Continental-scale isotope hydrology - UNM Digital Repository

University of New MexicoUNM Digital Repository

Earth and Planetary Sciences ETDs Electronic Theses and Dissertations

12-1-2014

Continental-scale isotope hydrologyScott Jasechko

Follow this and additional works at: https://digitalrepository.unm.edu/eps_etds

This Dissertation is brought to you for free and open access by the Electronic Theses and Dissertations at UNM Digital Repository. It has beenaccepted for inclusion in Earth and Planetary Sciences ETDs by an authorized administrator of UNM Digital Repository. For more information, pleasecontact [email protected].

Recommended CitationJasechko, Scott. "Continental-scale isotope hydrology." (2014). https://digitalrepository.unm.edu/eps_etds/40

https://digitalrepository.unm.edu?utm_source=digitalrepository.unm.edu%2Feps_etds%2F40&utm_medium=PDF&utm_campaign=PDFCoverPages

https://digitalrepository.unm.edu/eps_etds?utm_source=digitalrepository.unm.edu%2Feps_etds%2F40&utm_medium=PDF&utm_campaign=PDFCoverPages

https://digitalrepository.unm.edu/etds?utm_source=digitalrepository.unm.edu%2Feps_etds%2F40&utm_medium=PDF&utm_campaign=PDFCoverPages

https://digitalrepository.unm.edu/eps_etds?utm_source=digitalrepository.unm.edu%2Feps_etds%2F40&utm_medium=PDF&utm_campaign=PDFCoverPages

https://digitalrepository.unm.edu/eps_etds/40?utm_source=digitalrepository.unm.edu%2Feps_etds%2F40&utm_medium=PDF&utm_campaign=PDFCoverPages

mailto:[email protected]

Scott Jasechko Candidate

Earth and Planetary Sciences

Department

This dissertation is approved, and it is acceptable in quality and form for publication:

Approved by the Dissertation Committee:

Dr. Zachary D. Sharp , Co-chairperson

Dr. Peter J. Fawcett , Co-chairperson

Dr. Joseph Galewsky

Dr. Juske Horita

CONTINENTAL-SCALE ISOTOPE HYDROLOGY

by

SCOTT ALLAN JASECHKO

B.Sc., University of Victoria, 2009 M.Sc. University of Waterloo, 2011

DISSERTATION

Submitted in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

Earth and Planetary Sciences

The University of New Mexico Albuquerque, New Mexico

December, 2014

iii

DEDICATION

To Jennifer, Gordon, Glenn and Edith – for your love and your encouragement.

iv

ACKNOWLEDGMENTS

I am grateful to Zachary Sharp, Peter Fawcett and Joseph Galewsky for supporting, challenging and

guiding me throughout my Ph.D. education. I am thankful for my friends and mentors who continue

to grant me the joy of belonging in a community.

v

CONTINENTAL-SCALE ISOTOPE HYDROLOGY

By

Scott Jasechko

B.Sc., Physical Geography and Earth and Ocean Sciences, University of Victoria, 2009

M.Sc., Earth and Environmental Sciences, University of Waterloo, 2011

Ph.D., Earth and Planetary Sciences, University of New Mexico, 2014

ABSTRACT

Providing sustainable sources of fresh water for a growing population of 7 billion people is one of

the grand challenges of the 21st century. This dissertation outlines several applications of isotope

hydrology to address four previously unknown questions involving surface- and ground-water

resources at regional- to continental-spatial scales over contemporary- to millennial-temporal scales.

The four chapters in this dissertation investigate (1) the rate of plant transpiration, (2) the seasonality

of groundwater recharge, (3) the climate of the last ice age, and (4) the chemistry of Ugandan waters.

(1) Chapter one presents a new global compilation of lake water isotopic data, river isotopic data,

stand-level transpiration rates, and water use efficiency measurements, and analyzes the newly

synthesized data to show that plant transpiration is the largest water flux from Earth’s continents,

exceeding both physical evaporation and continental runoff. (2) Chapter two presents a new global

synthesis of rain, snow and groundwater isotopic compositions, and analyzes the paired

vi

precipitation-groundwater dataset to show that the percentage of precipitation that recharges aquifers

is at a maximum during the winter (extra-tropics) and wet (tropics) seasons. (3) Chapter three

presents a new global compilation of groundwater radiocarbon, tritium, and stable O and H isotopic

data, and maps the isotopic shift of meteoric waters since the last ice age. The analysis shows that the

majority (~90%) of precipitation during the last ice age had lower 18O/16O and 2H/1H ratios than the

modern day, except in some exclusively coastal locations. We also show that current isotope-enabled

general circulation models capture some, but not all, spatial variability in ice-age-to-late-Holocene

18O/16O and 2H/1H shifts, providing a new calibration tool that can be used to improve our

understanding of glacial climate dynamics. (4) Chapter four presents isotopic and chemical analyses

of Ugandan lake, river, rain, and ground water collected during a field expedition led in July of 2013.

Analysis of this new dataset reveals new estimates of lake water balances across Uganda.

vii

TABLE OF CONTENTS

DEDICATION .................................................................................................................................................. iii

ACKNOWLEDGMENTS ............................................................................................................................... iv

ABSTRACT .......................................................................................................................................................... v

TABLE OF CONTENTS ............................................................................................................................... vii

PREFACE............................................................................................................................................................. 1

CHAPTER 1 — GLOBAL PLANT TRANSPIRATION FLUXES ........................................................ 9

1.1 Abstract ...................................................................................................................................................... 9

1.2 Introduction ............................................................................................................................................... 9

1.3 Dataset and methods .............................................................................................................................. 32

1.4 Results ....................................................................................................................................................... 54

1.5 Discussion ................................................................................................................................................ 61

1.6 References ................................................................................................................................................ 63

CHAPTER 2 — THE SEASONALITY OF GLOBAL GROUNDWATER RECHARGE ............ 83

2.1 Abstract .................................................................................................................................................... 83

2.2 Introduction ............................................................................................................................................. 83

2.3 Dataset and methods .............................................................................................................................. 88

2.4 Results ..................................................................................................................................................... 101

2.5 Discussion .............................................................................................................................................. 106

2.6 References .............................................................................................................................................. 121

CHAPTER 3 — THE ISOTOPIC COMPOSITION OF ICE AGE GROUNDWATERS ........... 142

3.1 Abstract .................................................................................................................................................. 142

3.2 Introduction ........................................................................................................................................... 143

3.3 Dataset and Methods............................................................................................................................ 148

3.4 Results ..................................................................................................................................................... 151

3.5 Discussion .............................................................................................................................................. 164

3.6 References .............................................................................................................................................. 188

CHAPTER 4 — THE ISOTOPE HYDROLOGY OF UGANDA ..................................................... 212

4.1 Abstract .................................................................................................................................................. 212

4.2 Introduction ........................................................................................................................................... 212

4.3 Dataset and methods ............................................................................................................................ 214

4.4 Results ..................................................................................................................................................... 217

4.5 Discussion .............................................................................................................................................. 231

4.6 References .............................................................................................................................................. 241

viii

List of Figures

1-1. Schematic of fresh water fluxes ............................................................................................................... 10

1-2. Schematic of plant transpiration .............................................................................................................. 12

1-3. Water yields before and after the clearing of vegetation in experimental watersheds .................... 15

1-4. Compiled transpiration/evapotranspiration ratios ............................................................................... 17

1-5. Compiled transpiration/evapotranspiration ratios and precipitation rates ...................................... 28

1-6. Transpiration/evapotranspiration measurements sorted by technique............................................. 29

1-7. Compiled desert transpiration/evapotranspiration ratios and precipitation rates ........................... 30

1-8. Locations of transpiration study watersheds ......................................................................................... 34

1-9. Water use efficiency as a function of vapor pressure deficit ............................................................... 50

1-10. Spatial distribution of water use efficiency .......................................................................................... 50

1-11. The deuterium excess of 31 major rivers ............................................................................................. 52

1-12. The O and H isotopic composition of large lakes .............................................................................. 54

1-13. Heterogeneity of lake water O and H isotopes ................................................................................... 55

1-14. Temperature and O isotopic composition of Baikal and Tanganyika ............................................. 56

1-15. The isotopic composition of the North American Great Lakes at depth ...................................... 56

1-16. The transpiration rate calculated for 54 lake catchments grouped by biome................................. 58

1-17. The transpiration rate for 10% of Earth’s ice free land area ............................................................ 59

1-18. The transpiration rate for 73 catchments ............................................................................................. 60

1-19. Gross primary productivity for 10% of ice free land areas ............................................................... 61

2-1. An estimate of the global annual groundwater recharge ratio ............................................................ 86

2-2. Locations of paired precipitation-groundwater isotopic data ............................................................. 91

2-3. The change in meteoric 3H from 1930 to 2009..................................................................................... 96

2-4. The isotopic approach to quantifying recharge/precipitation seasonality ...................................... 100

2-5. Comparison of groundwater and precipitation O and H isotopic data .......................................... 101

ix

2-6. The seasonality of groundwater recharge ratios at 54 locations ....................................................... 103

2-7. Comparison of groundwater and precipitation isotopic compositions ........................................... 106

2-8. The seasonality of O and H isotopic data in precipitation ................................................................ 108

2-9. Seasonality of normalized difference vegetation indices across land surfaces ............................... 110

2-10. A comparison of modelled and isotope-based recharge ratio seasonality .................................... 113

2-11. Cross plot of modelled and isotope-based recharge ratio seasonalities ........................................ 115

3-1. Temperature changes from the last glacial maximum to the modern day ...................................... 145

3-2. Map of O and H isotopic change from the last ice age to the late-Holocene................................ 152

3-3. Ranges of isotopic change from the last ice age to the late-Holocene observed in records ........ 162

3-4. The difference between δ18Oice age and δ18Olate-Holocene with latitude ................................................. 163

3-5. The modelled (CCSM) precipitation δ18Olast glacial maximum – δ18Opre-industrial ........................................ 167

3-6. The modelled (ECHAM) precipitation δ18Olast glacial maximum – δ18Opre-industrial ................................... 168

3-7. The modelled (IsoGSM) precipitation δ18Olast glacial maximum – δ18Opre-industrial .................................... 169

3-8. The modelled (LMDZ) precipitation δ18Olast glacial maximum – δ18Opre-industrial ...................................... 170

3-9. Locations where all models agree on the sign of δ18Olast glacial maximum – δ18Opre-industrial ................... 171

3-10. Agreement for 3 of 4 models on the sign of δ18Olast glacial maximum – δ18Opre-industrial ........................ 172

4-1. The O and H isotopic composition of Ugandan waters ................................................................... 218

4-2. Sampling locations of Ugandan waters ................................................................................................. 221

4-3. A Piper diagram showing the hydrochemistry of Ugandan waters .................................................. 229

4-4. Stable-isotope-based evaporation/input ratios for 24 Ugandan Lakes ........................................... 230

4-5. O isotopic composition and conductivity of Ugandan lakes and groundwaters ........................... 232

4-6. Deuterium excess of Ugandan lakes and groundwaters with electrical conductivity .................... 233

4-7. The deuterium excess and sample elevation of Ugandan rivers and groundwaters ...................... 234

4-8. Stable-isotope-based evaporation/input ratios based on O and H isotopes ................................. 237

x

List of Tables

1-1. Compiled transpiration/evapotranspiration ratios ......................................................................... 19–26

1-2. Compiled transpiration/evapotranspiration sorted by ecoregion ...................................................... 27

1-3. Modelled transpiration/evapotranspiration fractions at a global scale ............................................. 32

1-4. Study lake information ........................................................................................................................ 35–36

1-5. Study lake hydrography ....................................................................................................................... 37–38

1-6. Transpiration model input parameters (isotope) ............................................................................ 44–45

1-7. Transpiration model input parameters (isotope, temperature and humidity) ............................ 45–46

1-8. Compiled large lake isotopic investigations ..................................................................................... 48–49

1-9. Compiled plant water use efficiencies..................................................................................................... 51

1-10. Deuterium excess of major rivers.......................................................................................................... 53

1-11. The terrestrial sublimation flux estimated in previous studies ......................................................... 62

2-1. Locations of paired groundwater and precipitation data ............................................................... 89–90

2-2. Seasonal groundwater recharge ratio results ............................................................................... 104–105

3-1. Modern and ice age physical and isotopic data for the oceans and the cryosphere ...................... 147

3-2. Differences in the δ18O value of the last ice age and the late-Holocene................................ 153–154

3-3. Observed ranges of δ18Oice age and δ18Olate-Holocene values in groundwaters ............................. 155–158

3-4. Speleothem δ18O from the last ice age to the late-Holocene ............................................................ 159

3-5. Ice core δ18O from the last ice age to the late-Holocene .................................................................. 160

4-1. The isotopic composition of Ugandan waters .................................................................................... 220

4-2. The isotopic composition of Ugandan waters sampled and measured in this study ........... 222–227

4-3. Major ion chemistry of Ugandan waters ..................................................................................... 235–236

1

PREFACE

This dissertation uses stable O and H isotopic compositions of meteoric waters to quantify

the sources and processes that govern the storage and movement of water on continents. This

preface is divided into two parts: (Part A) an outline of the publication yields from this dissertation,

specifically addressing the publication authorship guidelines as stipulated by The University of New

Mexico’s Department of Earth and Planetary Sciences, and (Part B) an outline for this dissertation,

with brief terminology and background information relevant to all four chapters within this

dissertation.

(Part A) This dissertation is linked to six planned-or-published peer reviewed publications.

Three articles have already been published in top-tier peer-reviewed journals. One other publication

is currently in review. Two manuscripts are being prepared for submission to a peer reviewed journal

at the time this dissertation is submitted. Chapter 1 is linked to three publications in the journals

Nature (two publications) and Agricultural and Forest Meteorology. Chapter 2 is linked to a

manuscript that is currently undergoing peer review. Publications linked to chapters 3 and 4 are being

prepared for submission at the time that this dissertation is being submitted.

S. Jasechko is the lead author of three published articles, is the second author on the third

publication that has only two authors, in total. S. Jasechko is the lead author on the fourth

publication that is currently in review, and will also be the lead author for publication six that results

from dissertation chapter 4. To follow the requirements of the Department of Earth and Planetary

Sciences, the contributions and roles of each co-author within each publication are outlined below.

Chapter 1 uses a global dataset of isotopic data compiled for lakes and rivers to quantify the

rate that plants uptake water. Chapter 1 has yielded three publications in peer reviewed journals, the

first of which was published in April-2013 in Nature, the second was published in February-2014 in

2

Nature, and the third was published in June-2014 in Agricultural and Forest Meteorology. The first

of three Chapter one publications included five analysis components, all of which were completed by

S. Jasechko in entirety: (i) compilation and synthesis of data, (ii) geospatial analyses, (iii) development

of methodology and equations, (iv) analysis of global remote sensing data, and (v) calculation of

transpiration fluxes. The manuscripts published in Nature were written by S. Jasechko with

comments and suggestions from Z. D. Sharp, J. J. Gibson, S. J. Birks, Y. Yi and P. J. Fawcett. The

publication in Agricultural and Forest Meteorology represents a collaboration between W.

Schlesinger and S. Jasechko, with W. Schlesinger and S. Jasechko sharing in all stages of manuscript

development (data compilation, figure development, statistical analyses and writing of the manuscript

text.).

Chapter 2 synthesizes global isotopic datasets of modern groundwater and precipitation and

analyzes the database to quantify the seasonal differences in groundwater recharge ratios at 54

globally-distributed locations, where the “groundwater recharge ratio” is defined as the fraction of

precipitation that recharges groundwater aquifers. Chapter 2 has yielded one manuscript that is in

press for publication in Water Resources Research at the time that this dissertation was submitted.

The Chapter 2 project included six parts: (i) compilation of a global groundwater isotopic dataset, (ii)

amalgamation of three continental-scale precipitation isotopic datasets, (iii) geospatial synthesis of

groundwater isotopic data with precipitation data, (iv) development of a new set of equations, (v)

calculation of groundwater recharge ratios, (vi) comparison of results with a state-of-the-art global

hydrological model. S. Jasechko led all components of this analysis, and worked together with

collaborators on two components: part (ii): “amalgamation of three continental-scale precipitation

isotopic datasets” (working with S. J. Birks and J. M. Welker), and part (vi): “comparison of results

with a state-of-the-art global hydrological model” (working with T. Gleeson and Y. Wada). The

manuscript was prepared by S. Jasechko (first author), and incorporates comments and suggestions

3

from all co-authors: S. J. Birks, T. Gleeson, Y. Wada, P. J. Fawcett, Z. D. Sharp, J. J. McDonnell and

J. M. Welker.

Chapter 3 uses a newly developed global groundwater dataset of radioactive carbon,

radioactive hydrogen, stable oxygen isotopes and stable hydrogen isotopes to delineate ice age

groundwaters and to map the distribution of 18O/16O and 2H/1H ratios of meteoric waters from the

last ice age. A manuscript linked to Chapter 3 — which will be submitted for publication — has been

prepared by S. Jasechko (first author) and is now circulating amongst co-authors at the time this

dissertation is submitted. The project included four components: (i) groundwater isotopic data

compilation, (ii) radiocarbon dating of groundwaters, (iii) statistical comparison of modern- and

paleo-groundwater isotopic compositions, (iv) geospatial comparison of groundwater isotopic

observations with the results of four isotope-enabled general circulation models run under last ice age

conditions. S. Jasechko led each component, and worked with leaders of four general circulation

models on component (iv): “geospatial comparison of groundwater isotopic observations with the

results of four isotope-enabled general circulation models run under last ice age conditions.” The

manuscript in preparation was written by S. Jasechko incorporates comments and suggestions from

all co-authors.

Chapter 4 uses a newly developed isotopic dataset of Ugandan groundwaters, lakes, rivers,

precipitation and springs to quantify hydrological processes controlling water availability throughout

the country. Chapter 4 presents a newly developed dataset resulting from fieldwork planned and led

by S. Jasechko, with on-the-ground collaborative support from M. Kizza (Makerere University) and

M. GebreEgziabher (Addis Ababa University). The cost of travel, lodging, transportation, sampling

equipment and geochemical analysis were supported in entirety by the combined graduate student

research funds awarded through four graduate student research grants to S. Jasechko: (i) the

Consortium of Universities for the Advancement of Hydrologic Science’s Pathfinder Fellowship, (ii)

4

the American Geophysical Union’s Horton Hydrology Research Grant, (iii) the Geological Society of

America’s Graduate Student Research Grant, and (iv) the Caswell-Silver Foundation’s Kelley-Silver

Graduate Fellowship research allotment. Special sampling equipment was loaned to S. Jasechko by T.

Fischer, L. Crossey and K. Karlstrom. Sample preparation and chemical analyses were completed by

S. Jasechko with guidance from A. S. Ali. Oxygen, hydrogen and carbon isotopic analyses were

completed by S. Jasechko with laboratory support from V. Atudorei. The results of this project will

be submitted to an appropriate peer reviewed journal, with S. Jasechko listed as the lead author.

(Part B) This dissertation is divided into four chapters (in order): (1) Global plant

transpiration fluxes, (2) The seasonality of global groundwater recharge, (3) A global database of ice

age groundwaters, and (4) The isotope hydrology of Uganda. The four chapters examine a variety of

spatial scales, ranging from local (~101 km2) and regional scales (~104 km2; e.g., Chapter 4) to

continental scales (~106 to 107 km2). The four chapters explore different time periods, ranging from

the climate of the last ice age (104 years before today) to the climate of the present day. The cross-

cutting theme that binds there four chapters into one dissertation is that all four chapters investigate

distributions of 18O/16O and 2H/1H ratios in environmental waters. An introduction to the

application of oxygen and hydrogen isotopes in hydrology is presented next before delving into each

chapter.

The elements of oxygen and hydrogen were first discovered in the 18th century by Henry

Cavendish and Antoine Lavoisier. However, it was not for another 150 years that the isotopes of

oxygen and hydrogen were first discovered. The first isotopes to be identified were of thorium and

uranium (McCoy and Ross, 1907) and were first acknowledged by F. Soddy (Soddy, 1913), who

received the Nobel Prize in 1922 for this work. Soddy used the term isotopes to describe

radionuclides that had different decay rates but seemed at the time to be identical in all other

manners: "Put colloquially, their atoms have identical outsides but different insides... These elements

5

which are identical in their whole chemical character and are not separable by any method of

chemical analysis are now called isotopes" (F. Soddy’s Nobel Prize address in 1922; within reference:

Soddy, 1966).

Soddy first identified differences in the radioactive decay rates of isotopes (Soddy, 1913).

Since his work more than 100 years ago, we have learned that small chemical differences between

stable isotopes exist. The differences arise due to differences in mass between isotopes, which is

defined by the sum of protons (Z) and neutrons (N) within the atomic nucleus (minus a small

amount of “missing” mass that has been converted to nuclear binding energy). The discovery of the

existence of stable isotopes of oxygen can be credited to Blackett (1925) who used photography to

document the production of 17O from the capture of an alpha particle (i.e., He2+, a particle

comprised of two neutrons and two protons) by a common nitrogen atom (14N). Soon after this

laboratory experiment, different stable isotopes of oxygen (16O, 17O, 18O) were discovered to be

naturally occurring within Earth’s atmosphere (Giauque and Johnson, 1929a; 1929b). The discovery

of a stable isotope of hydrogen is credited to Urey (1932) who applied electrolysis to natural waters

to extract deuterium and confirm the existence of two stable hydrogen isotopes in nature (2H, 1H).

The discovery of naturally occurring stable isotopes of O and H have led to a vast array of

hydrological and paleo-climate investigations. Landmark work in the 1950s and 1960s identified

several features of the global isotopic data that have been reproduced many times over since their

foundation (i.e., Friedman, 1953, Craig, 1961, Dansgaard, 1964): (i) the ratios of 18O/16O and 2H/1H

covary in precipitation (Friedman 1953; Craig, 1961), (ii) the isotopic composition of precipitation is

controlled by temperature-dependent fractionation during rainout, leading to lower 18O/16O and

2H/1H ratios farther from moisture sources (Dansgaard, 1964), (iii) the process of evaporation

changes 18O/16O and 2H/1H ratios in different proportions than condensation due to additional

kinetic (i.e., disequilibrium) isotope effects (Craig, 1961), (iv) plant transpiration does not modify the

6

isotopic composition of water (Wershaw et al., 1966), (v) the isotopic composition of waters has

changed over Earth’s history and provides information about past climates (e.g., Urey et al., 1951).

Many other interesting discoveries have been made in the field of stable isotope hydrology over the

past 60 years; however, aforementioned discoveries are of particular importance to the discoveries

outlined in the forthcoming chapters.

Last, before I begin chapter 1, some isotopic terminology must be presented. Isotopic data

are presented in per mille notation on a scale that ranges from −1000 to +∞. Delta notation is

described mathematically as δ = (Rsample/Rstandard) × 1000 ‰, where R represented the ratio of

18O/16O or the ratio of 2H/1H, and the subscripts sample and standard refer to the ratio in the

measured sample or in an international standard, respectively. The international standard most

commonly applied to O and H isotopes is oceanic water: “standard mean ocean water” (or, SMOW),

that has an 18O/16O ratio of 0.00200520±0.00000043 and a 2H/1H ratio of 0.00015575±0.00000008

(Baertschi, 1976; de Wit et al., 1980).

7

References (preface)

Baertschi, P. (1976), Absolute 18O content of standard mean ocean water, Earth and Planetary

Science Letters, 31, 341–344.

Blackett, P. M. S. (1927), The Ejection of Protons from Nitrogen Nuclei, Photographed by

the Wilson Method, Proceedings of the Royal Society of London, 107, 49–360.

Craig, H. (1961), Isotopic variations in meteoric waters, Science, 133, 1702–1703.

Dansgaard, W. (1964), Stable isotopes in precipitation, Tellus, 16, 436–468.

de Wit J.C., van der Straaten, C. M., Mook, W. G. (1980), Determination of the absolute

hydrogen isotopic ratio of V-SMOW and SLAP, Geostandards Newsletter 4, 33–36.

Friedman, I. (1953), Deuterium content of natural water and other substances. Geochimica

Cosmochimica Acta, 4, 89–103.

Giauque, W. F., and Johnson, H. L. (1929a), An isotope of oxygen of mass 17 in the earth’s

atmosphere, Nature, 123, 831.

Giauque, W. F., and Johnson, H. L. (1929b), An isotope of oxygen, mass 18, Nature, 123,

318.

McCoy, H. N., and Ross, W. H. (1907), The specific radioactivity of thorium and the

variation of the activity with chemical treatment and with time. Journal of the American Chemical Society,

29, 1709–1718.

Soddy, F. (1913), The Radio-elements and the Periodic Law, Chemical News, 107, 97–99.

8

Soddy, F. (1966), The Origins of the Conceptions of Isotopes, Nobel Lecture, December 12,

1922, In Nobel Lectures Including Presentation Speeches and Laureates' Biographies: Chemistry

1901–1921, New York: Elsevier, pp. 367–401.

Urey H. C., Brickwedde, F. G., and Murphy, G. M. (1932), A Hydrogen Isotope of Mass 2,

Physical Review, 39, 164–165.

Urey, H. C., Lowenstam, H. A., Epstein, S., & McKinney, C. R. (1951), Measurement of

paleotemperatures and temperatures of the Upper Cretaceous of England, Denmark, and the

southeastern United States, Geological Society of America Bulletin, 62, 399–416.

9

CHAPTER 1 — GLOBAL PLANT TRANSPIRATION FLUXES

1.1 Abstract

Terrestrial water stores are balanced by inputs from rainfall and snowfall and losses via evaporation,

transpiration, river discharges and submarine groundwater discharges. Two-thirds of all precipitation

on land surfaces is vaporized by transpiration or by evaporation, but current general circulation and

land surface models span a wide range of predicted transpiration/evaporation ratios. Here I analyze a

global dataset of river and lake water isotopic data to show that gas exchange at plant stoma

represents both (i) the largest outgoing water flux from Earth’s continents, and (ii) the greatest

assimilation of CO2 in the global climate system. This result suggests that current land surface and

climate models can prioritize biological, rather than physical (evaporation), water fluxes to enhance

predictions of water availability under varying climate and land use futures.

1.2 Introduction

Chapter 1 describes a new approach to quantifying transpiration using isotopic data in lakes

and rivers. The approach and results of Chapter 1 were published in April of 2013 (Jasechko et al.,

2013). Three subsequent works have been published (or are in review) since April of 2013 as a result

of this initial publication (Schlesinger and Jasechko, 2014; Jasechko, 2014, Evaristo et al., in review).

This chapter presents a background to plant transpiration investigations in hydrology, discusses the

isotopic dataset and approach taken to quantify transpiration, and concludes by discussing the

ramifications of this work and presenting a vision for this field moving ahead.

Water transport on continents is replenished by precipitation on land surfaces that provides

about 110,000 km3 of fresh water each year (Oki and Kanae, 2006). The path that water takes after

falling on the land surface involves mixing, storage and transportation either as a liquid (i.e.,

advection-dispersion through porous media, or streamflow) or through vapourization via

evaporation or plant transpiration. It has long been recognized that evapotranspiration outweighs

10

streamflow on continents by a factor of close to 2. Evapotranspiration consumes two-thirds of all

precipitation on continents, with an annual flux close to 70,000 km3/year (Jung et al., 2011).

Streamflow, on the other hand, has an annual flux of ~36,000 to ~38,000 km3/year (Dai and

Trenberth, 2002; Syed et al., 2010; Figure 1-1).

Figure 1-1. Schematic of current knowledge of fresh water fluxes on continents using rounded

numbers (note, submarine groundwater discharge not depicted, although this flux is expected to be

10 to 10,000 times less than continental runoff in rivers; Taniguchi et al., 2002).

Evapotranspiration is comprised of two components: plant transpiration (a biological

process) and evaporation (a physical process). Transpiration supports multiple life-sustaining roles

for plants. First, plants – like humans – require water for their cellular structures. Where humans are

about 70 % water, plants are ~80 % water. Transpiration supports cellular growth through the

provision of fresh water to plant cells. Second, plants move nutrients from the subsurface into

photosynthetically-active regions within the plant. For tall trees in forests this is often the canopy,

which can be in excess of 10s of meters above the ground surface. Third, plant transpiration requires

11

energy to convert liquid water into vapor (i.e., latent energy); plants capitalize upon this vaporization

energy requirement to cool off their leaf surfaces and, thus, moderate their growing leaf temperature

close to a cool, pan-biome temperature of 21°C (Helliker and Richter, 2008; Figure 1-2).

12

Figure 1-2. Schematic of gas exchange at plant stoma. Plants draw water from soil and groundwater

reservoirs, moving the water and entrained nutrients up the xylem by capitalizing on capillary action.

At stoma (upper right) plants passively release H2O (liquid to vapor conversion) via evaporation at leaf

surfaces (termed transpiration), which also cools leaf surfaces and maintains leaf temperatures that are

optimal for growth (Helliker and Richter, 2008).

13

The separate fluxes of evaporation and transpiration have been considered as a single

component in hydrological investigations, lumped into the single term: evapotranspiration. However,

separating the fluxes of evaporation and transpiration is important for hydroclimatology because the

processes of evaporation and transpiration are different, and will respond differently to land use and

climate modifications. Where evaporation is a physical process, transpiration is a biological process

that is central to primary production on continents: the largest carbon flux in the climate system

(~120 Gt C/year; Beer et al., 2010). The response of evaporation and transpiration to a changing

climate will be different. Evaporation is a physical process, and the potential for evaporation can

broadly be expected to increase with future warming, although some evaporation pan data suggest

that other factors may exert a stronger control than temperature alone (e.g., Roderick and Farquhar,

2002). The response of plant transpiration to a warming climate is, on the other hand, complicated

by several sometimes conflicting responses. For example, warmer temperatures and fertilization of

the biosphere through enriched atmospheric CO2 is expected to increase plant productivity, and

consequentially increase transpiration. However, the enrichment of CO2 in the atmosphere has also

been predicted to increase the water use efficiency of plants (i.e., the ratio of H2O transpired to CO2

uptake), which is expected to decrease transpiration. Indeed a shift to more water-efficient

ecosystems has recently been observed across a variety of biomes in North America (Keenan et al.,

2013). Examining the supplementary data within this publication (Keenan et al., 2013) shows that

water use efficiency may be changing at a greater rate (i.e., as percentage of the total flux) than

streamflow (e.g., Labat et al., 2002; Peterson et al., 2002; McClelland et al., 2006), precipitation

(Zhang et al., 2007) or evapotranspiration (Jung et al., 2010; Miralles et al., 2014), highlighting that

knowledge of the exact flux of transpiration on continents is important to accurately predicting

change in the global hydrological cycle. These qualitative predictions are further complicated by

expected limitations to the “CO2 fertilization effect” imparted by fast approaching nitrogen

limitations upon the extent of CO2 fertilization. The broad implication of this analysis, is that models

14

of the Earth’s critical zone and the climate that neglect any one of physics, chemistry or biology are

at risk of missing important feedbacks, interactions and thresholds between the atmosphere,

biosphere, hydrosphere and lithosphere.

Changing land uses via deforestation and agriculture change transpiration fluxes and

influence downstream liquid water yields in rivers. Deforestation by humans has, globally, led to a

decrease in natural evapotranspiration of ~3000 km3 per year (about 4% of global

evapotranspiration; Gordon et al., 2005; Jung et al., 2010), and irrigation for agriculture – which

reactivated groundwater that is part of long flow paths, often removed from the “active”

hydrosphere, on human time scales – has increased terrestrial vapor fluxes by ~2600 km3 per year

(Gordon et al., 2005). This pumping of unsustainable groundwater sources has been investigated and

mapped at a global scale (Wada et al., 2012).

Other examples of land use modifications and impacts upon downstream hydrology date

back to experimental watershed work completed in the 1970s- and 1980s. Bosch and Hewlett (1983)

present a series of watershed studies where river flows were measured before and after the complete

deforestation of the upstream watershed. Follow deforestation, water yields downstream increased by

75% due to reduced evapotranspiration fluxes following forest clearing, highlighting the important

role of transpiration in total evapotranspiration fluxes (average of n = 25 experimental watersheds

that were completely deforested; 10th-90th percentile spans +10% increase to 194% increase in water

yields; Figure 1-3; Bosch and Hewlett, 1983).

15

Figure 1-3. Water yields before and after the clearing of vegetation within experimental watersheds

show an increase in runoff following deforestation. Each point represents a single watershed, which

had its outflow monitored before and after forest clearing. Data presented in this figure from Bosch

and Hewlett (1983).

Changing climate chemistry is expected to impact transpiration, and will produce an

important feedback to regional warming. For example, a general circulation model (Community Land

and Community Atmosphere Model) simulation using a transpiration/evapotranspiration ratio of

40% (pers. comm. L. Cao; Cao et al., 2010) showed that the physiological response to a CO2 enriched

atmosphere was a decrease in transpiration that reduced latent heat fluxes on continents and

ultimately accounted for ~15% of land surface warming, with the remainder largely attributed to CO2

radiative forcing and other feedback mechanisms. Similarly, more than half (8.4%) of the predicted

increase in future global river discharges (predicted runoff increase of 15% of present day) in this

model were predicted to be derived from reduced transpiration water fluxes. Given the potential of

changes to transpiration to warm land surfaces (Cao et al., 2010) and the relatively rapid increases in

water use efficiency reported by Keenan et al. (2013) there is a need to quantify the proportion of

evapotranspiration completed by vegetation through transpiration.

16

An extensive review of ~100 peer-reviewed publications was completed to uncover studies

that have decoupled evapotranspiration into its components: evaporation, and transpiration

(Schlesinger and Jasechko, 2014, building upon a compilation originally presented by Schlesinger and

Bernhardt, 2013). Three groups of studies exist: (i) forest- or cropland-scale field measurements using

a suite of techniques (e.g., sap flow meters, radial flow meters, isotope partitioning), (ii) general

circulation models, and (iii) land surface models. These three groups of approaches are reviewed in

the coming sections.

More than 80 studies have quantified transpiration fluxes at forest stand scales over the past

50 years. The results of these studies were recently compiled and reviewed by Schlesinger and

Jasechko (2014). The locations and transpiration fluxes (reported as a percentage of annual

evapotranspiration) of the compiled studies are presented in Figure 1-4. Existing transpiration flux

measurements have been completed on all continents and span a variety of biomes with different

plant life forms. The studies use on a variety of different approaches to estimate transpiration as a

proportion of evapotranspiration.

17

Figure 1-4. (top). Locations of stand-level measurements transpiration (T) as a proportion of

evapotranspiration (ET; i.e., T/ET). Colors mark the share of total evapotranspiration accounted for

by transpiration alone (Figure reproduced from Schlesinger and Jasechko, 2014). (bottom) Ranges of

transpiration/evapotranspiration ratios compiled from 81 studies sorted into major biomes. Bars

mark the 25th-75th percentile range of compiled studies for each biome; whiskers mark the 10th-90th

percentile range of compiled studies for each biome. Colors delineate the annual flux of

evapotranspiration from each biome as a proportion of total terrestrial evapotranspiration, which is

~70,000 km3/year (Jung et al., 2010). Evapotranspiration rates across each biome were obtained

from long term mean annual satellite-based evapotranspiration flux data (Mu et al., 2011).

Transpiration can be estimated using a variety of approaches. The most commonly applied

approaches broadly fall under the category of hydroclimatological models that utilize meteorological

measurements and are often coupled to sap flow measurements or transpiration fluxes (43 studies

18

compiled by Schlesinger and Jasechko, 2014). Other approaches that have been used to measure

transpiration fluxes include radial sap flow meters (e.g., Nizinski et al., 2011), energy balance models

(e.g., Tajchman, 1972; Liu et al., 2012), stand level O and H isotope based models (e.g, Hsieh et al.,

1998; Ferretti et al., 2003; Wang et al., 2013), catchment scale O and H isotope based models (e.g.,

Telmer and Veizer, 2000; Gibson and Edwards, 2002), satellite-based estimates (e.g., Tian et al.,

2013) and a water balance approaches comparing water fluxes from control and bare-soil plots

(Schlesinger et al., 1987). A comprehensive review of available stand level measurements is presented

in Table 1-1.

19

Table 1-1. Compiled transpiration/evapotranspiration studies.

Ecoregion Country Latitude Longitude Location

1 Tropical

Rainforest India 22.5 87.3 Arabari Range

2 Temperate Forest Germany 52.4 13.8 Berlin

3 Temperate Forest Germany 52.4 13.8 Berlin

4 Temperate Forest United

Kingdom - - Pinus sylvestris plantation

5 Temperate Deciduous

Forests Russia 50.8 42.5 Tellermanovsky

6 Boreal Forest - - - -

7 Boreal Forest Germany 51.8 10.5 Harz Mountains

8 Temperate Grassland

United States of America

41.1 -104.7 Wyoming: High Plains Grasslands

Research Station

9 Tropical

Rainforest Puerto Rico 18.3 -65.7 Luquillo Experimental Forest

10 Temperate Forest Germany 48.0 11.6 Near Munich

11 Desert China 44.3 87.9 Gubantonggut Desert: Fukang

Station of Desert Ecology

12 Boreal Forest Canada 63.4 -114.3 Northwest Territories and Nunavut

13 Boreal Forest Canada 45.7 -76.9 Ottawa River basin

14 Tundra Canada 64.5 -112.7 Northwest Territories and Nunavut

15 Tropical

Grassland United States of America

20.1 -155.8 Kohala, Hawaii

16 Tropical



17 Tropical



18 Tropical





40.7 -104.8 Colorado: Central Plains

Experimental Range



35.0 -97.5 Oklahoma: Kessler Farm field

laboratory

21 Tropical

Rainforest Brazil -3.0 -60.0 Manaus

22 Tropical


23 Tropical


24 Tropical

Rainforest Brazil -3.1 -60.0 Ducke Forest Reserve

25 Tropical

Rainforest Indonesia -6.6 106.3 Janlappa Nature Reserve

20


26 Temperate Forest Czech

Republic 49.1 13.7 National Park Sumava


Kingdom 52.4 0.7 East Anglia


Kingdom 52.0 -3.5 East of Aberystwyth


Forests

The Netherlands

52.5 4.6 North Holland


Forests Germany 51.1 10.5

Hainich National Park, Central Germany


Forests Denmark 56.4 9.3 Hald Ege

32 Mediterranean

Shrubland United States of America

32.8 -116.4 Echo Valley, California

33 Mediterranean

Shrubland United States of America

32.8 -116.4 Echo Valley, California

34 Mediterranean

Shrubland Chile -33.1 -71.0 Fundo Santa Laura



40.7 -104.8 Colorado: Central Plains

Experimental Range



40.5 -104.8 Colorado: Long term Ecological

Research Station


China 37.6 101.7 Shidi


China 37.7 101.7 Gancaitan


China 30.9 91.1 Dangxiong


China 43.6 116.7 Neimeng

41 Steppe Tunisia 35.8 9.2 Southern Tunisia

42 Steppe China 43.5 116.7 Inner Mongolia Grassland

Ecosystem Research Station

43 Desert United States of America

36.6 -115.7 Nevada: Mojave Global Change

Facility


36.9 -116.6 Nevada test site

45 Desert Israel 30.9 34.4 Negev Desert


32.0 -112.9 Arizona: Ajo Mountains


31.7 -110.1 Arizona: Walnut Gulch

Experimental Watershed (Sonoran and Chihuahuan Deserts)

21





49 Wetland United States of America

41.1 -97.9 Nebraska, near Central City (Platte

River)

50 Agricultural Australia -34.3 142.2 Red Cliffs

51 Agricultural France 43.9 1.2 Auradé

52 Agricultural France 43.8 1.4 Lamasquère

53 Temperate Forest Japan 33.1 130.7 Kahoku Expt. Watershed, Kyushu

Island


Forests


36.0 -84.3 Oak Ridge, Tennessee

55 Mediterranean

Shrubland Israel 31.4 35.0 Yatir Forest


31.9 -110.8 Arizona: Sonoran Desert








31.4 -110.4 Huachuca Mountains


31.4 -110.4 Huachuca Mountains

61 Desert Israel 32.8 35.2 Alon ha’Galil

62 Agricultural Argentina -28.6 -66.8 Northwestern Argentina

63 Agricultural Vanuatu -15.4 167.2 Vanuatu Agricultural Research and

Technical Center

64 Temperate Forest United States of America

34.6 -111.8 Arizona, Beaver Creek


34.0 -85.8 Southeastern U.S.A.


35.1 -83.4 Cowetta (Pine)


44.2 -122.3 Oregon, Andrews watershed


Forests


35.1 -83.4 Cowetta (Hardwood)

69 Steppe Argentina -45.0 -70.0 Southern Argentina


35.8 -116.1 Mojave Desert

22


71 Tropical

Rainforest D.R. Congo -4.7 12.1 Pointe–Noire

72 Tropical

Grassland D.R. Congo -4.7 12.1 Pointe–Noire

73 Temperate Forest New Zealand -43.2 170.3 Okarito Forest, Westland


Forests Australia -32.3 117.9 Corrigin, Western Australia


Forests Portugal 38.5 -8.0 Herdade da Alfarrobeira


Forests France 48.7 7.1 Hesse


Forests


46.2 -89.3 Ottawa National Forest

78 Boreal Forest Sweden 60.0 17.3 Uppsala

79 Boreal Forest Sweden 60.0 17.3 Uppsala

80 Desert China 39.8 99.5 Heihe River Basin


32.5 -106.8 Jornada Experimental Range

23

Table 1-1. (continued)

Method P* T (%

of P)

E (% of

P)

Q (% of

P)

T/ET (%)

Reference

1 - 1623 45 56 45 Banerjee in Galoux et al., 1981

2 - 626 50 49 50 Lutzke & Simon in Galoux et al.,

1981

3 - 627 40 48 41 Lutzke & Simon in Galoux et al.,

1981

4 - 710 47 39 55 Rutter cited in Galoux et al., 1981

5 - 513 49 36 5 58 Molchanov cited in Galoux et al.,

1981

6 - 502 39 35 53 Ten studies by Molchanov 1963, cited by Choudhury et al., 1998

7 - 1237 19 26 42 Two studies by Delfs (1967), cited

by Choudhury et al., 1998

8 Diffusion porometer

365 65a Trlica and Biondini, 1990

9 Diurnal water table

changes 3725 14 9 61 Frangi and Lugo, 1985

10 Energy balance

model 725 37 22 41 63 Tajchman, 1972

11 Energy balance

model 150 38 62 38 Liu et al., 2012

12 Isotope-based (catchment)

340 71 18 12 81 Gibson and Edwards, 2002


872 45 8 85 Telmer and Veizer, 2000


310 34 8 58 80 Gibson and Edwards, 2002

15 Isotope-based (stand level)

1410* 32b 68 32 Hsieh et al., 1998


1410* 59 41 59 Hsieh et al., 1998


1380 61 39 61 Hsieh et al., 1998


2500 72 28 72 Hsieh et al., 1998


329 93 Ferretti et al., 2003


911 65-77 Wang et al., 2013

21 Model (with met.

data) 2000 49 26 26 65 Salati and Vose, 1984

22 Model (with met.

data) 2000 62 19 19 77 Salati and Vose, 1984

23 Model (with met.

data) 2232* 40 10 50 80 Shuttleworth, 1988

24

Method P* T (%

of P)

E (% of

P)

Q (% of

P)

T/ET (%)

Reference

24 Model (with met.

data) 2209 56 11 32 84 Leopoldo et al., 1995

25 Model (with met.

data) 2851 31 21 60 Calder et al., 1986

26 Model (with met.

data) 366 52a 53 52 Prazak et al., 1994

27 Model (with met.

data) 595 59 36 55 Gash and Stewart, 1997

28 Model (with met.

data) 2620 7 23 23 Hudson, 1988

29 Model (with met.

data) 234 93 17 84 Dolman, 1988

30 Model (with met.

data) 590

28-47

Gebauer et al., 2012

31 Model (with met.

data) 549 54 9 86 Ladekari, 1998

32 Model (with met.

data) 475 60 40 60 Poole et al., 1981

33 Model (with met.

data) 475 32 51 4 39 Poole et al., 1981

34 Model (with met.

data) 590 35 55 10 39 Poole et al., 1981

35 Model (with met.

data) 335 46 54 51 Lauenroth and Bradford, 2006

36 Model (with met.

data) 379* 67 33 0 67 Massman, 1992

37 Model (with met.

data) 350 39 73 39 Hu et al., 2009

38 Model (with met.

data) 477 37 67 37 Hu et al., 2009

39 Model (with met.

data) 580 56 83 56 Hu et al., 2009

40 Model (with met.

data) 580 39 49 44 Hu et al., 2009

41 Model (with met.

data) 144 45 55 0 45 Floret et al., 1982

42 Model (with met.

data) 275 55a 34 62 Huang et al., 2010

43 Model (with met.

data) 74 40a 60 40 Young et al., 2009

44 Model (with met.

data) 150 35 65 35 Smith et al., 1995

45 Model (with met.

data) 170 41 Littman and Veste, 2006

46 Model (with met.

data) 200 80 20 80 Liu et al., 1995

25

Method P* T (%

of P)

E (% of

P)

Q (% of

P)

T/ET (%)

Reference

47 Model (with met.

data) 223 64 64 Moran et al., 2009

48 Model (with met.

data) 233 79 79 Moran et al., 2009

49 Model (with met.

data) 687* 63a Kabenge and Irmak, 2012

50 Model (with met.

data) 476 67 63 52 Yunusa et al., 1997

51 Model (with met.

data) 615 42 51 46 Beziat et al., 2013

52 Model (with met.

data) 684 23 53 33 Beziat et al., 2013

53 Model (with met.

data), sap flow 2128 23 20 53 Kumagai et al., (in press)

54 Model (with met.

data), sap flow 1333 19a 14 58 Wilson et al., 2001

55 Model (with met.

data), sap flow 285 45 46 48 Raz-Yaseef et al., 2012

56 Model (with met.

data), sap flow 212 21a 27 47 Cavanaugh et al., 2011

57 Model (with met.

data), sap flow 260 21a 36 42 Cavanaugh et al., 2011

58 Model (with met.

data), sap flow 322 37 63 58 Scott et al., 2006

59 Model (with met.

data), sap flow 400 >45 Ffolliott et al., 2003

60 Model (with met.


61 Model (with met.


62 Model (with met.

data), sap flow 455* 70-80 Rousseaux et al., 2009

63 Model (with met.

data), sap flow 2763 68 Roupsard et al., 2006

64 Modelled (no obs.) 1085 49 15 41 76 Waring et al., 1981

65 Modelled (no obs.) 1225 49 15 38 77 McNulty et al., 1996




69 Modelled (no obs.) 150 34 56 10 38 Paruelo and Sala, 1995

70 Modelled (no obs.) 165 27 73 27 Lane et al., 1984

71 Radial flow meter 1019 81 12 87 Nizinski et al., 2011

72 Radial flow meter 1019 58 11 84 Nizinski et al., 2011

73 Sap flow 1127 8a 12 80 39 Barbour et al., 2005

74 Sap flow 265 53 78 40 Mitchell et al., 2009

26

Method P* T (%

of P)

E (% of

P)

Q (% of

P)

T/ET (%)

Reference

75 Sap flow 669 73 27 73 Paco et al., 2009

76 Sap flow 763 33a 15 69 Granier et al., 2000

77 Sap flow 896 65a Tang et al., 2006

78 Sap flow 250 46a 25 65 Grelle et al., 1997

79 Sap flow 271 51a Cienciala et al., 1997

80 Satellite-based 285 38-73

Tian et al., 2013

81 Water-balance;

control and bare plots

210 72 28 72 Schlesinger et al., 1987

Compiled transpiration/evapotranspiration ratios range from minimums of 23% (United

Kingdom, East of Aberystwyth; Hudson, 1988) to 93% (Colorado: Central Plains Experimental

Range; Ferretti et al., 2003; Table 1-1). The average T/ET ratio for compiled studies is 60%.

Compiled transpiration/evapotranspiration ratios are found to be highest in the tropics (e.g., tropical

rainforest T/ET of 70%±14%, tropical grassland T/ET of 62%±19%) and lower in Mediterranean

climates (47%±10%; Table 1-2; Figure 1-4). The highest evapotranspiration fluxes off of the

continents are from tropical regions. Spatially-weighting transpiration/evapotranspiration to the

percent of terrestrial evapotranspiration accounted for by each biome yields a

transpiration/evapotranspiration ratio of ~61%. This ratio is equivalent to a

transpiration/evaporation ratio of ~1.5, or a 50% greater transpiration flux than evaporation flux on

continents.

27

Table 1-2. Compiled transpiration/evapotranspiration (Schlesinger and Jasechko, 2014)

Ecoregion T/ET percent average ±1 s.d.

Land area (%)

P (mm/yr)

Percent of land

precipitation

ET (mm/yr)

Percent of

terrestrial ET

Tropical Rainforest

70±14 (n = 8) 16 1830 35 1076 33.1

Tropical Grassland

62±19 (n = 5) 12 950 14 583 13.9

Temperate Deciduous

Forests 67±14 (n = 9) 9 850 10 549 10.1

Boreal Forest 65±18 (n = 5) 14 500 8 356 9.5

Temperate Grassland

57±19 (n = 8) 8 470 5 332 5.4

Desert 54±18 (n = 14) 18 180 4 209 7.3

Temperate Coniferous

Forest 55±15 (n = 13) 4 880 4 458 3.4

Steppe 48±12 (n = 3) 4 440 2 467 3.4

Mediterranean shrubland

47±10 (n = 4) 2 480 1 302 1.0

The stand level measurements were scaled up in Schlesinger and Jasechko (2014) to estimate

global fluxes. However, we note that transpiration/evapotranspiration ratios in some studies neglect

understory transpiration fluxes, suggesting that the reported terrestrial

transpiration/evapotranspiration flux is likely to be a low end member of the actual terrestrial

transpiration/evapotranspiration ratio (Schlesinger and Jasechko, 2014).

The compiled data showed little spatial coherence in transpiration/evapotranspiration ratios.

First, studies completed at the same research site produced very different estimates of

transpiration/evapotranspiration. For example, Cavanaugh et al. (2011) and Moran et al. (2009) both

investigated a research site in Arizona (U.S.A.) and produced transpiration/evapotranspiration

estimates of 42% and 79%, respectively. The difference in these two results highlights the difficulty

28

associated with measuring transpiration fluxes and the uncertainties coupled to the scaling of point

(often tree-size scale) observations up to regional scales.

No trend was observed between precipitation rates and transpiration/evapotranspiration

ratios within the compiled data (Figure 1-5), highlighting that climate is not the only control upon

ecosystem productivity and primary production. Indeed, satellite based investigations of climate

controls upon terrestrial primary production reveals a three tier set of controls that includes

temperature, sunlight and water. Water is limiting in arid and semi-arid regions, but is a less

important control in other regions (e.g., the Amazon basin; Running et al., 2004). Primary production

in cold regions — which cover half of Earth’s of ice-free land surfaces (Jasechko et al., in review)

under the definition of Bates and Bilello (1966) — is limited by temperature and sunlight, and

primary production in tropical forests is limited by sunlight (Running et al., 2004).

Figure 1-5. Transpiration/evapotranspiration ratios compared to site-specific precipitation rates.

Reproduced from Schlesinger and Jasechko (2014).

29

Figure 1-6. Compiled estimates of transpiration/evapotranspiration ratios sorted into study

approach. Whiskers mark the 10th-90th percentile range of the data, the shaded rectangles mark the

25th-75th percentile range, the black line marks the median of each dataset.

Reducing the dataset in Figure 1-5 to include only studies within desert and steppe biomes

— which are expected to broadly be water-limited ecosystems — improves the trend between

precipitation and transpiration/evapotranspiration ratios slightly (R2 of 0.07; Figure 1-7) over the

entire, global compilation (R2 of 0.01). This suggests that further site-specific studies in deserts could

help to enhance our understanding of how water-limited ecosystems might respond to changes in

precipitation amounts.

30

Figure 1-7. Arid region transpiration/evapotranspiration ratios compared to site-specific precipitation

rates. A regression through the data reveals a significant (p < 0.05) trend towards higher

transpiration/evapotranspiration ratios with increasing precipitation amount.

Different methodologies used to calculate transpiration fluxes are found to produce slightly

different transpiration/evapotranspiration ratios. Isotope-based studies have a higher average

transpiration/evapotranspiration ratio of ~70%, whereas sap flow and meteorological models

suggest an average transpiration/evapotranspiration ratio of ~55% (Figure 1-6).

Several general circulation model based estimates of transpiration fluxes have been reported

over the past decade. The general circulation model estimates of transpiration/evapotranspiration

ratios are shown in Figure 1-1All alongside the compiled stand level data.

Generally, the GCMs have lower transpiration/evapotranspiration ratios than those

suggested by stand level measurements. GCM transpiration/evapotranspiration ratios range from

25% to 65%, whereas stand level transpiration measurements indicate a global transpiration flux of

closer to 60%, although this is likely to be a low end-member because many transpiration studies do

not include understory transpiration fluxes. Lawrence et al. (2007) first pointed out that the

Community Land Model (version 3) was underpredicting transpiration fluxes. General circulation

31

model estimates of transpiration/evapotranspiration range from 13 % (Community Land Model 3,

without improvements made by Lawrence et al., 2007), to 65 % (Lund–Potsdam–Jena model; Gerten

et al., 2005). A global land surface model that integrates satellite data (Miralles et al., 2011) has a

transpiration/evapotranspiration ratio of 80 %. A biophysical model developed by Choudhury et al.

(1998) proposes a transpiration/evapotranspiration ratio of 52 %. The broad range of

transpiration/evapotranspiration ratios estimated by earlier works (Table 1-3) highlights the immense

challenge of estimating this ratio. Upscaling a compilation of stand level measurements

(transpiration/evapotranspiration ratio of 61 %; Schlesinger and Jasechko, 2014) and a continental-

scale isotope-based approach (transpiration/evapotranspiration of 80 to 90 %; Jasechko et al., 2013)

suggest that the majority of general circulation models underestimate the role of transpiration in the

global water cycle, and that transpiration is the largest water flux from Earth’s continents.

32

Table 1-3. Global transpiration estimates.

Climate model Transpiration / Evapotranspiration Reference

Community Land Model

(version 3)

13 % (prior to improvements by

Lawrence et al., 2007) Lawrence et al., 2007

Community Land Model

(version 3)

41 % (with improvements by

Lawrence et al., 2007) Lawrence et al., 2007

Community Land Model 3.5

coupled to Community

Atmosphere Model 3.5

40 % Cao et al., 2010

Joint UK Land Environment

Simulator 38 % to 48 % Alton et al., 2009

Lund–Potsdam–Jena model 65 % Gerten et al., 2005

Global Soil Wetness Project 48 % Dirmeyer et al., 2006

n/a 52 % Choudhury et al., 1998

Global Land-surface

Evaporation: the Amsterdam

Methodology

80 % Miralles et al., 2011

Vegetation Integrative Simulator

for Trace Gases 24 % Ito and Inatomi, 2012

1.3 Dataset and methods

The development of isotope-based transpiration calculations is divided into three sections:

(i) development of a global lake water O and H isotope database and geospatial analysis of lake

catchments, (iii) calculation setup, geospatial data extraction, and analysis.

1.3.1 Development of a global lake water O and H isotope database

To develop a continental scale estimate of transpiration fluxes we required a continental

scale isotopic dataset. A lake-by-lake compilation of isotopic data was completed over four months

(September 2011 to December 2011) and the resulting compilation was presented at the American

Geophysical Union Fall Meeting in December of 2011 (Jasechko et al., 2011).

33

The dataset spans lakes from all continents with the exception of Antarctica. The dataset

contains 2129 measurements of δ18O and 2098 measurements of lake water δ2H from 73 unique

lakes compiled from 61 published datasets. Only lakes with surface areas on the order of 102-104 km2

were included in the large lake isotopic database.

The location of each of the compiled lakes is presented in Tables 1-4 and 1-5 and Figure 1-8.

Large lakes are concentrated geographically into scoured basins at the margins of the Laurentide and

Fennoscandanavian ice sheets that resided over North America and Eurasia during the last ice age.

These lakes include Great Bear, Great Slave, Lake Winnipeg, Lake Superior, Lake Huron, Lake

Michigan, Lake Erie and Lake Ontario in North America, and Lake Ladoga and Lake Onega in

Eurasia. Other large lakes are concentrated in geological rift valleys and include Lake Baikal and Lake

Tanganyika, which combine to a total volume that comprises more than one-third of all fresh water

at Earth’s surface. The majority of compiled lakes are exorheic (i.e., externally drained), with a

minority of endorheic (i.e., closed basin) lakes that include the Aral and Caspian Seas, Great Salt

Lake, and Lake Chad.

Before analyzing hydroclimate and hydrological data for each lake, the potential contributing

area to each lake was quantified by delineating watersheds for each of the 73 lakes in our database.

Lake catchment areas were delineated using the Shuttle Radar Topography Mission

(www2.jpl.nasa.gov/srtm) Digital Elevation Model and global river spatial data (waterbase.org).

Catchments were delineated by hand in a geographic information system on the basis of topographic

highs from the Shuttle Radar Topography Mission data and drainage basin data.

34

Figure 1-8. Locations of lake catchments studied in chapter one. The entire set of catchments covers

~10% of Earth’s surface. Small catchments are delineated with diamonds for clarity. Insets are

shown for the western region of North America, eastern Africa and the Tibetan plateau.

35

Table 1-4. Lake information

Lake Basin type Outflow Lat. Lon. Elevation (m.a.s.l.)

Abhe Endorheic - 11.1 41.8 240

Abiyata Endorheic - 7.8 38.7 1573

Afdera Endorheic - 13.3 40.9 -100

Albert Chain White Nile 1.7 30.9 615

Aral Sea Endorheic - 45.1 58.3 53

Athabasca Headwater Slave River 59.0 -110.0 213

Awasa Endorheic - 7.1 38.5 1708

Baikal Headwater Angara River 53.1 107.7 450

Baringo Endorheic - 0.6 36.1 970

Beysehir Endorheic - 37.7 31.5 1116

Biwa Headwater Seta River 35.3 136.2 86

Caspian Endorheic - 42.0 51.0 -28

Chad Endorheic - 13.0 14.2 244

Chamo Endorheic - 5.9 37.6 1110

Dagze Co Endorheic - 31.9 87.6 4478

Dead Sea Endorheic - 31.3 35.5 -420

Edward Headwater Semliki River -0.4 29.6 912

Egridir Endorheic - 38 30.9 924

Elephant Butte Headwater Rio Grande 33.4 107.2 1312

Erie Chain Niagara River 42.5 -79.6 173

Garda Headwater Mincio 45.6 10.7 65

Geneva Headwater Rhone River 46.4 6.6 372

Great Bear Headwater Great Bear R. 66.0 -120.0 156

Great Salt Endorheic - 41.2 -112.6 1270

Great Slave Chain Mackenzie R. 61.8 -114 176

Huron Chain St. Clair River 43.5 -82 176

Issyk-Kul Endorheic - 42.5 77.3 1600

Jackson Headwater Snake River 43.9 -110.6 2067

Kainji Headwater Niger River 10.4 4.6 139

Kivu Headwater Ruzizi River -2.0 29.0 1460

Kluane Headwater Kluane River 61.1 -138.5 781

Ladoga Chain Neva River 60.8 31.4 11

Lucern Headwater Reuss River 47.0 8.4 433

Malawi Headwater Shire River -12.0 34.5 471

Manasarovar Endorheic - 30.7 81.5 4584

Mar Chiquita Endorheic - -30.5 -62.7 67

36

Table 1-4. Lake information (continued)

Lake Basin type Outflow Lat. Lon. Elevation (m.a.s.l.)

Mead Chain Colorado River 36.1 -114.7 367

Michigan Chain Mackinac 42.4 -87.0 176

Naivasha Headwater - -0.8 36.4 1884

Namco Endorheic - 30.7 90.6 4718

Nasser Chain Nile 22.3 31.7 179

Ngangla Ringco Endorheic - 31.4 83.4 4724

Nicaragua Headwater San Juan River 11.2 -85.5 31

Oahe Chain Missouri River 44.4 -100.4 490

Okanagan Headwater Okanagan River 50.2 -119.4 345

Onega Headwater Svir River 61.9 35.4 56

Ontario Chain St. Lawrence R. 43.5 -79.4 86

Powell Headwater Colorado River 36.9 -111.5 1130

Poyang Headwater Changjiang 29.1 116.3 10

Qarhan Salt Lake Endorheic - 37.0 95.1 2685

Qinghai Hu Endorheic - 36.9 100.1 3200

Rukwa Endorheic - -8.4 32.7 800

Sakakawea Chain Missouri River 47.5 -101.4 561

Salton Sea Endorheic - 33.2 -115.7 -71

Sambhar Salt Endorheic - 27.0 75.1 360

Shala Endorheic - 7.4 38.6 1559

Superior Headwater St. Marys River 47.0 -85.2 183

Tahoe Headwater Truckee River 39.1 -120.1 1900

Tana Headwater Blue Nile 11.6 37.4 1790

Tanganyika Chain Rukuga River -4.9 29.5 773

Taro Co Endorheic - 31.1 84.3 4579

Taupo Headwater Waikato River -38.8 175.9 395

Titicaca Endorheic - -15.5 -69.4 3827

Tonlé Sap Chain Tonlé Sap River 11.6 104.9 14

Turkana Endorheic - 4.0 36.0 360

Valencia Endorheic - 10.2 -68.1 410

Van Endorheic - -38.7 -43.4 1646

Victoria Headwater White Nile -1.0 33.0 1133

Winnipeg Headwater Nelson River 52.1 -97.8 217

Yamdruk-tso Endorheic - 28.8 90.6 4458

Yellowstone Headwater Yellowstone R. 44.5 -110.4 2357

Zhari Namco Endorheic - 31.1 85.4 4624

Zige Tangco Endorheic - 32.0 90.8 4575

37

Table 1-5. Physical hydrology of lakes

Lake Catchment area

(km2) Open water

(km2) Volume

(km3) τ *

(years)

Abhe 94200 1600 6 50

Abiyata 10400 830 1.4 5

Afdera 7100 110 6 90

Albert 58800 5900 132 3

Aral Sea 949500 77900 193 6

Athabasca 271100 26900 110 1.8

Awasa 1500 100 1 10

Baikal 583200 37900 23600 280

Baringo 6600 150 0.2 2.3

Beysehir 15400 880 2 2

Biwa 3700 680 28 6.5

Caspian 3024400 428800 78000 260

Chad 976300 26200 72 4

Chamo 1900 320 4 17

Dagze Co 12800 640 3 6

Dead Sea 43200 1200 136 160

Edward 26800 2800 77 6

Egridir 3300 480 10 25

Elephant Butte 89900 820 2.5 2

Erie 103700 27300 484 2.1

Garda 2200 370 50 28

Geneva 7900 700 90 14

Great Bear 148500 41100 2300 55

Great Salt 81900 7700 20 6

Great Slave 702200 73400 2090 11

Huron 192100 66000 3540 15

Issyk-Kul 22000 6300 1740 170

Jackson 2000 150 6 7

Kainji 1565300 12800 15 1

Kivu 7500 2400 350 60

Kluane 5500 400 12 9

Ladoga 225800 34400 850 9

Lucern 2200 120 12 3

Malawi 124900 29100 7775 250

Manasarovar 5100 490 20 20

Mar Chiquita 129700 3100 6 3

*τ: approximate residence time of each lake

38

Table 1-5. Physical hydrology of lakes (continued)

Lake Catchment area

(km2) Open water

(km2) Volume (km3)

τ * (years)

Mead 147200 1100 25 2

Michigan 174400 60100 4920 64

Naivasha 3200 110 1 2

Namco 10700 1900 63 25

Nasser 2330500 16000 132 2

Ngangla Ringco 12500 610 5 6

Nicaragua 27900 8900 108 6

Oahe 150400 2400 25 1

Okanagan 6000 390 25 23

Onega 54500 12200 280 8

Ontario 82000 21000 1640 7

Powell 278600 3300 33 1.5

Poyang 161500 4100 3 1

Qarhan Salt 109700 820 50 8

Qinghai Hu 29600 4700 70 20

Rukwa 79300 3000 40 12

Sakakawea 461900 6400 29 1.4

Salton Sea 20000 930 9 3

Sambhar Salt 5900 20 0.2 1

Shala 4100 310 40 43

Superior 226200 92700 12100 88

Tahoe 1300 500 160 130

Tana 15000 3100 28 4

Tanganyika 230800 34000 19000 400

Taro Co 16800 830 5 6

Taupo 3500 630 60 12

Titicaca 56900 8600 900 160

Tonlé Sap 58800 ~3200 ~160 ~1

Turkana 180400 8700 200 40

Valencia 3000 360 6 10

Van 17100 3700 607 62

Victoria 264100 68400 2750 26

Winnipeg 1048200 88100 284 3

Yamdruk-tso 10000 1100 20 41

Yellowstone 2700 370 15 8

Zhari Namco 20100 1400 30 40

Zige Tangco 3300 190 3 19

*τ: approximate residence time of each lake

39

1.3.2 Calculation approach

To calculate transpiration rates we first developed a set of equations that can be applied to

estimate transpiration/evaporation ratios. A hydrological catchment’s water balance can be described

by water fluxes and changes to water storages (Equation 1.1):

QTExPIdt

dV Equation 1.1

where dV/dt is the rate of change in water storage in the catchment, I represents the flux of

precipitation entering the catchment plus any upstream liquid inflows from chain lake systems, E

represents physical evaporation losses from a catchment, T represents transpiration water losses

from a catchment, Q represents liquid losses via stream discharges and via groundwater recharge and

advection out of the basin, x represents the fraction of precipitation (P) that is intercepted by

vegetation and returned to the atmosphere through evaporation. At steady state Equation 1.1 reduces

to (Equation 1.2):

QTExPI Equation 1.2

In addition to the physical water balance, a steady state stable isotope mass balance can be

described as (Equation 1.3):

QTExPI QTEPI Equation 1.3

where δI is the flux-weighted isotopic composition of inputs (precipitation and chain lake inflows), δP

is the isotopic composition of precipitation, δE is the isotopic composition of evaporating moisture

(isotope fractionation labelled), δT is the isotopic composition of water used by plants in transpiration

(not isotope fractionation labelled) and δi is the isotopic composition of intercepted rain and snow.

40

Combining equations 1 and 2 yields a single equation representing the transpiration flux exiting a

hydrological catchment under steady state conditions (Equation 1.4):

ET

EPEQEI xPQIT

Equation 1.4

1.3.3 Geospatial analysis

Next, each of the inputs into equation 1.4 were quantified using multiple global geospatial

datasets. The following paragraphs examine each of the input parameters in equation 1.4 one by one.

The precipitation input to each hydrological catchment (P) was calculated using global high

resolution precipitation data spanning the continents (New et al., 2002). The catchment area of each

lake was calculated, as was the mean annual precipitation rate for each catchment. The two

components were multiplied together to estimate the annual flux of precipitation inputs for each

basin. Annual water inputs to each catchment (I) were calculated as the sum of precipitation inputs

(P) plus contributions from upstream chain lake systems.

The liquid fluxes out of each catchment (Q) were compiled on a river-by-river basis using

data within the primary literature. These water fluxes were also used to quantify chain lake inflows

into downstream lake basins where appropriate. Groundwater fluxes out of lakes were also collected

on a lake-by-lake basin for endorheic basins with known connections with regional groundwater flow

systems (e.g., Isiorho et al., 1996; Ojiambo et al., 2003).

The proportion of precipitation intercepted and returned to the atmosphere (x) was

calculated using satellite-based grids developed by Miralles et al. (2010) coupled to annual

precipitation rates (New et al., 2002).

41

δP, the flux-weighted isotopic composition of precipitation inputs entering each hydrological

catchment. δP was estimated for each hydrological catchment considering seasonal and spatial

variability in precipitation amounts. Geospatial grids of monthly precipitation isotope compositions

are available for download from waterisotopes.org following methods of Bowen and Revenaugh

(2003), Bowen and Wilkinson (2002) and Bowen (2010). The seasonality of precipitation amount was

quantified by flux weighting each grid call at a monthly time step (where δP(j) is the isotopic

composition of precipitation for month j, and Pj represents the monthly precipitation rate (mm per

month) at each grid cell). Our calculation also accounts for the spatial distribution of precipitation

was included by weighting the individual grid cells to their respective precipitation amounts (i.e., grid

cell i) following equation 1.5:

i

n

1i

i

ij

12

1j

Pj

12

1jn

1i

PP

PP

Pj

Equation 1.5

The isotopic composition of water inputs (δI) to each catchment was calculated by flux

weighting the isotopic compositions of precipitation (δP) against contributions from upstream lakes

(i.e., river inflows from upstream lakes).

The isotopic composition of evaporate from each catchment (δE) was calculated using an

evaporation model (Craig and Gordon, 1965; Equation 1.6):

K

KALake

Eh1

h*/*

Equation 1.6

where δLake is the isotopic composition of lake water (compiled from primary literature), α* is an

isotopic equilibrium fractionation factor (temperature dependent; temperature data from New et al.,

2002)), ε* is an equilibrium isotopic separation factor (approximated as: α* − 1), h is the relative

humidity of the catchment (calculated for each catchment using geospatial data from New et al.,

42

2002), δA is the isotopic composition of atmospheric vapor (estimated in two ways: once by using

precipitation as a liquid signature of atmospheric vapor, and back calculating the vapor isotope

composition using temperature data (New et al., 2002), and second by compiling δA values from an

isotope-enabled general circulation model developed by Yoshimura et al., 2008) and εK is a kinetic

isotopic separation factor calculated by CK·[1 – h] (Gonfiantini, 1986).

The isotopic composition of transpired moisture (δT) was estimated across the continents

using isotopic data for precipitation and seasonality in primary productivity. We weighted the

isotopic composition of precipitation15 spatially (i) to long-term monthly mean normalized difference

vegetation indices (NDVI; proxy for chlorophyll abundance), with NDVI values below zero set to a

value of zero. A range of two temporal (j) weighting approaches is used for δT, one weighted to

growing season (representing shallow rooted end-member; Equation 1.7) and another to monthly

precipitation (representing a deep rooted end-member, i.e., a phreatophyte; Equation 1.8):

in

1i

i

ij

12

1j

Pj

12

1jn

1i

SHALLOWTNDVI

NDVINDVI

NDVIj

Equation 1.7

in

1i

i

ij

12

1j

Pj

12

1jn

1i

DEEPTNDVI

NDVIP

Pj

Equation 1.8

Water use efficiency functions were compiled from the primary literature to develop

catchment wide estimates of water use efficiency. A review of water use efficiency data as a function

of humidity reveals differences between C3 and C4 plants (Table 1-9; reproduced from Jasechko et al.,

2013), such that a global grid of C3/C4 photosynthesis types was also sought after. We assessed

spatial variability in C3/C4 species abundances using grids developed by Still et al. (2003),

downloaded from http://webmap.ornl.gov/wcsdown/dataset.jsp?ds_id=932. Power regressions of

http://webmap.ornl.gov/wcsdown/dataset.jsp?ds_id=932

43

C3 and C4 datasets were applied to develop water use efficiency/climate relationships for each

photosynthetic pathway: C3: Water use efficiency = 4.21×(Vapor pressure deficit)-0.67 and C4: Water

use efficiency = 6.91×(Vapor pressure deficit)-0.40. Daytime vapor pressure deficit grids were then

estimated at a monthly time step by averaging the maximum and average monthly mean temperatures

at each grid cell (data from Hijmans et al., 2005) and catchment-wide water use efficiencies for each

basin were calculated (Figure 1-9, 1-10). Resulting transpiration fluxes were then converted into gross

primary productivity using the catchment water use efficiency data. The inputs for each calculation

are presented in Tables 1-6 to 1-8.

44

Table 1-6. Model input parameters (± 1 s.d. uncertainty shown)

Lake δ18OL (‰)

δ2HL (‰)

δ18OT (‰)

δ2HT

(‰) δ18OI (‰)

δ2HI

(‰)

Abhe 3.7±0.5 -4±4 0.2±1.4 12±11 -0.3±1 8±9

Abiyata 8.5±1.3 59±11 -1.8±1.3 -3±10 -2.0±1 -4±9

Afdera 6.4±0.5 28±2 0.9±1.5 14±11 0.5±1 13±9

Albert 5.2±0.5 37±4 -2.8±1.3 -10±10 -0.8±0.7 -1±7

Aral Sea 2.8±1.8 0±11 -5.8±2.7 -32±22 -9.9±1 -65±9

Athabasca -16.8±1.3 -131±4 -16.6±1.7 -127±14 -17.9±1 -137±9

Awasa 7.5±0.7 51±4 -1.7±1.4 -3±11 -2.3±1 -6±9

Baikal -15.8±0.3 -123±1 -12.2±1.2 -91±10 -12.3±1 -92±9

Baringo 7.5±1.3 42±8 -3.8±1.5 -17±12 -4.4±1 -21±9

Beysehir -1.4±0.9 -18±4 -6.8±1.9 -40±14 -8.2±1 -50±9

Biwa -7.0±0.5 -44±5 -8.1±1.2 -53±10 -8.0±1 -52±9

Caspian -1.7±0.2 -20±3 -10.5±2 -76±16 -11.6±1 -85±9

Chad 8.2±3.6 45±19 -1.8±1.9 -8±14 -3.2±1 -17±9

Chamo 7.7±0.8 50±3 -1.4±1.2 0±9 -1.4±1 0±9

Dagze Co -6.4±0.5 -69±4 -16.1±1.4 -115±10 -16.5±1 -117±9

Dead Sea 1.4±2.3 4±2 -5.1±1.6 -25±11 -6.1±1 -28±9

Edward 4.2±0.2 30±1 -3.5±1.3 -15±11 -3.7±1 -17±9

Egridir -2.4±0.6 -21±2 -7.4±1.7 -48±13 -8.5±1 -53±9

Elephant Butte -7.8±1.2 -68±6 -12.9±1.2 -91±10 -12.8±1 -93±9

Erie -6.6±0.3 -48±9 -7.1±1.9 -46±15 -7.6±0.5 -55±9

Garda -7.3±0.2 -55±1 -8.0±1.2 -52±9 -7.8±1 -51±9

Geneva -12.3±0.1 -88±2 -9.1±1.9 -61±14 -10.9±1 -74±9

Great Bear -18.7±0.5 -155±4 -17.2±3.7 -130±30 -22.3±1 -171±9

Great Salt -4.8±1.0 -67±9 -12.5±2.4 -93±18 -14.8±1 -110±9

Great Slave -17.8±0.3 -141±3 -16.5±2.3 -127±18 -18.6±0.9 -143±9

Huron -7.1±0.1 -54±2 -8.1±2.2 -54±17 -9.1±0.6 -64±6

Issyk-Kul -0.7±0.1 -9±2 -10.2±1.5 -62±15 -10.6±1 -72±9

Jackson -17.9±0.1 -141±4 -13.3±3.2 -96±26 -17.4±1 -129±9

Kainji -17±11 -2.2±2.2 -12±16 -4.5±1 -27±9

Kivu 1.5±1.4 18±6 -4.3±1.4 -20±12 -4.7±1 -25±9

Kluane -22.6±0.5 -177±3 -18.5±2.3 -149±16 -21.8±1 -169±9

Ladoga -9.5±0.5 -10.8±1.8 -78±15 -11.9±0.9 -76±8

Lucern -12.7±0.5 -9.9±1.7 -67±13 -11.0±1 -75±9

Malawi 2.0±0.1 12±1 -3.3±2 -14±16 -4.6±1 -24±9

Manasarovar -5.5±3.5 -58±16 -17.1±1.5 -112±12 -16.4±1 -117±9

Mar Chiquita 3.1±0.2 18±1 -5.4±1.4 -32±10 -5.0±1 -31±9

45

Table 1-6. Model input parameters (± 1 s.d. uncertainty shown; continued)

Lake δ18OL (‰)

δ2HL (‰)

δ18OT (‰)

δ2HT

(‰) δ18OI (‰)

δ2HI

(‰)

Mead -12.9±0.7 -103±5 -11.4±1.5 -85±12 -12.9±0.9 -97±8

Michigan -5.8±0.1 -44±1 -7.9±1.8 -54±14 -8.6±0.8 -61±8

Naivasha 4.6±1.1 26±7 -5.2±1.3 -28±10 -5.5±1 -29±9

Namco -7.3±0.4 -70±3 -17.8±1.3 -130±11 -17.5±1 -126±9

Nasser 0.0±1.4 8±8 -0.9±1.5 1±12 -1.5±1 -3±9

Ngangla Ringco -4.2±0.5 -57±4 -16.6±1.2 -120±10 -16.6±1 -118±9

Nicaragua -2.0±0.5 -9±4 -4.6±1.8 -27±15 -5.7±1 -37±9

Oahe -14.2±0.2 -116±2 -11.8±1.4 -88±11 -13.0±0.8 -98±8

Okanagan -11.4±0.5 -103±3 -12.8±1.9 -99±15 -14.5±1 -111±9

Onega -10.4±0.7 -10.8±2.2 -78±18 -12.8±1 -95±9

Ontario -6.6±0.1 -49±1 -8.2±2.1 -51±17 -7.4±0.3 -54±3

Powell -15.0±0.2 -115±2 -12.6±2.1 -93±16 -14.6±1 -107±9

Poyang -6.8±1.1 -38±9 -7.0±1.4 -46±11 -6.5±1 -42±9

Qarhan Salt 6.6±0.5 -16±4 -12.6±1.3 -92±10 -13.3±1 -95±9

Qinghai Hu 2.4±0.7 12±5 -12.5±1.4 -86±10 -12.0±1 -86±9

Rukwa 4.3±0.2 26±2 -3.6±1.7 -17±14 -4.7±1 -26±9

Sakakawea -15.5±0.2 -124±1 -13.3±1.8 -99±14 -14.5±1 -109±9

Salton Sea -3.6±2.4 -52±12 -6.5±1.8 -52±14 -8.4±1 -66±9

Sambhar Salt 11.5±11.2 -4.9±1.2 -31±10 -5.0±1 -30±9

Shala 7.5±0.7 52±3 -1.3±1.2 1±10 -1.4±1 0±9

Superior -8.6±0.1 -66±1 -9.6±2.1 -67±17 -11.4±1 -81±9

Tahoe -5.5±0.3 -59±16 -11.2±2.5 -87±18 -13.8±1 -103±9

Tana 4.5±0.9 35±6 -2.4±1.4 -9±11 -2.7±1 -11±9

Tanganyika 3.8±0.4 26±2 -3.3±1.6 -15±13 -4.0±1 -20±9

Taro Co -5.6±0.5 -68±4 -16.3±1.3 -118±10 -16.6±1 -119±9

Taupo -5.3±0.4 -33±3 -7.1±1.3 -44±10 -6.9±1 -43±9

Titicaca -3.8±0.7 -50±3 -12.7±1.7 -86±14 -13.6±1 -94±9

Tonlé Sap -5.2±1.0 -5.5±1.5 -34±12 -7.0±1 -25±6

Turkana 5.6±0.4 38±4 -1.6±1.2 -1±10 -1.7±1 -2±9

Valencia 22±4 -4.1±1.4 -29±11 -4.6±1 -32±9

Van 1.0±0.1 -7±0 -7.4±2.9 -45±22 -10.7±1 -70±9

Victoria 3.5±0.5 -3.5±1.3 -15±11 -3.6±1 -16±9

Winnipeg -10.4±0.5 -79±8 -11.6±2.3 -92±15 -14.3±1 -107±9

Yamdruk-tso -5.5±0.5 -68±4 -18.0±1.8 -136±17 -16.7±1 -121±9

Yellowstone -16.5±0.2 -135±4 -15.1±2.6 -118±17 -17.8±1 -133±9

Zhari Namco -6.7±0.5 -75±4 -17.3±1.3 -122±10 -17.0±1 -122±9

Zige Tangco -6.1±0.5 -68±4 -16.1±1.6 -115±12 -17.0±1 -122±9

46

Table 1-7. Model input parameters (± 1 s.d. uncertainty shown)

Lake δ18OP (‰)

δ2HP (‰)

δ18OA (‰)

δ2HA

(‰) TL

(°C) hA (%)

Abhe -0.3±1 8±9 -7.7±2 -51±33 26.1±1 67±3

Abiyata -2.0±1 -4±9 -10.5±1 -73±12 20.0±1 59±3

Afdera 0.5±1 13±9 -7.1±3 -46±37 27.1±1 68±3

Albert -2.9±1 -11±9 -11.3±1 -77±9 24.4±1 69±3

Aral Sea -9.9±1 -65±9 -18.4±1 -142±9 14.2±1 55±3

Athabasca -17.9±1 -137±9 -35.5±4 -288±85 -9.3±1 74±3

Awasa -2.3±1 -6±9 -11.5±1 -81±9 18.0±1 59±3

Baikal -12.3±1 -92±9 -30.6±2 -251±53 -8.4±1 79±3

Baringo -4.4±1 -21±9 -12.4±1 -85±9 24.2±1 54±3

Beysehir -8.2±1 -50±9 -16.7±1 -123±21 14.0±1 56±3

Biwa -8.0±1 -52±9 -18.0±2 -134±30 14.9±1 75±3

Caspian -11.6±1 -85±9 -17.7±1 -136±27 16.0±1 68±3

Chad -3.2±1 -17±9 -9.3±3 -69±39 27.4±1 36±3

Chamo -1.4±1 0±9 -10.4±1 -72±18 21.9±1 57±3

Dagze Co -16.5±1 -117±9 -28.9±4 -225±66 0.1±1 58±3

Dead Sea -6.1±1 -28±9 -13.1±1 -91±9 23.3±1 56±3

Edward -3.7±1 -17±9 -12.2±1 -85±9 23.6±1 70±3

Egridir -8.5±1 -53±9 -17.1±2 -126±26 14.7±1 55±3

Elephant Butte -12.8±1 -93±9 -25.6±2 -200±47 5.4±1 49±3

Erie -8.7±1 -58±9 -19.4±2 -146±30 11.0±1 74±3

Garda -7.8±1 -51±9 -16.0±1 -118±15 18.0±1 75±3

Geneva -10.9±1 -74±9 -17.2±2 -129±28 13.8±1 71±3

Great Bear -22.3±1 -171±9 -39.3±5 -318±96 -14.2±1 73±3

Great Salt -14.8±1 -110±9 -21.7±1 -171±24 16.2±1 46±3

Great Slave -18.9±1 -145±9 -36.9±4 -300±87 -11.6±1 74±3

Huron -10.1±1 -69±9 -23.7±2 -183±38 1.6±1 79±3

Issyk-Kul -10.6±1 -72±9 -20.7±1 -158±31 9.7±1 52±3

Jackson -17.4±1 -129±9 -24.9±3 -197±51 7.0±1 51±3

Kainji -4.5±1 -27±9 -9.2±3 -68±42 28.2±1 41±3

Kivu -4.7±1 -25±9 -13.8±1 -98±10 19.5±1 74±3

Kluane -21.8±1 -169±9 -31.0±4 -251±77 3.8±1 75±3

Ladoga -12.1±1 -90±9 -23.7±3 -188±52 4.9±1 85±3

Lucern -11.0±1 -75±9 -18.9±2 -143±38 10.9±1 76±3

Malawi -4.6±1 -24±9 -12.1±1 -84±9 24.1±1 71±3

Manasarovar -16.4±1 -117±9 -27.1±6 -209±98 0.5±1 60±3

Mar Chiquita -5.0±1 -31±9 -14.6±1 -107±12 21.2±1 71±3

47

Table 1-7. Model input parameters (± 1 s.d. uncertainty shown; continued)

Lake δ18OP (‰)

δ2HP (‰)

δ18OA (‰)

δ2HA

(‰) TL

(°C) hA (%)

Mead -12.0±1 -89±9 -19.5±1 -152±12 19.9±1 34±3

Michigan -9.0±1 -62±9 -22.6±2 -175±38 4.3±1 76±3

Naivasha -5.5±1 -29±9 -15.4±1 -111±17 14.0±1 68±3

Namco -17.5±1 -126±9 -28.6±4 -223±77 1.3±1 57±3

Nasser -1.5±1 -3±9 -10.1±1 -68±20 27.7±1 44±3

Ngangla Ringco -16.6±1 -118±9 -28.0±4 -217±79 -0.4±1 58±3

Nicaragua -5.7±1 -37±9 -13.7±1 -99±9 26.6±1 80±3

Oahe -12.3±1 -91±9 -23.4±2 -183±43 11.1±1 62±3

Okanagan -14.5±1 -111±9 -22.8±2 -184±42 10.0±1 60±3

Onega -12.8±1 -95±9 -24.4±3 -194±53 4.1±1 86±3

Ontario -9.9±1 -66±9 -22.8±2 -174±37 4.4±1 76±3

Powell -14.6±1 -107±9 -23.2±1 -181±29 11.9±1 46±3

Poyang -6.5±1 -42±9 -16.4±1 -121±20 21.8±1 76±3

Qarhan Salt -13.3±1 -95±9 -25.9±2 -201±44 2.4±1 48±3

Qinghai Hu -12.0±1 -86±9 -23.9±2 -185±35 4.2±1 49±3

Rukwa -4.7±1 -26±9 -12.6±1 -88±9 23.9±1 67±3

Sakakawea -14.5±1 -108±9 -25.4±3 -200±50 9.1±1 59±3

Salton Sea -8.4±1 -66±9 -15.5±1 -126±9 25.1±1 51±3

Sambhar Salt -5.0±1 -30±9 -12.7±1 -92±9 27.2±1 43±3

Shala -1.4±1 0±9 -10.5±1 -73±11 19.8±1 59±3

Superior -11.4±1 -81±9 -27.3±3 -215±54 -3.1±1 75±3

Tahoe -13.8±1 -103±9 -22.5±2 -181±46 7.9±1 53±3

Tana -2.7±1 -11±9 -11.1±1 -78±9 20.1±1 53±3

Tanganyika -4.1±1 -21±9 -12.2±1 -85±9 23.8±1 71±3

Taro Co -16.6±1 -119±9 -27.0±4 -209±76 1.9±1 59±3

Taupo -6.9±1 -43±9 -16.4±2 -123±26 13.4±1 81±3

Titicaca -13.6±1 -94±9 -21.9±3 -165±57 8.4±1 57±3

Tonlé Sap -6.3±1 -40±9 -14.5±1 -104±9 27.6±1 80±3

Turkana -1.7±1 -2±9 -9.4±2 -62±26 29.0±1 52±3

Valencia -4.6±1 -32±9 -12.6±1 -98±14 23.4±1 74±3

Van -10.7±1 -70±9 -18.2±2 -136±34 12.0±1 57±3

Victoria -3.6±1 -16±9 -12.3±1 -86±9 22.2±1 72±3

Winnipeg -14.3±1 -107±9 -22.2±2 -174±36 12.9±1 69±3

Yamdruk-tso -16.7±1 -121±9 -26.9±4 -210±71 3.1±1 55±3

Yellowstone -17.8±1 -133±9 -25.3±3 -200±54 6.6±1 51±3

Zhari Namco -17±1 -122±9 -27.8±5 -216±91 1.7±1 59±3

Zige Tangco -17±1 -122±9 -28.9±2 -225±40 0.7±1 59±3

48

Table 1-8. Lake isotope investigations

Lake n Reference

Abhe 1 Kebede et al., 2009

Abiyata 7 Kebede et al., 2009; Craig et al., 1977

Afdera 11 Gonfiantini et al., 1973

Albert 1 Bahati et al., 2005

Aral Sea 36 Oberhansli et al., 2009

Athabasca 4 Hitchon and Karouse, 1972; Wolfe et al., 2007

Awasa 33 Kebede et al., 2009; Craig et al., 1977; Darling et al., 1996

Baikal 32 Seal and Shanks, 1998

Baringo 3 Cerling et al., 1988; Becht et al., 2005

Beysehir 11 Dincer et al., 1968

Biwa 15 Taniguichi et al., 2001

Caspian 25 Froehlich et al., 2000

Chad 95 Fontes et al., 1970

Chamo 13 Kebede et al., 2009

Dagze Co 1 Yuan et al., 2011

Dead Sea 27 Gat et al., 1984

Edward 4 Rossel et al., 2006

Egridir 10 Dincer et al., 1968

Elephant Butte 12 Phillips et al., 2003; This work

Erie 151 Yang et al., 1996; Karim et al., 2008; Jasechko et al., 2014

Garda 176 Longinelli et al., 2008

Geneva 9 Fontes et al., 1970

Great Bear 32 Hitchon et Krouse, 1972; This work

Great Salt 32 Nielson and Bowen, 2010

Great Slave 7 Hitchon and Krouse, 1972; Brock et al., 2009

Huron 142 Yang et al., 1996; Karim et al., 2008; Jasechko et al., 2014

Issyk-Kul 7 Ricketts et al., 2001

Jackson 2 This work

Kainji 18 Zimmerman et al., 1976

Kivu 19 Cohen et al., 1997

Kluane 15 Brahney, 2007

Ladoga 3 Luz and Barkan et al., 2010

Lucern 1 Luz and Barkan et al., 2010

Malawi 21 Gonfiantini et al., 1979

Manasarovar 7 Yao et al., 2009

Mar Chiquita 31 Dapena et al., 1997

49

Table 1-8. Lake isotope investigations

Lake n Reference

Mead 12 Craig, 1966; This work

Michigan 80 Jasechko et al., 2014

Naivasha 9 Darling et al., 1997; Cerling et al., 1988; Becht et al., 2005

Namco 2 Liu et al., 2009

Nasser 41 Aly et al., 1993

Ngangla Ringco 1 Yuan et al., 2011

Nicaragua 1 Lachniet et al., 2002

Oahe 11 Kendall and Coplen, 2001

Okanagan 36 Wassenaar et al., 2011

Onega 2 Luz and Barkan, 2010

Ontario 68 Yang et al., 1996; Karim et al., 2008; Jasechko et al., 2014

Powell 118 Kendall and Coplen, 2001; This work

Poyang 35 Wenbin et al., 2007

Qarhan Salt 1 Yuan et al., 2011

Qinghai Hu 10 Henderson et al., 2010

Rukwa 4 Bergonzini et al., 2001

Sakakawea 15 Kendall and Coplen, 2001

Salton Sea 3 Mazzini et al., 2011

Sambhar Salt 8 Yadav et al., 1997

Shala 19 Craig et al., 1977

Superior 161 Karim et al., 2008; Jasechko et al., 2014

Tahoe 4 McKenna, 1992

Tana 52 Gonfiantini et al., 1973

Tanganyika 48 Craig et al., 1975

Taro Co 1 Yuan et al., 2011

Taupo 1 Stewart et al., 1981

Titicaca 12 Fontes et al., 1979

Tonlé Sap 14 Kabeya et al., 2008

Turkana 9 Cerling et al., 1988

Valencia 1 Friedman et al., 1964

Van 2 Kwiecien, 2011

Victoria 1 Beuing et al., 1997

Winnipeg 3 Buhay et al., 1998; This work

Yamdruk-tso 1 Yuan et al., 2011

Yellowstone 1 This work

Zhari Namco 1 Yuan et al., 2011

50

Figure 1-9. Compiled water use efficiency and vapor pressure deficit relationshiops (compiled data

and original references presented in Jasechko et al., 2013). Black lines mark C4 pathways and grey

lines mark C3 pathways. Thick black and red lines mark the regressions through the entire C4 (red)

and C3 (black) datasets, respectively.

Figure 1-10. Estimated spatial distributions of water use efficiency, accounting for climate and

photosynthesis types (C3 and C4) Figure based upon work by Jasechko et al. (2013).

51

Table 1-9. Plant water use efficiency (WUE) as a function of vapor pressure deficit (VPD)

Plant C3/C4 WUE

(mmol CO2/mol H2O) VPD range (kPa)

Acer saccharum, Betula alleghaniesis, Tsuga canadensis

C3 2.8×(VPD)−0.37 <0.1 to 1.7

Arachis C3 1.4 to 2.3 2.2

Deciduous C3 5.9×(VPD)−0.98 0.3 to 0.9

Encilia fariosa C3 3.5×(VPD)−1.02 1.1 to 3.8

Evergreen C3 7.1×(VPD)−0.93 0.3 to 0.9

Hordeum vulgare C3 3.0×(VPD)−0.39 0.6 to 1.9

Ipomoea vagans C3 8.0×(VPD)−0.77 0.6 to 3.0

Larrea tridentata C3 3.6×(VPD)−0.38 0.5 to 2.0

Nicotiana glauca C3 5.0×(VPD)−0.80 0.5 to 2.0

Olea europaea L. C3 5.2×(VPD)−0.94 0.2 to 6.8

Oryza sativa C3 4.8×(VPD)−0.48 0.5 to 2.0

Oryza sativa L. C3 1.8×(VPD)−0.28 0.3 to 1.6

Phalaris aquatica C3 3.2×(VPD)−0.44 0.5 to 2.0

Phaseolus vulgaris C3 4.8×(VPD)−0.76 0.5 to 2.0

Pinus sylvestris, Picea abies C3 5.8×(VPD)−0.39 0.1 to 1.3

Populus tremuloides C3 5.5×(VPD)−0.82 0.5 to 4.5

Prosopis juliflora C3 10.6×(VPD)−1.38 1.0 to 9.6

Pseudotsuga C3 8.9×(VPD)−0.49 0.3 to 3.2

Quercus C3 6.6×(VPD)−0.44 0.2 to 1.8

Salix viminalis C3 7.8×(VPD)−0.58 0.2 to 2.1

Triticum C3 3.0×(VPD)−0.67 0.5 to 1.5

Triticum C3 5.9×(VPD)−0.50 <0.1 to 2.8

Dactyloctenium aegyptium C4 14.3×(VPD)−0.73 1.2 to 3.8

Eragrostis tremula C4 17.9×(VPD)−1.12 2.1 to 5.0

Miscanthus giganteus, Spartina cynosuroides

C4 5.3×(VPD)−1.18 1.0 to 1.2

Paspalum plicatulum C4 5.7×(VPD)−0.35 0.5 to 2.0

Pleuraphis rigida C4 7.0×(VPD)−0.78 1.3 to 3.8

Schoenefeldia gracilis C4 10.1×(VPD)−1.05 0.9 to 3.0

Zea mays C4 8.9×(VPD)−0.35 <0.1 to 2.9

Zea mays C4 7.7×(VPD)−0.47 0.5 to 2.0

References for each of the above studies compiled within Jasechko et al. (2013)

52

A global in scale calculation was developed using a new global compilation of river isotopic

data (Table 1-10). A deuterium excess mass balance of the continents was used to estimate the global

transpiration/evapotranspiration ratio on land surfaces:

ET

EPEQEI

dd

ddxPddQddIT

Equation 1.9

where d represents the deuterium excess of each flux, I represents the flux of precipitation entering

the catchment, E represents physical evaporation losses from a catchment, T represents transpiration

water losses from a catchment, Q represents liquid losses via runoff and groundwater discharge out

of the basin, x represents the fraction of precipitation (P) that is intercepted by vegetation and

returned to the atmosphere through evaporation. The global water use efficiency was estimated by

spatially weighting our grid cell estimates of water use efficiency to mean annual mean normalized

difference vegetation indices (values less than zero assigned a value of zero), and was found to be

close to 3.2±0.9 mmol CO2 per mol H2O.

Figure 1-11. The deuterium excess of 31 major rivers (a) and associated annual streamflow (b).

Discharge data from Dai and Trenberth (2002).

53

Table 1-10. Deuterium excess of major rivers ranked by discharge

River Q (km3/y)** Rank** Deuterium excess* (‰)

Amazon 6642 1 9.8±1.8

Changjiang 944 4 8.8±2.5

Mississippi 610 6 8.5±1.8

Yenisey 599 7 8.2±2.6

Paraná 568 8 7.7±4.0

Lena 531 9 7.2±1.3

Mekong 525 10 5.9±4.5

Ob 412 13 5.8±2.1

St Lawrence 363 16 3.1±1.3

Amur 354 17 6.3±1.3

Mackenzie 290 19 -1.0±1.2

Columbia 252 21 6.6±1.6

Yukon 212 24 3.5±1.6

Danube 202 26 9.0±1.2

Fraser 144 30 2.9±3.8

Kolyma 118 35 5.7±1.9

Indus 104 38 13.5±5.9

Neva 79 45 6.4±1.0

Sacramento 69 50 8.6±0.9

Kuskokwim 57 54 5.7±1.2

Alabama 51 68 10.4±2.3

Stikine 51 69 8.3±1.1

Susquehanna 46 75 12.9±2.3

Susitna 45 78 4.7±1.1

Volta 37 86 1.3±6.9

Copper 34 96 6.5±1.4

Nushagak 31 109 5.7±1.3

Tombigbee 27 124 11.6±2.9

Colorado R. 12 165 -1.8±1.2

Brazos 7 180 2.9±5.4

Colorado (TX) 3 195 4.6±8.3

Rio Grande 2 196 -1.5±1.2

* Deuterium excess (d) defined as (Dansgaard, 1964): d = δ2H − 8∙δ18O (±1 s.d. shown), references

to original data sources presented in Jasechko et al. (2013).

** Runoff data from Dai and Trenberth (2002)

54

1.4 Results

Figure 1-12 presents the range of δ18O and δ2H values observed in Earth’s large lakes. The

lowest δ18O and δ2H values in large lakes are generally found at high altitudes and latitudes, whereas

the highest δ18O and δ2H values are found in lakes that are located at low latitudes and altitudes. The

entire dataset spans the range of −23‰ to +15‰ in δ18O and −180‰ to +80‰ in δ2H values. The

majority of lakes are found to plot below a regression of meteoric waters (i.e., the “global meteoric

water line;” Craig, 1961) because of kinetic isotope effect occurring during the process of

evaporation (Craig, 1961).

Figure 1-12. The stable O and H isotopic composition of Earth’s large lakes and inland/semi-

enclosed seas. The lowest δ18O and δ2H values are observed at high altitudes and latitudes (e.g.,

Kluane Lake) and the highest δ18O and δ2H values are observed at low latitudes and altitudes (e.g.,

Lake Turkana). Dots mark individual water samples, with shaded areas enclosing all points for a

single lake (“convex hull” – from Jasechko et al., 2013). References presented in Table 1-8.

55

Some lakes have greater internal variability in δ18O and δ2H values than others. Generally,

stratified and shallow lakes (e.g., Lake Chad, Great Salt Lake and the Aral Sea) have larger variations

in δ18O and δ2H values than well mixed, deep lakes (e.g., the North American Laurentian Great

Lakes or Lake Baikal; Figure 1-13). Stratified lakes (e.g., Tanganyika) have different δ18O values at the

lake surface compared to values at depth (Figure 1-14); whereas, well-mixed lakes such as Lake Baikal

(Figure 1-14) and each of the North American Great Lakes (Figure 1-15) have relatively homogenous

δ18O and δ2H values.

Figure 1-13. The internal variability in a selection of large lake δ18O and δ2H values. The upper pane

highlights two well-mixed, deep (>400 m maximum depth) lakes that have homogenous isotopic

compositions. The lower pane delineates heterogeneous lake δ18O and δ2H values found in shallow

large lakes and inland seas.

56

Figure 1-14. Temperature (top row) and δ18O (bottom row) profiles for the two largest (volumetric)

lakes on Earth: Tanganyika (left) and Baikal (right; data from Craig, 1975 and Seal and Shanks, 1998).

Baikal has a heterogenous temperature profile, but a homogenous isotopic composition. Tanganyika

has a heterogeneous isotopic profile and a heterogenous temperature profile.

Figure 1-15. The isotopic composition and temperature (April) of the North American Great Lakes

(profile shown here spans Superior, Huron, Erie and Ontario, but skips Michigan, which has the

highest δ18O and δ2H values of the Laurentian Great Lakes; from Jasechko et al., 2014).

57

The rate of transpiration spanning 10% of Earth’s ice free area based on stable isotopic data

is presented in Figure 1-17, both as a percentage of total evapotranspiration (Figure 1-17 a) and as a

transpiration rate (Figure 1-17 b). The rate of transpiration is greatest in the humid tropics where

primary production is rarely limited by water and temperature (e.g., east African Great Lakes; Figure

1-16; Running et al., 2004). The rate of transpiration – in our dataset – is smallest in the boreal forest,

where growth is limited to a short growing season due to the pronounced seasonality of the high

latitudes. Transpiration rates are also discovered to be small in arid climates, where growth is

expected to be limited by water supplies.

Global transpiration fluxes calculated by a deuterium excess mass balance of continental

waters support conclusions reached using our global lake dataset: transpiration is the largest H2O flux

from the continents. Our global analysis suggests that 80 to 90% of vapor flows from the continents

are funneled through transpiration, with smaller percentages left to evaporation or sublimation.

Volumetrically, isotopic data support a transpiration flux of ~60,000 km3 of water per year, which

has a corresponding latent energy requirement of 33 W/m2, suggesting that a substantial percentage

of radiation absorbed by Earth’s surfaces is appropriated to the vaporization of water at plant leaf

surfaces (“solar absorbed at the surface” reported as ranging between 147 W/m2 and 174 W/m2

(Trenberth et al., 2009).

58

Figure 1-16. The transpiration rate for seven ecozones (each bar is one lake catchment). The total

evapotranspiration flux for each catchment is marked by a black line, and the median result of Monte

Carlo calculation realizations is represented by a square. Bars extend to the 25th-75th percentiles of

Monte Carlo realizations.

59

Figure 1-17. Transpiration rates for 10% of ice free land areas. (a) Transpiration is shown both as a

proportion of total evapotranspiration (%; pane a) and as a rate (mm H2O year-1; b).

60

Figure 1-18. Catchment-by-catchment transpiration rates for 73 lakes. Colors mark ecoregions, with

grey bars marking lake catchments that fall into more than one ecoregion. Total evapotranspiration

rates are marked as a dash, whereas the median of calculation results is marked by a square.

Global primary production assimilates ~123±8 Gt of carbon each year (Beer et al., 2010).

The transpiration fluxes reported in this study can be used to calculate gross primary production by

applying the water use efficiency data calculated within each catchment to transpiration fluxes (i.e.,

converting mm H2O transpired per year into g C assimilated per year). Gross primary production rate

calculated using transpiration fluxes are presented in Figure 1-19.

61

Figure 1-19. Gross primary productivity (catchment averages) for 10% of Earth’s ice free land area

calculated by coupling isotope-based transpiration fluxes to water use efficiency data.

1.5 Discussion

Transpiration is found to account for more than two-thirds of evapotranspiration in more

than 80% of catchments studied. We find that even though potential evaporation rates likely exceed

transpiration on land surfaces, the rate of evaporation is limited by the small area of open water on

“land” surfaces (~3%, globally; Downing et al., 2006). Therefore our results suggest that biological

fluxes of water into the atmosphere is the greatest vapor flow from the continents, rather than

evaporation. Plant roots tap into ground- and soil-water reservoirs and effectively move these

underground water sources upward to Earth’s boundary layer for evaporation at leaf surfaces,

whereas evaporation is limited in its water supply to water that is at or near the surface.

Our analysis neglects snow sublimation, which has been proposed as a non-fractionating

process (although more recent work suggests that sublimation is indeed a fractionation labelled

process; Koeniger et al., 2006), is thought to be very small at pan-continental scales. A compilation of

62

three global climate and land surface model estimates places sublimation at less than 2% of terrestrial

evapotranspiration (not considered in one, ~1% in another, and ~2% in the third). Although locally

sublimation may be an important H2O vaporization process, current land surface and general

circulation models suggest that >98% of continental vapor flows are represented by transpiration,

interception and evaporation (i.e., the vapor flows considered in this study).

Table 1-11. Published estimates of sublimation relative to terrestrial evapotranspiration

Study Sublimation / total evapotranspiration

Model or methodology

Dirmeyer et al., 2006

<1%

Calculation: Sublimation of 0.35 mm/mo (i.e., 4.2 mm/year) is shown. Even including seasonally snow covered regions and ice-caps that cover 46,000,000 km2 (see *), this calculation yields a sublimation flux of ~200 km3/yr, or less than 0.5 % of terrestrial ET.

Lawrence et al., 2007

Not considered Community Land Model Version 3

Miralles et al., 2011

2% Global Land-surface Evaporation: the Amsterdam Methodology

* National Snow and Ice Data Center: Snow and Climate. nsidc.org/cryosphere/snow/climate.html

The connections between carbon and water fluxes made in this study highlight a novel

approach for quantifying water and carbon fluxes on continents. Continental water and carbon cycles

are connected by water use efficiency ratios – compiled in this study, for the first time – highlighting

an opportunity for atmospheric models to take advantage of this natural H2O-CO2 accounting

system on continents. Because of the higher proportion of transpiration/evapotranspiration

discovered here, this analysis suggests that biological changes due to climate and land use

modifications will exert a dominant impact upon fresh water fluxes, and associated transports of

nutrients, contaminants, sediment and other solutes. The results of this study also highlight that

changes to vegetation in the past, such as the evolution and spread of the C4 photosynthetic pathway

or the emergence of vascular plants onto continents, are likely to have profoundly modified the

global water and carbon cycles.

63

1.6 References

Alton, P., Fisher, R., Los, S., and Williams, M. (2009), Simulations of global

evapotranspiration using semiempirical and mechanistic schemes of plant hydrology, Global

Biogeochemical Cycles, 23, GB4032.

Aly, A. I. M., Froehlich, K., Nada, A., Hamza, M. and Salem, W. M. (1993), Study of

environmental isotope distribution in the Aswan High Dam Lake (Egypt) for estimating the

evaporation of lake water and its recharge to adjacent groundwater. Environmental Geochemistry and

Health, 15, 37–49.

Bahati, G., Pang, Z., Armannsson, H., Isabirye, E.M. and Kato, V. (2005), Hydrology and

reservoir characteristics of three geothermal systems in Western Uganda, Geothermics, 34, 568–591.

Barbour, M. M., Hunt, J. E., Walcroft, A. S., Rogers, G. N. D., McSeveny, T. M. and

Whitehead, D. (2005), Components of ecosystem evaporation in a temperate coniferous rainforest,

with canopy transpiration scaled using sap–wood density, New Phytologist, 165, 549–558.

Bates, R. E. and Bilello, M. (1966), Defining the cold regions of the northern hemisphere,

U.S.A/ Cold Regions Research and Engineering Laboratory, Technical Report 178.

Becht, R., Mwango, F. and Muno, F. A. (2005), Groundwater links between Kenyan Rift

Valley lakes (eds. Odada et al.) 7 – 14 (Proceedings of 11th World Lakes Conference, Nairobi,

Kenya, 2005).

Beer, C. et al. (2010), Terrestrial gross carbon dioxide uptake: global distribution and

covariation with climate, Science, 329, 834–838.

64

Bergonzini, L., Gibert, E., Winckel, A. and Merdaci, O. (2001), Bilans hydrologique et

isotopique (18O et 2H) du Lac Massoko, Tanzanie. Quantification des échanges lac–eaux souterraines.

C. R. Acad. Sci. Paris, Sciences de la Terre et des Planètes 333, 617–623.

Beuning K. R. M., Kelts, K., Ito, E. and Johnson, T. C. (1997), Paleohydrology of Lake

Victoria, East Africa, inferred from 18O/16O ratios in sediment cellulose. Geology, 25, 1083–1086.

Beziat, P., Rivalland, V., Tallec, T., Jarosz, N., Boulet, G., Gentine, P. and Ceschia, E. (2013),

Evaluation of a simple approach for crop evapotranspiration partitioning and analysis of the water

budget distribution for several crop species, Agricultural and Forest Meteorology, 177, 46–56.

Bosch, J. M., and J. D. Hewlett (1983), A review of catchment experiments to determine the

effect of vegetation changes on water yield and evapotranspiration, Journal of Hydrology, 55, 3–23.

Bowen, G. J. (2010), Statistical and geostatistical mapping of precipitation water isotope

ratios. In Isoscapes, Springer, Netherlands, pp. 139–160.

Bowen, G. J., and Revenaugh, J. (2003), Interpolating the isotopic composition of modern

meteoric precipitation. Water Resources Research, 39, 1299.

Bowen, G. J., and Wilkinson, B. (2002), Spatial distribution of δ18O in meteoric precipitation,

Geology, 30, 315–318.

Brahney, J. Paleolimnology of Kluane Lake. (2007), 116 pp. (M.Sc. thesis, Simon Fraser

University, Vancouver, British Columbia, Canada)

Brock, B. E., Yi, Y., Clogg–Wright, K. P., Edwards, T. W. D. and Wolfe, B. B. (2009),

Multi–year landscape–scale assessment of lakewater balances in the Slave River Delta, NWT, using

water isotope tracers, Journal of Hydrology, 379, 81–91.

65

Buhay, W. M. and Betcher, R. N. (1998), Paleohydrologic implications of 18O enriched Lake

Agassiz water. Journal of Paleolimnology, 19, 285–296.

Kendall, C. and Coplen, T. B. (2001), Distribution of oxygen–18 and deuterium in river

waters across the United States. Hydrological Processes, 15, 1363–1393.

Calder, I. R., Wright, I. R. and Murdiyarso, D. (1986), A study of evaporation from tropical

rainforest—West Java, Journal of Hydrology, 89, 13–31.

Cao, L., Bala, G., Caldeira, K., Nemani, R., and Ban–Weiss, G. (2010), Importance of carbon

dioxide physiological forcing to future climate change, Proceedings of the National Academy of Sciences,

107, 9513–9518.

Cavanaugh, M. L., Kurc, S. A. and Scott, R. L. (2011), Evapotranspiration partitioning in

semiarid shrubland ecosystems, a two–site evaluation of soil moisture content on transpiration,

Ecohydrology, 4, 671–681.

Cerling, T. E., Bowman, J. R. and O’Neil J. R. (1988), An isotopic study of a fluvial

lacustrine sequence: the Plio–Pleistocene Koobi Fora sequence, East Africa, Palaeogeography,

Palaeoclimatology, Palaeoecology, 63, 335–356.

Choudhury, B. J. and DiGirolamo, N. E. (1998), A biophysical process–based estimate of

global land surface evaporation using satellite and ancillary data. I. Model description and comparison

with observations, Journal of Hydrology, 205, 164–185.

Choudhury, B. J., DiGirolamo, N. E., Susskind, J., Darnell, W. L., Gupta, S. K., and Asrar,

G. (1998), A biophysical process–based estimate of global land surface evaporation using satellite and

ancillary data, II. Regional and global patterns of seasonal and annual variations, Journal of Hydrology,

205, 186–204.

66

Cienciala, E., Kucera, J., Lindroth, A., Cermak, J., Grelle, A. and Halldin, S. (1997), Canopy

transpiration from a boreal forest in Sweden during a dry year, Agricultural and Forest Meteorology, 86,

157–167.

Cohen, A. S., Talbot, M. R., Awramik, S. M., Dettman, D. L. and Abell, P. (1997), Lake level

and paleoenvironmental history of Lake Tanganyika, Africa, as inferred from late-Holocene and

modern stromatolites, Geological Society of America Bulletin, 109, 444–460.

Craig, H. (1975), Lake Tanganyika geochemical and hydrographic study: 1973 expedition.

Scripps Institute of Oceanography, Reference 75–5, La Jolla, California, 83 pp.

Craig, H. (1975), Lake Tanganyika geochemical and hydrographic study: 1973 expedition.

Report 75–5, 83 pp. (Scripps Institute of Oceanography La Jolla, California, 1975).

Craig, H. (1996), Isotopic composition of the Red Sea and Salton Sea geothermal brines.

Science, 154, 1544–1548.

Craig, H. and Gordon, L. I. (1965), in Stable Isotopes in Oceanographic Studies and

Paleotemperatures (ed. Tongiorgi, E.) Lab. Geol. Nucl., pp. 9–130.

Craig, H., Lupton, J. E. and Horowiff, R. M. (1977), Isotope Geochemistry and Hydrology

of geothermal waters in the Ethiopian rift valley, Report 77–14, 140 pp. (Scripps Institute of

Oceanography, La Jolla, California).

Dai, A., and Trenberth, K. E. (2002), Estimates of freshwater discharge from continents:

Latitudinal and seasonal variations, Journal of Hydrometeorology, 3, 660–687.

Dapena, C. and Panarello, H. O. (1997), Isotopic study of the “Laguna Mar Chiquita,”

Córdoba, Argentina and its hydrogeological and paleoclimatological implications. 7–15 (Proc. Int.

Symp. International Atomic Energy Agency, Vienna).

67

Darling, W. G., Berhanu, G. and Arusei, M. K. (1996), Lake–groundwater relationships and

fluid–rock interaction in the East African Rift Valley: isotopic evidence. Journal of African Earth

Sciences, 22, 423–431.

Dinçer, T. (1968), The use of oxygen–18 and deuterium concentrations in the water balance

of lakes, Water Resources Research, 4, 1289–1305.

Dirmeyer, P. A., Gao, X., Zhao, M., Guo, Z., Oki, T., and Hanasaki, N. (2006), GSWP–2:

multimodel analysis and implications for our perception of the land surface, Bulletin of the American

Meteorological Society, 87, 1381–1397.

Dolman, A. J (1988), Transpiration of an oak forest as predicted from porometer and

weather data, Journal of Hydrology, 97, 225–234.

Downing, J. A. et al. The global abundance and size distribution of lakes, ponds, and

impoundments. Limnol. Oceanogr. 51, 2388–2397 (2006).

Evaristo, J., Jasechko, J., McDonnell, J. J. (in review), Global separation of plant

transpiration from groundwater recharge and streamflow.

Ferretti, D.F., Pendall, E., Morgan, J. A., Nelson, J. A., LeCain, D. and Mosier, A. R. (2003),

Partitioning evapotranspiration fluxes from a Colorado grassland using stable isotopes: Seasonal

variations and ecosystem implications of elevated atmospheric CO2, Plant and Soil, 254, 291–303.

Ffolliott, P.F., Gottfried, G. J., Cohen, Y. and Schiller, G. (2003), Transpiration by dryland

oaks, studies in the south–western United States and northern Israel, Journal of Arid Environments, 55,

595–605.

Floret, C., Pontanier, R., and Rambal, S. (1982), Measurement and modeling of primary

production and water use in a South Tunisian steppe, Journal of Arid Environments, 5, 77–90.

68

Fontes, J.–Ch. and Gonfiantini, R. (1970), Composition isotopique et origine de la vapeur

d’eau atmosphérique dans la région du Lac Leman, Earth and Planetary Science Letters, 7, 325–329.

Fontes, J–Ch., Boulange, B., Carmouze, J.P. and Florkowski, T. Preliminary oxygen–18 and

deuterium study of the dynamics of Lake Titicaca. 145–150 (Proc. Int. Symp. International Atomic

Energy Agency, Vienna.

Fontes, J–Ch., Gonfiantini, R. and Roche, M. A. (1970), Deutérium et oxygène–18 dans les

eaux du lac Tchad. 387–404 (Proc. Int. Symp. International Atomic Energy Agency, Vienna).

Frangi, J. L. and Lugo, A. E. (1985), Ecosystem dynamics of a subtropical floodplain forest,

Ecological Monographs, 55, 351–369.

Friedman, I., Redfeld, A. C., Scohoen, B., Harris, J. (1964), The variation of the deuterium

content of natural waters in the hydrologic cycle. Reviews of Geophyics, 2, 177–224.

Froehlich, K. (2000), Evaluating the water balance of inland seas using isotopic tracers: the

Caspian Sea experience, Hydrological Processes, 14, 1371–1383.

Galoux, A., Benecke, P., Gietl, G., Hager, H., Kayser, C., Kiese, O., Knoerr, K. R., Murphy,

C. E., Schnock, G. and Sinclair, T. R. (1981), Radiation, heat, water and carbon dioxide balances, In

D.E. Reichle (ed). Dynamic Properties of Forest Ecosystems, Cambridge University Press, pp. 87–

204.

Gash, J. H. C. and Stewart, J. B. (1997), The evaporation from Thetford Forest during 1975,

Journal of Hydrology, 35, 385–396.

Gat, J. R. (1984), The stable isotope composition of Dead Sea waters, Earth and Planetary

Science Letters, 71, 361–376.

69

Gebauer, T., Homa, V. and Leuschner, C. (2012), Canopy transpiration of pure and mixed

forest stands with variable abundance of European beech, Journal of Hydrology, 442/443, 2–14.

Gerten, D., Hoff, H., Bondeau, A., Lucht, W., Smith, P., and Zaehle, S. (2005),

Contemporary “green” water flows: simulations with a dynamic global vegetation and water balance

model, Physics and Chemistry of the Earth, 30, 334–338.

Gibson, J.J. and Edwards, T. W. D. (2002), Regional water balance trends and evaporation–

transpiration partitioning from a stable isotope survey of lakes in southern Canada, Global

Biogeochemical Cycles 16.

Gonfiantini, R. (1986), Environmental isotopes in lake studies, In Handbook of

Environmental Isotope Geochemistry (Eds. P. Fritz, J.C. Fontes), Elsevier, pp. 113–168.

Gonfiantini, R., Borsi, S., Ferrara, G. and Panichi, C. (1973), Isotopic composition of waters

from the Danakil Depression (Ethiopia), Earth and Planetary Science Letters, 18, 13–21.

Gonfiantini, R., Zuppi, G. M., Eccles, D. H. and Ferro, W. (1979), Isotope investigation of

Lake Malawi. (Proc. Int. Symp. International Atomic Energy Agency, Vienna).

Gordon, L. J., Steffen, W., Jönsson, B. F., Folke, C., Falkenmark, M. and Johannessen, Å.

(2005), Human modification of global water vapor flows from the land surface, Proceedings of the

National Academy of Sciences of the United States of America, 102, 7612–7617.

Granier, A., Biron, P. and Lemoine, D. (2000), Water balance, transpiration and canopy

conductance in two beech stands. Agricultural and Forest Meteorology, 100, 291–308.

Grelle, A., Lundberg, A., Lindroth, A., Moren, A. S. and Cienciala, E. (1997), Evaporation

components of a boreal forest, variations during the growing season, Journal of Hydrology, 197, 70–87.

70

Helliker, B. R., and Richter, S. L. (2008), Subtropical to boreal convergence of tree–leaf

temperatures. Nature, 454, 511–514.

Henderson A. C. G., Holmes, J. A. and Leng, M. J. (2010), late-Holocene isotope hydrology

of Lake Qinghai, NE Tibetan Plateau: effective moisture variability and atmospheric circulation

changes. Quaternary Science Reviews, 29, 2215–2223.

Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., and Jarvis, A. (2005). Very high

resolution interpolated climate surfaces for global land areas. International journal of climatology,

25(15), 1965–1978.

Hitchon, B. and Krouse, H. R. (1972), Hydrogeochemistry of surface waters of the

Mackenzie River drainage basin, Canada—III. Stable isotopes of oxygen, carbon and sulphur,

Geochimica et Cosmochimica Acta, 36, 1337–1357.

Hsieh, J. C. C., Chadwick, O. A., Kelly. E. F. and Savin, S. M. (1998), Oxygen isotopic

composition of soil water, Quantifying evaporation and transpiration, Geoderma, 82, 269–293.

Hu, Z. et al. (2009), Partitioning of evapotranspiration and its controls in four grassland

ecosystems, Application of a two–source model, Agricultural and Forest Meteorology, 149, 1410–1420.

Huang, X., Hao, Y., Wang, Y., Cui, X., Mo, X. and X. Zhou (2010), Partitioning of

evapotranspiration and its relation to carbon dioxide fluxes in Inner Mongolia steppe, Journal of Arid

Environments, 74, 1616–1623.

Hudson, J. A (1988), The contribution of soil moisture storage to the water balances of

upland forested and grassland catchment, Hydrologic Science Journal, 33, 289–308.

71

Isiorho, A. A., Matisoff and Wehn, G. K. S. (1996), Seepage relationships between Lake

Chad and the Chad aquifers, Ground Water, 34, 819–826. relationships between Lake Chad and the

Chad aquifers. Ground Water 34, 819–826 (1996).

Ito, A., and Inatomi, M. (2012). Water–use efficiency of the terrestrial biosphere: a model

analysis focusing on interactions between the global carbon and water cycles. Journal of

Hydrometeorology, 13, 681–694.

Jasechko, S. (2011), Stable isotope mass balance of the Laurentian Great Lakes, (M.Sc. thesis,

University of Waterloo, Waterloo, Ontario, Canada).

Jasechko, S. (2014), Global plant breathing revealed by stable isotopes, American

Geophysical Union Hydrology Section July 2014 Newsletter.

Jasechko, S., Birks, S. J., Gleeson, T., Wada, Y., Fawcett, P. J., Sharp, Z. D., McDonnell, J. J.

and Welker, J. M. (in review), The pronounced seasonality of global groundwater recharge.

Jasechko, S., Gibson, J. J. and Edwards, T. W. D. (2014), Stable isotope mass balance of the

Laurentian Great Lakes. Journal of Great Lakes Research, 40, 336–346.

Jasechko, S., Gibson, J. J., YI, Y., Birks, S. J., and Sharp, Z. D. (2011). Stable isotope

composition of Earth's large lakes. In AGU Fall Meeting Abstracts (0358).

Jasechko, S., Sharp, Z. D., Gibson, J. J., Birks, S. J., Yi, Y., and Fawcett, P. J. (2013),

Terrestrial water fluxes dominated by transpiration, Nature, 496, 347–350.

Jung, M. et al. (2010), Recent decline in the global land evapotranspiration trend due to

limited moisture supply, Nature, 467, 951–954.

72

Kabenge, I. and Irmak, S. (2012), Evaporative losses from a common reed–dominated

peachleaf willow and cottonwood riparian community, Water Resources Research, 48,

doi,10.1029/2012WR011902.

Karim, A., Veizer, J. and Barth, J. (2008), Net ecosystem production in the great lakes basin

and its implications for the North American missing carbon sink: A hydrologic and stable isotope

approach, Global Planetary Change, 61, 15–27.

Kebede, S., Travi, Y. and Rozanski, K. (2009), The δ18O and δ2H enrichment of Ethiopian

lakes, Journal of Hydrology, 365, 173–182 (2009).

Keenan, T. F., Hollinger, D. Y., Bohrer, G., Dragoni, D., Munger, J. W., Schmid, H. P., and

Richardson, A. D. (2013), Increase in forest water–use efficiency as atmospheric carbon dioxide

concentrations rise, Nature, 499, 324–327.

Koeniger, P., Hubbart, J. A., Link, T., Marshall, J. D. Isotopic variation of snow cover and

streamflow in response to changes in canopy structure in a snow–dominated mountain catchment.

Hydrol. Processes 22, 557–566 (2006).

Kumagai, T., Takiko Tateishi, M., Miyazawa, Y., Kobayashi, M., Yoshifugi, N., Komatsu, H.

and T. Shimizu (2014), Estimation of annual forest evapotranspiration from a coniferous plantation

in Japan (1), Water use components in Japanese cedar stands, Journal of Hydrology, 508, 66–76.

Kwiecien, O. et al. (2011), Hydrological Conditions in Eastern Anatolia Over the Last CA.

500 KA – First Insights from Lake Van. Presentation at the American Geophysical Union Fall

Meeting 2011, December 5–9, San Francisco, U. S. A.

Labat, D., Goddéris, Y., Probst, J. L., and Guyot, J. L. (2004), Evidence for global runoff

increase related to climate warming. Advances in Water Resources, 27, 631–642.

73

Lachniet, M. S. and Patterson, W. P. (2002), Stable isotope values of Costa Rican surface

waters. Journal of Hydrology, 260, 135–150.

Ladekarl, U. L (1998), Estimation of the components of soil–water balance in a Danish oak

stand from measurements of soil moisture using TDR, Forest Ecology and Management, 104, 227–238.

Lane, L.J., Romney, E. M. and Hakonson, T. E. (1984), Water–balance calculations and net

production of perennial vegetation in the northern Mojave desert. Journal of Range Management, 37, 12–

18.

Lauenroth, W. K. and Bradford, J. B. (2006), Ecohydrology and the partitioning AET

between transpiration and evaporation in a semiarid steppe, Ecosystems, 9, 756–767.

Lawrence, D. M., Thornton, P. E., Oleson, K. W., and Bonan, G. B. (2007), The partitioning

of evapotranspiration into transpiration, soil evaporation, and canopy evaporation in a GCM:

impacts on land–atmosphere interaction, Journal of Hydrometeorology, 8, 862–880.

Leopoldo, P. R., Franken, W. K. and Nova, N. A. V. (1995), Real evapotranspiration and

transpiration through a tropical rain forest in central Amazonia as estimated by the water–balance

method, Forest Ecology and Management, 73, 185–195.

Littmann, T. and M. Veste (2006), Determination of actual evapotranspiration and

transpiration in desert sand dunes (Negev Desert) using different approaches, Forestry Studies in China,

8, 1–9.

Liu, B.L., Phillips, F., Hoines, S., Campbell, A. R. and Sharma, P. (1995), Water movement

in desert soil traced by hydrogen and oxygen isotopes, chloride, and 36Cl, southern Arizona, Journal of

Hydrology, 168, 91–110.

74

Liu, R., Li, Y. and Wang, Q–X. (2012), Variations in water and CO2 fluxes over a saline

desert in western China, Hydrological Processes, 26, 513–522.

Liu, X. and Chen, J. (2009), Studying of Model of Stable Isotope Fractionation in Lake –

Taking the Nam Co Lake as an example. Proceedings of the International Forum on Porous Flow

and Applications, Wuhan City, China.

Longinelli, A. et al. (2008), A stable isotope study of the Garda lake, Northern Italy: its

hydrological balance, Journal of Hydrology, 360, 103–116.

Luz B. and Barkan E. (2010), Variations of 17O/16O and 18O/16O in meteoric waters,

Geochimica et Cosmochimica Acta, 74, 6276–6286.

Massman, W. J (1992), A surface–energy balance method for partitioning evapotranspiration

data into plant and soil components for a surface with partial canopy cover, Water Resources Research,

28, 1723–1732.

Mazzini, A., Svensen, H., Etiope, G., Onderdonk, N. and Banks, D. (2011), Fluid origin, gas

fluxes and plumbing system in the sediment–hosted Salton Sea Geothermal System (California,

USA). Journal of Volcanology and Geothermal Research, 205, 67–83.

McClelland, J. W., S. J. Déry, B. J. Peterson, R. M. Holmes, and E. F. Wood (2006), A pan–

arctic evaluation of changes in river discharge during the latter half of the 20th century, Geophys.

Res. Lett., 33, L06715.

McKenna, S. A., Ingraham, N., Jacobson, R. L. and Cochran, G. F. (1992), A stable isotopic

study of Bank storage mechanisms in the Truckee River Basin, Journal of Hydrology, 134, 203–219.

McNulty, S. G., Vose, J. M. and Swank, W. T. (1996), Loblolly pine hydrology and

productivity across the southern United States, Forest Ecology and Management, 86, 241–251.

75

Miralles, D. G. et al. (2014), El Niño–La Niña cycle and recent trends in continental

evaporation, Nature Climate Change, 4, 122–126.

Miralles, D. G., De Jeu, R. A., Gash, J. H., Holmes, T. R., and Dolman, A. J. (2011),

Magnitude and variability of land evaporation and its components at the global scale. Hydrology and

Earth System Sciences, 15, 967–981.

Miralles, D. G., Gash, J. H., Holmes, T. R., de Jeu, R. A. and Dolman, A. J. (2010), Global

canopy interception from satellite observations, Journal of Geophysical Research: Atmospheres, 115,

D16122.

Mitchell, P. J., Veneklaas, E., Lambers, H. and Burgess, S. S. O. (2009), Partitioning of

evapotranspiration in a semi–arid eucalypt woodland in south–western Australia, Agricultural and

Forest Meteorology, 149, 25–37.

Moran, M. S., Scott, R. L., Keefer, T. O., Emmerich, W. E., Hernandez, M., Nearing, G. S.,

Paige, G. B., Cosh, M. H. and O’Neill, P.E. (2009), Partitioning evapotranspiration in semiarid

grassland and shrubland ecosystems using time series of soil surface temperature. Agricultural and

Forest Meteorology, 149, 59–72.

Mu, Q., M. Zhao, S. W. Running (2011), Improvements to a MODIS Global Terrestrial

Evapotranspiration Algorithm, Remote Sensing of Environment, 115, 1781–1800

New, M., Lister, D., Hulme, M. and Makin, I. (2002), A high–resolution data set of surface

climate over global land areas, Climate Research, 21, 1–25.

Nielson, K. E. and Bowen, G. J. (2010), Hydrogen and oxygen in brine shrimp chitin reflect

environmental water and dietary isotopic composition, Geochimica et Cosmochimica Acta, 74, 1812–1822.

76

Nizinski, J. J., Galat, G. and Galat–Luong, A. (2011), Water balance and sustainability of

Eucalyptus plantations in the Kouilou Basin (Congo–Brazzaville), Russian Journal of Ecology, 42, 305–

314.

Oberhänsli, H., Weise, S. M. and Stanichny, S. (2009), Oxygen and hydrogen isotopic water

characteristics of the Aral Sea, Central Asia, Journal of Marine Systems, 76, 310–321.

Ojiambo, S. B., Lyons, W. B., Welch, K. A., Poreda, R. J. and Johannesson, K. H. (2003),

Strontium isotopes and rare earth elements as tracers of groundwater–lake water interactions, Lake

Naivasha, Kenya, Applied Geochemistry, 18, 1789–1805.

Oki, T., and Kanae, S. (2006), Global hydrological cycles and world water resources, Science,

313, 1068–1072.

Paco, T.A., David, T. S., Henriques, M. O., Pereira, J. S., Valente, F., Banza, J., Pereira, F. L.,

Pinto, C. and David, J. S. (2009), Evapotranspiration from a Mediterranean evergreen oak savannah.

The role of trees and pasture, Journal of Hydrology, 369, 98–106.

Paruelo, J. M. and Sala, O. E. (1995), Water losses in the Patagonian steppe—a modeling

approach, Ecology, 76, 510–520.

Peterson, B. J., Holmes, R. M., McClelland, J. W., Vörösmarty, C. J., Lammers, R. B.,

Shiklomanov, A. I., Shiklamanov, I. A., and Rahmstorf, S. (2002), Increasing river discharge to the

Arctic Ocean, Science, 298, 2171–2173.

Phillips, F. M., Mills, S., Hendrickx, J. M. H. and Hogan, J. (2003), Environmental tracers

applied to quantifying causes of salinity in arid–region rivers; results from the Rio Grande Basin,

southwestern USA, Developments in Water Science, 50, 327– 334.

77

Poole, D.K., Roberts, S. W. and Miller, P.C. (1981), Water utilization, in P.C. Miller (ed).

Resource Use by Chaparral and Matorral, Springer, pp. 123–149.

Prazak, J., Sir, M. and Tesar, M. (1994), Estimation of plant transpiration from

meteorological data under conditions of sufficient soil moisture, Journal of Hydrology, 162, 409–427.

Raz–Yaseef, N., Yakir, D., Schiller, G. and Cohen, S. (2012), Dynamics of

evapotranspiration partitioning in a semi–arid forest as affected by temporal rainfall patterns,

Agricultural and Forest Meteorology, 157, 77–85.

Ricketts, R. D. Johnson, T. C., Brown, E. T., Rasmussen, K. A. and Romanovsky, V. V.

(2001), Trace element and stable isotope study of the Holocene paleoclimate of Lake Issyk–Kul,

Palaeogeography, Palaeoclimatology, Palaeoecology, 176, 207–227.

Roderick, M. L., and Farquhar, G. D. (2002), The cause of decreased pan evaporation over

the past 50 years, Science, 298, 1410–1411.

Roupsard, O. et al. (2006), Partitioning energy and evapotranspiration above and below a

tropical palm canopy, Agricultural and Forest Meteorology, 139, 252–268.

Rousseaux, M. C., Figuerola,, P. I., Corfrea–Tedesco, G. and Searles, P. S. (2009), Seasonal

variation in sap flow and soil evapotranspiration in an olive (Olea europaea L.) grove under two

irrigation regimes in an arid region of Argentina, Agricultural Water Management, 96, 1037–1044.

Running, S. W., Nemani, R. R., Heinsch, F. A., Zhao, M., Reeves, M., and Hashimoto, H.

(2004), A continuous satellite–derived measure of global terrestrial primary production. Bioscience, 54,

547–560.

Russell, J. M. and Johnson, T. C. (2006), The water balance and stable isotope hydrology of

Lake Edward, Uganda–Congo, Journal of Great Lakes Research, 32, 77–90 (2006).

78

Salati, E. and Vose, P. B. (1984), Amazon basin—a system in equilibrium, Science, 225, 129–

138.

Schlesinger, W. H., and Bernhardt, E. S. (2013), Biogeochemistry: an analysis of global

change, Academic press.

Schlesinger, W. H., and Jasechko, S. (2014), Transpiration in the global water cycle,

Agricultural and Forest Meteorology, 189, 115–117.

Schlesinger, W. H., Fonteyn, P. J. and Marion, G. M. (1987), Soil moisture content and plant

transpiration in the Chihuahuan desert of New Mexico, Journal of Arid Environments, 12, 119–126.

Scott, R. L., Huxman, T. E., Travis, E. J., Cable, W. L. and Emmerich, W. E. (2006),

Partitioning of evapotranspiration and its relation to carbon dioxide exchange in a Chihuahuan

Desert grassland, Hydrological Processes, 20, 3227–3243.

Seal, R. R. and Shanks, W. C. (1998), Oxygen and hydrogen isotope systematics of Lake

Baikal, Siberia: implications for paleoclimate studies, Limnology and Oceanography, 43, 1251–1261.

Shuttleworth, W. J (1988), Evaporation from Amazonian rainforest, Proceedings of the Royal

Society of London, 233B, 321–346.

Smith, S. D., Herr, C. A., Leary, K. L. and Piorkowski, J. M. (1995), Soil–plant water

relations in a Mojave desert mixed shrub community, A comparison of three geomorphic surfaces,

Journal of Arid Environments, 29, 339–351.

Stewart, M. K. and Taylor, C. B. Environmental isotopes in New Zealand hydrology: 1

Introduction: The role of oxygen–18, deuterium, and tritium in hydrology. New. Zeal. J. Sci. 24, 295–

311 (1981).

79

Still, C. J., Berry, J. A., Collatz, G. J., and DeFries, R. S. (2003), Global distribution of C3 and

C4 vegetation: carbon cycle implications. Global Biogeochemical Cycles, 17(1), 6–1.

Syed, T. H., Famiglietti, J. S., Chambers, D. P., Willis, J. K., and Hilburn, K. (2010), Satellite–

based global–ocean mass balance estimates of interannual variability and emerging trends in

continental freshwater discharge. Proceedings of the National Academy of Sciences, 107, 17916–17921.

Tajchman, S. L (1972), The radiative and energy balance of coniferous and deciduous

forests, Journal of Applied Ecology, 9, 359–375.

Tang, J., Bolstad, P. V., Ewers, B. E., Desai, A. R., Davis, K. J. and Carey, E. V. (2006), Sap

flux–upscaled canopy transpiration, stomatal conductance, and water use efficiency in an old growth

forest in the Great Lakes region of the United States, Journal of Geophysical Research, 111, doi,

10.1029/2005JG000083

Taniguchi, M., Burnett, W. C., Cable, J. E., and Turner, J. V. (2002), Investigation of

submarine groundwater discharge, Hydrological Processes, 16, 2115–2129.

Taniguchi, M., Nakayama, T., Tase, N. and Shimada, J. (2001), Stable isotope studies of

precipitation and river water in the Lake Biwa basin, Japan, Hydrological Processes, 14, 539–556.

Telmer, K. and Veizer, J. (2000), Isotopic constraints on the transpiration, evaporation,

energy, and gross primary production budgets of a large boreal watershed, Ottawa River basin,

Canada, Global Biogeochemical Cycles, 14, 149–165.

Tian, F., Qiu, G. Y., Yang, Y., Lu, Y. and Xiong, Y. (2013), Estimation of evapotranspiration

and its partition based on an extended three–temperature model and MODIS products, Journal of

Hydrology, 498, 210–220.

80

Trenberth, K. E., Fasullo, J. T., and Kiehl, J. (2009), Earth's global energy budget, Bulletin of

the American Meteorological Society, 90, 311–323.

Trlica, M. J. and Biondini, M. E. (1990), Soil–water dynamics, transpiration, and water losses

in a crested wheatgrass and native shortgrass ecosystem, Plant and Soil, 126, 187–201.

Wada, Y., L. P. H. van Beek, and M. F. P. Bierkens (2012), Nonsustainable groundwater

sustaining irrigation: A global assessment, Water Resources Research, 48, W00L06.

Wang, L. X., Niu, S. L., Good, S. P., Soderberg, K., McCabe, M. F., Sherry, R. A., Luo, Y.

Q., Zhou, X. H., Xia, J. Y. and Caylor, K. K. (2013), The effect of warming on grassland

evapotranspiration partitioning using laser–based isotope monitoring techniques, Geochimica et


Waring, R. H., Rogers, J. J. and Swank, W.T. (1981), Water relations and hydrologic cycles,

In Dynamic Properties of Forest Ecosystems (ed. D.E. Reichle) Cambridge University Press, pp.

205–264.

Wassenaar, L. I., Athanasopoulos, P. and Hendry, M. J. (2011), Isotope hydrology of

precipitation, surface and ground waters in the Okanagan Valley, British Columbia, Canada. Journal of

Hydrology, 411, 37–48.

Wenbin, Z., Wang, M., Chunhua, H. and Huayin, X. X. (2007), Preliminary isotope studies in

Poyang Lake, 427–435 (Proc. Int. Symp. International Atomic Energy Agency, Vienna).

Wilson, K. B., Hanson, P. J., Mulholland, P. J., Baldocchi, D. D. and Wullschleger, S. D.

(2001), A comparison of methods for determining forest evapotranspiration and its components,

sap–flow, soil water budget, eddy covariance, and catchment water balance, Agricultural and Forest

Meteorology, 106, 153–168.

81

Wolfe, B.B. et al. (2007), Classification of hydrological regimes of northern floodplain basins

(Peace–Athabasca Delta, Canada) from analysis of stable isotopes (δ18O, δ2H) and water chemistry.

Hydrological Processes, 21, 151–168.

Yadav, D. N. (1997), Oxygen isotope study of evaporating brines in Sambhar Lake,

Rajasthan, India, Chemical Geology, 138, 109–118.

Yang, C., Telmer, K. and Veizer, J. (1996), Chemical dynamics of the St. Lawrence riverine

system: δDH2O, δ13CDIC, δ34Ssulphate, and dissolved 87Sr/86Sr, Geochimica et Cosmochimica Acta, 60, 851–

866.

Yao, Z. J. et al. (2009), Characteristics of isotope in precipitation, river water and lake water

in the Manasarovar Basin of Qinghai: Tibet Plateau, Environmental Geology, 57, 551–556.

Yoshimura, K., Kanamitsu, M., Noone, D. & Oki, T. Historical Isotope Simulation using

Reanalysis Atmospheric Data, J. Geophys. Res. 113, D19108 (2008).

Young, M. H., Caldwell, T. G., Meadows, D. G. and Fenstermaker, L.F (2009), Variability of

soil physical and hydraulic properties at the Mojave Global Change Facility, Nevada, Implications for

water budget and evapotranspiration, Journal of Arid Environments, 73, 733–744.

Yuan, F. et al. (2011), Evaporative enrichment of oxygen–18 and deuterium in lake waters

on the Tibetan Plateau, Journal of Paleolimnology, 46, 291–307.

Yunusa, I. A. M., Walker, R. R. and Guy, J. R. (1997), Partitioning of seasonal

evapotranspiration from a commercial furrow–irrigated Sultana vineyard, Irrigation Science, 18, 45–54.

Zhang, X., Zwiers, F. W., Hegerl, G. C., Lambert, F. H., Gillett, N. P., Solomon, S., Stott, P.

A., and Nozawa, T. (2007), Detection of human influence on twentieth–century precipitation trends,

Nature, 448, 461–465.

82

Zimmermann, U., Baumann, U., Imevbore, A., Henderson, F. and Adeniji, H. A. (1976),

Study of the mixing patterns of Lake Kainji using stable isotopes, Catena, 3, 63–76.

83

CHAPTER 2 — THE SEASONALITY OF GLOBAL GROUNDWATER

RECHARGE

2.1 Abstract

Groundwater is recharged as rain and snowmelt infiltrate underground into aquifers.

Groundwater is a vital resource that sustains 40% of crop irrigation. Many studies report annual

groundwater recharge rates, yet few studies report seasonal differences in groundwater recharge rates,

particularly in light of differing precipitation fluxes between seasons. In this chapter I define the

“groundwater recharge ratio” as the proportion of rain and snow that recharges groundwater

aquifers. On the basis of a newly compiled set of 54 paired precipitation-groundwater isotopic data, I

show that groundwater recharge ratios are highest during the winter in most arid and temperate

climates, and are at a maximum during the wet season in the tropics. The isotope-based seasonal

assessment of groundwater recharge ratios are compared with the outputs of a global hydrological

model (PCR-GLOBWB), and the model is found to compare closely with the isotope observations in

most, but not all locations. The seasonal difference in the efficiency of groundwater recharge

suggests that changes to winter (temperate and arid regions) and wet season (tropics) hydrological

processes will be the most important to future changes in groundwater recharge fluxes.

2.2 Introduction

Groundwater supplies one third of modern-day human water uses (Wada et al., 2014) and

represents the lion’s share (~99%) of unfrozen terrestrial water (Aeschbach-Hertig and Gleeson,

2012). Groundwater is replenished by rain and snowmelt that infiltrates through the critical zone near

to Earth’s surface and into aquifers. Groundwater is depleted by natural discharges of groundwater

flow paths into the water at the surface – such as streams, lakes and seas – and also is depleted by

human extractions via wells. Humans need groundwater to sustain modern livelihoods. Groundwater

supplied drinking water for two billion people, and sustains about 40% of global cropland irrigation

84

(Siebert et al., 2010; Foley et al., 2011). Although groundwater is a pivotal component of modern

human livelihoods, the extractions of groundwater by humans are unsustainable and are draining

aquifers at the global- (Konikow and Kendy, 2005; Wada et al., 2010; Konikow, 2011; Gleeson et al.,

2012) and regional scales (Rodell et al., 2009; Famiglietti et al., 2011; Scanlon et al., 2012; Feng et al.,

2013; Steward et al., 2013; Voss et al., 2013; Joodaki et al., 2014). Unsustainable groundwater

extractions have been spotlighted in multiple regional scale studies including the northern Gangetic

Plain (India, Rodell et al., 2009), the North China Plain (Feng et al., 2013), the Middle East (Voss et

al., 2013; Joodaki et al., 2014), the High Plains of the central United States of America (Scanlon et al.,

2012; Steward et al., 2013) and the Californian Central Valley (western U.S.A., Famiglietti et al., 2011;

Scanlon et al., 2012) and the Colorado River basin (Castle et al., 2014). To reverse these examples of

regional-scale, non-sustainable pumping, groundwater managers will need to set and achieve long

term pumping rate goals that will realize sustainable withdrawals (Gleeson et al., 2012; Aeschbach-

Hertig and Gleeson, 2012). However, these pumping rate goals must be established in the face of a

changing climate, and, therefore, a moving target. In order to predict future groundwater

replenishment rates, it is important that the best information possible be made available regarding

natural groundwater recharge fluxes and their controlling processes which include: the physical state,

amount and intensity of precipitation; topography; water table characteristics; geology; soil type;

vegetation characteristics; boundary layer climatology; irrigation return flows).

Some previous work has evaluated controls upon groundwater recharge fluxes. A

compilation of chloride mass balance recharge estimates suggests that plant life form distributions are

a leading determinant for groundwater recharge, falling second only to precipitation amounts in

terms of importance (Kim and Jackson, 2012). Work presented in Chapter one has indeed shown

that transpiration is a dominant process in the global hydrological cycle (Jasechko et al., 2013).

85

Knowledge of annual groundwater recharge fluxes are a common research target in regional

and continental scale scientific investigations (e.g., Scanlon et al., 2006; Döll and Fielder, 2007; Wada

et al., 2010). Fewer studies have explored seasonal differences in recharge fluxes as a proportion of

precipitation. Understanding the seasonal distribution of groundwater recharge is important because

climate change will impact the hydrology of each season in different ways.

Here we define the groundwater recharge ratio: “the groundwater recharge (R) flux as a

proportion of precipitation (P): R/P.” Previous modelling studies have estimated that the annual

groundwater recharge ratio is close to ~10% (Figure 2-1). The groundwater recharge is estimated to

be lowest in arid climates (average of 4%) and higher in boreal, temperate, and moist tropical forests

(averages recharge ratios of ~14%, ~15%, and ~16%, respectively; recharge estimates from Döll and

Fielder, 2007 and precipitation data from the Global Precipitation Climatology Project, accessed at

www.gewex.org). However, these estimates are highly uncertain as a result of land use and irrigation

return flows not embedded within most hydrological models, in addition to the immense challenges

associated with accurately representing complex interactions of plants, rocks, and climate at Earth’s

critical zone (where these interactions are at a maximum).

86

Figure 2-1. The global groundwater recharge ratio (recharge data from Döll and Fielder, 2007;

precipitation data from the Global Precipitation Climatology Project: www.gewex.org) in map form

(a) and presented as ecozone statistics (b; colored bars mark 25th-75th percentiles, lines mark 10th-90th

percentile distribution).

Previous fieldwork has revealed that winter groundwater recharge ratios are higher than

summer groundwater recharge ratios (Heppner et al., 2007; Jukić and Denić-Jukić, 2009; Yeh and

Famiglietti, 2009; Dripps and Bradbury, 2010; Dripps, 2012; Leterme et al., 2012). Seasonality of

groundwater recharge ratios have been assessed in Belgium (Leterme et al., 2012), Greenland

(Leterme et al., 2012), the northeastern U.S.A. (Heppner et al., 2007; Yeh and Famiglietti, 2009;

Dripps and Bradbury, 2010; Dripps, 2012) and Croatia (Jukić and Denić-Jukić, 2009). In some cases,

summer groundwater recharge has been shown to be restricted solely to high intensity

thundershowers (Wisconsin, U.S.A.; Dripps, 2012). Field based monitoring of groundwater recharge

in Tanzania has shown that groundwater recharge ratios are at their highest when rainfall is most

intense (Taylor et al., 2013), suggesting that an intensifying hydrosphere (Durack et al., 2012) could,

in fact, be beneficial from the sole standpoint of groundwater recharge fluxes. Temperate climate

groundwater recharge has been found to be extremely and rapid process during snowmelt (Gleeson

87

et al., 2009), with the ice content of the shallow subsurface being a controlling factor upon on the

proportion of snowmelt that recharges the subsurface aquifers (Granger et al., 1984). Groundwater

recharge investigations in the mid-western United States of America has found that snowmelt can

comprise two thirds of annual groundwater recharge (Delin et al., 2007; Dripps, 2012). Yet, in spite

of these examples of seasonal biases in the efficiency of groundwater recharge, different recharge

ratios between different seasons have not been observed in in all cases (e.g., Spain, Leterme et al.,

2012), opening an opportunity to calculate and assess the potential for seasonality in groundwater

recharge ratios across different biomes with different lithologies, plant life forms and hydroclimates.

In this chapter, I hypothesize that by coupling groundwater and precipitation isotopic data,

one may calculate seasonal differences in the groundwater recharge ratio. Several studies have

compared precipitation and groundwater isotopic compositions. These studies have found

differences in some cases, and no differences in other cases, between precipitation and groundwater

δ18O and δ2H values.

Studies finding similarities in the isotopic compositions of flux-weighted annual precipitation

and modern groundwater include locations such as China (Li et al., 2000), Finland (Kortelainen,

2004), France (Genty et al., 2014), Israel (Even et al., 1986), Italy (Madonia et al., 2013), Korea (Lee

et al., 1999; Lee and Kim, 2007), New Zealand (Williams and Fowler, 2002), Tasmania (Goede et al.,

1982), the United Kingdom (Darling and Bath, 1988; Darling et al., 2003) and the United States of

America (Yonge et al., 1985; van Beynen and Febbroriello, 2006). These finding suggest at first

glance – without statistical analysis, per se – that groundwater recharge ratios in these sites are similar

year round.

Studies finding differences in the isotopic compositions of flux-weighted annual

precipitation and modern groundwater include field sites in South Africa (Vogel et al., 1963), the

south-western United States (Arizona, Simpson et al., 1972, Kalin, 1994; Nevada, Winograd et al.,

88

1998), the north-eastern United States (Pennsylvania, O’driscoll et al., 2005; Vermont: Abbott et al.,

2000), central Canada (Alberta, Maulé et al., 1994; Grasby et al., 2010), southern Canada (Ontario;

Huddart et al., 1999), French Guyana (Negrel et al., 2010), St. Croix (Gill, 1994), Spain (Julian et al.,

1992), Barbados, Puerto Rico and Guam (Jones et al., 2000; 2003). These differences have been

interpreted as a reflection of higher groundwater recharge ratios during winter (Vogel et al., 1963;

Simpson et al., 1972; Maulé et al., 1994; Kalin, 1994; Winograd et al., 1998; Abbott et al., 2000;

O’driscoll et al., 2005) and wet seasons (Jones et al., 2000; 2003; Negrel; et al., 2010). These isotope-

based results have never been synthesized at a global scale, nor have all studies quantitatively assessed

the seasonal difference in groundwater recharge ratios, expressing these observations qualitatively

instead.

The objective of this chapter of my dissertation is to test for, and quantify, seasonal

differences in groundwater recharge ratios across a variety of field sites by analyzing a newly

compiled global dataset of precipitation and groundwater isotopic data.


Here I calculate the seasonality of groundwater recharge ratios (two seasons) for 54 globally-

distributed locations (Figure 2-2). I analyze global isotopic data for precipitation from regional and

global monitoring networks (Araguás-Araguás et al., 2000; Welker, 2000; Birks and Edwards, 2009;

Welker, 2012) and compare precipitation isotopic data to nearby groundwater isotopic data that have

been compiled from previous field reports. Precipitation data are available through the International

Atomic Energy Agency (e.g., Araguás-Araguás et al., 2000), the United States Network for Isotopes

in Precipitation (Welker, 2000; Welker, 2012) and the Canadian Network for Isotopes in

Precipitation (Birks and Edwards, 2009). Groundwater isotopic data were compiled from >40

published datasets within the primary literature. Original field studies have been properly credited

and are referenced within Table 2-1.

89

Table 2-1. Locations of paired precipitation and groundwater isotopic data

Station Data Lon. Lat. Aquifer Reference

Cayenne IAEA -52.4 4.8 Guyana Shield Negrel and Petelet-Giraud,

2010

Taguac IAEA 144.8 13.6 Guam caves Jones and Banner, 2003

Seawell IAEA -59.5 13.1 Barbados aqfr. Jones et al., 2000

Jakarta IAEA 106.8 -6.2 Jakarta aqfr. Kagabu et al., 2011

New Dehli IAEA 77.2 28.6 Gangetic Plain Das et al., 1988; Lorenzen et

al., 2012

Dar es Salaam IAEA 39.2 -6.9 Coastal aqfr. Bakari et al., 2012

Addis Ababa IAEA 38.7 9.0 Akaki Volcanics Demlie et al., 2007; Kebede

et al., 2007; Rango et al., 2010; Bretzler et al., 2011

Santa Maria IAEA -120.5 34.9 CA Coast www.waterqualitydata.us

Beit Dagan IAEA 34.8 32.0 Israel coast aqfr. Yechieli et al., 2008

Pisa IAEA 10.4 43.7 Pisa Plain Grassi and Cortecci, 2005

Trout Lake USNIP -89.7 46.1 Surficial aqfr. www.waterqualitydata.us

Yellowstone USNIP -110.4 44.9 Alluvial aqfr. www.waterqualitydata.us

Smith's Ferry USNIP -116.1 44.3 Idaho Batholith Schlegel et al., 2009

Lake Geneva USNIP -88.5 42.6 Surficial aqfr. www.waterqualitydata.us

East MA USNIP -71.2 42.4 Surficial aqfr. www.waterqualitydata.us

Niwot Saddle USNIP -105.6 40.1 Surficial aqfr. www.waterqualitydata.us

Wye USNIP -76.2 38.9 Aquia aqfr. Aeschbach-Hertig et al. 2002

Purdue Agr. USNIP -87.5 38.7 Surficial aqfr. www.waterqualitydata.us

Clinton Stn. USNIP -78.3 35.0 Atlantic Plain www.waterqualitydata.us

Caddo Valley USNIP -93.1 34.2 MI River Valley www.waterqualitydata.us

Coffeeville USNIP -89.8 34.0 MI Embayment www.waterqualitydata.us

Saturna CNIP -123.2 48.8 Surficial aqfr. Allan, 2003

Ottawa CNIP -75.7 45.3 Surficial aqfr. Praamsma et al., 2009

Wallingford IAEA -1.1 51.6 London Chalk Elliot et al., 1999; Darling et

al., 1997

P. Douradas IAEA -7.6 40.4 Serra da Estrela Carreira et al., 2011

Krakow IAEA 19.9 50.1 Malm

Limestones Zuber et al. 2004;

Samborska et al., 2012

Cuxhave IAEA 8.7 53.9 N. German Bsn. Kloppman et al., 1998

Orleans IAEA 1.9 47.9 Paris Bsn. Kloppman et al., 1998

Melbourne IAEA 145.0 -37.8 Yarra Bsn. Tweed et al., 2004

Newcastle IAEA -104.2 43.9 Surficial aqfr. www.waterqualitydata.us

Little Bighorn USNIP -107.4 45.6 Surficial aqfr. www.waterqualitydata.us

Lamberton USNIP -95.3 44.2 Mt. Simon aqfr. Berg and Person, 2012;

www.waterqualitydata.us

N. Platte Agr. USNIP -100.8 41.1 N. High Plains McMahon et al., 2006

Mon Mouth USNIP -90.7 40.9 Surficial aqfr. www.waterqualitydata.us

Great Plains USNIP -97.5 35.0 Arbuckle aqfr. www.waterqualitydata.us

Edmonton CNIP -113.5 53.6 Surficial aqfr. Maule et al., 1994

Saskatoon CNIP -106.6 52.1 Dalmeny aqfrs. Fortin et al., 1991

Wynyard CNIP -104.2 51.8 Surficial aqfr. unpublished data

90

Station Data Lon. Lat. Aquifer Reference

Esther CNIP -110.2 51.7 Surficial aqfr. Wallick, 1981

Calgary CNIP -114.0 51.0 Surficial aqfr. Lanza, 2009; Cheung and Mayer, 2009; Rock and

Mayer, 2009

Icelandic Park / Gimli

USNIP / CNIP

-97.8 48.8 Winnipeg fm. Ferguson et al., 2007

Craters of the Moon

USNIP -113.6 43.5 Surficial aqfr. www.waterqualitydata.us

Pinedale USNIP -109.8 42.9 Colorado Plat. www.waterqualitydata.us

Sand Spring USNIP -107.7 40.5 Surficial aqfr. www.waterqualitydata.us

Smith Valley USNIP -119.3 38.8 Basin & Range www.waterqualitydata.us

Tuscon ** -110.8 32.2 Tucson Basin Cunningham et al., 1998

Chihuahua IAEA -106.1 28.6 Chihuahua Plain Wassenaar et al., 2009

Alice Springs IAEA 133.9 -23.8 Amadeus Bsn. Wischusen et al., 2000

Zhangye IAEA 100.4 38.9 Hexi Corridor Qin et al., 2011

Yinchuan IAEA 106.2 38.5 Yinchuan Plain Wang, L. et al., 2012

Yellowknife CNIP -114.3 62.3 Con Mine Douglas et al., 2000

Whitehorse CNIP -135.1 60.7 Surficial aqfr. Carey and Quinton, 2005

Chapais CNIP -75.0 49.8 Surficial aqfr. Boutin, 2009

91

Figure 2-2. Locations where precipitation and groundwater isotopic data are available. Circles mark

54 study sites where sufficient groundwater and precipitation were available to assess groundwater

recharge ratio seasonality. Diamonds mark locations where only a comparison of groundwater and

precipitation isotopic data could be made (i.e., no seasonal recharge ratio

Most studies have only grab samples of groundwater, and do not report long term

monitoring isotopic data. However, the multi-year isotopic monitoring studies of groundwater δ18O

and δ2H values show little seasonal variability, suggesting that grab samples are suitable archives of

multi-year recharge fluxes. Examples of long term groundwater monitoring for isotopic data include

records analyzed in Finland (Kortelainen et al., 2004), Italy (Iacumin et al., 2009), the United

Kingdom (Darling et al., 2003), New Zealand (Williams and Fowler, 2002), eastern Canada (Savard et

al., 2007) and France (Genty et al., 2014. The temporal homogeneity of groundwater δ18O and δ2H

values is interpreted to be the result of hydrodynamic dispersion and multi-year groundwater

residence times.

92

Precipitation isotopic data has been collected for more than 50 years by the International Atomic

Energy Agency (Araguás-Araguás et al., 2000) and other country-wide precipitation networks

(Welker, 2000; Kurita et al., 2004; Birks and Edwards, 2009; Welker 2012, Liu et al., 2013). The

analysis of precipitation data for this study involved to steps: (i) calculation of amount-weighted

annual precipitation isotopic compositions, and (ii) calculation of amount-weighted precipitation

isotopic compositions for two six-month seasons for each meteorology station. Seasons are defined

as winter and summer in the extra-tropics, and the wettest and driest consecutive six-month interval

in the tropics.

First, the amount-weighted isotopic composition of precipitation (δP(annual)) was calculated following

(Equation 2.1):

δP(annual) =∑ δP(i)Pi

12i=1

∑ Pi12i=1

Equation 2.1

where δP(i) is the monthly isotopic composition of precipitation during month i, and Pi is the amount

of precipitation (i.e., the rate) during month i.

Second, the amount-weighted isotopic composition of season 1 (δP(season 1); defined as October-March

in the northern hemisphere extra-tropics, and the wettest consecutive six month interval in the

tropics) and season 2 (δP(season 2); defined as April-September in the extra-tropics, and the driest

consecutive six month interval in the tropics) precipitation was calculated following (Equations 2.2

and 2.3):

δP(season 1) =δP(10)P10+δP(11)P11+δP(12)P12+δP(1)P1+δP(2)P2+δP(3)P3

P10+P11+P12+P1+P2+P3 Equation 2.2

δP(season 2) =δP(4)P4+δP(5)P5+δP(6)P6+δP(7)P7+δP(8)P8+δP(9)P9

P4+P5+P6+P7+P8+P9 Equation 2.3

93

Following the same symbology as outlined for Equation 2.1. Southern hemisphere (e.g., Melbourne,

Australia) sites had winter and summer months inverted.

Before completing an analysis of groundwater and precipitation data at similar locations,

paleo-groundwater were required to be delineated and removed from the analysis because these

groundwaters are not reflective of the modern climate where precipitation measurements have been

made. Indeed fossil groundwaters that recharged during the Pleistocene (i.e., fossil groundwaters)

have been shown to be different than modern groundwaters by −8 to +2 ‰ in δ18O due to different

Pleistocene hydroclimatology (e.g., Plummer, 1993; Edmunds, 2009) or due to subglacial recharge of

groundwaters beneath the Laurentide and Fennoscandanavian ice sheets that resided over the

northern portions of Eurasia and North America 20,000 years ago (e.g., Estonia, Karro et al., 2004;

central Canada, Grasby and Chen, 2005; reviews by Jiráková et al., 2011 and McIntosh et al., 2012).

Groundwater δ18O and δ2H values, well depths, and 3H and 14C radioactivity levels were

compiled from earlier works (Table 2-1). Considerations of (i) possible effects of evaporation during

groundwater recharge, and (ii) possible shifts in δ18O and δ2H values related to paleoclimates

recorded in fossil groundwaters were made before comparing precipitation and groundwater stable

isotopic data.

First, partially evaporated groundwater samples were removed from our analysis using the

deuterium excess parameter (Dansgaard, 1964). Partial evaporation leads to changes in isotopic

compositions along δ2H/δ18O slopes of less than eight because of differences in the vapor pressures

of the 1H1H16O, 1H1H18O and 2H1H16O isotopologues. The deuterium excess parameter (d = δ2H –

8×δ18O; Dansgaard, 1964) integrates information within both δ18O and δ2H values and is used here

to test for modifications to the isotopic composition of groundwaters due to partial evaporation.

Samples bearing an evaporative signature will have a lower deuterium excess than that of meteoric

waters, which have a global mean deuterium excess of close to +10 ‰. All groundwater samples with

94

a deuterium excess value of less than zero were removed from this analysis to ensure that the

calculation of seasonal groundwater recharge ratios was not biased to evaporative influences.

Second, groundwater ages in excess of ~10,000 years were removed from this analysis on

the basis of 3H, 14C and well depths because fossil groundwaters have different δ18O and δ2H values

from modern groundwaters (Plummer, 1993; Edmunds and Milne, 2001; Grasby and Chen, 2005;

Karro et al., 2004; Edmunds, 2009; Jiráková et al., 2011; McIntosh et al., 2012). Samples with a 14C

concentration of greater than 60 p.m.C. were included in this study, as the maximum groundwater

age of samples with 60 p.m.C. can be no more than 5,000 years. Paleo-groundwater shifts in δ18O

and δ2H values do not become apparent until ~12 ka (Edmunds and Milne, 2001; Edmunds, 2009;

Darling, 2011), such that groundwaters with 14C activities of exceeding 60 p.m.C. should be suitable

for comparison with modern precipitation.

A similar delineation of “modern” groundwater can be derived from tritium groundwater

data, a commonly applied age tracer in groundwater investigations. Here I a mixing model that

accounts for groundwater residence time and mixing of different groundwaters with different ages

(i.e., time elapsed since recharge). I apply a mixing model that defines modern and old groundwater

using the year 1950 as a threshold, where “post-1950 groundwater” is defined as groundwater having

recharged after the year 1950 and “pre-1950 groundwater” is defined as groundwater that recharged

prior to 1950.

To use the compiled 3H groundwater data to quantify the mixing components of post-1950

and pre-1950 groundwater an estimate of the activity of 3H in meteoric waters was required. I

downloaded and analyzed a global dataset of 3H in precipitation measurements made since ~1960 at

various locations by the International Atomic Energy Agency. Records of pre-1950 3H activities in

meteoric waters are available from wine and ice cores compiled by Kotzer et al. (2000).

95

The 3H mixing model used to calculate pre- and post-1950 age components of each

groundwater sample is presented next. The mass (m) fraction of groundwater having recharged after

the year 1950 for a water sample (mpost-1950/msample) is calculated as:

mpost−1950

msample=

Hsample− Hpre−195033

Hpost−19503 − Hpre−1950

3 Equation 2.4.

where 3Hsample represents the 3H concentration of a given sample, 3Hpre-1950 represents the range of

possible 3H values for groundwater that recharged prior to 1950, and or 3Hpost-1950 represents the

range of possible 3H values for groundwater that recharged after 1950.

The calculation includes spatio-temporal variations in meteoric tritium activities in addition

to the radioactive decay of 3H. Possible 3Hpre-1950 and 3Hpost-1950 concentrations were determined using

linear regressions of latitude against the annual average 3H concentration in precipitation at various

locations (Figure 2-3).

96

Figure 2-3. The variations of tritium in meteoric water over time. The left pane shows linear

regressions through International Atomic Energy Agency precipitation stations (mean annual 3H

values computed for every site, for every year). The color of each line marks the corresponding year

that the regression was completed. The right pane shows an example of changes to 3H in

precipitation over time for 45°N calculated using the regressions presented in the left pane. The

squares and diamonds mark wine and ice core data (Kotzer et al., 2000). The darker lines and points

show the 2009-equivilent 3H activity after considering radioactive decay, whereas the lighter (grey)

points and line mark the uncorrected (i.e., “real time”) 3H activity of precipitation.

Regressions of 3H and latitude were developed using all International Atomic Energy

Stations with data for any given year (the number of stations available ranged from 12 to 89 sites,

annually). Regressions for the southern and northern hemisphere were completed separately because

of known inter-hemispheric differences in the 3H activity of precipitation, imparted because the

atmosphere is not completely mixed (Rozanski et al., 1991). The latitude and sample date for every

groundwater well location was entered into the latitude-time regressions of 3H in precipitation to

develop a range of possible 3Hpost-1950 and 3Hpre-1950 activities, with considerations for radioactive decay

made by calculating an equivalent 3H concentration for the date that each groundwater sample was

collected (i.e., meteoric tritium decay corrected up to the date that the compiled groundwater sample

was collected; 3H half-life of 12.3 years).

97

3Hsample values (i.e., measured groundwater tritium activity) and corresponding ranges for

3Hpost-1950 and 3Hpre-1950 were input into equation 2.4 to quantify the mixing proportion of “modern”

(i.e., post-1950) groundwater within each groundwater sample. All samples being comprised of

>80% “post-1950 groundwater” (median value from calculation used) were included in this study, as

these were presumed to have recharged during the contemporary climate, where precipitation data is

also available. All samples that did not meet this threshold were removed from our calculation, as

mixing with paleo-waters could not be precluded. This dataset reduction step is likely to be

conservative as many “3H-dead” (i.e., below detection tritium activities) groundwater samples may

have recharged more recently than the mid-Holocene, and, therefore, could have been compared to

modern precipitation in principle.

Finally, 90 percent of samples obtained from depths shallower than 40 meters underground

were found to meet the aforementioned “modern groundwater” criteria set for 14C and 3H data.

Therefore, a depth threshold of 40 meters below ground level was set as a threshold for modern

groundwater for compiled datasets that present groundwater δ18O or δ2H measurements but do not

present 14C and 3H data. Additional care was taken on an aquifer-by-aquifer basis where paleo-waters

are known to occur (e.g., Ferguson et al., 2007) in order to ensure that paleo-water isotopic data did

not propagate into the calculation of groundwater recharge

Now that modern groundwaters have been delineated using the above 3H, 14C and well

depth based methods, a stable isotope based calculation of groundwater recharge ratio seasonality

can proceed. To calculate the seasonal difference in groundwater recharge ratios, we compare

modern groundwaters (delineated sing 14C, 3H and well depths as per the preceding paragraphs) with

modern precipitation data by combining a water budget (equation 2.5) and an isotopic (equation 2.6)

mass balance:

Pannual = Pseason 1 + Pseason 2 Equation 2.5

98

PannualδP(annual) = Pseason 1δP(season 1) + Pseason 2δP(season 2) Equation 2.6

where Pannual, Pseason 1 and Pseason 2 are the precipitation rates for the year (i.e., annual), for season 1 (i.e.,

winter in the extra-tropics, and the wet season in the tropics), and for season 2 (summer in the extra-

tropics, and the dry season in the tropics). Similarly, δP(annual), δP(season 1) and δP(season 2) are the amount-

weighted isotopic compositions for annual, season 1 or season 2 time intervals. Combining equations

2.5 and 2.6 yields an isotope-based solution for the contribution of season 2 (i.e., summer or dry

season) rainfall to total annual precipitation:

Pseason 2

Pannual=

δP(annual)−δP(season 1)

δP(season 2)−δP(season 1) Equation 2.7

A similar set of equations can be derived for groundwater recharge rates (R) rather than precipitation

rates.

Rannual = Rseason 1 + Rseason 2 Equation 2.8

Rannualδgroundwater = Rseason 1δP(season 1) + Rseason 1δP(season 2) Equation 2.9

where Rannual, Rseason 1 and Rseason 2 are annual, season 1 and season 2 recharge rates, and δgroundwater is

the isotopic composition of recently recharged groundwater. Combining equations 2.8 and 2.9 yields

the an equation representing theproportion of season 2 recharge as a ratio of annual recharge f

(equation 2.10).

Rseason 2

Rannual=

δgroundwater−δP(season 1)

δP(season 2)−δP(season 1) Equation 2.10

Combining equations 2.7 and 2.10 yields the isotope-based equation for the recharge ratio during the

summertime (extra-tropics) or during the dry season (tropics; Rseason 2/Pseason 2; equation 2.11).

99

Rseason 2

Pseason 2=


δP(annual)−δP(season 1)(

Rannual

Pannual) Equation 2.11

A similar derivation (i.e., equations 4 – 10) can be made to calculate the recharge ratio during season

1 (Rseason 1/Pseason 1; equation 2.12):

Rseason 1

Pseason 1=


δP(annual)−δP(season 2)(

Rannual

Pannual) Equation 2.12

Finally, the isotope-based equation representing the seasonal difference in the groundwater recharge

ratio (R/P) between season 1 and season 2 can be made – without knowledge of annual precipitation

and recharge fluxes – by combining equations 2.11 and 2.12, yielding (Equation 2.13):

(R/P)season 1

(R/P)season 2= (


δP(annual)−δP(season 2)) / (


δP(annual)−δP(season 1)) Equation 2.13

This isotopic derivation of seasonal differences in groundwater recharge ratios is presented

schematically in Figure 2-4 (lower axis).

100

Figure 2-4. A schematic representation of the isotope-based approach to estimating seasonal

differences in the groundwater recharge ratio, defined as the proportion of rain and snow that

infiltrates into groundwater aquifers. The four isotopic data shown are: the flux weighted isotopic

composition of season one precipitation (δP(season 1)), the flux weighted isotopic composition of season

two precipitation (δP(season 2)), the flux weighted isotopic composition of annual precipitation (δP(annual)),

and the isotopic composition of groundwater (δgroundwater).

Uncertainties were estimated by completing the calculation using every combination of input

data and subsequently computing percentile ranges from the various calculation results on a site-by-

site basis. The calculation of seasonality in groundwater recharge ratios was only made for locations

that had at least three groundwater δ18O or δ2H values and three annual amount-weighted δ18O and

δ2H values for precipitation. 16 stations were excluded in the analysis because no precipitation data

were available for the summer or the winter season (e.g., Damascus, Syria) or because the δ18O and

δ2H values of winter and summer precipitation were not consistently higher or lower than the

opposing season (e.g., Quincy and Kennedy Space Center in Florida, U.S.A.). Locations that not

101

included in this study of seasonal differences in the groundwater recharge ratio are marked as

diamonds in Figure 2-2.

2.4 Results

Paired measurements of the isotopic composition of precipitation and groundwater at 54

globally-distributed locations are shown in Figure 2-5. Results show that, for the majority of samples,

precipitation and groundwater isotopic compositions are similar, or that groundwater δ18O or δ2H

values are lower than annual precipitation δ18O or δ2H values.

Figure 2-5. Comparison of groundwater and mean annual precipitation isotopic data at 54 globally-

distributed locations. Vertical error bars mark one standard deviation of inter-annual variability in

amount-weighted isotopic compositions of precipitation. Horizontal error bars bracket one standard

deviation of groundwater isotopic data.

102

Isotope-based calculation results of seasonal differences of groundwater recharge ratios (i.e.,

(R/P)winter/(R/P)summer) are presented in Figure 2-6. Similarly, Table 2-2 presents 25th-75th percentile

ranges of our isotope-based calculations of (i) the ratio of the summer groundwater recharge fluxes

relative to winter groundwater recharge fluxes (Rsummer/Rwinter), (ii) summer recharge efficiencies

(Rsummer/Psummer), and (iii) winter recharge efficiencies (Rwinter/Pwinter) for each study site.

Winter groundwater recharge ratios are higher than summer groundwater recharge ratios for

93% of desert (7 of a total of 9), temperate grassland (11 of a total of 13) or temperate forest

locations (16 of a total of 18; median of δ18O-based results of Monte-Carlo realizations). Winter

recharge is at least twice as effective (i.e., higher recharge/precipitation ratio) as summer recharge for

half of all temperate grasslands and temperate forests (15 of 31 locations) and for three-quarters of

deserts and xeric shrublands (7 of 9 locations). Also, one quarter of temperate or arid locations have

a winter groundwater recharge efficiency that is more than five times that of the summer.

Seasonal changes in the groundwater recharge ratios for tropical climates (n = 7) show that

all of the tropical sites tested here have higher groundwater recharge ratios during the wet season

relative to the dry season (i.e., (R/P)wet >> (R/P)dry; Figure 5). Only a few locations were available for

Mediterranean climates (n = 3) and boreal forests (n = 3). Mediterranean climates examined here

showed very little variability between summer and winter precipitation δ18O and δ2H values, resulting

in highly uncertain isotope-based calculations of groundwater recharge ratios for these coastal

locations (i.e., small change between δP(summer) and δP(winter); Figure 6). The few boreal sites (n = 3)

have a similar groundwater recharge ratio during the summer and winter seasons. Analysis of

groundwater recharge ratios in boreal forests are limited by the lack of groundwater isotopic data,

likely associated with the low boreal population density (~2.5 persons/km2) relative to the global

average (~50 persons/km2; population dataset from

www.sedac.ciesin.columbia.edu/data/collection/gpw-v3).

http://www.sedac.ciesin.columbia.edu/data/collection/gpw-v3

103

Figure 2-6. Seasonal differences in groundwater recharge ratios (recharge/precipitation: R/P)

between the (a) summer and winter seasons (extra-tropics), or between the (b) wet and dry seasons

(tropics). Colored bars mark the 25th-75th percentile ranges of calculation results and lines mark the

10th-90th percentile range of calculation results.

104

Table 2-2. Seasonal groundwater recharge ratios (isotope-based)

Sta

tio

n

δ18O

P(a

nn

ual

)

δ2H

P(a

nn

ual

)

δ18O

P(s

um

mer

) -

δ18O

P(w

inte

r)

δ2H

P(s

um

mer

) -

δ2H

P(w

inte

r)

Rsu

mm

er/

Rw

inte

r

(R/

P) s

easo

n 1

(%)

(R/

P) s

easo

n 2

(%)

Cayenne −2.2 −10 1.4 4 0 0 - 39 0

Taguac −5.3 −33 2.2 19 0 - 0.3 65 - 100 0 - 48

Seawell −1.9 −6 1.8 13 0 - 0.1 17 - 35 0 - 6

Jakarta −5.6 −35 1.1 9 0 - 0.2 48 - 100 0 - 26

New Dehli −5.8 −38 5.0 41 0 - 0.3 11 - 23 0 - 21

Dar es Salaam −2.6 −12 1.7 15 0 - 0.3 3.9 - 16 0

Addis Ababa −1.3 +3 0.9 9 0 29 - 96 0

Santa Maria −5.0 −35 2.0 12 0 - 0.1 0 - 30 0 - 100

Beit Dagan −5.1 −22 1.7 6 0 - 0.4 0 - 15 0 - 34

Pisa −5.5 −33 0.7 n/a 0 - 0.2 34 - 75 0 - 10

Trout Lake −11.1 −77 6.1 49 0 - 0.6 85 - 100 0 - 24

Yellowstone −16.2 −122 9.4 69 0.2 - 0.6 12 - 26 3.2 - 6

Smith's Ferry −15.6 −118 4.9 36 0 - 0.3 11 - 16 0 - 5

Lake Geneva −7.6 −53 4.5 32 0.4 - 1.1 33 - 39 21 - 24

East MA −7.5 −51 2.2 25 0 - 1.4 17 - 56 0 - 30

Niwot Saddle −17.6 −130 8.5 65 0.3 - 0.7 3.9 - 5 2.2 - 4.4

Wye −7.3 −44 2.8 17 0 - 1.7 1.2 - 16 16 - 26

Purdue Agr. −5.7 −33 3.4 24 0 - 1.5 22 - 40 0 - 18

Clinton Stn. −5.0 −29 1.8 16 0 - 0.8 0 - 27 3.6 - 18

Caddo Valley −4.9 −27 2.1 15 0 - 0.6 16 - 21 0.8 - 9

Coffeeville −5.0 −32 1.5 12 0 - 1.9 0 - 24 21 - 72

Saturna −10.9 −79 2.2 14 0.2 - 2.4 6 - 57 64 - 100

Ottawa −11.0 −75 5.5 38 0.6 - 1.7 46 - 85 40 - 71

Wallingford −7.2 −49 1.5 10 0 - 0.6 15 - 54 0 - 25

P. Douradas −7.6 −45 0.9 6 0 - 0.5 12 - 42 0 - 39

Krakow −9.1 −65 3.8 29 0.2 – 1.0 22 - 38 4.4 - 13

Cuxhave −7.0 −49 1.4 9 0 - 0.5 22 - 51 0 - 17

Orleans −6.9 −46 1.8 13 0.1 - 1.4 0 - 16 0 - 6

Melbourne −5.0 −28 1.4 15 0 - 0.1 8 - 25 0 - 1.4

Newcastle −11.2 −89 4.3 47 0 - 2.0 1.3 - 8 0 - 1.2

Little Bighorn −15.1 −115 5.9 44 0 - 0.5 1.9 - 3.1 0 - 0.4

Lamberton −7.6 −51 6.7 37 0.6 - 2.0 31 - 47 7 - 11

N. Platte Agr. −9.0 −61 5.6 53 1.3 - 4.6 13 - 36 5 - 8

105

Sta

tio

n

δ18O

P(a

nn

ual

)

δ2H

P(a

nn

ual

)

δ18O

P(s

um

mer

) -

δ18O

P(w

inte

r)

δ2H

P(s

um

mer

) -

δ2H

P(w

inte

r)

Rsu

mm

er/

Rw

inte

r

(R/

P) s

easo

n 1

(%)

(R/

P) s

easo

n 2

(%)

Mon Mouth −6.7 −41 3.5 24 0.5 - 2.1 14 - 25 8 - 15

Great Plains −5.8 −35 2.4 17 0.7 - 4.7 0 - 22 22 - 50

Edmonton −17.6 −131 10.6 84 1.1 - 2.7 68 - 100 43 - 56

Saskatoon −14.3 −111 9.0 76 0.1 - 0.5 up to 100 10 - 27

Wynyard −16.0 −124 7.8 62 0.6 - 1.4 78 - 100 33 - 52

Esther −15.7 −124 10.0 72 0.3 - 1.0 up to 100 17 - 37

Calgary −17.8 −138 8.6 49 0.2 - 3.7 48 - 100 36 - 63

Gimli −14.0 −102 11.3 72 1.8 - 2.8 4.1 - 6 4.4 - 5

Craters of Moon −16.9 −128 3.3 52 0.1 - 0.5 0 - 0.1 0 - 0.1

Pinedale −14.8 −110 9.9 38 0.1 - 0.7 6 - 14 2.1 - 6

Sand Spring −12.8 −96 6.5 61 0 - 0.1 21 - 31 0 - 1.6

Smith Valley −12.4 −94 3.8 24 0 - 0.4 33 - 100 0 - 55

Tuscon −7.1 −53 2.5 8 0 13 - 25 0

Chihuahua −4.1 −26 6.1 42 0 - 1.9 0 - 18 0 - 2.4

Alice Springs −5.2 −22 1.6 20 0 - 0.6 0 - 15 0 - 0

Zhangye −6.7 −46 9.4 61 0.6 - 2.9 1.2 - 2.2 0.3 - 0.5

Yinchuan −7.4 −48 8.9 50 0.8 - 1.9 4.6 - 14 0 - 0.7

Yellowknife −20.7 −158 2.5 21 0 - 1.9 0 - 64 56 - 100

Whitehorse −21.3 −164 4.9 31 0.2 - 2.9 48 - 100 25 - 66

Chapais −13.5 −97 5.2 45 1.2 - 1.5 54 - 100 44 - 64

** Seawell (Barbados) recharge data from Jones and Banner, 2000

* Taguac (Guam) recharge data from Jocson et al., 2002

106

2.5 Discussion

Recharge ratios were calculated for 54 aquifer-precipitation pairings. Further, an additional

16 sites were available for a comparison of precipitation and groundwater δ18O and δ2H values, but

were not suited for quantifying groundwater recharge ratios due to the lack of summer or winter

precipitation end-members. A comparison of δ18O and δ2H values for the amount-weighted isotopic

composition of precipitation and groundwater is shown in Figure 2-7 for these 70 locations (average

±1 s.d. uncertainty). Groundwater matched the amount-weighted precipitation from nearby

monitoring stations within 1 ‰ for δ18O and within 9 ‰ for δ2H for half of the locations in this

study or 2 ‰ for δ18O and within 16 ‰ for δ2H for four-fifths of study locations.

Figure 2-7. Differences in the stable oxygen and hydrogen isotopic compositions of amount-

weighted precipitation (δP(annual)) and local groundwaters (δGroundwater). Error bars mark one standard

deviation from the mean.

107

The difference between precipitation and groundwater isotopic compositions ranges from

+1.8 ‰ to −5.6 ‰ for δ18O and from +9 ‰ to −45 ‰ for δ2H. The closest match between the

isotopic composition of groundwater and precipitation were found in the tropics. All locations

having an average groundwater δ18O value of greater than −5 ‰ have an amount-weighted

precipitation value that matches groundwater is within 1.5 ‰. In contrast, regions with lower δ18O

groundwater values have a broader range of differences between groundwater and precipitation. At

locations where groundwater δ18O values are less than −10 ‰ (n=24) the range in δ18OGroundwater −

δ18OP(annual) was between −5.6 ‰ and +1.0 ‰.

More tightly constrained groundwater-precipitation isotopic data in regions with higher

δ18O values is reconciled by an examination of seasonal fluctuations in δ18O and δ2H values.

Regions having higher δ18O and δ2H values also have more subdued seasonal fluctuations in the

isotopic composition of precipitation. Conversely, regions with lower δ18Oprecipitation and δ2Hprecipitation

values tend to exhibit greater seasonal changes in the isotopic composition of precipitation (Figure 2-

8).

108

Figure 2-8. The absolute value of the difference between summer and winter δ18O values for 333

globally-distributed locations. The top pane shows the seasonality of δ18O on a global map, and this

spatial presentation reveals that locations having the greatest seasonality in precipitation δ18O are

located at high latitudes or far from continents. The bottom pane presents a cross plot of the

seasonality of δ18O in precipitation, with each point representing on precipitation monitoring station.

The most subdue seasonal fluctuations in δ18O are also locations that have high overall δ18O values,

and tend to be located in the humid tropics.

The difference between summer (April to September) and winter (October to March) δ18O

values is less than 2 ‰ for the overwhelming majority (95 %) of stations that have an amount-

weighted δ18Oprecipitation value greater than −3 ‰ (i.e., 18 of 19 stations). Conversely, the difference

between summer and winter δ18O values is greater than 5‰ for the majority (87 %) of stations

having an amount-weighted precipitation δ18O value below −15 ‰ (i.e., 27 of 31 stations).

109

Geographically, stations located within the tropics have an average difference between winter and

summer δ18O values of 2.3 ‰ (s.d. of 1.6 ‰, n = 46), whereas locations in the extra-tropics have an

average difference between winter and summer δ18O values of 5.0 ‰ (s.d. of 4.0 ‰, n = 176).

Offsets have also been reported for North America, where surface water is isotopically light

compared to rainfall, due in large part to snow fall and snow melt water inputs in the western North

American watersheds compared to the central and eastern regions of North America (Dutton et al.,

2005).

Overall, it appears that groundwater values may be of use as a proxy for the long-term

annual amount-weighted isotopic composition of precipitation in some cases, but that the application

of an offset may be appropriate because the majority of groundwaters have lower δ18O and δ2H

values than amount-weighted annual precipitation. There may be the potential to develop predictive

models of the isotopic composition of groundwater that can complement existing global maps of the

isotopic composition of precipitation (Bowen and Wilkinson, 2002; Bowen and Revenaugh, 2003).

Now this discussion will turn attention from raw isotopic datasets to groundwater recharge

ratios. As a reminder, this chapter shows that arid and temperate climates have higher winter

recharge ratios than summer recharge ratios. This suggests that a given unit change in winter

precipitation will be more important than the same unit change in summer precipitation, from a

groundwater recharge perspective.

The high groundwater recharge ratios found during the winter in arid and temperate climates

may be due to seasonal changes in evapotranspiration potential. Many arid and temperate climates

examined here have pronounced seasonal differences in surface temperatures and plant productivity.

Lower recharge ratios during summertime are explained in part by the higher potential for

evapotranspiration. Higher winter recharge ratios are explained in part by lower potentials for

evapotranspiration because of reduced atmospheric temperatures and dormant vegetation (Welker et

110

al., 1991, Blumenthal et al., 2008, Chimner and Welker, 2005; Anderson-Smith et al., 2014). A global

map of the seasonality in chlorophyll abundance, calculated using long-term monthly mean values of

the normalized difference vegetation index (NDVI), highlights the pronounced seasonality of plant

growth (Figure 2-9).

Figure 2-9. The ratio of summer (six-month) and winter (six-month) normalized difference

vegetation index spanning Earth’s continents (stippled areas highlight areas having <10% difference

between summer and winter normalized difference vegetation indices; southern hemisphere locations

have had summer/winter months reversed relative to the northern hemisphere).

One quarter of continental areas – mostly located in the tropics – have less than a 10%

difference between summer and winter plant productivity (stippled regions in Figure 2-9), suggesting

no a dominant growing season in these regions. The greatest intra-annual changes in plant activity is

found in cold regions (defined as having at least one month with a mean temperature <0°C, Bates

and Bilello, 1966), which cover one half of the continents (New et al., 2002). Cold regions have

normalized difference vegetation indices that are 14 times higher during the summer relative to

during the winter (global average), whereas non-cold regions have an average normalized difference

111

vegetation indices that have more subdued intra-annual variations (non-cold-region

NDVIsummer/NDVIwinter have a global average value of 1; Figure 2-9).

Some cold regions have seasonally frozen ground during the winter that inhibits winter

groundwater recharge (Hayashi et al., 2003; Cable et al., 2013). This seasonal blocking of recharge

may have an effect, but overall it does not appear to override the seasonality of groundwater recharge

ratios in temperate regions. The effect of a temporally variable “frozen ground aquitard” may be

offset by elevated groundwater recharge during the rapid melt of seasonal snowpack. Groundwater

recharge in the United States is more than double monthly precipitation during snowmelt, implying

that snowmelt constitutes an important component of annual recharge (Dripps and Bradbury, 2010;

Dripps, 2012; Allan et al., 2014).

There are four temperate locations that show summer groundwater recharge ratios that are

higher than winter recharge ratios: Coffeeville (Mississippi, in the southern U.S.A.), Great Plains

Apiaries (Oklahoma, in the south-central U.S.A.) Saturna Island (British Columbia, off the coast of

western Canada) and Wye (Maryland, in eastern U.S.A.). It is noteworthy that all of these locations

do not have a large winter snowpack (i.e., less than 5 mm of snow-water equivalent in February, as

obtained from long-term monthly mean snow water equivalent data analyzed from passive

microwave satellite products: www.globsnow.info). Large-scale mapping can provide better

knowledge of the importance of snowmelt to annual groundwater recharge. For some locations that

have a Mediterranean-type precipitation seasonal pattern (e.g., Saturna Island), wintertime storage

may fill and inhibit recharge, generating runoff instead (Sayama et al., 2011). This could in part help

to explain the isotope-based observation of higher summer recharge ratios, although more detailed

research in these locations is supported by the global perspective presented here.

Groundwater recharge ratios in the tropics are higher during the wet season than during the

dry season in all cases examined. This finding suggests that more intense rainfall leads to higher

112

recharge as a proportion of precipitation (i.e., more intense rain leads to higher groundwater recharge

ratios). This finding is consistent with previous isotope and water-level monitoring based work in

Namibia, Uganda, Ethiopia and Tanzania (Wanke et al., 2008; Owor et al., 2009; Walraevens et al.,

2009; Taylor et al., 2013). Each of these studies found that groundwater recharge is most efficient

during high intensity rainfall events in the tropics. This finding implies that possible increases in the

frequency of high-intensity rainfall events under in intensifying water cycle (Durack et al., 2012) may

enhance groundwater availability in some tropical locations. However, a more intense water cycle

may elevate geohazard risks to local communities (Belle et al., 2013).

Uncertainty in isotope-based calculations of the groundwater recharge ratio in tropical

settings are greater than uncertainties than regions with more pronounced seasonality because of the

small differences between summer and winter precipitation isotopic compositions (average of 1.9‰).

The intra-annual variability in δ18O values of precipitation is presented in Figure 2-8. Inland and

high-latitude locations are characterized by a greater intra-annual range in δ18O and δ2H values than

coastal sites and the tropics. Subdued seasonality of δ18O in the tropics results in higher uncertainties

in the seasonality of the groundwater recharge ratio, suggesting that isotope-based approaches to

calculating seasonal differences in groundwater recharge ratios will be better constrained outputs in

hydro-climates characterized by pronounced seasonality. In spite of the high uncertainties, there exist

more than 60 tropical locations with long-term precipitation isotopic data (International Atomic

Energy Agency database: www-naweb.iaea.org/napc/ih/IHS_resources_gnip.html), highlighting an

unfilled opportunity to calculate groundwater recharge ratios should groundwater investigations be

completed at these locations.

Next, I compare the isotope based observations of groundwater recharge ratio seasonality

with outputs from a global hydrological model (pers. comm. Y. Wada; e.g., Gleeson et al., 2012). The

113

spatial comparison of the isotope-based groundwater recharge ratios with a global hydrological

model is shown in Figure 2-10 (PCR-GLOBWB; Wada et al., 2010).

Figure 2-10. The seasonality of groundwater recharge ratios from isotope-based calculations (this

study; points) and a global hydrological model (PCR-GLOBWB; Wada et al., 2010).

114

PCR-GLOB (Wada et al., 2010) is a global hydrological model that simulates water balances

at a 0.5o×0.5o spatial resolution and a daily temporal resolution. The model is setup with two soil

layers and an underlying groundwater aquifer. The model simulates exchanges such as infiltration,

capillary action between the layers, and also simulates exchanges between the top soil horizon and

the atmosphere via rainfall, snowmelt, evaporation and transpiration, canopy interception and

snowpack storages. The groundwater aquifer in the model is representative of the deeper subsurface,

such that vegetation is not considered to play a critical role in these exchanges via hydraulic lift or

plant transpiration. Groundwater storage is parameterized using geospatial lithology and topography

datasets. A detailed description of the model is presented within works by Wada et al. (2012a; b).

A cross plot comparison of median groundwater recharge ratios from the isotope- and

model-based approaches show that PCR-GLOBWB outputs match isotopic outputs (within error) in

most, but not all locations in the extra-tropics (a range of PCR-GLOB modelled recharge ratios

falling within 100 km of each study location were used to bound possible model recharge ratio

values). The 10th-90th percentile range of isotope-based recharge ratios overlaps with modeled PCR-

GLOBWB recharge ratios in 85% of extra-tropical locations analyzed in this chapter (Figure 2-11).

115

Figure 2-11. Comparison of recharge ratios calculated using isotope-based and hydrological

modelling based approaches for (a) summer (April-September) and (b) winter (October-March).

Error bars (x-axis) mark the 10th-90th percentile ranges of isotope-based calculations (squares mark

the median). PCR-GLOB error bars mark the range of model results within 100 km of each study

location. Colors for each square correspond to ecoregions as shown in previous figures in this

chapter.

116

The extra-tropical locations where PCR-GLOBWB recharge ratios do not overlap within the

10th-90th percentiles of isotope-based groundwater recharge ratios are all located in regions that have

a February snowpack of between 18 and 81 mm (now water equivalent; Trout Lake and Craters of

the Moon in the U.S.A., and Edmonton, Saskatoon, Wynyard and Esther in Canada, long-term

monthly means of snow storages from www.globsnow.info). Part of the model-isotope differences

observed may be the result of the fact that the isotope-based calculation assesses the seasonal

distribution of recharge relative to the timing of precipitation, not necessarily the timing of recharge.

For example, recharge of snow falling during the winter (October to March) but not melting until the

spring season (e.g., April to June) is – from the isotopic standpoint – winter recharge. Whereas –

from the model standpoint – this snowpack-delayed recharge is considered as summer recharge.

Other sources of discrepancy between the isotope and model groundwater recharge ratio

estimates include cropland irrigation return flow that are not incorporated into PCR-GLOBWB, nor

the isotope based model, per se (as this flow would be evident in the groundwater isotopic data, but

will not be included in precipitation fluxes). Irrigation return flows can constitute an overwhelming

component of groundwater recharge in some regions, such as the California Central Valley, for

example (Faunt, 2009). Irrigation can also aid recharge indirectly by enhancing the proportion of

rainfall that infiltrates (e.g., Chiew and McMahon, 1991). Further, PCR-GLOBWB does not include

groundwater recharge from lakes, wetlands and rivers that may account for some component of

recharge in arid and semi-arid regions.

The broader implications of this study are three fold: (i) implications for climate change, (ii)

impliactions for paleoclimatology, and (iii) implications for ecosystem ecology.

Current climate model projections of groundwater recharge are highly uncertain because of

large differences between different general circulation models, different downscaling methods and

variable coupling with hydrological models (Crosbie et al., 2011). Previous works that have assessed

117

the potential for change to groundwater recharge have found that different climate models range

both in the direction and magnitude of predicted changes to groundwater recharge. The differences

range from ±20 to ±50% changes to future groundwater recharge (Allan et al., 2010; Stoll et al.,

2011; Dams et al., 2012). Very few models have quantified changes to the intra-annual distribution of

groundwater recharge (Dams et al., 2012). Therefore, current models are likely to be overlooking

potentially important changes to individual seasons and their associated impacts upon the annual

groundwater recharge flux. The isotope based recharge ratios presented here may be used to assess

the most important seasonal hydrological processes governing groundwater recharge under a future,

warmer and more energetic water cycle.

In temperate regions, our results suggest that a higher percentage of winter precipitation is

able to recharge aquifers. This finding suggests that a unit change to winter precipitation will be more

important, from a groundwater recharge perspective, than the same unit change to summer

precipitation. The bias towards winter recharge could also be altered if the factors limiting summer

recharge occur, such as summer evapotranspiration and storm intensities. The observed bias towards

winter precipitation recharge in the extra-tropics can been attributed to the seasonal filtering of

precipitation, whereby greater proportions of winter precipitation reach the water table relative to the

total summer precipitation amount. This result is interpreted to be due to the high evapotranspiration

rates that limit the amount of summer precipitation that recharges.

In tropical settings, we found that recharge ratios are highest during the rainy season. This

finding supports the integration of rainfall intensity and intra-annual distributions of rainfall amounts

as a central component of future forecasts of groundwater recharge in a warming climate. Existing

studies of site-specific modeling in Uganda have found that by including intra-annual variability in

precipitation amounts, and variable rainfall intensities, into projections of future groundwater

recharge fluxes substantially modifies the projection of future groundwater availability, changing the

118

prediction from a 55% decrease to, instead, a 53% increase in annual groundwater recharge (Mileham

et al., 2009). Given the large number of precipitation monitoring stations (e.g., 330 locations in

Figure 2-8) and the equations and approach derived in this chapter, a new opportunity now exists to

quantify groundwater recharge ratios across the continents using isotopic data for groundwaters. The

incorporation of these data as calibration and validation toolsets in groundwater-equipped general

circulation models may help to confirm the validity of projections from these models. Similarly, the

paired investigation of precipitation and groundwater isotopic compositions at the same field site can

be used to test isotope-enabled general circulation models’ conceptualization of

groundwater/surface-water interactions (ECHAM: Hoffman et al., 1998; CCSM: Noone et al., 2002;

IsoGSM: Yoshimura et al., 2003; GISS: Schmidt et al., 2007; LMDZ4: Risi et al., 2010; iLOVECLIM:

Roche et al., 2013).

Our finding that groundwater recharge fluxes do not match precipitation fluxes one-to-one

(Figure 2-7) has three partially-overlapping implications for the interpretation of isotope-based

paleoclimate proxies such as fossil groundwaters and speleothems.

First, changes to the seasonality of precipitation fluxes may not be recorded – isotopically –

on a one-to-one basis in paleoclimate records involving subsurface waters such as paleo-

groundwaters, smectite, tree rings, speleothems and vein calcite (e.g., Winograd et al., 1992; Plummer,

1993; McCarroll and Loader, 2004; Asmerom et al., 2010; Stevenson et al., 2010, Winnick et al., 2013;

Mix and Chamberlain, in press). Groundwater recharge is generally a more efficient process during

the winter relative to the summer as shown in this study. Paleoclimate records based on

groundwaters may be more tightly linked (i.e., biased) to changes in winter (or, wet season) climate,

relative to summer (or, dry season). This realization map help to explain some of the discrepancies

that have been observed in fossil groundwaters and lake sediment records located near to one

another. For example, paleo-limnologic records of Owens Lake, California show δ18O shifts of up to

119

10 ‰ during the past 500,000 years (Smith and Bischoff, 1997; Menking et al., 1997). Conversely,

groundwater-precipitated calcite from nearby Devils Hole, Nevada shows a much smaller range of

δ18O shifts (less than 3 ‰) over the past 500,000 years (Winograd et al., 1992).

Second, the dramatic shifts in climate and biomes from the last ice age to today — such as

the shift from deserts to forested climates in parts of in Europe and Alaska (Williams, 2003) — may

have modified the recharge ratios in these settings, and thereby created changes to groundwater δ18O

values. Our limited set of boreal observations in this study are of particular interest for further word

because of the apparent similarity between precipitation and groundwater isotopic compositions in

this zone. The boreal biome shifted to lower latitudes during the last glacial maximum (Williams,

2003), and could have modified the seasonality of groundwater recharge ratios, too. This research

calls for more work in boreal sites that have long-term precipitation δ18O and δ2H data in order to

further test this observation.

Third, seasonal changes in the isotopic composition of precipitation, shown in Figure 2-8,

provide some information for the possible range of δ18O shifts in paleoclimate records that can be

attributed to changes in the seasonality of precipitation. Seasonality is commonly discussed as a

potential source isotopic change amongst other factors such as differences in paleo-ocean δ18O,

atmospheric and sea surface temperatures and air mass trajectories. For example, a complete

shutdown of precipitation from a single six month (summer or winter) interval can account for a

shift no greater than ~9 ‰ in δ18O (much less in most regions), if the seasonality of precipitation

δ18O were similar in the past to today. Some lacustrine paleoclimatic records show more than 9 ‰

variation during the Pleistocene (e.g., Owens Lake, California; Smith and Bischoff, 1997), and this

analysis may help to put quantitative bounds on the magnitudes of δ18O and δ2H shifts that may be

attributed to seasonality when interpreting paleoclimate records.

120

Finally, ecosystem ecology is linked to groundwater recharge fluxes. The groundwater

recharge ratio patterns assessed here span a variety of biomes with different plant life forms, with

unique temporal and spatial partitioning of water sources by vegetation with different rooting and

growth patterns (Ehleringer and Cooper 1992; Dodd et al., 1998; Alstad et al., 1999; Welker 2000;

Dawson et al., 2002; Kulmatiski et al., 2010; Leffler and Welker, 2013). In deserts, temperate

grasslands and temperate forests, seasonal hydrological processes support the growth of a diversity of

life forms (grasses and shrubs, trees and understory plants) that utilize soil- and ground-water

resources from different depths and are thereby linked to water movements close to Earth’s surface.

Developing an improved understanding of the seasonal changes in vegetation and coupled feedbacks

to groundwater recharge – such as interception, transpiration and hydraulic redistribution – will help

to better predict how large-scale biome shifts may impact groundwater. For example, ongoing tree

death due to mountain pine beetle infestation has recently been shown to reduce transpiration fluxes,

resulting in a one-third increase in groundwater fluxes that becomes particularly apparent in late

summer (Bearup et al., 2014). Changing seasonality in groundwater recharge fluxes due to vegetation

shifts have important implications for aquatic species that depend upon groundwater refugia for

habitat (e.g., Power et al., 1999). Plant life forms are expected to shift in a warming climate, and yet

these shifts will likely contain surprises such as recent work that has shown that some plant species

move downhill (toward warmer temperatures) as climate warms, an unexpected response likely

related to plant’s selection of optimal water requirements over temperature trends (Crimmins et al.,

2011; Harsch and Janneke, 2014). Continuing to monitor groundwater and precipitation isotopic

compositions can help to quantify vegetation water sources and to assess eco-hydrological feedbacks

as transpiration fluxes are modified by changing human land uses (Gordon et al., 2005) and plant

water use efficiencies (Keenan et al., 2013).

121

2.6 References

Abbott, M. D., Lini, A., and Bierman, P. R. (2000), δ18O, δD and 3H measurements

constrain groundwater recharge patterns in an upland fractured bedrock aquifer, Vermont, USA.,


Aeschbach-Hertig, W., and Gleeson, T. (2012), Regional strategies for the accelerating global

problem of groundwater depletion, Nature Geoscience, 5, 853–861, doi:10.1038/ngeo1617.

Aeschbach-Hertig, W., Stute, M., Clark, J. F., Reuter, R. F., and Schlosser, P. (2002), A

paleotemperature record derived from dissolved noble gases in groundwater of the Aquia Aquifer

(Maryland, USA), Geochimica et Cosmochimica Acta, 66, 797–817.

Allen, D. M. (2004), Sources of ground water salinity on islands using 18O, 2H, and 34S.

Ground Water, 42, 17–31.

Allen, D. M., Cannon, A. J., Toews, M. W., and Scibek, J. (2010), Variability in simulated

recharge using different GCMs, Water Resources Research, 46, W00F03. doi: 10.1029/2009WR008932

Allen, D. M., Stahl, K., Whitfield, P. H., and Moore, R. D. (2014)1 Trends in groundwater

levels in British Columbia. Canadian Water Resources Journal/Revue canadienne des ressources

hydriques, (Ahead of print).

Alstad, K. P., Welker, J. M., Williams, S. and Trilica, M. J. (1999), Carbon and water relations

of Salix monticola in response to winter browsing and changes in surface water hydrology: an

isotopic study using δ13C and δ18O, Oecologia, 120, 375–385.

Anderson-Smith, A., Sullivan, P. F. and Welker, J. M. (2014), Increases in tundra shrub

density is manifested in vegetation spectral properties and corresponds to greater CO2 sequestration,

Global Change Biology (in press).

122

Araguás-Araguás, L., Froehlich, K., and Rozanski, K. (2000), Deuterium and oxygen-18

isotope composition of precipitation and atmospheric moisture, Hydrological Processes 14, 1341–1355.

Asmerom, Y., Polyak, V. J., and Burns, S. J. (2010), Variable winter moisture in the

southwestern United States linked to rapid glacial climate shifts, Nature Geoscience, 3, 114–117.

Bakari, S. S., Aagaard, P., Vogt, R. D., Ruden, F., Brennwald, M. S., Johansen, I., and

Gulliksen, S. (2012), Groundwater residence time and paleorecharge conditions in the deep confined

aquifers of the coastal watershed, South-East Tanzania, Journal of Hydrology, 466, 127–140.

Bates, R. E. and Bilello, M. A. (1966), Defining the cold regions of the northern hemisphere,

Hanover, NH: CRREL, Technical Report 178.

Bearup, L., Maxwell, R. M., Clow, D. W., and McCray, J. E. (2014), Hydrological effects of

forest transpiration loss in bark beetle-impacted watersheds, Nature Climate Change, Advance online

publication (April 20), doi:10.1038/nclimate2198

Belle, P., Aunay, B., Bernardie, S., Grandjean, G., Ladouche, B., Mazué, R. and Join, J.-L.

(2013), The application of an innovative inverse model for understanding and predicting landslide

movements (Salazie cirque landslides, Reunion Island), Landslides, 1–13.

Berg, J. A., and Pearson, S. R. (2011), South-central Minnesota groundwater monitoring of

the Mt. Simon aquifer: Minnesota, Department of Natural Resources, Ecological and Water

Resources Division Report, 92 pp.

Birks, S. J., and Edwards, T. W. D. (2009), Atmospheric circulation controls on precipitation

isotope–climate relations in western Canada, Tellus B, 61, 566–576.

123

Blumenthal, D., Chimner, R., Welker, J. M., and Morgan, J. (2008), Increased snow facilitates

plant invasion in mixedgrass prairie, New Phytologist, 179, 440-448. doi:10.1111/j.1469-

8137.2008.02475.x.

Boutin, P. (2009), Etude geochmique des eaux souterraines à la mine joe mann,

Chibougamau,Québec, M.Sc. Thesis at Université du Québec, 141 pp.

Bowen, G. J., and Revenaugh, J. (2003), Interpolating the isotopic composition of modern

meteoric precipitation, Water Resources Research, 39, 1299. doi:10.1029/2003WR002086

Bowen, G. J., and Wilkinson, B. (2002), Spatial distribution of δ18O in meteoric

precipitation, Geology 30, 315–318.

Bretzler, A., Osenbrück, K., Gloaguen, R., Ruprecht, J. S., Kebede, S., and Stadler, S. (2011),

Groundwater origin and flow dynamics in active rift systems–A multi-isotope approach in the Main

Ethiopian Rift, Journal of Hydrology, 402, 274–289.

Burchuladze, A. A., Chudy, M., Eristavi, I. V., Pagava, S. V., Povinec, P., Sivo, A., and

Togonidze, G. I. (1989), Anthropogenic 14C variations in atmospheric CO2 and wines, Radiocarbon,

31, 771–776.

Cable, J., Olge, K., Bolton, D., Bentley, L., Romonvsky, V., Iwata, H., Harazona, Y., and

Welker, J. M. (2013), Permafrost thaw effects boreal deciduous plant transpiration through increased

soil water, increased thaw and warmer temperatures, Ecohydrology DOI: 10.1002/eco.1423.

Carey, S. K., and Quinton, W. L. (2005), Evaluating runoff generation during summer using

hydrometric, stable isotope and hydrochemical methods in a discontinuous permafrost alpine

catchment, Hydrological Processes, 19, 95–114.

124

Carreira, P. M., Marques, J. M., Marques, J. E., Chaminé, H. I., Fonseca, P. E., Santos, F. M.,

Moura, R. M., and Carvalho, J. M. (2011), Defining the dynamics of groundwater in Serra da Estrela

Mountain area, central Portugal: an isotopic and hydrogeochemical approach, Hydrogeology Journal, 19,

117–131.

Castle, S. L., Thomas, B. F., Reager, J. T., Rodell, M., Swenson, S. C. and Famiglietti, J. S.

(2014), Groundwater Depletion During Drought Threatens Future Water Security of the Colorado

River Basin. Geophysical Research Letters, doi: 10.1002/2014GL061055.

Cheung, K. and Mayer, B. (2007), Chemical and isotopic characterization of shallow

groundwater from selected monitoring wells in Alberta: Part I: 2006-2007, Alberta Environment

Report, 88 pp.

Chiew, F. H. S. and McMahon, T. A. (1991), Groundwater recharge from rainfall and

irrigation in the Campaspe River Basin, Soil Research, 29, 651–670.

Chimner, R. A., and Welker, J. M. (2005), Ecosystem respiration responses to experimental

manipulations of winter and summer precipitation in a Mixedgrass Prairie, WY, USA., Biogeochemistry,

73, 257–270. doi:10.1007/s10533-004-1989-6.

Crimmins, S. M., Dobrowski, S. Z., Greenberg, J. A., Abatzoglou, J. T., and Mynsberge, A.

R. (2011), Changes in climatic water balance drive downhill shifts in plant species’ optimum

elevations. Science 331, 324-327.

Crosbie, R. S., Dawes, W. R., Charles, S. P., Mpelasoka, F. S., Aryal, S., Barron, O., and

Summerell, G. K. (2011), Differences in future recharge estimates due to GCMs, downscaling

methods and hydrological models, Geophysical Research Letters, 38, L11406,

doi:10.1029/2011GL047657

125

Cunningham, E. E., Long, A., Eastoe, C., and Bassett, R. L. (1998), Migration of recharge

waters downgradient from the Santa Catalina Mountains into the Tucson basin aquifer, Arizona,

USA., Hydrogeology Journal, 6, 94–103.

Dams, J., Salvadore, E., Van Daele, T., Ntegeka, V., Willems, P., Batelaan, O., and Hendricks

Franssen, H. J. (2012), Spatio-temporal impact of climate change on the groundwater system,

Hydrology and Earth System Sciences 16, 1517–1531.


Darling, W. G., and Bath, A. H. (1988), A stable isotope study of recharge processes in the

English Chalk, Journal of Hydrology, 101, 31–46.

Darling, W. G., Edmunds, W. M., and Smedley, P. L. (1997), Isotopic evidence for

palaeowaters in the British Isles, Applied Geochemistry, 12, 813–829.

Darling, W. G., Bath, A. H., and Talbot, J. C. (2003), The O and H stable isotope

composition of freshwaters in the British Isles. 2, surface waters and groundwater, Hydrology and Earth

System Sciences, 7, 183–195.

Das, B. K., Kakar, Y. P., Moser, H., and Stichler, W. (1988), Deuterium and oxygen-18

studies in groundwater of the Delhi area, India, Journal of Hydrology, 98, 133-146.

Dawson, T. E., Mambelli, S., Plamboeck, A. H., Templer, P. H., and Tu, K. P. (2002), Stable

isotopes in plant ecology. Annual review of ecology and systematics, 33, 507–559.

Delin, G. N., Healy, R. W., Lorenz, D. L., and Nimmo, J. R. (2007), Comparison of local-to

regional-scale estimates of ground-water recharge in Minnesota, USA., Journal of Hydrology, 334, 231–

249.

126

Demlie, M., Wohnlich, S., Gizaw, B., and Stichler, W. (2007), Groundwater recharge in the

Akaki catchment, central Ethiopia: evidence from environmental isotopes (δ18O, δ2H and 3H) and

chloride mass balance, Hydrological Processes, 21, 807-818.

Dodd, M. B., Lauenroth, W. K., and Welker, J. M. (1998), Differential water resource use by

herbaceous and woody plants in a shortgrass steppe community. Oecologia, 117, 504–512.

Döll, P., and Fiedler, K. (2007), Global-scale modeling of groundwater recharge, Hydrology

and Earth System Sciences Discussions, 4, 4069–4124.

Douglas, M., Clark, I. D., Raven, K., and Bottomley, D. (2000), Groundwater mixing

dynamics at a Canadian Shield mine, Journal of Hydrology, 235, 88–103.

Dripps, W. R., and Bradbury, K. R. (2010), The spatial and temporal variability of

groundwater recharge in a forested basin in northern Wisconsin, 24, 383–392.

Dripps, W. R. (2012), An Integrated Field Assessment of Groundwater Recharge, Open

Hydrology Journal, 6, 15–22.

Durack, P. J., Wijffels, S. E., and Matear, R. J. (2012), Ocean salinities reveal strong global

water cycle intensification during 1950 to 2000, Science, 336, 455–458.

Dutton, A. L., Wilkinson, B. H., Bowen, G., Welker, J. M., and Lohmann, K. C. (2005),

Comparison of river water and precipitation δ18O across the 48 contiguous United States. Hydrological

Processes, 19, 3551–3572.

Edmunds, W. M., and Milne, C. J. (Eds.) (2001), Palaeowaters in coastal Europe: evolution

of groundwater since the late Pleistocene. Geological Society of London.

127

Edmunds, W. M. (2009), Palaeoclimate and groundwater evolution in Africa—implications

for adaptation and management. Hydrological Sciences Journal, 54, 781–792.

Ehleringer, J. R. and Cooper, T. A. (1992), On the role of orientation in reducing

photoinhibitory damage in photosynthetic-twig desert shrubs, Plant, Cell and Environment, 15, 301–306.

doi: 10.1111/j.1365-3040.1992.tb00977.x

Elliot, T., Andrews, J. N., and Edmunds, W. M. (1999), Hydrochemical trends,

palaeorecharge and groundwater ages in the fissured Chalk aquifer of the London and Berkshire

Basins, UK, Applied Geochemistry, 14, 333–363.

Even, H., Carmi, I., Magaritz, M., and Gerson, R. (1986), Timing the transport of water

through the upper vadose zone in a karstic system above a cave in Israel, Earth Surface Processes and

Landforms, 11, 181–191.

Famiglietti, J. S., Lo, M., Ho, S. L., Bethune, J., Anderson, K. J., Syed, T. H., Swenson, S. C.,

de Linage, C. R., and Rodell, M. (2011), Satellites measure recent rates of groundwater depletion in

California's Central Valley, Geophysical Research Letters, 38, L03403, doi:10.1029/2010GL046442.

Faunt, C. C. (Ed.), (2009), Groundwater availability of the central valley aquifer, California,

United States Geological Survey Professional Paper 1766, pp. 225.

Feng, W., Zhong, M., Lemoine, J. M., Biancale, R., Hsu, H. T., and Xia, J. (2013), Evaluation

of groundwater depletion in North China using the Gravity Recovery and Climate Experiment

(GRACE) data and ground‐based measurements, Water Resources Research, 49, 2110–2118.

Ferguson, G. A., Betcher, R. N., and Grasby, S. E. (2007), Hydrogeology of the Winnipeg

formation in Manitoba, Canada, Hydrogeology Journal, 15, 573–587.

128

Foley, J. A., Ramankutty, N., Brauman, K. A., Cassidy, E. S., Gerber, J. S., Johnston, M.,

Mueller, N. D., O’Connell, C., Ray, D. K., West, P. C., Balzer, C., Bennett, E. M., Carpenter, S. R.,

Hill, J., Monfreda, C., Polasky, S., Rockström, J., Sheehan, J., Siebert, S., Tilamn, D., and Zaks, D. P.

(2011). Solutions for a cultivated planet, Nature, 478, 337–342.

Fortin, G., van der Kamp, G., Cherry, J. A. (1991), Hydrogeology and hydrochemistry of an

aquifer-aquitard system within glacial deposits, Saskatchewan, Canada, Journal of Hydrology, 126, 265–

292.

Friedman, I. (1953), Deuterium content of natural waters and other substances, Geochimica et


Genty, D., Labuhn, I., Hoffmann, G., Danis, P. A., Mestre, O., Bourges, F., Wainer, K.,

Massault, M., Van Exter, S., Régnier, E., Orengo, P., Falourd, S., and Minster, B. (2014), Rainfall and

cave water isotopic relationships in two South-France sites, Geochimica et Cosmochimica Acta, 131, 323–

343.

Gibson, J. J., Birks, S. J., and Edwards, T. W. D. (2008), Global prediction of δA and δ2H-

δ18O evaporation slopes for lakes and soil water accounting for seasonality, Global Biogeochemical Cycles,

22, GB2031. doi:10.1029/2007GB002997

Gill, I. (1994), Groundwater geochemistry of the Kingshill aquifer system, St. Croix,

Environmental Geosciences, 1, 40–49.

Gleeson, T., Novakowski, K., and Kyser, T. K. (2009), Extremely rapid and localized

recharge to a fractured rock aquifer, Journal of Hydrology 376, 496–509.

Gleeson, T., Wada, Y., Bierkens, M. F., and van Beek, L. P. (2012), Water balance of global

aquifers revealed by groundwater footprint, Nature, 488, 197–200.

129

Goede, A., Green, D. C., and Harmon R. S. (1982), Isotopic composition of precipitation,

cave drips and actively forming speleothems at three Tasmanian cave sites, Helictite, 20, 17–29.

Gordon, L. J., Steffen, W., Jönsson, B. F., Folke, C., Falkenmark, M. and Johannessen, Å.

(2005), Human modification of global water vapor flows from the land surface, Proceedings of the

National Academy of Sciences of the United States of America, 102, 7612–7617.

Granger, R. J., Gray, D. M., and Dyck, G. E. (1984), Snowmelt infiltration to frozen Prairie

soils, Canadian Journal of Earth Sciences, 21, 669–677

Grasby, S. E., and Chen, Z. (2005), Subglacial recharge into the Western Canada

Sedimentary Basin—Impact of Pleistocene glaciation on basin hydrodynamics, Geological Society of

America Bulletin, 117, 500–514.

Grasby, S. E., Osborn, J., Chen, Z., and Wozniak, P. R. (2010), Influence of till provenance

on regional groundwater geochemistry. Chemical Geology, 273, 225–237.

Grassi, S., and Cortecci, G. (2005), Hydrogeology and geochemistry of the multilayered

confined aquifer of the Pisa plain (Tuscany–central Italy), Applied Geochemistry, 20, 41–54.

Harsch, M. A. and HilleRisLambers, J. (2014), Species distributions shift downward across

western North America, Global Change Biology, doi: 10.1111/gcb.12697

Hayashi, M., van der Kamp, G., and Schmidt, R. (2003), Focused infiltration of snowmelt

water in partially frozen soil under small depressions, Journal of Hydrology, 270, 214–229.

Heppner, C. S., Nimmo, J. R., Folmar, G. J., Gburek, W. J., and Risser, D. W. (2007),

Multiple-methods investigation of recharge at a humid-region fractured rock site, Pennsylvania, USA.

Hydrogeology Journal, 15, 915–927.

130

Hoffmann, G., Werner, M., and Heimann, M. (1998), Water isotope module of the ECHAM

atmospheric general circulation model: A study on timescales from days to several years, Journal of

Geophysical Research: Atmospheres, 103, 16871–16896.

Huddart, P. A., Longstaffe, F. J., and Crowe, A. S. (1999), δD and δ18O evidence for inputs

to groundwater at a wetland coastal boundary in the southern Great Lakes region of Canada, Journal

of Hydrology, 214, 18–31.

Iacumin, P., Venturelli, G., and Selmo, E. (2009), Isotopic features of rivers and

groundwater of the Parma Province (Northern Italy) and their relationships with precipitation. Journal

of Geochemical Exploration, 102, 56–62.

Jasechko, S., Sharp, Z. D., Gibson, J. J., Birks, S. J., Yi, Y., and Fawcett, P. J. (2013),

Terrestrial water fluxes dominated by transpiration, Nature, 496, 347–350.

Jiráková, H., Huneau, F., Celle-Jeanton, H., Hrkal, Z., and La Coustumer, P. L. (2011),

Insights into palaeorecharge conditions for European deep aquifers, Hydrogeology Journal, 19, 1545–

1562.

Jocson, J.M.U., Jenson, J.W., and Contractor, D.N. (2002), Recharge and Aquifer response:

Northern Guam Lens Aquifer, Guam, Mariana Islands, Journal of Hydrology, 260, 231–254.

Jones, I. C., Banner, J. L., and Humphrey, J. D. (2000), Estimating recharge in a tropical

karst aquifer, Water Resources Research, 36, 1289–1299.

Jones, I. C., and Banner, J. L. (2003), Estimating recharge thresholds in tropical karst island

aquifers: Barbados, Puerto Rico and Guam, Journal of Hydrology, 278, 131–143.

131

Joodaki, G., Wahr, J. and Swenson, S. (2014), Estimating the human contribution to

groundwater depletion in the Middle East, from GRACE data, land surface models, and well

observations, Water Resources Research, 50, doi:10.1002/2013WR014633.

Jukić, D., and Denić-Jukić V. (2009), Groundwater balance estimation in karst by using a

conceptual rainfall–runoff model, Journal of Hydrology, 373, 302–315.

Julian, J., Araguás-Araguás, L., Rozanski, K., Benavente, J., Cardenal, J., Hidalgo, M. C.,

Garcia-Lopez, S., Martinez-Garrido, J. C. Moral, F., and Olias. M. (1992), Sources of precipitation

over South‐Eastern Spain and groundwater recharge. An isotopic study, Tellus B, 44, 226–236.

Kagabu, M., Shimada, J., Delinom, R., Tsujimura, M., and Taniguchi, M. (2011),

Groundwater flow system under a rapidly urbanizing coastal city as determined by

hydrogeochemistry, Journal of Asian Earth Sciences, 40, 226–239.

Karro, E., Marandi, A., and Vaikmäe, R. (2004), The origin of increased salinity in the

Cambrian-Vendian aquifer system on the Kopli Peninsula, northern Estonia. Hydrogeology Journal, 12,

424–435.

Kalin, R. M. (1994), The hydrogeochemical evolution of the groundwater of the Tucson

Basin with application to 3-dimensional groundwater flow modelling, Ph.D. thesis at the University

of Arizona, 510 pp.

Kebede, S., Travi, Y., Asrat, A., Alemayehu, T., Ayenew, T., and Tessema, Z. (2008),

Groundwater origin and flow along selected transects in Ethiopian rift volcanic aquifers, Hydrogeology

Journal, 16, 55–73.

132

Keenan, T. F., Hollinger, D. Y., Bohrer, G., Dragoni, D., Munger, J. W., Schmid, H. P., and

Richardson, A. D. (2013), Increase in forest water-use efficiency as atmospheric carbon dioxide

concentrations rise, Nature, 499, 324–327.

Kim, J. H., and Jackson, R. B. (2012), A global analysis of groundwater recharge for

vegetation, climate, and soils. Vadose Zone Journal, 11, doi:10.2136/vzj2011.0021RA

Kloppmann, W., Dever, L., and Edmunds, W. M. (1998), Residence time of Chalk

groundwaters in the Paris Basin and the North German Basin: a geochemical approach, Applied

Geochemistry, 13, 593–606.

Konikow, L. F. (2011), Contribution of global groundwater depletion since 1900 to sea‐level

rise, Geophysical Research Letters, 38, L17401.

Konikow, L. F., and Kendy, E. (2005), Groundwater depletion: A global problem,

Hydrogeology Journal, 13, 317–320, doi:10.1007/s10040-004-0411-8

Kortelainen, N. M., and Karhu, J. A. (2004), Regional and seasonal trends in the oxygen and

hydrogen isotope ratios of Finnish groundwaters: a key for mean annual precipitation, Journal of

Hydrology, 285, 143–157.

Kotzer, T. G., Kudo, A., Zheng, J., and Workman, W. (2000), Natural and anthropog en ic

levels of tritium in a Canadian Arctic ice core, Agassiz Ice Cap, Ellesmere Island, and comparison

with other radionuclides. Journal of Glaciology 46, 35–40.

Kulmatiski, A., Beard, K. H., Verweij, R. J., and February, E. C. (2010), A depth‐controlled

tracer technique measures vertical, horizontal and temporal patterns of water use by trees and grasses

in a subtropical savanna. New Phytologist, 188, 199–209.

133

Kurita, N., Yoshida, N., Inoue, G., and Chayanova, E. A. (2004), Modern isotope

climatology of Russia: A first assessment, Journal of Geophysical Research: Atmospheres, 109, D03102,

doi:10.1029/2003JD003404.

Lanza, S. (2009), Groundwater anammox at an industrial site in Calgary, M.Sc. Thesis at the

University of Calgary, 96 pp.

Lee, K. S., Wenner, D. B., and Lee, I. (1999), Using H-and O-isotopic data for estimating the

relative contributions of rainy and dry season precipitation to groundwater: example from Cheju

Island, Korea, Journal of Hydrology, 222, 65–74.

Lee, K. S., and Kim, Y. (2007), Determining the seasonality of groundwater recharge using

water isotopes: a case study from the upper North Han River basin, Korea. Environmental Geology, 52,

853–859.

Leffler, J., and Welker, J. M. (2013), Long-term increases in snow elevate leaf N and

photosynthesis in Salix arctica: response to a snow fence experiment in NW Greenland, Environmental

Research Letters, 8, 025023.

Leterme, B., Mallants, D., and Jacques, D. (2012), Sensitivity of groundwater recharge using

climatic analogues and HYDRUS-1D, Hydrology and Earth System Sciences, 16, 2485–2497.

Li, B., Yuan, D., Qin, J., Lin, Y., and Zhang, M. (2000), Oxygen and carbon isotopic

characteristics of rainwater, drip water and present speleothems in a cave in Guilin area, and their

environmental meanings. Science in China Series D: Earth Sciences, 43, 277–285.

Liu, Z., Bowen, G., Yoshimura, K., and Welker, J. M. (2013), Pacific North American

teleconnection controls on precipitation isotopes (δ18O) across the contiguous USA and adjacent

regions: A GCM-Based Analysis, Journal of Climate, 27, 1046-1061, doi:10.1175/JCLI-D-13-00334.1.

134

Liu, Z., Yoshimura, K., Bowen, G. J., Buenning, N. H., Risi, C., Welker, J. M. and Yuan, F.

(2014), Paired oxygen isotope records reveal modern North American atmospheric dynamics during

the Holocene, Nature Communications, 5, doi:10.1038/ncomms4701

Lorenzen, G., Sprenger, C., Baudron, P., Gupta, D., and Pekdeger, A. (2012), Origin and

dynamics of groundwater salinity in the alluvial plains of western Delhi and adjacent territories of

Haryana State, India, Hydrological Processes, 26, 2333–2345.

Madonia, P., D’Aleo, R., Di Maggio, C., Favara, R., and Hartwig, A. (2013), The use of

shallow dripwater as an isotopic marker of seepage in karst areas: A comparison between Western

Sicily (Italy) and the Harz Mountains (Germany), Applied Geochemistry, 34, 231–239.

Maulé, C. P., Chanasyk, D. S., and Muehlenbachs, K. (1994), Isotopic determination of

snow-water contribution to soil water and groundwater, Journal of Hydrology, 155, 73–91.

McCarroll, D., and Loader, N. J. (2004), Stable isotopes in tree rings, Quaternary Science

Reviews, 23, 771–801.

McIntosh, J. C., Schlegel, M. E., and Person, M. (2012), Glacial impacts on hydrologic

processes in sedimentary basins: evidence from natural tracer studies, Geofluids, 12, 7–12.

doi:10.1111/j.1468-8123.2011.00344.x

McMahon, P. B., Bohlke, J. K., and Carney, C. P. (2007), Vertical gradients in water

chemistry and age in the northern High Plains aquifer, Nebraska, 2003, US Department of the

Interior, US Geological Survey Scientific Investigations Report 2006–5294, 66 pp.

Menking, K. M., Bischoff, J. L., Fitzpatrick, J. A., Burdette, J. W., and Rye, R. O. (1997),

Climatic/hydrologic oscillations since 155,000 yr BP at Owens Lake, California, reflected in

abundance and stable isotope composition of sediment carbonate, Quaternary Research, 48, 58–68.

135

Mileham, L., Taylor, R. G., Todd, M., Tindimugaya, C., and Thompson, J. (2009), The

impact of climate change on groundwater recharge and runoff in a humid, equatorial catchment:

sensitivity of projections to rainfall intensity, Hydrological Sciences Journal, 54, 727–738.

Mix, H. T., and Chamberlain, C. P. (2014), Stable isotope records of hydrologic change and

paleotemperature from smectite in Cenozoic western North America. Geochimica et Cosmochimica Acta,

doi: 10.1016/j.gca.2014.07.008

Négrel, P., and Giraud, E. P. (2010), Geochemistry, isotopic composition (δ18O, δ2H,

87Sr/86Sr, 143Nd/144Nd) in the groundwater of French Guiana as indicators of their origin,

interrelations, Comptes Rendus Géosciences, 342, 786–795.

New, M., Lister, D., Hulme, M., and Makin, I. (2002), A high-resolution data set of surface


Noone, D., and Simmonds, I. (2002), Associations between δ18O of water and climate

parameters in a simulation of atmospheric circulation for 1979–95, Journal of Climate, 15, 3150–3169.

O'driscoll, M. A., DeWalle, D. R., McGuire, K. J., and Gburek, W. J. (2005), Seasonal 18O

variations and groundwater recharge for three landscape types in central Pennsylvania, USA., Journal

of Hydrology, 303, 108–124.

Owor, M., Taylor, R. G., Tindimugaya, C., and Mwesigwa, D. (2009), Rainfall intensity and

groundwater recharge: empirical evidence from the Upper Nile Basin. Environmental Research Letters, 4,

035009.

Plummer, L. N. (1993), Stable isotope enrichment in paleowaters of the southeast Atlantic

Coastal Plain, United States, Science, 262, 2016–2020.

136

Power, G., Brown, R. S. and Imhof, J. G. (1999), Groundwater and fish—insights from

northern North America, Hydrological Processes, 13, 401–422

Praamsma, T., Novakowski, K., Kyser, K., and Hall, K. (2009), Using stable isotopes and

hydraulic head data to investigate groundwater recharge and discharge in a fractured rock aquifer,


Qin, D., Qian, Y., Han, L., Wang, Z., Li, C., and Zhao, Z. (2011). Assessing impact of

irrigation water on groundwater recharge and quality in arid environment using CFCs, tritium and

stable isotopes, in the Zhangye Basin, Northwest China, Journal of Hydrology, 405, 194–208.

Rango, T., Petrini, R., Stenni, B., Bianchini, G., Slejko, F., Beccaluva, L., and Ayenew, T.

(2010), The dynamics of central Main Ethiopian Rift waters: Evidence from δD, δ18O and 87Sr/86Sr

ratios, Applied Geochemistry, 25, 1860–1871.

Risi, C., Bony, S., Vimeux, F., and Jouzel, J. (2010), Water‐stable isotopes in the LMDZ4

general circulation model: Model evaluation for present‐day and past climates and applications to

climatic interpretations of tropical isotopic records, Journal of Geophysical Research: Atmospheres, 115,

D12118. doi:10.1029/2009JD013255

Roche, D. M. (2013), δ18O water isotope in the iLOVECLIM model (version 1.0)-Part 1:

Implementation and verification, Geoscientific Model Development Discussions, 6, 1481–1491.

Rock, L., and Mayer, B. (2009), Identifying the influence of geology, land use, and

anthropogenic activities on riverine sulfate on a watershed scale by combining hydrometric, chemical

and isotopic approaches, Chemical Geology, 262, 121–130.

Rodell, M., Velicogna, I., and Famiglietti, J. S. (2009), Satellite-based estimates of

groundwater depletion in India, Nature, 460, 999–1002.

137

Rozanski, K., Gonfiantini, R., and Araguás-Araguás, L. (1991), Tritium in the global

atmosphere: distribution patterns and recent trends, Journal of Physics G: Nuclear and Particle Physics, 17,

S523.

Samborska, K., Różkowski, A., and Małoszewski, P. (2013), Estimation of groundwater

residence time using environmental radioisotopes (14C, T) in carbonate aquifers, southern Poland,

Isotopes in Environmental and Health Studies, 49, 73–97.

Savard, M. M., Paradis, D., Somers, G., Liao, S., and van Bochove, E. (2007), Winter

nitrification contributes to excess NO3− in groundwater of an agricultural region: A dual‐isotope

study, Water Resources Research, 43, W06422, doi:10.1029/2006WR005469.

Sayama, T., McDonnell, J. J., Dhakal, A. and Sullivan, K. (2011), How much water can a

watershed store? Hydrological Processes, 25, 3899–3908. doi:10.1002/hyp.8288

Siebert, S, Burke, J., Faures, J. M., Frenken, K., Hoogeveen, J., Döll, P., and Portmann, F. T.

(2010), Groundwater use for irrigation – a global inventory, Hydrology and Earth System Sciences, 14,

1863–1880, doi:10.5194/hess-14-1863-2010

Scanlon, B. R., Keese, K. E., Flint, A. L., Flint, L. E., Gaye, C. B., Edmunds, W. M., and

Simmers, I. (2006), Global synthesis of groundwater recharge in semiarid and arid regions,

Hydrological Processes, 20, 3335–3370.

Scanlon, B. R., Faunt, C. C., Longuevergne, L., Reedy, R. C., Alley, W. M., McGuire, V. L.,

and McMahon, P. B. (2012), Groundwater depletion and sustainability of irrigation in the US High

Plains and Central Valley, Proceedings of the National Academy of Sciences, 109, 9320–9325.

138

Schmidt, G. A., LeGrande, A. N., and Hoffmann, G. (2007), Water isotope expressions of

intrinsic and forced variability in a coupled ocean‐atmosphere model, Journal of Geophysical Research:

Atmospheres, 112, D10103, doi:10.1029/2006JD007781.

Simpson, E. S., Thorud, D. B., and Friedman, I. (1972), Distinguishing seasonal recharge to

groundwater by deuterium analysis in southern Arizona, Proceedings of the Reeding Symposium,

International Association of Scientific Hydrology, 113–121.

Smith, G.I., and Bischoff, J. L., Eds. (1997), An 800,000-year Paleoclimatic Record from

Core OL-92, Owens Lake, Southeast California, Geological Society of America Special Paper 317, 37–47.

Stevenson, B. A., Kelly, E., McDonald, E., Busacca, A., and Welker, J. M. (2010), Oxygen

isotope ratios in Holocene carbonates across a climatic gradient, eastern Washington State, USA:

Evidence for seasonal effects on pedogenic mineral isotopic composition, The Holocene. 20, 575–583,

doi:10.1177/0959683609356588.

Steward, D. R., Bruss, P. J., Yang, X., Staggenborg, S. A., Welch, S. M., and Apley, M. D.

(2013), Tapping unsustainable groundwater stores for agricultural production in the High Plains

Aquifer of Kansas, projections to 2110, Proceedings of the National Academy of Sciences, 110, E3477–

E3486.

Stoll, S., Franssen, H. J., Butts, M., and Kinzelbach, W. (2011), Analysis of the impact of

climate change on groundwater related hydrological fluxes: a multi-model approach including

different downscaling methods, Hydrology and Earth System Sciences, 15, 21–38. doi:10.5194/hess-15-21-

2011

139

Taylor, R. G., Todd, M. C., Kongola, L., Maurice, L., Nahozya, E., Sanga, H., and

MacDonald, A. M. (2013), Evidence of the dependence of groundwater resources on extreme rainfall

in East Africa, Nature Climate Change, 3, 374–378.

Tweed, S. O., Weaver, T. R., and Cartwright, I. (2005), Distinguishing groundwater flow

paths in different fractured-rock aquifers using groundwater chemistry: Dandenong Ranges,

southeast Australia, Hydrogeology Journal, 13, 771–786.

Vachon, R. W., Welker, J. M., White, J. W. C., and Vaughn, B. H. (2010), Monthly

precipitation isoscapes (δ18O) of the United States: Connections with surface temperatures, moisture

source conditions, and air mass trajectories, Journal of Geophysical Research: Atmospheres, 115, D21126.

doi:10.1029/2010JD014105.

Van Beynen, P., and Febbroriello, P. (2006), Seasonal isotopic variability of precipitation and

cave drip water at Indian Oven Cave, New York. Hydrological Processes, 20, 1793–1803.

Vogel, J. C., Ehhalt, D., and Roether, W. (1963), A survey of the natural isotopes of water in

South Africa, Proceedings of Tokyo Conference on Radioisotopes in Hydrology, 407–416.

Voss, K. A., Famiglietti, J. S., Lo, M., Linage, C., Rodell, M., and Swenson, S. C. (2013),

Groundwater depletion in the Middle East from GRACE with implications for transboundary water

management in the Tigris‐Euphrates‐Western Iran region, Water Resources Research, 49, 904–914.

Wada, Y., van Beek, L. P. H., van Kempen, C. M., Reckman, J. W. T. M., Vasek, S.,

Bierkens, M. F. P. (2010), Global depletion of groundwater resources. Geophysical Research. Letters, 38,

L20402. doi:10.1029/2010GL044571.

140

Wada, Y., L. P. H. van Beek, and M. F. P. Bierkens (2012a), Nonsustainable groundwater

sustaining irrigation: A global assessment, Water Resources Research, 48, W00L06,

doi:10.1029/2011WR010562.

Wada, Y., L. P. H. van Beek, F. C. S. Weiland, B. F. Chao, Y.-H. Wu, and M. F. P. Bierkens

(2012b), Past and future contribution of global groundwater depletion to sea-level rise, Geophysical

Research Letters, 39, L09402, doi:10.1029/2012GL051230.

Wada, Y., Wisser, D., and Bierkens, M. F. P. (2014), Global modeling of withdrawal,

allocation and consumptive use of surface water and groundwater resources, Earth System Dynamics

Discussions, 4, 355–392. doi:10.5194/esd-5-15-2014

Wallick, E. I. (1981), Chemical evolution of groundwater in a drainage basin of Holocene

age, east-central Alberta, Canada. Journal of Hydrology, 54, 245–283.

Walraevens, K., Vandecasteele, I., Martens, K., Nyssen, J., Moeyersons, J., Gebreyohannes,

T., de Smedt, F., Poesen, J., Deckers, J. and Van Camp, M. (2009), Groundwater recharge and flow

in a small mountain catchment in northern Ethiopia. Hydrological Sciences Journal, 54, 739–753.

Wanke, H., Dünkeloh, A., and Udluft, P. (2008), Groundwater recharge assessment for the

Kalahari catchment of north-eastern Namibia and north-western Botswana with a regional-scale

water balance model, Water Resources Management, 22, 1143–1158.

Wang, L., Hu, F., Yin, L., Wan, L., and Yu, Q. (2013), Hydrochemical and isotopic study of

groundwater in the Yinchuan plain, China, Environmental Earth Sciences, 69, 2037–2057.

Wassenaar, L. I., Van Wilgenburg, S. L., Larson, K., and Hobson, K. A. (2009), A

groundwater isoscape (δD, δ18O) for Mexico, Journal of Geochemical Exploration, 102, 123–136.

141

Welker, J. M., McClelland, S., and Weaver, T. W. (1991), Soil water retention after natural

and simulated rainfall on a temperate grassland. Theoretical and Applied Climatology, 44, 447-453.

Welker, J. M. (2000), Isotopic (δ18O) characteristics of weekly precipitation collected across

the USA: an initial analysis with application to water source studies. Hydrological Processes, 14, 1449–

1464.

Welker, J. M. (2012), ENSO effects on the isotopic (δ18O, δ2H and d-excess) of precipitation

across the US using a long-term network (USNIP), Rapid Communication in Mass Spectrometery, 17,

1655–1660.

West, J. B., Bowen, G. J., Cerling, T. E., and Ehleringer, J. R. (2006), Stable isotopes as one

of nature's ecological recorders. Trends in Ecology and Evolution, 21, 408–414.

Williams, P. W., and Fowler, A. (2002), Relationship between oxygen isotopes in rainfall,

cave percolation waters at Waitomo, New Zealand, Journal of Hydrology, 41, 53–70.

Williams, J. W. (2003), Variations in tree cover in North America since the last glacial

maximum, Global and Planetary Change, 35, 1–23.

142

CHAPTER 3 — THE ISOTOPIC COMPOSITION OF ICE AGE

GROUNDWATERS

3.1 Abstract

In Chapter 3 I present a global compilation of the isotopic composition of groundwater

recharge from the late-Holocene (δ18Olate-Holocene) and the last ice age (δ18Oice age). Changes to meteoric

water δ18O values from the last ice age to the late-Holocene are described herein as Δδ18Oice age

(where, Δδ18Oice age = δ18Olast ice age − δ18Olate-Holocene). Groundwater Δδ18Oice age values range from −3.6

‰ (i.e., δ18Olast ice age < δ18Olate-Holocene) to +2.0 ‰ (i.e., δ18Olast ice age > δ18Olate-Holocene). More than 90%

of study aquifers have δ18Olast ice age < δ18Olate-Holocene, in spite of higher-than-modern seawater δ18O

values during the last ice age. The few study aquifers where δ18Olast ice age > δ18Olate-Holocene are found

exclusively within 300 km of coasts and generally confined to the subtropics. Groundwater Δδ18Oice

age values are closer to zero (average groundwater Δδ18Oice age of −0.6 ‰) than Greenland and

Antarctic ice cores (average polar ice core Δδ18Oice age of −5.5 ‰). Δδ18Oice age values from four

different isotope-enabled general circulation models are able to reproduce some but not all ice-age-

to-late-Holocene δ18O shifts (pre-industrial and last glacial maximum climate simulations). Each of

the four models do not reproduce the negative Δδ18Oice age values over tropical Africa and South

America, potentially reflecting imperfect parameterization of convective precipitation. The four

isotope enabled general circulation models have a similar sign of Δδ18Oice age for about half of Earth’s

areas, generally showing multi-model agreement upon positive Δδ18Oice age over the tropical oceans,

and negative Δδ18Oice age over extra-tropical land areas. However, simulated Δδ18Oice age values do not

reproduce the observed negative Δδ18Oice age values over Africa and Brazil, potentially due to different

or incomplete model parameterization of convective rainfall during the last glacial maximum and

present day.

143

3.2 Introduction

The isotopic composition of groundwater recharge from the last ice age has been used to

improve the scientific community’s understanding of water availability under climates of the past for

more than a half century (e.g., Thatcher et al., 1961; Tamers, 1967; Gat et al., 1969; Salati et al., 1974).

The use of paleo-groundwaters to reconstruct past climates has both advantages and disadvantages in

comparison to other types of paleoclimate records. For example, shifts in climate on time scales of

100 to 103 years that can be distinguished in lacustrine (e.g., von Grafenstein et al., 1999) and

speleothem (e.g., Denniston et al., 2007) records often cannot be identified in groundwater records

because of hydrodynamic dispersion during multi-millennia groundwater residence times. However,

groundwaters have an advantage over other paleoclimate records because paleo-groundwaters are a

direct sample of paleo-precipitation and because paleo-groundwaters are found in many regions. As

such, the chemistry of groundwaters provide insights into hydrologic and climate changes since the

last ice age such as changes to air mass trajectories (Rozanski et al., 1985; Legrande and Schmidt,

2009) and land surface temperatures (Stute et al., 1989; 1995a; 1995b; Clark et al., 1998; Aeschbach-

Hertig et al., 2002). Groundwater paleoclimate archives are not subject to complicating effects that

must be accounted for in other paleoclimate archives before interpreting the isotopic composition of

paleowaters. For example, extracting quantitative paleoclimate information from the isotopic

compositions of lake sediments, tree rings or speleothems is challenging due to several factors,

including: (1) uncertainty in paleo-temperatures, which directly impact water-proxy isotopic

fractionation factors (e.g., lake sediment calcite, diatom silica and sediment cellulose; tree ring

cellulose; speleothem calcite), (2) uncertainty in the magnitude of evaporation-induced 18O-

enrichment of surface waters prior to preservation in proxy records (e.g., lake sediment calcite,

diatom silica and sediment cellulose; Leng and Marshall, 2004), (3) uncertainty and variability in the

timing and seasonality of mineral precipitation and bioform growth, which impacts both the isotopic

composition of the water being preserved in the proxy record, and the water-proxy fractionation

144

factor due to seasonality in temperatures (e.g., lake sediment calcite, diatom silica and sediment

cellulose), (4) uncertainty in the impact of diagenesis (e.g., travertine; O’Brien et al., 2006) and (5)

uncertainty in paleo-atmospheric humidity (e.g., tree ring cellulose; Roden and Ehleringer, 1999),

each of which impacts the isotopic offset between paleo-waters and the preserved proxy record. In

contrast, the relationship between paleo-precipitation groundwater isotope compositions is more

direct and reliably identifiable. A recent study of 70 paired precipitation-groundwater isotopic

datasets found systematic relationships between the isotopic composition of annual precipitation and

groundwater. Differences between modern annual precipitation and groundwater isotopic

compositions are related to the ratio of recharge as a proportion of precipitation (i.e.,

recharge/precipitation: Jasechko et al., 2014).

Ice-age-to-late-Holocene changes to isotopic compositions measured in proxy records have

been ascribed in earlier works to several partially overlapping influences. Perhaps the two best-

constrained and global-in-scale changes from the last ice age to the late-Holocene are (i) the change

to atmospheric and surface ocean temperatures (MARGO Members, 2009; Annan and Hargreaves,

2013), and (ii) the change to the isotopic composition of the ocean (Emiliani, 1955; Dansgaard and

Tauber, 1969; Schrag et al., 1996). Atmospheric temperatures have increased by a global average of

about 4°C since the last glacial maximum, as constrained by compilations of proxy-based

reconstructions (Shakun and Carlson, 2010, Annan and Hargreaves, 2013). Climate warming over the

past 20,000 years is thought to have been greatest in the extra-tropics (average increase of 6.3°C for

latitudes of greater than 25°; Annan and Hargreaves, 2013) and more subdued in the tropics (average

increase of 1.7°C for latitudes of less than 25°; Annan and Hargreaves, 2013; Figure 3-1), although

some terrestrial noble gas based temperature reconstructions suggest much greater tropical cooling

(e.g., eastern Brazil 5.4°C cooler than today during the last glacial maximum; Stute et al., 1995b).

145

Figure 3-1. The change in surface air temperatures from the last glacial maximum to the preindustrial

era (gridded data from Annan and Hargreaves, 2013). (a) Percentile ranges of temperature changes

since the last glacial maximum for 10 degree latitudinal bands. Blue shading mark 25th-75th percentile

ranges and the thin horizontal lines mark 10th-90th percentile ranges. The grey band shows the

globally-averaged estimate of temperature change since the last glacial maximum of −4.0±0.8 °C

(Annan and Hargreaves, 2013). (b) Gridded surface air temperature anomaly from the last glacial

maximum to the preindustrial era (Annan and Hargreaves, 2013).

The δ18O value of the last glacial period ocean was 1.0±0.1 ‰ higher than the modern

ocean, as constrained by paleo-ocean water samples collected from pore waters trapped within sea

floor sediments (Schrag et al., 2002; where δ18O = (18O/16Osample) / (18O/16Ostandard – 1)×1000). In

addition to differences in temperatures and ocean water isotope compositions, a variety of additional

explanations for observed changes to δ18O values found in paleoclimate records have been proposed,

including variations in hurricane and storm intensity (e.g., Plummer et al., 1993), changes to large-

scale atmospheric circulation patterns (e.g., Rozanski et al., 1985; Weyhenmeyer et al., 2000;

McDermott et al., 2001; Asmerom et al., 2010), shifts in monsoon strength (e.g., Denniston et al.,

2000; 2013; Lachniet et al., 2004; Liu et al., 2007; Pausata et al., 2011), fluctuations in the seasonality

of precipitation (e.g., Cruz et al., 2005), and modifications to El Niño-Southern Oscillation patterns

(e.g., Vuille and Werner, 2005).

146

In this study I present a global compilation of proxy isotope records of groundwaters (n =

65), speleothems (n = 15) and ice cores (n = 11) spanning both the last ice age and the late-

Holocene. Global compilations already exist for speleothems (Shah et al., 2013) and polar ice cores

(Pedro et al., 2011; Stenni et al., 2011; Clark et al., 2012); this compilation is the first global

compilation of isotopic data for groundwaters from the last ice age, building upon existing reviews of

paleowaters across Europe (Edmunds and Milne, 2001; Jiráková et al., 2011) and Africa (Edmunds,

2009).

The objective of this study is to develop and analyze a global database of ice age

groundwater chemistry and constrain the processes and mechanisms controlling meteoric water

isotopic changes since the last ice age. This new compilation provides a global scale perspective that

can be used to quantitatively interpret the magnitudes of δ18O and δ2H anomalies observed within

various Quaternary climate records and to validate outputs from isotope-enabled general circulation

models.

147

Table 3-1. Modern and ice age physical and isotopic data for the oceans and the cryosphere

Present day Ocean Ice:

Antarctica Ice:

Greenland Ice: Laurentide and Fennoscandinavian

Volume 13.7 × 109 km3 d 28 × 106 km3 d 3.1 × 106 km3 0 km3

Depth 3800 m - - -

δ18O value 0 ‰ e −20 to −60 ‰ f −30 to −45 ‰

-

Last glacial maximum

Ocean Ice:

Antarctica Ice:

Greenland Ice: Laurentide and Fennoscandinavian

Volume a 13.2 × 109 km3 d 38 × 106 km3 d 4.4 × 106 km3 g 20 to 60 × 106 km3

Depth b 3665 to 3680 m - - -

δ18O value c +1.0±0.1 ‰ e −20 to −60 ‰ f −30 to −45 ‰

h −22 to −25 ‰

Sea level change *

b change of −120 m to −135 m

d −19.2 m d −3.1 m g −40 to −115 m

a Lambeck et al., 2000

b Clark and Mix, 2002

c Schrag et al., 2002

d Huybrechts., 2002 (maximum values shown)

e Range of Antarctic ice cores: Byrd Glacier, (Blunier and Brook, 2001) Dome Fuji, (Kawamura et al., 2007) Dronning Maud, (EPICA Community, 2006) Law Dome, (Pedro et al., 2011) Siple Dome (Pedro et al., 2011) and TALD Ice (Buiron et al., 2011) f Range from NGRIP1 core

g Range of model predictions from Beghin et al., 2013

h Subglacial recharge from the Laurentide and Fennoscandinavian ice sheets (Karro et al., 2004; Grasby and Chen, 2005; Ferguson et al., 2007; Stotler et al., 2010) * Presented as relative to the modern ocean level

148

3.3 Dataset and Methods

I compiled 18O/16O ratios, 2H/1H ratios, 13C/12C ratios, 3H activities, 14C activities for 1640

groundwater samples collected from 65 aquifers as reported in 68 publications. Each compiled

aquifer dataset has between 4 and 182 groundwater samples (average of 27) that had previously been

analyzed for both stable oxygen and hydrogen isotopic compositions and for radioactive carbon

activities (14C). 14C data were required to ensure that compiled samples identifiably recharged during

the last ice age. A mean 14C-based groundwater age (t, the time elapsed since recharge) was calculated

for each sample by accounting for the radioactive decay of 14C and for the dissolution of 14C-dead

aquifer materials (Clark and Fritz, 1997):

𝑡 = −8267 × 𝑙𝑛 (𝐴

𝑞×𝐴𝑜) Equation 3.1

where t is the time elapsed since the groundwater sample recharged (i.e., groundwater age), A is the

measured 14C activity in a groundwater sample, Ao is the initial 14C activity (100 pmC) and q is a

correction factor applied to account for the dissolution of aquifer material with zero 14C (i.e., 14C-

dead). In cases where δ13C data was available q was calculated as:

𝑞 =𝛿13𝐶𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑 − 𝛿13𝐶𝑎𝑞𝑢𝑖𝑓𝑒𝑟

𝛿13𝐶𝑟𝑒𝑐ℎ𝑎𝑟𝑔𝑒 − 𝛿13𝐶𝑎𝑞𝑢𝑖𝑓𝑒𝑟 Equation 3.2

where δ13Cmeasured, δ13Caquifer and δ13Crecharge represent the carbon isotope composition of a groundwater

sample, the aquifer and recharging groundwater. δ13Caquifer was set to 1.1±1.6 ‰ PDB as determined

by the 25th to 75th percentile range of δ13C values for 16359 rock and sediment samples compiled and

presented by Veizer et al. (1999). δ13Crecharge was set to −12.8±3.1 ‰ PDB as determined by the 25th to

75th percentiles of compiled δ13C values for 261 groundwater samples having a 14C activity of greater

than 90 p.m.C. (i.e., recently recharged water bearing near-atmospheric radioactive activities;

Burchuladze et al., 1989). q was set to 0.86±0.14 in cases where δ13Cmeasured data were unavailable, as

149

determined by the most common δ13C-based q values (q = 0.86±0.14 represents the average and one

standard deviation of δ13C-based q values).

Equivalent calendar year ages were estimated from 14C-ages by applying a polynomial fit of

compiled 14C-to-calender age corrections (Fairbanks et al., 2005). Samples were then divided into two

age categories: (i) the late-Holocene (14C-based age of less than 5,000 calendar years before present,

or a 3H activity of greater than 5 T.U.), or (ii) the last ice age (14C-based age of 19,500 to ~50,000

calendar years before present (Clark et al., 2009) and samples with 14C activities below analytical

detection). An upper ice age limit of ~50,000 years before present was set because of limitations with

14C age calculations, even though the most recent ice age extends to ~110,000 years before present

(Lisiecki and Raymo, 2005).

Groundwater δ18O and δ2H values for the late-Holocene and the last ice age were analyzed

on an aquifer-by-aquifer basis. Only aquifers with a minimum of two samples dated to both the late-

Holocene and the last glacial time periods were included in this analysis. Comparisons of isotopic

data for the last ice age and the late-Holocene were made by subtracting median δ18O and δ2H values

from each age group, with errors calculated by maximizing the 25th to 75th percentile distributions for

the two data groups (i.e., late-Holocene and last glacial period age groups). Samples were omitted

from our analysis if they exhibited an evaporative signature (δ2H – 8×δ18O of less than 0), contained

a mixture of modern and ice age groundwater (3H activity of greater than 1 tritium unit and a 14C-age

of more than 19,500 calendar years before present), were suspected to have mixed with intruding

seawater (e.g., Bouchaou et al., 2008; Morrissey et al., 2010) or were presumed to have been

recharged by subglacial meltwaters from the Fennoscandinavian (e.g., Karro et al., 2004) or the

Laurentide (e.g., Grasby and Chen, 2005; Ferguson et al., 2007; Stotler et al., 2010) ice sheets (review

by McIntosh et al., 2012).

150

A correction to speleothem δ18O values was applied because of the different H2O-CaCO3

isotopic fractionation factor for the last ice age and the late-Holocene imparted by the different

atmospheric temperatures during each time period (Shakun and Carlson, 2010). Temperature-based

H2O-CaCO3 fractionation factors were obtained from O’Neil et al. (1969) with temperatures

calculated under the assumption that atmospheric temperatures are indicative of temperatures in the

shallow subsurface. Temperatures for the late-Holocene were assumed to be equivalent to modern

mean annual near surface temperatures (New et al., 2002), potentially introducing <1°C of error

because of temperature change throughout the last 5,000 years (Marcott et al., 2013). Adding 1°C of

added uncertainty into late-Holocene temperature equates to an added ±0.4 ‰ (δ18O) of uncertainty

in the temperature-corrected difference between ice age and late-Holocene δ18O values (O’Neil et al.,

1969). Last glacial period temperatures were calculated by applying the temperature offset of the last

glacial maximum (Figure 3-1; Annan and Hargreaves, 2013) to gridded values of modern mean

annual air temperatures (New et al., 2002).

With the help of F. Pausata, M. Werner, C. Risi, K. Yoshimura I assembled modelled

precipitation Δδ18Oice age values from four isotope-enabled general circulation models: (i) CCSM3

(e.g., Pausata et al., 2011), (ii) ECHAM (e.g., Hoffman et al., 1998; Werner et al., 2011), (iii) IsoGSM

(e.g., Yoshimura et al., 2003) and (iv) LMDZ4 (e.g., Risi et al., 2010a). The models span a range of

spatio-temporal resolutions and isotopic/atmospheric parameterizations that are explained in detail

in the above references. Simulations run for the last glacial maximum and pre-industrial time periods

were assembled to analyze global grids of the amount-weighted isotopic composition of precipitation

for each time period. General circulation model outputs were compared to ice age groundwater data

by extracting model estimates of the annual isotopic composition of precipitation at the locations of

each of the 65 aquifers analyzed in this study. We acknowledge that the general circulation models

explicitly analyze the last glacial maximum and the pre-industrial climate scenarios, whereas the

151

groundwater aquifers integrate hydroclimatology over longer (103 year) time scales that will damp

abrupt δ18O changes because of storage and mixing.

3.4 Results

Groundwater, speleothem and ice core data sources, locations and isotopic changes since the

last glacial maximum are presented in Figure 3-2 and in Tables 3-2 and Table 3-4. Fossil groundwater

δ18O records are unevenly distributed amongst Europe (n = 13), Africa (n = 19), Asia (n = 13),

Oceania and the Malay Archipelago (n = 4), North America (n = 13) and South America (n = 3;

Table 3-2), with 30% of records located in the tropics and 70% of records located in the extra-tropics

(tropics defined as spanning 0° to 25° latitude). The magnitude of change in meteoric δ18O from the

last ice age is described herein as Δδ18Oice age (where Δδ18Oice age = δ18Olast ice age − δ18Olate-Holocene).

152

Figure 3-2. Meteoric water δ18O changes from the last ice age (19,500 to ~50,000 years ago) to the

late-Holocene (within past ~5,000 years): Δδ18Oice age = δ18Olast ice age − δ18Olate-Holocene.

153

Table 3-2. Groundwater datasets compiled in this study

Country Aquifer Citation(s)

Algeria Great Oriental Erg: CI Edmunds et al., 2003

Botswana Kalahari: Ntane Kulongoski et al., 2004

Botswana Lokalane-Nakojane Rahube, 2003

Burkina Faso Taoudeni basin Huneau et al., 2011

Chad Chad aquifer Edmunds, 2009

Egypt Nubian aquifer Patterson et al., 2005

Mali Mali aquifer Edmunds, 2009

Morocco N. Morocco aquifer Winckel et al., 2002

Morocco Tadla basin Bouchaou et al., 2009

Morocco Nappe des sables Castany et al., 1974

Namibia Omatako basin Külls, 2000

Niger Djardo-Bilma Dodo and Zuppi, 1997; 1999

Niger Irhazer: CI Andrews et al., 1994

Niger Illumeden: CT Beyerle et al. 2003

Nigeria Chad basin Maduabuchi et al., 2006

Senegal Senegalese CT Castany et al., 1974

South Africa Uitenhage aquifer Heaton et al., 1986

Tunisia Kairouan Plain Derwich et al., 2012

Zimbabwe Zimbabwe aquifer Larsen et al., 2002

Australia Canning basin Harrington et al., 2011

Australia Ngalia and Amadeus Leaney and Allison, 1986

Australia Murray aquifer Cresswell et al., 1999

Bangladesh Bengal basin Aggarwal et al., 2000

China Songnen plain Chen et al. 2011

China Hexi Corridor: east Gates et al., 2008

China North China Plain Kreuzer et al. 2009

China Yuncheng basin Currell et al., 2010

India Cuddalore sandstone Sukhija et al., 1998

India Tiruvadanai aquifer Kumar et al., 2009

Indonesia Jakarta basin Geyh and Sofner, 1989

Israel Israel coastal aquifer Yechieli et al., 2008

Israel Dead Sea rift valley Gat and Galai, 1982

Kuwait Kuwait aquifer Robinson and Gunatilaka, 1991; Al-Ruwaih et al., 2004

Oman Batinah coastal plain Weyhenmeyer et al., 2002

Oman Najd aquifer Al-Mashaikhi et al., 2012

Syria Aleppo basin Stadler et al., 2012

Belgium Ledo-Paniselian Blaser et al., 2010

Czech Rep. Sokolov aquifer Noseck et al., 2009

France Bathonian coast Barbecot et al., 2000

France Lorraine sandstone Celle-Jeanton et al., 2009

France Aquitaine basin Jiráková et al., 2009

Hungary Great Hungarian Plain Stute and Deak, 1989

154

Country Aquifer Citation(s)

Hungary Pannonian basin Varsanyi et al., 2011

Poland Mazovian basin Zuber et al., 2000

Poland S. Poland carbonates Samborska et al., 2012

Poland Malm limestone Zuber et al. 2004

Portugal Sado basin Fernandes and Carreira, 2008

United Kingdom Lincolnshire limestone Darling et al., 1997

United Kingdom Chalk aquifer Darling and Bath, 1988; Dennis et al., 1997; Elliot et al., 1999; Gooddy et al., 2006

U.S.A. Columbia Flood Bslts. Douglas et al., 2007

U.S.A. Black Hills: Pahasapa Back et al., 1983

U.S.A. Idaho Batholith Schlegel et al., 2009

U.S.A. Cambrian-Ordovician Siegel, 1991

U.S.A. High Plains: North Gosselin et al., 2001

U.S.A. Mahomet aquifer Hackley et al., 2010

U.S.A. Aquia aquifer Aeschbach-Hertig et al. 2002

U.S.A. High Plains: Central Clark et al. 1998

U.S.A. San Juan Basin Stute et al., 1995a

U.S.A. Middle Rio Grande Plummer et al., 2011

U.S.A. Los Angeles Basin Swarzenski et al., 2013

U.S.A. Floridan aquifer Clark et al., 1997

U.S.A. Floridan surficial aqfr. Morrissey et al., 2010

Brazil Portigar basin: Acu Salati et al., 1974

Brazil Botacatu: central Gouvea da Silva, 1983

Brazil Botucatu: south Roboucas and Santiago, 1989

155

Table 3-2 (continued). Observed Δδ18Oice age values in groundwaters

Country Aquifer Lon. Lat. Δδ18Oice age

(‰ V−SMOW)

Africa

Algeria Great Oriental Erg: CI 5.9 32.4 −0.5 (−1.6 to −0.3)

Botswana Kalahari: Ntane 25.2 −24.0 −0.5 (−0.7 to −0.3)

Botswana Lokalane-Nakojane 22.0 −22.3 −1.1 (−1.2 to −0.9)

Burkina Faso Taoudeni basin −4.7 12.8 −0.5 (−1.0 to −0.3)

Chad Chad aquifer 18.3 11.2 −0.9 (−2.4 to −0.5)

Egypt Nubian aquifer 28.9 25.7 −1.6 (−3.6 to −0.3)

Mali Mali aquifer −7.2 15.2 −0.5 (−1.3 to +0.1)

Morocco N. Morocco aquifer −4.9 34.0 −0.6 (−2.2 to 1.4)

Morocco Tadla basin −6.7 32.6 −0.9 (−1.5 to −0.1)

Morocco Nappe des sables −14.5 15.4 +0.2 (−0.5 to +0.8)

Namibia Omatako basin 17.9 −20.1 −0.9 (−1.3 to −0.1)

Niger Djardo-Bilma 12.9 18.9 +2.0 (−0.4 to +2.5)

Niger Irhazer: CI 7.5 17.3 −0.9 (−2.3 to +0.2)

Niger Illumeden: CT 2.8 13.6 −3.0 (−3.7 to −2.1)

Nigeria Chad basin 13.0 12.0 −0.3 (−2.2 to +0.5)

Senegal Senegalese CT −16.4 15.2 +0.3 (−0.6 to +1.0)

South Africa Uitenhage aquifer 25.5 −33.7 −0.5 (−1.0 to −0.4)

Tunisia Kairouan Plain 10.0 35.5 +0.2 (−0.9 to +0.4)

Zimbabwe Zimbabwe aquifer 28.1 −19.5 −0.9 (−1.3 to −0.5)

Asia and western Pacific

Australia Canning basin 125.1 −17.5 −1.0 (−2.3 to +0.9)

Australia Ngalia and Amadeus 131.9 −23.4 −0.3 (−0.6 to +1.1)

Australia Murray aquifer 140.2 −34.2 −0.3 (−1.1 to +0.1)

Bangladesh Bengal basin 90.0 23.6 +1.6 (+0.9 to +2.3)

China Songnen plain 124.5 45.9 −0.2 (−0.8 to 0.3)

China Hexi Corridor: east 102.1 38.7 −1.4 (−2.4 to −0.3)

China North China Plain 114.9 38.0 −2.3 (−2.6 to −1.7)

China Yuncheng basin 110.6 35.0 −1.1 (−2.1 to −0.1)

India Cuddalore sandstone 79.5 11.4 +0.8 (0.1 to +1.5)

India Tiruvadanai aquifer 78.7 10.0 −0.9 (−1.5 to −0.5)

Indonesia Jakarta basin 106.8 −6.3 +0.1 (−0.1 to +0.5)

Israel Israel coastal aquifer 34.8 32.0 +0.2 (−0.1 to +0.5)

Israel Dead Sea rift valley 35.2 30.7 −1.4 (−2.0 to −0.8)

Kuwait Kuwait aquifer 47.7 29.8 −1.6 (−2.1 to −1.3)

Oman Batinah coastal plain 57.7 23.6 +1.1 (−0.2 to +2.0)

Oman Najd aquifer 53.9 18.1 −0.6 (−3.4 to +2.3)

Syria Aleppo basin 37.3 35.9 −1.4 (−2.5 to −0.1)

Europe

Belgium Ledo-Paniselian 3.5 51.2 −0.5 (−1.0 to +0.1)

Czech Rep. Sokolov aquifer 12.7 50.2 −0.8 (−0.8 to −0.3)

France Bathonian coast −0.2 49.2 −0.4 (−0.6 to −0.3)

France Lorraine sandstone 6.6 48.9 −1.0 (−1.5 to −0.8)

156

Country Aquifer Lon. Lat. Δδ18Oice age

(‰ V−SMOW)

France Aquitaine basin −0.4 45.9 −1.5 (−2.5 to −0.1)

Hungary Great Hungarian Plain 20.8 47.6 −1.8 (−2.3 to −1.3)

Hungary Pannonian basin 20.1 46.3 −3.6 (−4.1 to −2.7)

Poland Mazovian basin 21.0 52.2 0.0 (−0.4 to +0.2)

Poland S. Poland carbonates 19.2 50.6 −2.0 (−2.5 to −1.1)

Poland Malm limestone 19.8 50.0 −1.0 (−1.9 to +0.4)

Portugal Sado basin −8.5 38.3 +0.1 (−0.3 to +0.2)

United Kingdom Lincolnshire limestone −0.4 52.7 −0.4 (−0.4 to −0.2)

United Kingdom Chalk aquifer −1.4 51.5 −0.4 (−0.4 to −0.3)

The Americas

U.S.A. Columbia Flood Basalts −119.0 46.6 −2.8 (−3.6 to −1.2)

U.S.A. Black Hills: Pahasapa −103.5 44.3 −0.4 (−0.9 to +1.6)

U.S.A. Idaho Batholith −116.1 43.7 −0.7 (−1.1 to +0.1)

U.S.A. Cambrian-Ordovician −93.2 42.9 0.0 (−0.6 to +0.5)

U.S.A. High Plains: North −101.3 40.9 +0.3 (−1.3 to +2.2)

U.S.A. Mahomet aquifer −88.8 39.9 −0.2 (−0.5 to +0.1)

U.S.A. Aquia aquifer −76.6 38.7 0.0 (−0.4 to +0.3)

U.S.A. High Plains: Central −101.0 37.5 −2.6 (−4.3 to −1.4)

U.S.A. San Juan Basin −107.8 36.5 −3.1 (−3.4 to −2.5)

U.S.A. Middle Rio Grande −106.4 35.1 −0.6 (−2.8 to +0.8)

U.S.A. Los Angeles Basin −118.2 33.8 −1.4 (−2.0 to −0.8)

U.S.A. Floridan aquifer −82.1 32.0 +1.0 (+0.6 to +1.3)

U.S.A. Floridan surficial aqfr. −81.0 26.7 +1.8 (+0.6 to +2.1)

Brazil Portigar basin: Acu −38.0 −5.6 −2.2 (−3.3 to −1.9)

Brazil Botacatu: central −48.7 −22.3 0.0 (−1.4 to +1.8)

Brazil Botucatu: south −52.9 −28.1 −1.5 (−1.9 to −1.0)

157

Table 3-3. Observed ranges of groundwater δ18Oice age and δ18Olate-Holocene (Shown as δ18OHolo.) values

Aquifer δ18OHolo. HighHolo. LowHolo. δ18Oice age Highice age Lowice age

Great Oriental Erg −7.9 −7.0 −8.0 −8.4 −8.3 −8.6

Kalahari: Ntane −5.6 −5.2 −5.8 −6.1 −5.9 −6.1

Lokalane-Nakojane −6.1 −6.1 −6.3 −7.2 −7.2 −7.3

Taoudeni basin −5.2 −5.0 −5.4 −5.8 −5.7 −5.9

Chad aquifer −4.3 −3.5 −4.6 −5.3 −5.1 −5.9

Nubian aquifer −8.9 −7.0 −9.9 −10.5 −10.2 −10.6

Mali aquifer −6.0 −5.4 −6.5 −6.5 −6.4 −6.7

N. Morocco aquifer −6.4 −6.0 −6.6 −7.0 −5.2 −8.2

Tadla basin −5.6 −5.1 −6.4 −6.6 −6.5 −6.6

Nappe des sables −6.1 −5.6 −6.6 −5.9 −5.8 −6.1

Omatako basin −8.4 −8.1 −9.1 −9.3 −9.2 −9.3

Djardo-Bilma −10.1 −7.9 −10.4 −8.1 −7.9 −8.2

Irhazer: CI −6.4 −5.5 −7.0 −7.3 −6.8 −7.7

Illumeden: CT −4.4 −3.8 −5.2 −7.4 −7.3 −7.6

Chad basin −5.9 −4.4 −6.4 −6.2 −5.9 −6.6

Senegalese CT −6.2 −5.6 −6.5 −5.9 −5.5 −6.2

Uitenhage aquifer −4.9 −4.5 −5.0 −5.4 −5.4 −5.5

Kairouan Plain −5.8 −5.4 −5.9 −5.6 −5.5 −6.2

Zimbabwe aquifer −6.0 −5.7 −6.3 −6.9 −6.8 −7.0

Canning basin −6.6 −5.5 −8.4 −7.6 −7.5 −7.8

Ngalia/Amadeus −4.3 −4.0 −4.5 −4.5 −4.4 −5.1

Murray aquifer −7.0 −6.8 −8.4 −7.3 −7.2 −7.4

Bengal basin −4.7 −4.2 −5.3 −3.1 −3.0 −3.3

Songnen plain −10.0 −9.5 −10.3 −10.2 −10.0 −10.3

Hexi Corridor: east −9.1 −8.4 −9.8 −10.5 −10.1 −10.8

North China Plain −8.6 −8.3 −8.9 −10.8 −10.6 −10.9

Yuncheng basin −9.2 −8.4 −9.4 −10.3 −9.5 −10.5

Cuddalore sst. −5.6 −5.5 −5.8 −4.9 −4.4 −5.4

Tiruvadanai aquifer −4.1 −3.7 −4.4 −5.0 −4.9 −5.2

Jakarta basin −6.1 −5.7 −6.2 −6.0 −5.6 −6.2

Israel coastal −4.7 −4.5 −5.0 −4.5 −4.5 −4.6

Dead Sea rift valley −4.8 −4.8 −5.4 −6.2 −6.2 −6.8

Kuwait aquifer −2.9 −2.6 −3.0 −4.5 −4.4 −4.7

Batinah coast −2.7 −1.6 −3.4 −1.6 −1.4 −1.8

Najd aquifer −3.1 −0.7 −5.4 −3.6 −3.2 −4.1

Aleppo basin −6.0 −5.4 −6.9 −7.4 −6.9 −7.9

Ledo-Paniselian −6.5 −6.1 −6.9 −7.0 −6.8 −7.1

158

Aquifer δ18OHolo. HighHolo. LowHolo. δ18Oice age Highice age Lowice age

Sokolov aquifer −9.0 −8.9 −9.1 −9.8 −9.4 −9.8

Bathonian coast −6.6 −6.5 −6.7 −7.0 −7.0 −7.1

Lorraine sandstone −8.9 −8.7 −9.0 −10.0 −9.8 −10.2

Aquitaine basin −5.8 −5.6 −6.2 −7.3 −6.3 −8.1

Grt Hungarian Plain −9.5 −9.3 −9.6 −11.3 −10.9 −11.6

Pannonian basin −9.4 −9.1 −9.6 −13.0 −12.3 −13.2

Mazovian basin −10.2 −10.0 −10.2 −10.1 −10.0 −10.3

S. Poland −9.9 −9.6 −10.1 −11.8 −11.2 −12.1

Malm limestone −10.1 −10.0 −11.1 −11.1 −10.7 −11.9

Sado basin −4.8 −4.4 −4.8 −4.7 −4.7 −4.7

Lincolnshire limest. −7.8 −7.8 −7.9 −8.2 −8.1 −8.2

Chalk aquifer −7.4 −7.4 −7.4 −7.8 −7.7 −7.8

Columbia Floods −15.3 −14.8 −16.4 −18.1 −17.6 −18.5

Black Hills −17.1 −16.7 −17.4 −17.5 −15.8 −17.6

Idaho Batholith −16.8 −16.5 −17.4 −17.5 −17.2 −17.6

Cambrian-Ordo. −8.8 −8.2 −9.2 −8.8 −8.7 −8.8

High Plains: North −9.9 −9.3 −10.8 −9.6 −8.5 −10.6

Mahomet aquifer −6.8 −6.7 −7.0 −7.0 −6.9 −7.2

Aquia aquifer −7.1 −7.0 −7.1 −7.1 −6.8 −7.4

High Plains: Cent. −9.5 −8.1 −10.5 −12.1 −11.9 −12.4

San Juan Basin −11.4 −11.2 −11.7 −14.5 −14.2 −14.6

Middle Rio Grande −11.8 −10.2 −12.9 −12.5 −12.1 −13.0

Los Angeles Basin −7.3 −7.2 −7.3 −8.6 −8.1 −9.2

Floridan aquifer −4.7 −4.6 −4.7 −3.7 −3.4 −4.0

Floridan surf. aqfr. −3.4 −2.4 −3.5 −1.6 −1.4 −1.8

Portigar basin: Acu −2.3 −1.4 −2.5 −4.5 −4.4 −4.6

Botacatu: central −8.6 −7.4 −9.3 −8.6 −7.5 −8.8

Botucatu: south −6.2 −5.9 −6.4 −7.6 −7.4 −7.8

* High and Low refer to 25th and 75th percentile ranges of modern (mod. i.e., late-Holocene) and

glacial (i.e., last ice age) data groups.

159

Table 3-4. Speleothem δ18O from the last ice age to the late-Holocene

Cave Country Reference Lon. Lat. Δδ18Oice age

(‰ V−SMOW)

Corr.x

Speleothems

Gunung Buda Borneo Partin et al., 2007 114.8 4.0 +1.9 (+1.8 to +2.4)

0.9±0.2

Botuverá Cave Brazil Cruz et al., 2005; Wang et al., 2007

−49.2 −27.2 −0.7 (−1.1 to −0.3)

0.9±0.2

Dongge t China Dykoski et al., 2005; Yuan et al., 2004

108.1 25.3 +2.5 (+2.1 to +3.2)

0.9±0.2

Hulu * China Wang et al., 2001 119.2 32.5 +1.6 (+1.4 to +1.9)

1.0±0.2

Jiuxian t China Cai et al., 2010 109.1 33.6 +1.0 (−0.8 to +3.7)

1.0±0.2

Yaman Cave t China Yang et al., 2010 107.9 24.5 +2.9 (+2.3 to +3.3)

0.9±0.2

Soreq Israel Bar-Matthews et al., 2003

35.0 31.5 +2.3 (+2.1 to +2.5)

1.0±0.1

Peqin* Israel Bar-Matthews et al., 2003

35.2 32.6 +2.1 (+1.8 to +2.3)

0.9±0.2

Jerusalem W. Israel Frumkin et al., 1999 35.2 31.7 +1.6 (+1.4 to +2.3)

1.0±0.2

NW South Island

New Zealand

Williams et al., 2010 172.0 −42.0 +0.4 (+0.2 to +0.6)

1.0±0.3

Cold Air Cave South Africa

Holmgren et al., 2003 29.1 −24.0 +1.2 (+0.7 to +1.7)

1.0±0.1

Sofular Turkey Fleitmann et al., 2009 31.9 41.4 −4.7 (−4.8 to −4.5)

1.0±0.2

Fort Stanton* U.S.A. Asmerom et al., 2010 −105.3 33.3 −2.0 (−2.8 to −1.2)

1.0±0.2

Cave of the bells*

U.S.A. Wagner et al., 2010 −110.8 31.8 −2.4 (−2.6 to −2.4)

1.0±0.2

Moomi* Yemen Shakun et al., 2007 54.0 12.5 +2.3 (+1.7 to +2.9)

0.9±0.2

* early Holocene value used (i.e., shift likely larger than shown)

t values from 15.0 ka used

x Calcite-water fractionation correction subtracted from raw observed Δδ18Oice age to correct for the

4.0±0.8 °C colder climate (Annan and Hargreaves, 2013) at the last glacial stage (from O’Neil et al.,

1969; modern temperatures from New et al., 2002; Δδ18Oice age values in preceding column are shown

in raw (i.e, uncorrected) form.

160

Table 3-5. Ice core δ18O from the last ice age to the late-Holocene

Ice core Country Reference Lon. Lat. Δδ18Oice age

(‰ V−SMOW)

Ice cores

Sajama Bolivia Thompson et al., 1998 −63.9 −18.1 −4.9 (−3.7 to −5.6)

Huascaran Peru Thompson et al., 1995 −77.6 −9.1 −6.5 (−5.9 to −7.4)

Qinghai-Tibetan

Tibet Thompson et al., 1997 81.5 35.3 −0.9 (+2.0 to −3.2)

TALD Ice Antarctica Buiron et al., 2011 159.2 −72.8 −3.9 (−3.3 to −4.3)

Byrd Glacier Antarctica Blunier and Brook, 2001

−119.5 −80.0 −6.0 (−5.0 to −6.9)

Dome Fuji Antarctica Kawamura et al. 2007 39.7 −77.3 −3.6 (−2.7 to −4.5)

Dronning Maud

Antarctica EPICA Community Members, 2006

2.0 −75.0 −5.2 (−4.5 to −6.0)

Law Dome Antarctica Pedro et al. 2011 112.8 −66.8 −6.9 (−6.0 to −7.5)

Siple Dome Antarctica Pedro et al. 2011 −148.8 −81.7 −7.1 (−6.2 to −7.9)

Renland ice core

Greenland Vinther et al., 2008 −27.0 71.0 −4.1 (−2.8 to −4.9)

NGRIP1 Greenland Vinther et al., 2006 −42.3 75.1 −7.3 (−5.5 to −8.7)

161

The magnitude of change in δ18O values from the last ice age (19,500 to 50,000 years before

present) to the late-Holocene (<5,000 years before present; i.e., Δδ18Oice age) is shown in Figures 3-3

and 3-5. The Δδ18Oice age value of groundwater ranges from −3.6 ‰ (i.e., δ18Olast ice age < δ18Olate-

Holocene) to +2.0 (i.e., δ18Olast ice age > δ18Olate-Holocene), with more than 90 percent of aquifers having

negative Δδ18Oice age values (Figure 3-4). No systematic latitudinal trend in Δδ18Oice age values can be

observed for either the fossil groundwater or speleothem records (Figure 3-4), unlike temperature

(Figure 3-1). However, cases where δ18Oice age values exceed δ18Olate-Holocene values are constrained to

coastal aquifers in the subtropics (e.g., Bangladesh: +1.6 ‰, less than 300 km from the coast; Florida:

+1.0 and +1.8 ‰, less than 100 km from the coast; southern India: +0.9 ‰, less than 100 km from

the coast). In comparison, aquifers characterized by lower δ18Oice age values than δ18Olate-Holocene values

are found in both coastal regions and farther inland. Aquifers located farthest from coastlines exhibit

the lowest Δδ18Oice age values (e.g., Hungary: −3.6 ‰, ~500 km inland; New Mexico: −3.1 ‰, ~1000

km inland; Niger: −3.0 ‰, ~800 km inland). Greenland and Antarctic ice cores have consistently

negative Δδ18Oice age values that are of a greater magnitude (average of −5.5 ‰, range from −3.6 ‰

to −7.3 ‰) than groundwater Δδ18Oice age values (average of −0.6 ‰, range from −3.6 ‰ to +2.0 ‰;

Figure 3-3).

162

Figure 3-3. The Δδ18Oice age value of

groundwaters, speleothems and ice

cores. Colored bars mark the 25th-

75th percentile ranges of late-

Holocene and last glacial stage

datasets for each aquifer (blue

shades, unique to geographic

regions), speleothem (red), and ice

core (light brown marks non-polar,

dark brown marks ice cores for

Antarctica and Greenland).

Speleothem data are corrected for

the different isotope effects during

precipitation due to different ice age

and modem temperatures. An early

Holocene δ18O range was used for

the Byrd, Dronning Maud, Law

Dome ice cores and Dongge, Fort

Stanton, Hulu, Jiuxian and Moomi

speleothems due to lacking late-

Holocene data in these records. See

Tables 3-2 through 3-5 for

descriptions.

163

Figure 3-4. (top pane) Latitudinal variations of Δδ18Oice age values of groundwater (circles, each circle

is one aquifer), ice cores (squares) and caves (i.e., speleothems; triangles). Dashed lines mark 10°

zonal means of terrestrial precipitation δ18O values predicted by four different general circulation

models (CCSM, ECHAM, LMDZ and IsoGSM). (bottom pane) Histogram of observed Δδ18Oice age

values for in speleothems, ice cores and groundwaters (n = 92 records, in total). Red bars mark

records where δ18Oice age > δ18Olate-Holocene, blue bars mark records where δ18Oice age < δ18Olate-Holocene.

164

3.5 Discussion

3.5.1 Ice age groundwaters as a paleoclimate proxy

The isotopic composition of groundwater from the last ice age provides a valuable tracer of

the isotopic composition of past meteoric waters. The meteoric nature of ice-age-to-late-Holocene

δ18O and δ2H shifts found in the compiled groundwater data demonstrates that paleo-groundwaters

with minimal evaporative influence can be readily identified, making them valuable archives of

paleoclimate information. Such identification of evaporative influence is often difficult to decouple

for other records based solely upon δ18O or δ2H values (e.g., lake sediments), highlighting the value

of groundwater archives for paleoclimate investigations. The ability to measure both δ18O and δ2H

values of groundwater and ice core paleoclimate records is another primary advantage of these “dual

isotope” (i.e., both δ18O and δ2H) paleoclimate records over “single isotope” paleoclimate records

that can only provide either δ18O (e.g., speleothem, lake sediment carbonate, diatom or cellulose

records) or δ2H (e.g., lake sediment leaf wax records) values, but not both. Because of the ability to

examine deuterium excess values, ice age groundwater records may be better suited than speleothems

for determining changes to moisture sources as recently evidenced by isotope enabled general

circulation model reanalysis of ice core isotopic data (Lewis et al., 2013).

Where groundwater data may be advantageous over other records in its availability of “dual

isotope” data, these records suffer in temporal resolution. Lake sediment, ice core and speleothem

records can often be resolved at time scales of 100 to 103 years, whereas groundwater paleoclimate

records can be resolved at time scales of >103 years because of uncertainties in corrected 14C-based

groundwater ages and because of hydrodynamic dispersion that “smears” the groundwater isotopic

record. The impact of hydrodynamic dispersion and long groundwater residence times may help to

explain a portion of the discrepancies in the magnitude of δ18O shifts observed in lake sediment and

groundwater δ18O records at similar locations, just as comparing the standard deviation of daily

precipitation will differ from the standard deviation of monthly precipitation at the same location.

165

Each type of paleoclimate record has advantages and disadvantages, and all records are

useful to advancing our understanding of the climate during the last ice age. Groundwater records of

last glacial climate are globally-distributed and are able to be analyzed for “dual isotopes” to confirm

the meteoric nature of the paleoclimate record as completed in this study. However, paleo-

groundwater records of past climates have a poor temporal resolution (>103 years) that negates the

detection of rapid and dramatic shifts in climate. Speleothem isotopic records of last glacial climate

have high temporal resolution (100 to 102 years) but only have a single isotope available (δ18O in

carbonate) and are not as common (n = 15) as groundwater records (n = 65). Ice core records of last

glacial climate can be analyzed for both oxygen and hydrogen isotopic data and have a high temporal

resolution, but are very uncommon on land masses other than Antarctica and Greenland. Lake

sediment isotopic records of last glacial climate can have a high temporal resolution and are available

for a multitude of globally-distributed locations, however, lake sediment records have large

uncertainties in reconstructing past changes to meteoric δ18O because of the need to (i) quantify the

temperature of the water that the paleoclimate archive (e.g., lake sediment diatom, cellulose and

carbonate; speleothem carbonate) precipitated from in the past, and (ii) know the impact of

evaporation upon isotopic composition of water in the past, both of which are highly difficult to

reconstruct considering that most lake sediment archives are “single isotope” records (i.e., one of

δ18O or δ2H analyzed).

3.5.2 Isotope-enabled general circulation models

The δ18O value of annual precipitation from four isotope-enabled general circulation models

analyzed for the pre-industrial era and the last glacial maximum scenarios are shown in Figures 3-5,

3-6, 3-7 and 3-8. Points in each of the figures mark the observed ice-age-to-modern differences in

δ18O observed in groundwater, speleothems and ice cores. Locations where three or four models

agree on the sign of Δδ18Oice age are shown in Figures 3-9 and 3-10.

166

Generally, modelled Δδ18Oice age values are lowest over the Fennoscandanavian and

Laurentide ice sheets (less than 3 per mille), and highest over the tropical oceans. Positive modelled

Δδ18Oice age values occur near to coasts in the tropics and subtropics. Extra-tropical land surfaces

generally have negative Δδ18Oice age values, whereas tropical land surface Δδ18Oice age values are more

variable in their sign amongst the models (Figure 3-9 and 3-10). The disagreement amongst the four

models on the sign of Δδ18Oice age values is potentially related to different parameterizations of

convective rainfall along air mass trajectories, which is a leading control upon precipitation δ18O

values in tropical regions (Risi et al., 2008; Risi et al., 2010b; Lee et al., 2009; 2012; Lekshmy et al.,

2014; Samuels-Crow et al., 2014).

Simulated Δδ18Oice age values reproduce the sign of observed Δδ18Oice age values across North

America and Europe (extratropics) better than over Africa and South America (Figure 3-9 and 3-10).

Simulated isotopic compositions of rain over tropical Africa and South America have both

disagreement amongst different models on Δδ18Oice age values, and also Δδ18Oice age disagreement

between simulated (generally positive) and observed (generally negative) values. The negative

Δδ18Oice age values across Africa are consistent with enhanced air mass distillation during transport

due to higher-than-modern upstream rainout during the last ice age. Assuming that convection is a

leading control upon the isotopic composition of tropical precipitation, the models perhaps

overestimate the change in convection from the last glacial maximum to the present day. The

stronger agreement between simulated and observed Δδ18Oice age values in the extratropics relative to

the tropics suggests that models may simulate isotopic distillation via frontal advective hydroclimates

better than via convective rainout. The poorer simulation of Δδ18Oice age values in the tropics than in

the extratropics is consistent with other works that show that simulating the isotopic composition of

convective rains is highly sensitive to model parameterization (Lee et al., 2009).

167

Figure 3-5. The modelled difference in the δ18O value of precipitation from the last glacial maximum

to the pre-industrial time period (CCSM, pers. comm. F. Pausata): δ18Olast glacial maximum – δ18Olate-Holocene.

Circles show the observed Δδ18Oice age values (median) from groundwater, speleothem and ice core

records compiled and analyzed in this study.

168


to the pre-industrial time period (ECHAM, pers. comm. M. Werner): δ18Olast glacial maximum – δ18Olate-

Holocene. Circles show the observed Δδ18Oice age values (median) from groundwater, speleothem and ice

core records compiled and analyzed in this study.

169


to the pre-industrial time period (IsoGSM, pers. comm. K. Yoshimura): δ18Olast glacial maximum – δ18Olate-

Holocene. Circles show the observed Δδ18Oice age values (median) from groundwater, speleothem and ice

core records compiled and analyzed in this study.

170


to the pre-industrial time period (LMDZ, pers. comm. C. Risi): δ18Olast glacial maximum – δ18Olate-Holocene.

Circles show the observed Δδ18Oice age values (median) from groundwater, speleothem and ice core

records compiled and analyzed in this study.

171

Figure 3-9. Locations where all four models agree on the sign of Δδ18Oice age values (i.e., positive or

negative). Red colors mark regions where there is unanimous prediction of higher-than-modern δ18O

values at the last ice age amongst the four models, whereas blues colors mark regions where there is

unanimous prediction of lower-than-modern δ18O values at the last ice age amongst the four models.

The shades of red and blue are the multi-model average of modelled ice-age-to-late-Holocene

changes in the δ18O value of meteoric water. White regions show areas where at least one of the four

models predicts a different sign of Δδ18Oice age values (i.e., some models predict negative glacial to

modern shifts, other models predict positive glacial to modern shifts).

172

Figure 3-10. Locations where at least three of four models agree on the sign of Δδ18Oice age values (i.e.,

positive or negative). Red colors mark regions where there is higher-than-modern δ18O values at the

last ice age amongst the models, whereas blues colors mark regions where there is lower-than-

modern δ18O values at the last ice age amongst the models. The shades of red and blue are the multi-

model average of modelled ice-age-to-late-Holocene changes in the δ18O value of meteoric water.

White regions show areas where at least one of the four models predicts a different sign of Δδ18Oice

age values (i.e., some models predict negative glacial to modern shifts, other models predict positive

glacial to modern shifts).

173

Models predict similar Δδ18Oice age values in some regions (e.g., precipitation over the

tropical oceans) and different Δδ18Oice age values in other regions (e.g., Africa). Figure 3-9 delineates

locations where all four models agree on the sign of Δδ18Oice age values (i.e., all models are positive, or

all models are negative), and shows that all models agree on the sign of Δδ18Oice age over ~40% of

Earth’s surface. All four models agree on the sign of Δδ18Oice age values for half of continental areas

and for one-third ocean areas. For continental precipitation, 80% of locations where all models agree

on the sign of Δδ18Oice age values have a unanimously negative simulated Δδ18Oice age value. Conversely,

75% of cases where all models agree on the sign of over-ocean precipitation Δδ18Oice age values have a

unanimously positive simulated Δδ18Oice age value.

All four models have positive Δδ18Oice age values over the western and southern Pacific

Ocean, the tropical and mid-latitude Atlantic Ocean, the southeastern United States of America,

northeast Brazil, western Africa, eastern China and southwestern Australia. All four models predict

negative Δδ18Oice age values over the western United States of America, and northern and western

Canada, the southern margins of Argentina, northern Europe, the Norwegian Sea, throughout

Russia, Kazakhstan, Uzbekistan, and Turkmenistan, and over northern Mongolia and the Tibetan

plateau.

The general circulation model ice-age-to-modern δ18O changes agree with the observations

for some, but not all, locations. In general, simulated Δδ18Oice age values match observed Δδ18Oice age

values more closely in the extratropics than in the tropics. For example, general observed Δδ18Oice age

patterns over North America and Europe are reproduced by most general circulation models. In

contract, observed Δδ18Oice age values over Africa and South America are not reproduced by most

models. The modelled Δδ18Oice age reproduces some of the observed positive and observed negative

Δδ18Oice age values in groundwaters, speleothems and ice cores (Figure 3-9 and 3-10), highlighting the

174

much greater potential of these models for reconstruction of Δδ18Oice age values than temperature-

δ18O regressions, alone (e.g., Dansgaard, 1964).

3.5.3 Regional Δδ18Oice age values

3.5.3.1 Australia and Oceania

Australian records of Δδ18Oice age (n=3) range from -1.0‰ (Canning Basin) to -0.3‰ (Ngalia

and Amadeus, Murray aquifers; Table 3-2). General circulation models predict positive Δδ18Oice age

values across Australia; whereas the three observed groundwater records have negative Δδ18Oice age

values. Observed Δδ18Oice age values are similar for all three Australian records despite different

climates amongst the records that range from humid northern regions (Canning Basin) to more arid

interior settings (Ngalia and Amadeus basins).

Spatial differences in climate change across the Australia continent are evidenced by higher-

than-modern lake levels during the last glacial maximum in southeastern Australia (Galloway, 1965;

Williams, 2001), but lower-than-modern ice age lake levels in central Australia (Hope, 2005). The

climate at the last ice age in parts of Australia was more arid (Nanson et al., 1992), dustier (Chen et

al., 1993) and ~10°C cooler (Miller et al., 1997) than present day. Observations of higher-than-

modern ice age lake levels are attributed to lower evaporative potential at the last glacial maximum

(Hope, 2005). Observed negative Δδ18Oice age values are consistent with cooler-than-modern

condensation temperatures (i.e., enhanced air mass distillation) supported by 10°C cooler land

surface temperatures (Miller et al., 1997). Alternatively, atmospheric models suggest that precipitation

was more seasonal during the last glacial maximum than today due to cooler-than-modern sea surface

temperatures (Hope, 2005) able to produce negative Δδ18Oice age values.

Oceania records of paleoclimate include groundwater data for the Jakarta basin (Geyh and

Sofner, 1989), and speleothem data in Borneo (Partin et al., 2007) and New Zealand (Williams et al.,

175

2010). Oceania isotopic records of speleothems (Partin et al., 2007) and groundwaters (Aggarwal et

al., 2004) from Vietnam, Thailand, The Philippines and Borneo each have near-zero or positive

Δδ18Oice age values that have been attributed to ice-age-to-modern changes in monsoonal strength

and atmospheric convection (Aggarwal et al., 2004; Partin et al., 2007). Simulated Δδ18Oice age values

are generally positive or near-zero over Bangladesh, Vietnam, Thailand, The Philippines and Borneo

(Figure 3-9 and 3-10) consistent with the sign of observed Δδ18Oice age values. Simulated precipitation

δ18O values overlying Borneo are controlled by changes to precipitation amount caused by spatial

shifts in the position of the intertropical convergence zone (Lewis et al., 2010; 2011), suggesting that

the +1‰ higher-than-modern ice age seawater value was offset in part by drier-than-modern climate

during the last glacial maximum(applying interpretation of Lewis et al., 2011).

3.5.3.2 Southeast Asia

Southeast Asian Δδ18Oice age values range from −2.3 ‰ to +1.9 ‰ (n = 13). The highest

regional Δδ18Oice age values are found in Bangladesh (Δδ18Oice age of +1.6 ‰; Aggarwal et al., 2000) and

in central and south-eastern China (Δδ18Oice age of 0.0 ‰ to +1.9 ‰; Wang et al., 2001; Yuan et al.,

2004; Dykoski et al., 2005; Cai et al., 2010; Yang et al., 2010). The high Bangladeshi Δδ18Oice age value

of +1.6 ‰ cannot be explained solely by ice-age-to-modern changes in seawater δ18O (δ18Oice age

seawater > δ18Omodern seawater by +1 ‰), suggesting that changes to temperature and humidity of the over-

ocean moisture sources, air mass rainout history, precipitation seasonality, or seasonal filtering of

groundwater recharge must have occurred in Bangladesh between the last ice age and the late-

Holocene.

Chinese speleothem records located between latitudes 25°N to 35°N have near-zero or

positive Δδ18Oice age values. The Chinese speleothem records have been interpreted to reflect the

strength of the East Asian (Wang et al., 2001; Dykoski et al., 2005; Cosford et al., 2008) or Indian

monsoons (Pausata et al., 2011). Recent work proposes that interpreting Chinese speleothem isotopic

176

data as records of the summer monsoon strength is incorrect (Caley et al., 2014). Proxy evidence

suggest a weaker-than-modern summer monsoon at the last glacial maximum (Wang et al., 2001) and

stronger-than-modern winter precipitation (Sagawa et al., 2011; Clark et al., 2012). The North China

Plain (northeastern China; Zongyu et al., 2005) and the eastern Hexi Corridor (northern China; Gates

et al., 2008) aquifers have the lowest Δδ18Oice age values observed across east Asia (Δδ18Oice age of −1.4

‰ and −2.3 ‰). Combining northern Chinese groundwater Δδ18Oice age observations (Zongyu et al.,

2005; Gates et al., 2008) with the observed positive Δδ18Oice age values of across central China (Wang

et al., 2001; Yuan et al., 2004; Dykoski et al., 2005; Cai et al., 2010; Yang et al., 2010) reveals a south-

to-north decrease in Δδ18Oice age (Figure 3-2).

The observed north-to-south Δδ18Oice age decrease is spatially consistent with intra-annual

precipitation δ18O seasonality across southeastern Asia. Previous studies have identified increasing

precipitation δ18O values from the coast (i.e., Hong Kong) to inland China (e.g., Zhangye) during the

wet season, sharply contrasting spatial patterns expected from Rayleigh distillation (Aragúas-Aragúas

et al., 1998). This pattern has been interpreted as the maximum northward extent of the intertropical

convergence zone (i.e., broad scale Hadley circulation; Aragúas-Aragúas et al., 1998). However, more

recent work suggests that low wet-season precipitation δ18O values over southern Chinese are

controlled by the deflection of westerlies from the Tibetan Plateau, whereas precipitation δ18O over

northern China is controlled by local-scale precipitation fluxes and subsequent evaporation of falling

raindrops (Lee et al., 2012). Therefore observed Δδ18Oice age values in southern China may be

reflective of broader scale atmospheric circulation pattern changes, whereas Δδ18Oice age over northern

China could reflect ice-age-to-modern changes to local meteorology. The source of precipitation over

China varies on intra-annual time scales, and about half of all rainfall is sourced from continental

moisture recycling (Lewis et al., 2013). Generally, Chinese atmospheric vapor sourced from the

Indian Ocean is at a maximum during the summer, whereas Pacific-sourced moisture is greatest

during the winter (Lewis et al., 2013). The strong modelled intra-annual variation in moisture sources

177

over China (Lewis et al., 2013) suggests that observed Δδ18Oice age values may represent broad-scale

changes to moisture sources and associated air mass trajectories that have resulted in a strengthening

of monsoon rains from the last ice age to the present day.

General circulation models predict positive Δδ18Oice age values near to the Chinese coastlines,

and negative Δδ18Oice age values in western and northern China (Figures 3-9 and 3-10), consistent with

observed south-to-north decrease in Δδ18Oice age values. Generally, spatial patterns of the sign of the

multi-model average Δδ18Oice age agrees with the sign of the observed Δδ18Oice age (Figures 3-9 and 3-

10) in these monsoonal regions. A single hydrological process that explains all observed Δδ18Oice age

values is not identifiable nor expected given the variety of processes controlling modern precipitation

in southeastern Asia (Aragúas-Aragúas et al., 1998; Lee et al., 2012). However, the strong inter-model

agreement on Δδ18Oice age values and model capture of the south-to-north decrease in Δδ18Oice age

implies that general circulation models reproduce the broad atmospheric boundary defining the

different hydrological processes governing southern vs. northern Chinese precipitation regimes.

3.5.3.3 The Middle East

The Middle East has four Δδ18Oice age records ranging from −1.6 ‰ (Kuwait) to +1.4 ‰

(Yemen); all four sites in the Middle East are located within 100 km of a coast. Records collected in

Kuwait and Yemen have both been interpreted to reflect an ice age climate that was wetter than

today’s (Al-Ruwaih and Shehata, 2004; Shakun et al., 2007), although the Δδ18Oice age value is of a

different sign (i.e., Kuwait being negative, and Yemen positive). Groundwater noble gas records in

Oman reveal a 6.5°C temperature increase from the last ice age to the late-Holocene (Weyhenmeyer

et al., 2000). Groundwaters have an ice-age-to-late-Holocene deuterium excess increase (i.e., dice age <

dmodern) interpreted to be the result of a switch in the moisture source to Oman: from the Indian

Ocean during the last ice age, to the Mediterranean Ocean today (Weyhenmeyer et al., 2000) that may

be a leading control upon other records in the region.

178

3.5.3.4 Africa

African Δδ18Oice age values range from −3.0 ‰ to near-zero (Figure 3-2). 80% of African

Δδ18Oice age values are negative. Near-zero Δδ18Oice age values are generally found near to coasts (e.g.,

Senegal Δδ18Oice age of +0.3 ‰) whereas the lowest African Δδ18Oice age value is located in Niger

(Δδ18Oice age of −3.0 ‰) 800 kilometers from the coast. Records located north and south of the

equator have negative Δδ18Oice age values.

Northern African changes to hydrological processes are complicated by multiple interlinked

controls such as the strength of Atlantic meridional overturning circulation (Jullien et al., 2007) and

meridional shifts in the position of the intertropical convergence zone (Arbuszewski et al., 2013).

Paleowater isotopic records indicate that northern Africa was 2-3°C cooler than today (Guendouz et

al., 1998) and that westerly winds transporting moisture to northern Africa were stronger than

present day (Sultan et al., 1997; Abouelmagd et al., 2014). North African Δδ18Oice age observations

were also likely impacted by higher-than-modern sea surface humidity as evidenced by lower ice age

deuterium excess values in paleowaters (Rozanski, 1985). Potentially cooler-than-modern final air

mass condensation temperatures during the last ice age coupled to changes in moisture source and

sea surface temperature and humidity have each been suggested and result in unanimously negative

Δδ18Oice age values across northern Africa.

Southern Africa lacustrine sediment records recovered at Lake Tanganyika and Malawi show

that the eastern Africa was 2°C to 4°C cooler than modern, and that the isotopic composition of leaf

waxes was highly variable between the early and late-Holocene (Powers et al., 2005; Tiereny, 2008;

2013). These records are interpreted as indicative of precipitation variations imparted by changes to

Indian Ocean temperatures (Tiereny, 2008; 2013); although this interpretation is not supported by all

(Schefuß et al., 2011). Pollen records suggest that the African tropics were both cooler and more arid

at the last glacial maximum (Gasse, 2000). General circulation model simulate lower-than-modern ice

179

age precipitation fluxes over tropical Africa (Otto-Bliesner et al., 2006). Pollen records and climate

model simulation models both suggest a more arid region, indicative of lower-than-modern moisture

recycling during the last ice age. Rainfall originates from both Indian and Atlantic Oceanic sources,

with Atlantic-sourced moisture travelling across the Congo rainforest (Levin et al., 2009). A

reduction in continental moisture recycling is consistent with the observed negative Δδ18Oice age values

across southern tropical Africa. Model simulations of Heinrich event precipitation δ18O confirm

changes to moisture recycling and transport distance can change Δδ18Oice age values over southern

Africa. Higher-than-modern upwind convection during the last ice age may have produced negative

Δδ18Oice age values (e.g., Lekshmy et al., 2014) consistent with observed negative Δδ18Oice age values.

However, stronger-than-modern convective rainout at the last ice age is contrary to the cooler-than-

modern land surface temperatures, suggesting that increases to transport distance and vapor origin

changes are more likely sources of the observed negative Δδ18Oice age values (Lewis et al., 2010).

Isotope enabled general circulation model Δδ18Oice age values and observed groundwater

Δδ18Oice age values are shown in Figures 3-9 and 3-10. The four general circulation models do not

agree with each other nor with multiple compiled Δδ18Oice age values over the majority of Africa

(Figure 3-9 and Figure 3-10). While the source of this discrepancy remains unclear, different

parameterizations of convective rainfall amongst the models may help to explain disagreements

between models (e.g., inter-model differences in the timescale for consumption of convective

available potential energy; Lee et al., 2009). Indeed, recent work has shown that convection, not

precipitation amount (the “amount effect”), drives tropical variations in meteoric water δ18O values

(Lekshmy et al., 2014). However, the observed negative Δδ18Oice age values are consistent with higher-

than-modern upwind convection during the last ice age. Higher-than-modern convection during the

last ice age is difficult to reconcile given the cooler-than-modern land surface temperatures at the last

ice age (Figure 3-1). However, the observed negative Δδ18Oice age values must reflect rainout

180

processes, since ice-age-to-modern changes to seawater δ18O induce an opposing effect (i.e., positive

Δδ18Oice age) to observations.

3.5.3.5 Europe and Mediterranean nations

Europe and nations bordering the eastern Mediterranean Sea have Δδ18Oice age values ranging

from −5.7 ‰ to near-zero (n = 20; Figure 3-2). 80% of European Δδ18Oice age values are negative.

Δδ18Oice age values are generally higher in western Europe (+0.1 ‰ to −1.5 ‰ in Portugal and the

United Kingdom and France) than in eastern Europe (−1.8 ‰ to −3.6 ‰ in Poland and Hungary).

The lowest ice Δδ18Oice age value is observed in a speleothem in Turkey near to the Black Sea (−5.7

‰), which is interpreted to be the dominant source of moisture over the region (Fleitmann et al.,

2009). The interpretation of a change in moisture source is consistent with recent reporting of lower-

than-modern deuterium excess values during the last ice age (Arslan et al., 2013) and pollen records

indicative of a drier ice age climate in eastern Turkey (Kaplan, 2013). Indeed there is large potential

for an ice-age-to-modern change in the moisture sources and air mass trajectories over Turkey given

the large number of potential moisture sources (e.g., Black Sea, Mediterranean Sea, Atlantic Ocean)

and the cave’s location near to the margin of the Fennoscandanavian ice sheet at the last glacial

maximum. The highest European Δδ18Oice age value (near-zero) is found along the Portugal coast

(Galego Fernandes and Carreira, 2008). The near-zero Δδ18Oice age value in the Portugal aquifer

suggests that the effect of the higher ice age δ18Oseawater value is cancelled out by a combination of ice-

age-to-modern changes in sea surface temperature and humidity, cooler condensation temperatures

at the last ice age and/or greater fluxes of winter precipitation entering aquifers at the last glacial

maximum.

Positive Δδ18Oice age values are observed in the eastern Mediterranean speleothems found in

Israel (Frumkin et al., 1999; Bar-Matthews et al., 2003; Ayalon et al., 2013), although groundwater

aquifers in eastern Israel and in Syria have negative Δδ18Oice age values. These records are near to one

181

another, such that the opposing sign of Δδ18Oice age observed in each record is surprising. The

groundwater record may record information from higher recharge-zone elevations located ~102 km

from the measurement location due to advection along regional-scale flowpaths. This compilation

and spatial analysis advocated for further comparative research into speleothem and groundwater

isotopic compositions to ensure each record indeed reflects paleo-meteoric water δ18O values

unaltered by subsequent effects (e.g., partial evaporation, etc.).

Some European aquifers have a prolonged gap in 14C-based groundwater ages interpreted to

be the result of the inhibition of recharge due to permafrost aggradation (Darling, 2004). Changes to

freeze-thaw conditions of the ground surface between the last ice age the modern climate may have

also impacted the seasonality of groundwater recharge ratios (Darling, 2011; Jasechko et al., 2014),

suggesting that recharge dynamics may represent a process that has not yet been applied to reconcile

observed Δδ18Oice age values. Indeed, pollen records indicate that northern Europe was tundra-like at

the last glacial maximum and that southern Europe was semi-arid, receiving ~300 mm less

precipitation than modern day (Clark et al., 2012 and references therein). The glacial-to-modern

transition from semi-arid deserts to temperate forests may have modified the seasonality of the

groundwater recharge ratio as evidenced by modern day recharge being much more efficient during

the winter (Jasechko et al., 2014). This potential ice-age-to-modern change in recharge/precipitation

ratios would have likely enhanced winter recharge fluxes resulting in negative shifts in Δδ18Oice age

values consistent with observations.

General circulation model outputs of Δδ18Oice age values over Europe are unanimously

negative (i.e., all four models agree on the sign of Δδ18Oice age Figure 3-9; 3-10), with the exception of

southern Portugal and Spain. The model predictions across Europe closely match the observations of

Δδ18Oice age values in groundwaters and speleothems. Earlier works have suggested the European

moisture sources and air mass trajectories have not changed considerably since the last ice age

182

(Rozanski et al., 1985; Loosli et al., 2001). The match between simulated and modelled negative

Δδ18Oice age values implies that the last ice age had cooler final air mass condensation temperatures,

higher winter groundwater recharge ratios or higher proportions of winter precipitation as a

proportion of annual totals. However, modelled Δδ18Oice age values agree less frequently over Israel

and Syria, where aquifer and speleothem observations also show both positive and negative Δδ18Oice

age values. The conflicting model outputs and observations of Δδ18Oice age values in the eastern

Mediterranean suggest that moisture sources, air mass trajectories and meteorology is highly sensitive

to change in the eastern Mediterranean, and that this sensitivity varies over distances of ~102

kilometers.

3.5.3.6 South America

South American Δδ18Oice age values range from −6.5 ‰ to 0.0 ‰ (Figure 3-2), with the

lowest values found in ice cores in the Andes (Bolivia: δ18O anomaly of −4.9 ‰; Thompson et al.,

1998; Bolivia: δ18O anomaly of −6.5 ‰; Thompson et al., 1998). The Δδ18Oice age values found in the

ice cores have been interpreted to have been coupled to substantial cooling of the tropics (quoted as

8°C to 12°C; Thompson et al., 1995), and may also be related to changes in moisture recycling over

the Amazon, which is the dominant moisture source to the Andes (Thompson et al., 1998).

Paleowater Δδ18Oice age data is available in the semi-arid eastern portion of Brazil. The interpretation

of this record was that rainfall was higher-than-modern during the Pleistocene (Salati et al., 1974),

consistent with greater upwind convective rainfall leading to negative Δδ18Oice age values. However,

recent work proposes that precipitation was lower-than-modern in eastern Brazil at the last glacial

maximum (Cruz et al., 2009; Clark et al., 2012). Eastern Brazilian precipitation is anti-phased with

precipitation fluxes in the South American monsoon region (Cruz et al., 2009) where ice age

precipitation fluxes are thought to be higher-than-modern (Wang et al., 2007). This aquifer is located

~100 km from the Atlantic Ocean at a latitude of 5°S, and the region was 5.4°C cooler than today

during the last glacial maximum (Stute et al., 1995b). The intra-annual variability in the isotopic

183

composition of precipitation is subdued, with the summer/wet-season (April to September) having a

nearly identical isotopic composition (−2.2‰) to that of the winter/dry-season (−2.0‰; data from

Ceara Mirim, Brazil; data accessed from www.iaea.org/water), suggesting that changes to the

seasonality of precipitation amounts or the seasonality of the groundwater recharge ratio are not the

source of observed negative Δδ18Oice age value in eastern Brazil. Possible processes that may explain

the negative Δδ18Oice age values in eastern Brazil include higher-than-modern upwind convection

during the last ice age (Salati et al., 1974), supported as a leading control on Δδ18Oice age by general

circulation model simulations that suggest local rainfall amounts govern precipitation δ18O (Lewis et

al., 2010). Observed Δδ18Oice age values in eastern Brazil support the interpretation of Salati et al.

(1974) that eastern Brazil was wetter than today during the last glacial maximum.

General circulation models have unanimously positive Δδ18Oice age values over semi-arid

eastern Brazil (Figures 3-9 and 3-10). Interestingly, the Δδ18Oice age value observed in this region is

negative (Salati et al., 1974). It is clear that the models have not captured all processes in this region,

as the predicted Δδ18Oice age values are of a different sign than the observed Δδ18Oice age value. I have

ruled out seasonality of precipitation fluxes as the sole process controlling Δδ18Oice age values in this

region. However, changes to moisture sources, air mass recycling, rainout history and moisture

recycling (i.e., processes ii through v) may each be an important control upon Δδ18Oice age values in

eastern Brazil. Similarly, upstream convective rainstorms (potentially not accurately parameterized

within all general circulation models) stronger-than-modern during the last ice age could have

contributed to observed negative Δδ18Oice age values.

3.5.3.7 North America

North American Δδ18Oice age records are all located in the United States of America and range

from −3.1 ‰ to +1.8 ‰ (n = 14). Easternmost USA has Δδ18Oice age values that are positive or near-

zero. The positive Δδ18Oice age values are highest in Florida (latitude: 27°N; Δδ18Oice age of +1.8 ‰)

184

and decrease northward through Georgia (latitude: 32°N; Δδ18Oice age of +1.0 ‰) to coastal Maryland

(latitude 39°N; Δδ18Oice age of 0.0 ‰). The decreasing Δδ18Oice age values observed with increasing

latitude along the USA eastern seaboard that may be partially explained by the isotopic distillation of

air masses advecting northward from the tropics. The effect of the higher ice age δ18Oseawater values

has been shown to be offset by the lowering of the sea level during the last ice age which increases

the distance-to-the-coast because of sea level regression (Clark et al., 1997; Aeschbach-Hertig et al.,

2002). Other potentially important processes that may reconcile the observed Δδ18Oice age values

include changes to seasonal precipitation rates, changes to moisture recycling due to differing

Pleistocene vegetation in the region (Harrison et al., 2003) or changes to hurricane frequency and

intensity (i.e., precipitation seasonality; Plummer, 1993) could have impacted Δδ18Oice age values.

Further, recent research show that seawater δ18O values changed over time in the Gulf of Mexico

(i.e., one of the moisture sources to central and southeastern USA; Feng et al., 2014). δ18Oseawater

changes from the last ice age to the present day due to fluctuations in Mississippi River discharge

may have impacted terrestrial Δδ18Oice age values, with higher Mississippi discharges leading to lower

seawater δ18O and lower terrestrial precipitation δ18O values.

Westernmost USA has negative Δδ18Oice age values (e.g., Los Angeles basin Δδ18Oice age of

−1.4 ‰), contrasting Δδ18Oice age values observed along the eastern coast at similar latitudes.

Although the reason for this east-coast/west-coast difference may have multiple explanations, higher

than modern winter precipitation fluxes during the last ice age could invoke a negative Δδ18Oice age

value consistent with observations.

Central USA has the lowest Δδ18Oice age values that range from −0.6‰ to −3.1‰. The

southwestern USA was 5°C cooler than today during the last glacial maximum (Stute et al., 1995a).

The low inland Δδ18Oice age values observed in central North America are consistent with the

enhanced isotopic distillation of moisture advecting overland due to cooler final condensation

185

temperatures, or higher proportions of annual precipitation falling during the winter season. The

observed Δδ18Oice age values in these records have been attributed to lower-than-modern summer

precipitation fluxes during the late Pleistocene (New Mexico, Phillips et al., 1986), latitudinal shifts in

the positions of the polar jet stream and the intertropical convergence zone (New Mexico, Asmerom

et al., 2010) and changes to over-ocean humidity, temperature or moisture sources (Idaho, Schlegel et

al., 2009). Pollen records indicate widespread forests throughout the present day deserts of the

American southwest, indicative of wetter-than-modern conditions at the last glacial maximum

(Williams, 2003). An isotopic record at Cave of the Bells is interpreted to reflect southwestern aridity,

with reductions in paleo-δ18O interpreted to reflect a cooler and a wetter climate (Arizona; Wagner et

al., 2010). Extending this interpretation to the observed negative Δδ18Oice age values throughout the

southwest USA, the groundwater Δδ18Oice age values suggest that the American southwest was both

cooler and more humid during the last ice age compared to present day. The source of higher-than-

modern ice age humidity may be linked to changes in air mass trajectories and moisture sources to

the southwestern USA (Asmerom et al., 2010; Wagner et al., 2010). Further, the strong intra-annual

variability in the isotopic composition of modern day precipitation (more than a 7 ‰ difference

between the summer and winter δ18O values) suggests that increases to winter precipitation or higher

recharge/precipitation ratios could also contribute to the observed negative Δδ18Oice age values in

southwestern USA groundwaters.

General circulation model results Δδ18Oice age values are generally positive along the eastern

seaboard, the Gulf States and the central plains of the USA (Figures 3-9; 3-10). The modelled

Δδ18Oice age results are consistent with the sign of Δδ18Oice age values observed in aquifers across

Florida, Georgia and Maryland (Plummer, 1993; Clark et al., 1997; Aeschbach-Hertig et al., 2002;

Morrissey et al., 2010; Figures 3-9 and 3-10), although no isotopic record of the last glacial maximum

is available for aquifers across the Gulf States (e.g., Edwards Aquifer, Texas). The general circulation

models Δδ18Oice age values are generally negative west of the Rocky Mountains, consistent with the

186

sign of observed Δδ18Oice age values in Colorado, New Mexico and Idaho (Clark et al., 1998; Stute et

al., 1995a; Schlegel et al., 2009; Asmerom et al., 2010).

Conclusions

The Δδ18Oice age of groundwater aquifers compiled in this study ranges from −3.6 ‰ (i.e.,

δ18Oice age < δ18Olate-Holocene) to +2.0 ‰ (i.e., δ18Oice age > δ18Olate-Holocene). ~90% of aquifers have

negative Δδ18Oice age values. Aquifers with positive Δδ18Oice age values are found exclusively near to

coasts. Future research may capitalize upon the broad availability of groundwater isotopic records of

last ice age and late-Holocene climate in order to isolate and constrain hydrological processes

responsible for observed Δδ18Oice age values using general circulation models (e.g., Lewis et al., 2010).

Further, observed Δδ18Oice age values are compared to general circulation model outputs of Δδ18Oice age

values. The general circulation models agree in the sign and magnitude of Δδ18Oice age values for

some, but not all locations. This synthesis and sensitivity analysis advocates for the use of

quantitative models when interpreting precipitation δ18O paleoclimate records.

Regional paleoclimate signals show that during the last ice age:

- Australia was more arid, ~10°C cooler, and had either greater contributions of winter

precipitation or increased rainout and isotopic fractionation of air masses potentially due to

cooler-than-modern atmospheric condensation temperatures.

- Southern Chinese summer monsoons were weaker-than-modern, winter rainfall was higher-

than-modern. Northern China was 5°C cooler and more humid than present climate

conditions.

- The Middle East was 6.5°C cooler than today and received greater vapour fluxes from the

Indian Ocean than present day, producing a wetter overall climate during the last ice age.

- Northern Africa was 2°C to 3°C cooler than modern climate and had greater vapor influxes

from westerly moisture sources, creating a more humid climate than present day.

187

- Southern African was 2°C to 4°C cooler than modern, had higher-than-modern vapor inputs

from westerly moisture sources, potentially lower-than-modern moisture recycling over the

Congo rainforest and potentially greater-than-modern upwind convective rainfall.

- European climate was 3°C to 9°C cooler than present day, had broadly similar-to-present

vapor inflows from westerly moisture sources, and may have had substantially higher-than-

modern winter groundwater recharge ratios or cooler final condensation temperatures (i.e.,

greater upwind air mass distillation) leading to observed unanimously negative Δδ18Oice age

values.

- Eastern Brazil was 5°C cooler and was more humid than present day climate due to greater-

than-modern monsoonal rainstorms.

- The southwestern USA was 5°C cooler than today (Stute et al., 1995a), was more humid

than modern climate, and potentially received greater-than-modern vapor fluxes from

westerly moisture sources or higher-than-modern winter precipitation fluxes.

Acknowledgements

I thank C. Risi, F. Pausata, K. Yoshimura and M. Werner for their time and help compiling

results from the isotope enabled general circulation models. I also thank A. Lechler, F. Pausata and

T. Gleeson for the valuable insights that have improved this chapter.

188

3.6 References

Abouelmagd, A. et al. (2012), Toward a better understanding of palaeoclimatic regimes that

recharged the fossil aquifers in North Africa: Inferences from stable isotope and remote sensing data,

Palaeogeography, Palaeoclimatology, Palaeoecology, 329, 137-149.

Aeschbach-Hertig, W., Stute, M., Clark, J. F., Reuter, R. F., and Schlosser, P. (2002), A

paleotemperature record derived from dissolved noble gases in groundwater of the Aquia Aquifer

(Maryland, USA), Geochimica et Cosmochimica Acta, 66, 797–817.

Aggarwal, P. K. et al. (2000), A report on isotope hydrology of groundwater in Bangladesh:

implications for characterization and mitigation of arsenic in groundwater, International Atomic

Energy Agency, Department of Technical Co-operation, Vienna (Austria).

Aggarwal, P. K., K. Fröhlich, K. M. Kulkarni, and L. L. Gourcy (2004), Stable isotope

evidence for moisture sources in the Asian summer monsoon under present and past climate

regimes, Geophysical Research Letters, 31, L08203.

Al-Mashaikhi, K., Oswald, S., Attinger, S., Büchel, G., Knöller, K., and Strauch, G. (2012),

Evaluation of groundwater dynamics and quality in the Najd aquifers located in the Sultanate of

Oman, Environmental Earth Sciences, 66, 1195–1211.

Al-Ruwaih, F. M., and Shehata, M. (2004), Hydrochemical processes and environmental

isotopic study of groundwater in Kuwait, Water International, 29, 158–166.

Andrews, J. N., Fontes, J. C., Aranyossy, J. F., Dodo, A., Edmunds, W. M., Joseph, A., and

Travi, Y. (1994), The evolution of alkaline groundwaters in the continental intercalaire aquifer of the

Irhazer Plain, Niger, Water Resources Research, 30, 45–61.

189

Annan, J. D., and Hargreaves, J. C. (2013), A new global reconstruction of temperature

changes at the Last Glacial Maximum. Climate of the Past, 9, 367–376.

Aragúas-Aragúas, L., K. Froehlich, and Rozanski, K. (1998), Stable isotope composition of

precipitation over Southeast Asia, Journal of Geophysical Research, 103, 721–742.

Arbuszewski, J. A., Cléroux, C., Bradtmiller, L., and Mix, A. (2013), Meridional shifts of the

Atlantic intertropical convergence zone since the Last Glacial Maximum, Nature Geoscience, 6, 959–

962.

Arslan, S., Yazicigil, H., Stute, M., and Schlosser, P. (2013), Environmental isotopes and

noble gases in the deep aquifer system of Kazan Trona Ore Field, Ankara, central Turkey and links

to paleoclimate, Quaternary Research, 79, 292–303.

Asmerom, Y., Polyak, V. J., and Burns, S. J. (2010), Variable winter moisture in the

southwestern United States linked to rapid glacial climate shifts, Nature Geoscience, 3, 114–117.

Ayalon, A., Bar-Matthews, M., Frumkin, A., and Matthews, A. (2013), Last Glacial warm

events on Mount Hermon: the southern extension of the Alpine karst range of the east

Mediterranean, Quaternary Science Reviews, 59, 43–56.

Back, W., Hanshaw, B. B., Plummer, L. N., Rahn, P. H., Rightmire, C. T., and Rubin, M.

(1983), Process and rate of dedolomitization: mass transfer and 14C dating in a regional carbonate

aquifer, Geological Society of America Bulletin, 94, 1415–1429.

Bar-Matthews, M., Ayalon, A., Gilmour, M., Matthews, A., and Hawkesworth, C. (2003),

Sea-land oxygen isotopic relationships from planktonic foraminifera and speleothems in the Eastern

Mediterranean region and their implication for paleorainfall during interglacial intervals, Geochimica et


190

Barbecot, F., Marlin, C., Gibert, E., and Dever, L. (2000), Hydrochemical and isotopic

characterisation of the Bathonian and Bajocian coastal aquifer of the Caen area (northern France),

Applied Geochemistry, 15, 791–805.

Beyerle, U., Rueedi, J., Leuenberger, M., Aeschbach‐Hertig, W., Peeters, F., Kipfer, R., and

Dodo, A. (2003), Evidence for periods of wetter and cooler climate in the Sahel between 6 and 40

kyr BP derived from groundwater, Geophysical Research Letters, 30, 1173.

Blaser, P. C., Kipfer, R., Loosli, H. H., Walraevens, K., Van Camp, M., and Aeschbach‐

Hertig, W. (2010), A 40 ka record of temperature and permafrost conditions in northwestern Europe

from noble gases in the Ledo‐Paniselian Aquifer (Belgium), Journal of Quaternary Science, 25, 1038–

1044.

Blunier, T., and Brook, E. J. (2001), Timing of millennial-scale climate change in Antarctica

and Greenland during the last glacial period. Science, 291, 109–112.

Bouchaou, L., Michelot, J. L., Vengosh, A., Hsissou, Y., Qurtobi, M., Gaye, C. B., Bullen, T.

D., and Zuppi, G. M. (2008), Application of multiple isotopic and geochemical tracers for

investigation of recharge, salinization, and residence time of water in the Souss–Massa aquifer,

southwest of Morocco, Journal of Hydrology, 352, 267–287.

Bouchaou, L. et al. (2009), Origin and residence time of groundwater in the Tadla basin

(Morocco) using multiple isotopic and geochemical tools, Journal of Hydrology, 379, 323–338.

Buiron, D. et al. (2011), TALDICE-1 age scale of the Talos Dome deep ice core, East

Antarctica, Climate of the Past, 7, 1–16.

191

Burchuladze, A. A., Chudy, M., Eristavi, I. V., Pagava, S. V., Povinec, P., Sivo, A., and

Togonidze, G. I. (1989), Anthropogenic 14C variations in atmospheric CO2 and wines, Radiocarbon,

31, 771–776.

Cai, Y., Tan, L., Cheng, H., An, Z., Edwards, R. L., Kelly, M. J., Kong, X., and Wang, X.

(2010), The variation of summer monsoon precipitation in central China since the last deglaciation,

Earth and Planetary Science Letters, 291, 21–31.

Caley, T., Roche, D. M., Renssen, H. (2014), Orbital Asian summer monsoon dynamics

revealed using an isotope-enabled global climate model, Nature Communications, 10, 105–148.

Castany, G., Marce, A., Margat, J., Moussu, H., Vuillaume, Y., and Evin, J. (1974), An

environmental isotope study of the groundwater regime in large aquifers. In: Isotope techniques in

groundwater hydrology 1974, Vol. I.

Celle-Jeanton, H., Huneau, F., Travi, Y., and Edmunds, W. M. (2009), Twenty years of

groundwater evolution in the Triassic sandstone aquifer of Lorraine: impacts on baseline water

quality. Applied Geochemistry, 24, 1198–1213.

Chen, X. Y., Bowler, J. M. and Magee, J. W. (1993). Late Cenozoic stratigraphy and

hydrologic history of Lake Amadeus, a central Australian playa, Australian Journal of Earth Sciences,

40, 1–14.

Chen, Z., Wei, W., Liu, J., Wang, Y., and Chen, J. (2011), Identifying the recharge sources

and age of groundwater in the Songnen Plain (Northeast China) using environmental isotopes.

Hydrogeology Journal, 19, 163–176.

Clark, I. D., and Fritz, P. (1997). Environmental isotopes in hydrogeology. CRC press.

192

Clark, J. F., Stute, M., Schlosser, P., Drenkard, S., and Bonani, G. (1997), A tracer study of

the Floridan aquifer in southeastern Georgia: Implications for groundwater flow and paleoclimate,

Water Resources Research, 33, 281-289.

Clark, J. F., Davisson, M. L., Hudson, G. B., and Macfarlane, P. A. (1998), Noble gases,

stable isotopes, and radiocarbon as tracers of flow in the Dakota aquifer, Colorado and Kansas,


Clark, P. U., Dyke, A. S., Shakun, J. D., Carlson, A. E., Clark, J., Wohlfarth, B., Mitrovica, J.

X., Hostetler, S. W., and McCabe, A. M. (2009), The last glacial maximum, Science, 325, 710–714.

Clark, P. U. et al. (2012), Global climate evolution during the last deglaciation, Proceedings of

the National Academy of Sciences, 109, E1134–E1142.

Cosford, J., Qing, H., Yuan, D., Zhang, M., Holmden, C., Patterson, W., and Hai, C. (2008),

Millennial-scale variability in the Asian monsoon: Evidence from oxygen isotope records from

stalagmites in southeastern China, Palaeogeography, Palaeoclimatology, Palaeoecology, 266, 3–12.

Cresswell, R., Wischusen, J., Jacobson, G., and Fifield, K. (1999), Assessment of recharge to

groundwater systems in the arid southwestern part of Northern Territory, Australia, using chlorine–

36, Hydrogeology Journal, 7, 393–404.

Cruz, F. W., Burns, S. J., Karmann, I., Sharp, W. D., Vuille, M., Cardoso, A. O., Ferrari, J.

A., Dias, P. L. S., and Viana, O. (2005), Insolation-driven changes in atmospheric circulation over the

past 116,000 years in subtropical Brazil. Nature, 434, 63–66.

Cruz, F. W. et al. (2009), Orbitally driven east–west antiphasing of South American

precipitation. Nature Geoscience, 2, 210–214.

193

Currell, M. J., Cartwright, I., Bradley, D. C., and Han, D. (2010). Recharge history and

controls on groundwater quality in the Yuncheng Basin, north China, Journal of Hydrology, 385, 216–

229.


Dansgaard, W., and Tauber, H. (1969), Glacier oxygen-18 content and Pleistocene ocean

temperatures, Science, 166, 499–502.

Darling, W. G. (2004), Hydrological factors in the interpretation of stable isotopic proxy data

present and past: a European perspective, Quaternary Science Reviews, 23, 743–770.

Darling, W. G. (2011), The isotope hydrology of quaternary climate change, Journal of Human

Evolution, 60, 417–427.

Darling, W. G., and Bath, A. H. (1988), A stable isotope study of recharge processes in the

English Chalk, Journal of Hydrology, 101, 31–46.

Darling, W. G., Edmunds, W. M., and Smedley, P. L. (1997), Isotopic evidence for

palaeowaters in the British Isles, Applied Geochemistry, 12, 813–829.

Dennis, F., Andrews, J. N., Parker, A., Poole, J. and Wolf, M. (1997), Isotopic and noble gas

study of Chalk groundwater in the London Basin, England. Applied Geochemistry, 12, 763–773.

Denniston, R. F., González, L. A., Asmerom, Y., Reagan, M. K., and Recelli-Snyder, H.

(2000), Speleothem carbon isotopic records of Holocene environments in the Ozark Highlands,

USA. Quaternary International, 67, 21–27.

194

Denniston, R. F., González, L. A., Asmerom, Y., Sharma, R. H., and Reagan, M. K. (2000),

Speleothem evidence for changes in Indian summer monsoon precipitation over the last ∼2300

years, Quaternary Research, 53, 196–202.

Derwich, L. J., Zouar, K., and Michelot, J. L. (2012), Recharge and paleorecharge of the deep

groundwater aquifer system in the Zeroud Basin (Kairouan plain, Central Tunisia), Quaternary

International, 257, 56–63.

Dodo, A., and Zuppi, G. M. (1997), Groundwater flow study in the Bilma–Djado Basin

(Niger) by means of environmental isotopes, Comptes Rendus de l'Academie des Sciences. Serie 2, Sciences de

la Terre et des Planetes, 30, 845–852.

Dodo, A., and Zuppi, G. M. (1999), Variabilité climatique durant le Quaternaire dans la

nappe du Tarat (Arlit, Niger), Comptes Rendus de l'Académie des Sciences–Series IIA–Earth and Planetary

Science, 328, 371–379.

Douglas, A. A., Osiensky, J. L., & Keller, C. K. (2007). Carbon–14 dating of ground water in

the Palouse Basin of the Columbia River basalts, Journal of Hydrology, 334, 502–512.

Dykoski, C. A., Edwards, R. L., Cheng, H., Yuan, D., Cai, Y., Zhang, M., Lin, Y., Qing, J.,

An, Z., and Revenaugh, J. (2005), A high-resolution, absolute-dated Holocene and deglacial Asian

monsoon record from Dongge Cave, China. Earth and Planetary Science Letters, 233, 71–86.

Edmunds, W. M. (2009), Palaeoclimate and groundwater evolution in Africa—implications

for adaptation and management. Hydrological Sciences Journal, 54, 781–792.

Edmunds, W. M. et al. (2003), Groundwater evolution in the Continental Intercalaire aquifer

of southern Algeria and Tunisia: trace element and isotopic indicators, Applied Geochemistry, 18, 805–

822.

195

Edmunds, W., and C. Milne (Eds.) (2001), Palaeowaters in coastal Europe: Evolution of

Groundwater since the late Pleistocene, Geol. Society Special. Publication, 189, Geological Society of

London, London.

Elliot, T., Andrews, J. N., and Edmunds, W. M. (1999), Hydrochemical trends,

palaeorecharge and groundwater ages in the fissured Chalk aquifer of the London and Berkshire

Basins, UK, Applied Geochemistry, 14, 333–363.

Emiliani, C. (1955), Pleistocene temperatures, The Journal of Geology, 538–578.

EPICA community members (2006), One-to-one coupling of glacial climate variability in

Greenland and Antarctica. Nature, 444, 195–198.

Fairbanks, R. G., Mortlock, R. A., Chiu, T. C., Cao, L., Kaplan, A., Guilderson, T. P.

Fairbanks, T. W., Bloom, A. L., Grootes, P. M., and Nadeau, M.-J. (2005), Radiocarbon calibration

curve spanning 0 to 50,000 years BP based on paired 230Th/234U/238U and 14C dates on pristine

corals, Quaternary Science Reviews, 24, 1781-1796.

Feng, W., Casteel, R. C., Banner, J. L., and Heinze-Fry, A. (2014), Oxygen isotope variations

in rainfall, drip-water and speleothem calcite from a well-ventilated cave in Texas, USA: Assessing a

new speleothem temperature proxy, Geochimica et Cosmochimica Acta, 127, 233–250.

Ferguson, G. A., Betcher, R. N., and Grasby, S. E. (2007), Hydrogeology of the Winnipeg

formation in Manitoba, Canada, Hydrogeology Journal, 15, 573–587.

Fleitmann, D. et al. (2009), Timing and climatic impact of Greenland interstadials recorded

in stalagmites from northern Turkey, Geophysical Research Letters, 36, L19707.

Frumkin, A., Ford, D. C., and Schwarcz, H. P. (1999), Continental oxygen isotopic record of

the last 170,000 years in Jerusalem, Quaternary Research, 51, 317–327.

196

Galego Fernandes, P., and Carreira, P. M. (2008), Isotopic evidence of aquifer recharge

during the last ice age in Portugal, Journal of Hydrology, 361, 291–308.

Gasse F. (2000), Hydrological changes in the African tropics since the Last Glacial

Maximum, Quaternary Science Reviews, 19, 189–211.

Gat, J.R., Mazor, E. and Tzur, Y., (1969), The stable isotope composition of mineral waters

in the Jordan Rift Valley, Journal of Hydrology, 7, 334–352.

Gates, J. B., Edmunds, W. M., Darling, W. G., Ma, J., Pang, Z., and Young, A. A. (2008),

Conceptual model of recharge to southeastern Badain Jaran Desert groundwater and lakes from

environmental tracers, Applied Geochemistry, 23, 3519–3534.

Geyh, M. A., and Sofner, B. (1989), Groundwater analysis of environmental carbon and

other isotopes from the Jakarta Basin Aquifer, Indonesia, Radiocarbon, 31, 919–925.

Gooddy, D. C., Darling, W. G., Abesser, C., and Lapworth, D. J. (2006), Using

chlorofluorocarbons (CFCs) and sulphur hexafluoride SF6 to characterise groundwater movement

and residence time in a lowland Chalk catchment, Journal of Hydrology, 330, 44–52.

Gosselin, D. C., Harvey, F. E., and Frost, C. D. (2001), Geochemical evolution of ground

water in the Great Plains (Dakota) Aquifer of Nebraska: implications for the management of a

regional aquifer system, Ground Water, 39, 98–108.

Gouvea da Silva, R. B. (1983), Estudo hidroquımico e isotópico das águas subterrâneas do

aquıfero Botucatu no estado de Sao Paulo, Ph. D. thesis, University of Sao Paulo, Sao Paulo, Brazil.

Grasby, S. E., and Chen, Z. (2005), Subglacial recharge into the Western Canada

Sedimentary Basin—Impact of Pleistocene glaciation on basin hydrodynamics, Geological Society of

America Bulletin, 117, 500–514.

197

Guendouz, A., Moulla, A. S., Edmunds, W. M., Shand, P., Poole, J., Zouari, K., and Mamou,

A. (1998), Palaeoclimatic information contained in groundwaters of the Grnad Erg Oriental,

northern Africa. In: Isotope Techniques in the study of Environmental Change, International Atomic

Energy Agency.

Hackley, K. C., Panno, S. V., and Anderson, T. F. (2010), Chemical and isotopic indicators

of groundwater evolution in the basal sands of a buried bedrock valley in the midwestern United

States: Implications for recharge, rock–water interactions, and mixing, Geological Society of America

Bulletin, 122, 1047–1066.

Harrington, G., Stelfox, L., Gardner, W. P., Davies, P., Doble, R., and Cook, P. G. (2011),

Surface water–groundwater interactions in the lower Fitzroy River, Western Australia, CSIRO

Publications.

Harrison, S. P., and Prentice, I. C. (2003), Climate and CO2 controls on global vegetation

distribution at the last glacial maximum: analysis based on palaeovegetation data, biome modelling

and palaeoclimate simulations, Global Change Biology, 9, 983–1004.

Heaton, T. H. E., Talma, A. S., and Vogel, J. C. (1986), Dissolved gas paleotemperatures and

18O variations derived from groundwater near Uitenhage, South Africa, Quaternary Research, 25, 79–

88.

Hoffmann, G., Werner, M., and Heimann, M. (1998), Water isotope module of the ECHAM

atmospheric general circulation model: A study on timescales from days to several years. Journal of

Geophysical Research: Atmospheres, 103, 16871–16896.

Holmgren, K., Lee-Thorp, J. A., Cooper, G. R., Lundblad, K., Partridge, T. C., Scott, L.,

Sithaldeen, R., Talma, A. S., and Tyson, P. D. (2003), Persistent millennial-scale climatic variability

over the past 25,000 years in Southern Africa, Quaternary Science Reviews 22, 2311–2326.

198

Hope, P. (2005), The Weather and Climate of Australia at the Last Glacial Maximum, Ph.D.

Dissertation, The University of Melbourne, 292 pp.

Huneau, F. et al. (2011), Flow pattern and residence time of groundwater within the south–

eastern Taoudeni sedimentary basin (Burkina Faso, Mali), Journal of Hydrology, 409, 423–439.

Jasechko, S., Birks, S. J., Gleeson, T., Wada, Y., Fawcett, P. J., Sharp, Z. D., McDonnell, J. J.

and Welker, J. M. (2014), The pronounced seasonality of global groundwater recharge. Water

Resources Research, doi: 10.1002/2014WR015809.

Jiráková, H., Huneau, F., Celle-Jeanton, H., Hrkal, Z., and Le Coustumer, P. (2009),

Palaeorecharge conditions of the deep aquifers of the Northern Aquitaine region (France), Journal of

Hydrology, 368, 1-16.

Jiráková, H., Huneau, F., Celle-Jeanton, H., Hrkal, Z., and La Coustumer, P. L. (2011),

Insights into palaeorecharge conditions for European deep aquifers, Hydrogeology Journal, 19, 1545–

1562.

Jones, I. C., Banner, J. L., and Humphrey, J. D. (2000), Estimating recharge in a tropical

karst aquifer, Water Resources Research, 36, 1289–1299.

Kaplan, G. (2013), Palynological analysis of the Late Pleistocene terrace deposits of Lake

Van, eastern Turkey: Reconstruction of paleovegetation and paleoclimate, Quaternary International,

292, 168–175.

Karro, E., Marandi, A., and Vaikmäe, R. (2004), The origin of increased salinity in the

Cambrian-Vendian aquifer system on the Kopli Peninsula, northern Estonia, Hydrogeology Journal, 12,

424–435.

199

Kawamura, K., et al. (2007), Northern Hemisphere forcing of climatic cycles in Antarctica

over the past 360,000 years. Nature, 448, 912–916.

Kreuzer, A. M., von Rohden, C., Friedrich, R., Chen, Z., Shi, J., Hajdas, I., Kipfer, R., and

Aeschbach–Hertig, W. (2009), A record of temperature and monsoon intensity over the past 40 kyr

from groundwater in the North China Plain, Chemical Geology, 259, 168–180.

Külls, C. (2000), Groundwater of the North–Western Kalahari, Namibia. Ph.D. Thesis,

Julius–Maximilian University of Würzburg.

Kulongoski, J. T., Hilton, D. R., and Selaolo, E. T. (2004), Climate variability in the

Botswana Kalahari from the late Pleistocene to the present day, Geophysical Research Letters, 31,

L10204.

Kumar, S. U., Sharma, S., Navada, S. V., and Deodhar, A. S. (2009), Environmental isotopes

investigation on recharge processes and hydrodynamics of the coastal sedimentary aquifers of

Tiruvadanai, Tamilnadu State, India, Journal of Hydrology, 364, 23–39.

Lachniet, M. S., Asmerom, Y., Burns, S. J., Patterson, W. P., Polyak, V. J., and Seltzer, G. O.

(2004), Tropical response to the 8200 yr BP cold event? Speleothem isotopes indicate a weakened

early Holocene monsoon in Costa Rica, Geology, 32, 957–960.

Lambeck, K., Yokoyama, Y., Johnston, P., and Purcell, A. (2000), Global ice volumes at the

Last Glacial Maximum and early Lateglacial, Earth and Planetary Science Letters, 181, 513–527.

Larsen, F., Owen, R., Dahlin, T., Mangeya, P., & Barmen, G. (2002). A preliminary analysis

of the groundwater recharge to the Karoo formations, mid–Zambezi basin, Zimbabwe, Physics and

Chemistry of the Earth, 27, 765–772.

200

Leaney, F. W., and Allison, G. B. (1986), Carbon–14 and stable isotope data for an area in

the Murray Basin: its use in estimating recharge. Journal of Hydrology, 88, 129–145.

Lee, J.-E., R. Pierrehumbert, A. Swann, and B. R. Lintner (2009), Sensitivity of stable water

isotopic values to convective parameterization schemes, Geophys. Res. Lett., 36, L23801,

Lee, J.-E., Risi, C., Fung, I., Worden, J., Scheepmaker, R. A., Lintner, B., and Frankenberg,

C. (2012), Asian monsoon hydrometeorology from TES and SCIAMACHY water vapor isotope

measurements and LMDZ simulations: Implications for speleothem climate record interpretation,

Journal of Geophysical Research, 117, D15112.

LeGrande, A. N., and Schmidt, G. A. (2009), Sources of Holocene variability of oxygen

isotopes in paleoclimate archives, Climate of the Past, 5, 1133–1162.

Lekshmy, P. R., Midhun, M., Ramesh, R., and Jani, R. A. (2014), 18O depletion in monsoon

rain relates to large scale organized convection rather than the amount of rainfall. Scientific Reports, 4.

Leng, M. J., and Marshall, J. D. (2004), Palaeoclimate interpretation of stable isotope data

from lake sediment archives, Quaternary Science Reviews, 23, 811–831.

Levin, N. E., Zipser, E. J. and Cerling, T. E. (2009), Isotopic composition of waters from

Ethiopia and Kenya: Insights into moisture sources for eastern Africa, Journal of Geophysical Research,

114, D23306.

Lewis, S. C., LeGrande, A. N., Kelley, M., and Schmidt, G. A. (2010), Water vapour source

impacts on oxygen isotope variability in tropical precipitation during Heinrich events, Climate of the

Past, 6, 325–343.

201

Lewis, S. C. et al. (2011). High-resolution stalagmite reconstructions of Australian–

Indonesian monsoon rainfall variability during Heinrich stadial 3 and Greenland interstadial 4, Earth

and Planetary Science Letters, 303, 133–142.

Lewis, S. C., LeGrande, A. N., Kelley, M. and Schmidt, G. A. (2013), Modeling insights into

deuterium excess as an indicator of water vapor source conditions, Journal of Geophysical Research, 118,

243–262.

Lisiecki, L. E., and Raymo, M. E. (2005), A Pliocene-Pleistocene stack of 57 globally

distributed benthic δ18O records, Paleoceanography, 20, PA1003.

Liu, X., Shen, J., Wang, S., Wang, Y., and Liu, W. (2007), Southwest monsoon changes

indicated by oxygen isotope of ostracode shells from sediments in Qinghai Lake since the late

Glacial, Chinese Science Bulletin, 52, 539–544.

Loosli, H. H. et al. (2001), Isotopic methods and their hydrogeochemical context in the

investigation of palaeowaters, Geological Society, London, Special Publications, 189, 193–212.

Maduabuchi, C., Faye, S., and Maloszewski, P. (2006), Isotope evidence of palaeorecharge

and palaeoclimate in the deep confined aquifers of the Chad Basin, NE Nigeria, Science of the total

environment, 370, 467–479.

Marcott, S. A., Shakun, J. D., Clark, P. U., and Mix, A. C. (2013), A reconstruction of

regional and global temperature for the past 11,300 years, Science 339, 1198–1201.

MARGO Members (2009), Constraints on the magnitude and patterns of ocean cooling at

the Last Glacial Maximum, Nature Geoscience, 2, 127–132.

202

McDermott, F., Mattey, D. P., and Hawkesworth, C. (2001), Centennial-scale Holocene

climate variability revealed by a high-resolution speleothem δ18O record from SW Ireland, Science,

294, 1328–1331.

McIntosh, J. C., Schlegel, M. E., and Person, M. (2012), Glacial impacts on hydrologic

processes in sedimentary basins: evidence from natural tracer studies, Geofluids, 12, 7–12.

Miller, G. H., Magee, J. W. and Jull, A. J. T. (1997). Low-latitude glacial cooling in the

Southern Hemisphere from amino-acid racemization in emu eggshells, Nature, 385, 241–244.

Morrissey, S. K., Clark, J. F., Bennett, M., Richardson, E., and Stute, M. (2010),

Groundwater reorganization in the Floridan aquifer following Holocene sea-level rise. Nature

Geoscience, 3, 683–687.

Nanson, G., Price, D. and Short, S. (1992). Wetting and drying of Australia over the past 300

ka, Geology, 20, 791–794.

New, M., Lister, D., Hulme, M., and Makin, I. (2002), A high-resolution data set of surface


Noseck, U. et al. (2009), Carbon chemistry and groundwater dynamics at natural analogue

site Ruprechtov, Czech Republic: insights from environmental isotopes, Applied Geochemistry, 24,

1765–1776.

O'Brien, G. R., Kaufman, D. S., Sharp, W. D., Atudorei, V., Parnell, R. A., and Crossey, L. J.

(2006), Oxygen isotope composition of annually banded modern and mid-Holocene travertine and

evidence of paleomonsoon floods, Grand Canyon, Arizona, USA, Quaternary Research, 65, 366–379.

O'Neil, J. R., Clayton, R. N., and Mayeda, T. K. (1969), Oxygen isotope fractionation in

divalent metal carbonates, The Journal of Chemical Physics, 51, 5547–5558.

203

Otto-Bliesner, B. L., Brady, E. C., Clauzet, G., Tomas, R., Levis, S., and Kothavala, Z.

(2006), Last glacial maximum and Holocene climate in CCSM3, Journal of Climate, 19, 2526–2544.

Partin, J. W., Cobb, K. M., Adkins, J. F., Clark, B., and D. P. Fernandez (2007), Millennial-

scale trends in west Pacific warm pool hydrology since the Last Glacial Maximum, Nature, 449, 452–

455.

Patterson, L. J. et al. (2005), Cosmogenic, radiogenic, and stable isotopic constraints on

groundwater residence time in the Nubian Aquifer, Western Desert of Egypt, Geochemistry, Geophysics,

Geosystems, 6.

Pausata, F. S., Battisti, D. S., Nisancioglu, K. H., and Bitz, C. M. (2011), Chinese stalagmite

δ18O controlled by changes in the Indian monsoon during a simulated Heinrich event. Nature

Geoscience, 4, 474–480.

Pedro, J. B., Van Ommen, T. D., Rasmussen, S. O., Morgan, V. I., Chappellaz, J., Moy, A.

D., Masson-Delmotte, V. and Delmotte, M. (2011), The last deglaciation: timing the bipolar seesaw.

Climate of the Past, 7, 671–683.

Phillips, F. M., Peeters, L. A., Tansey, M. K., and Davis, S. N. (1986), Paleoclimatic

inferences from an isotopic investigation of groundwater in the central San Juan Basin, New Mexico,

Quaternary Research, 26, 179–193.

Plummer, L. N. (1993). Stable isotope enrichment in paleowaters of the southeast Atlantic

Coastal Plain, United States, Science, 262, 2016–2020.

Plummer, L. N., Bexfield, L. M., Anderholm, S. K., Sanford, W. E., and Busenberg, E.

(2004), Hydrochemical tracers in the middle Rio Grande Basin, USA: 1. Conceptualization of

groundwater flow, Hydrogeology Journal, 12, 359–388.

204

Powers, L. A., Johnson, T. C., Werne, J. P., Castañeda, I. S., Hopmans, E. C., Sinninghe

Damsté, J. S., and Schouten, S. (2005), Large temperature variability in the southern African tropics

since the Last Glacial Maximum, Geophysical Research Letters, 32, L08706.

Rahube, T. B. (2003), Recharge and groundwater resources evaluation of the Lokalane–

Ncojane Basin (Botswana) using numerical modelling. MSc Thesis, International Institute for

Geoinformation Science and Earth Observation, Enschede, Netherlands, 104.

Risi, C., S. Bony, F. Vimeux, L. Descroix, B. Ibrahim, E. Lebreton, I. Mamadou, and B.

Sultan (2008), What controls the isotopic composition of the African monsoon precipitation?

Insights from event-based precipitation collected during the 2006 AMMA field campaign, Geophysical

Research Letters, 35, L24808.

Risi, C., Bony, S., Vimeux, F., and Jouzel, J. (2010a), Water‐stable isotopes in the LMDZ4

general circulation model: Model evaluation for present‐day and past climates and applications to

climatic interpretations of tropical isotopic records, Journal of Geophysical Research: Atmospheres, 115,

D12118.

Risi, C., Bony, S., Vimeux, F., Frankenberg, C., Noone, D., and Worden, J. (2010b),

Understanding the Sahelian water budget through the isotopic composition of water vapor and

precipitation, Journal of Geophysical Research: Atmospheres, 115, D24110.

Robinson, B. W., and Gunatilaka, A. (1991), Stable isotope studies and the hydrological

regime of sabkhas in southern Kuwait, Arabian Gulf, Sedimentary Geology, 73, 141–159.

Roboucas, A. C., and Santiago, M. F. (1989), 14C analyses of groundwater from the Botucatu

aquifer system in Brazil, Radiocarbon, 31, 926–935.

205

Roden, J. S., and Ehleringer, J. R. (1999), Hydrogen and oxygen isotope ratios of tree-ring

cellulose for riparian trees grown long-term under hydroponically controlled environments, Oecologia,

121, 467–477.

Rozanski, K. (1985). Deuterium and oxygen-18 in European groundwaters—links to

atmospheric circulation in the past, Chemical Geology 52, 349–363.

Sagawa, T., Yokoyama, Y., Ikehara, M., and Kuwae, M. (2011), Vertical thermal structure

history in the western subtropical North Pacific since the Last Glacial Maximum, Geophysical Research

Letters, 38, L00F02.

Salati, E., Menezes Leal, J., and Mendes Campos, M. (1974), Environmental isotopes used in

a hydrogeological study of northeastern Brazil, In: Isotope techniques in groundwater hydrology

1974, Vol. I. 379–398.

Salati, E., Dall'Olio, A., Matsui, E., and Gat, J. R. (1979), Recycling of water in the Amazon

basin: an isotopic study, Water Resources Research, 15, 1250–1258.

Samborska, K., Różkowski, A., and Małoszewski, P. (2013), Estimation of groundwater

residence time using environmental radioisotopes (14C, T) in carbonate aquifers, southern Poland,

Isotopes in Environmental and Health Studies, 49, 73-97.

Samuels-Crow, K. E., Galewsky, J., Hardy, D. R., Sharp, Z. D., Worden, J., and Braun, C.

(2014), Upwind convective influences on the isotopic composition of atmospheric water vapor over

the tropical Andes, Journal of Geophysical Research – Atmospheres, 119, 7051–7063,

Schefuß, E., Kuhlmann, H., Mollenhauer, G., Prange, M., and Pätzold, J. (2011), Forcing of

wet phases in southeast Africa over the past 17,000 years, Nature, 480, 509–512.

206

Schlegel, M. E., Mayo, A. L., Nelson, S., Tingey, D., Henderson, R., and Eggett, D. (2009),

Paleo-climate of the Boise area, Idaho from the last glacial maximum to the present based on

groundwater δ2H and δ18O compositions, Quaternary Research, 71, 172–180.

Schrag, D. P., Hampt, G.,,and Murray, D. W. (1996), Pore fluid constraints on the

temperature and oxygen isotopic composition of the glacial ocean, Science, 272, 1930–1932.

Schrag, D. P., Adkins, J. F., McIntyre, K., Alexander, J. L., Hodell, D. A., Charles, C. D., and

McManus, J. F. (2002), The oxygen isotopic composition of seawater during the Last Glacial

Maximum, Quaternary Science Reviews, 21, 331–342.

Shah, A. M., Morrill, C., Gille, E. P., Gross, W. S., Anderson, D. M., Bauer, B. A. Buckner,

R. and Hartman, M. (2013), Global speleothem oxygen isotope measurements since the Last Glacial

Maximum, Dataset papers in Geosciences, 9 pp.

Shakun, J. D., and Carlson, A. E. (2010), A global perspective on Last Glacial Maximum to

Holocene climate change, Quaternary Science Reviews, 29, 1801–1816.

Shakun, J. D., Burns, S. J., Fleitmann, D., Kramers, J., Matter, A., and Al-Subary, A. (2007),

A high-resolution, absolute-dated deglacial speleothem record of Indian Ocean climate from Socotra

Island, Yemen, Earth and Planetary Science Letters, 259, 442–456.

Siegel, D. I. (1991), Evidence for dilution of deep, confined ground water by vertical

recharge of isotopically heavy Pleistocene water, Geology, 19, 433–436.

Stadler, S., Geyh, M. A., Ploethner, D., and Koeniger, P. (2012), The deep Cretaceous

aquifer in the Aleppo and Steppe basins of Syria: assessment of the meteoric origin and geographic

source of the groundwater, Hydrogeology Journal, 20, 1007–1026.

207

Stotler, R. L., Frape, S. K., Ruskeeniemi, T., Pitkänen, P., and Blowes, D. W. (2012), The

interglacial–glacial cycle and geochemical evolution of Canadian and Fennoscandian Shield

groundwaters, Geochimica et Cosmochimica Acta, 76, 45–67.

Stute, M., and Deak, J. (1989), Environmental isotope study 14C, 13C, 18O, D, noble gases on

deep groundwater circulation systems in Hungary with reference to paleoclimate, Radiocarbon, 31,

902–918.

Stute, M., Clark, J. F., Schlosser, P., Broecker, W. S., and Bonani, G. (1995a), A 30,000 yr

continental paleotemperature record derived from noble gases dissolved in groundwater from the

San Juan Basin, New Mexico, Quaternary Research, 43, 209–220.

Stute, M., Forster, M., Frischkorn, H., Serejo, A., Clark, J. F., Schlosser, P., Broecker, W. S.,

and Bonani, G. (1995b), Cooling of tropical Brazil (5°C) during the Last Glacial Maximum, Science,

269, 379–379.

Sukhija, B. S., Reddy, D. V., and Nagabhushanam, P. (1998), Isotopic fingerprints of

paleoclimates during the last 30,000 years in deep confined groundwaters of Southern India,

Quaternary Research, 50, 252–260.

Sultan, M., Sturchio, N., Hassan, F. A., Hamdan, M. A. R., Mahmood, A. M., Alfy, Z. E.,

and Stein, T. (1997), Precipitation source inferred from stable isotopic composition of Pleistocene

groundwater and carbonate deposits in the Western desert of Egypt, Quaternary Research, 48, 29–37.

Swarzenski, P. W., Baskaran, M., Rosenbauer, R. J., Edwards, B. D., and Land, M. (2013), A

combined radio–and stable–isotopic study of a California coastal aquifer system. Water, 5, 480–504.

208

Tamers M. A. (1967), Radiocarbon ages of groundwater in an arid zone unconfined aquifer,

In: Stout G. E. (ed.), Isotope techniques in the hydrologic cycle. American Geophysical Union

Monograph 11, 143–152.

Thatcher, L., Rubin, M., and Brown, G. F. (1961), Dating desert groundwater, Science, 134,

105–106.

Thompson, L. G., Mosley-Thompson, E., Davis, M. E., Lin, P. N., Henderson, K. A., Cole-

Dai, J., Bolzan, J. F., and Liu, K. B. (1995), Late glacial stage and Holocene tropical ice core records

from Huascaran, Peru. Science, 269, 46–50.

Thompson, L. O., Yao, T., Davis, M. E., Henderson, K. A., Mosley-Thompson, E., Lin, P.

N., Beer, J., Synal, H.-A., Cole-Dai, J., and Bolzan, J. F. (1997), Tropical climate instability: The last

glacial cycle from a Qinghai-Tibetan ice core. Science, 276, 1821–1825.

Thompson, L. G. et al. (1998), A 25,000-year tropical climate history from Bolivian ice cores.

Science, 282, 1858–1864.

Tierney, J. E., Russell, J. M., Huang, Y., Damsté, J. S. S., Hopmans, E. C., and Cohen, A. S.

(2008), Northern hemisphere controls on tropical southeast African climate during the past 60,000

years, Science, 322, 252–255.

Tierney, J. E., Smerdon, J. E., Anchukaitis, K. J., and Seager, R. (2013), Multidecadal

variability in East African hydroclimate controlled by the Indian Ocean, Nature, 493, 389-392.

Varsányi, I., Palcsu, L., and Kovács, L. Ó. (2011), Groundwater flow system as an archive of

palaeotemperature: Noble gas, radiocarbon, stable isotope and geochemical study in the Pannonian

Basin, Hungary, Applied Geochemistry, 26, 91–104.

209

Veizer, J., et al. (1999). 87Sr/86Sr, δ13C and δ18O evolution of Phanerozoic seawater. Chemical

Geology, 161, 59–88.

Vinther, B. M. et al. (2006), A synchronized dating of three Greenland ice cores throughout

the Holocene, Journal of Geophysical Research, 111, D13102.

Vinther, B. M., Clausen, H. B., Fisher, D. A., Koerner, R. M., Johnsen, S. J., Andersen, K.

K., Dahl-Jensen, D., Rasmussen, S. O., Steffensen, J. P., and Svensson, A. M. (2008), Synchronizing

ice cores from the Renland and Agassiz ice caps to the Greenland Ice Core Chronology, Journal of

Geophysical Research: Atmospheres, 113, D08115.

von Grafenstein, U., Erlenkeuser, H., Brauer, A., Jouzel, J., and Johnsen, S. J. (1999), A Mid-

European Decadal Isotope-Climate Record from 15,500 to 5000 Years B.P., Science, 284, 1654–1657.

Vuille, M., and Werner, M. (2005), Stable isotopes in precipitation recording South American

summer monsoon and ENSO variability: observations and model results, Climate Dynamics, 25, 401–

413.

Wagner, J. D. M., Cole, J. E., Beck, J. W., Patchett, P. J., Henderson, G. M., and Barnett, H.

R. (2010), Moisture variability in the southwestern United States linked to abrupt glacial climate

change, Nature Geoscience 3, 110–113.

Wang, Y. J., Cheng, H., Edwards, R. L., An, Z. S., Wu, J. Y., Shen, C-C., and Dorale, J. A.

(2001), A high-resolution absolute-dated late pleistocene monsoon record from Hulu Cave, China,

Science, 294, 2345–2348.

Wang, X. F. et al. (2007), Millennial-scale precipitation changes in southern Brazil over the

past 90,000 years, Geophysical Research Letters, 34, L23701.

210

Werner, M., Langebroek, P. M., Carlsen, T., Herold, M., and Lohmann, G. (2011), Stable

water isotopes in the ECHAM5 general circulation model: Toward high‐resolution isotope modeling

on a global scale, Journal of Geophysical Research: Atmospheres, 116, D15109.

Weyhenmeyer, C. E., Burns, S. J., Waber, H. N., Aeschbach-Hertig, W., Kipfer, R., Loosli,

H. H., and Matter, A. (2000), Cool glacial temperatures and changes in moisture source recorded in

Oman groundwaters, Science, 287, 842–845.

Weyhenmeyer, C. E., Burns, S. J., Waber, H. N., Macumber, P. G., and Matter, A. (2002),

Isotope study of moisture sources, recharge areas, and groundwater flow paths within the eastern

Batinah coastal plain, Sultanate of Oman, Water Resources Research, 38, 1184.

Williams J.W. (2003), Variations in tree cover in North America since the last glacial

maximum, Global Planetary Change, 35, 1–23.

Williams, M., Prescott, J. R., Chappell, J., Adamson, D., Cock, B., Walker, K. and Gell, P.

(2001). The enigma of a late Pleistocene wetland in the Flinders Ranges, South Australia, Quaternary

International, 83-85, 129–144.

Williams, P. W., Neil, H. L., and Zhao, J. X. (2010), Age frequency distribution and revised

stable isotope curves for New Zealand speleothems: palaeoclimatic implications. International Journal of

Speleology, 39, 99–112.

Winckel, A., Marlin, C., Dever, L., Morel, J., Morabiti, K., Makhlouf, M. B., and Chalouan,

A. (2002), Recharge altitude estimation of thermal springs using stable isotopes in Morocco, Comptes

Rendus Geoscience, 334, 469–474.

211

Yang, Y., Yuan, D., Cheng, H., Zhang, M., Qin, J., Lin, Y., XiaoYan, Z. and Edwards, R. L.

(2010), Precise dating of abrupt shifts in the Asian Monsoon during the last deglaciation based on

stalagmite data from Yamen Cave, Guizhou Province, China, Science China Earth Sciences, 53, 633–641.

Yechieli, Y., Kafri, U., and Sivan, O. (2009), The inter–relationship between coastal sub–

aquifers and the Mediterranean Sea, deduced from radioactive isotopes analysis. Hydrogeology Journal,

17, 265–274.

Yoshimura, K., Oki, T., Ohte, N., and Kanae, S. (2003), A quantitative analysis of short‐term

18O variability with a Rayleigh‐type isotope circulation model, Journal of Geophysical Research:

Atmospheres, 108, 4647.

Yuan, D. et al. (2004), Timing, Duration, and Transitions of the Last Interglacial Asian

Monsoon, Science, 23, 575–578.

Zongyu, C., Jixiang, Q., Jianming, X., Jiaming, X., Hao, Y., and Yunju, N. (2003),

Paleoclimatic interpretation of the past 30 ka from isotopic studies of the deep confined aquifer of

the North China plain, Applied Geochemistry, 18, 997–1009.

Zuber, A., Weise, S. M., Osenbrück, K., Pajnowska, H., and Grabczak, J. (2000), Age and

recharge pattern of water in the Oligocene of the Mazovian basin (Poland) as indicated by

environmental tracers, Journal of Hydrology, 233, 174–188.

Zuber, A., Weise, S. M., Motyka, J., Osenbrück, K., and Różański, K. (2004), Age and flow

pattern of groundwater in a Jurassic limestone aquifer and related Tertiary sands derived from

combined isotope, noble gas and chemical data, Journal of Hydrology, 286, 87–112.

212

CHAPTER 4 — THE ISOTOPE HYDROLOGY OF UGANDA

4.1 Abstract

In the final chapter of this dissertation I present the results of a comprehensive sampling

campaign and stable O and H isotopic investigation of Ugandan rivers, lakes, wetlands and

groundwaters. Surface- and ground-waters in Uganda have δ18O values ranging between −4.0 ‰ and

+8.7 ‰, δ2H values ranging between −13.2 ‰ and +55.5 ‰, and deuterium excess values ranging

between −17 ‰ and +22‰. The highest δ18O and δ2H values and lowest deuterium excess values

are found in Ugandan lakes; whereas, the lowest δ18O and δ2H values and highest deuterium excess

values are found in springs and river waters sourced from the Rwenzori mountains of southwest

Uganda. I analyze the isotopic composition of lake waters using a stable-isotope-mass-balance to

calculate the fraction of evaporation as a proportion of water inputs to 24 lakes. I show that a sample

of lake water analyzed for O and H isotopes, coupled to the application of a stable-isotope-mass-

balance, can rapidly delineate well flushed (low evaporation/input ratio) and terminal

(evaporation/input ratio of close to 1) lake systems.

4.2 Introduction

In Uganda, 70% of the 35 million people living there have access to an improved water

source, ranking Uganda 148 out of 179 nations reporting in 2010 (Millennium Development Goals

Indicators). Groundwater is the primary drinking water source for 80% of Ugandans and cultivated

lands cover one third of the country, highlighting the importance of agriculture (usually rain-fed) to

the Ugandan economy. Lake Victoria, located in southeastern Uganda, sustains a commercial fishery,

supports over-lake transportation, feeds municipal water supplies and generates hydroelectric power

at the Kiira and Nalubaale power stations near Jinja.

Uganda is a landlocked nation. It is situated in the humid tropics of east Africa, bordered by

Sudan (north), Kenya (east), Tanzania (south), Rwanda (southwest) and the Democratic Republic of

213

the Congo (west). The country is constrained between 1°S and 4°N latitude and 29°E to 35°E

longitude and covers a total of 240,000 km2. Land surface elevations range between 600 to 5,200

meters above sea level, with 80% of the country resting between 900 and 1,500 meters above sea

level. The majority of land surfaces have been converted to rain-fed croplands. Natural Ugandan

vegetation that remains intact includes closed canopy forests of the relatively-wet southwest and

open shrublands of the relatively-arid northwest. Regolith depths can extend to depths of 30m below

groundwater surfaces, and the bedrock geology includes endogenous granulites in central Uganda

and metasedimentary rocks along the western arm of the east African rift system, located in western

Uganda (Taylor and Howard, 1998a; 1998b; 2000).

The hydrography of Uganda ranges from flashy systems in the steep mountainous systems

of western Uganda, to slow-flowing and vast wetlands in the subdued relief in central Uganda, to

semi-arid ephemeral flow systems on the westward slopes of Mt. Kenya and other mountains in

northeastern Uganda. Annual rainfall ranges from minimums of ~700 mm per year in the northeast

to maximums of ~1,500 mm per year in the southwest. Lake Victoria – the second largest area of

fresh surface water on Earth – borders Uganda’s southeastern margins and its outflow generates the

headwaters of the White Nile. Uganda’s drainage system is dominated by two flow systems: (i)

drainage into Lake Victoria and Lake Kyoga, and (ii) drainage into the rift lakes of western Uganda

(i.e., Lakes George, Edward, and Albert). The two drainage systems converge at the northern margin

of Lake Albert before flowing northward into Sudan.

Stable isotope investigations of Ugandan waters have targeted improved knowledge of

groundwaters (Taylor and Howard, 1996; 1998a; Tindimugaya et al., 2007), surface waters (Russell

and Johnson, 2006) and geothermal systems (Kato, 2000; Bahati et al., 2005). Groundwater stable

isotopes have revealed that recharge occurs almost exclusively during the rainy season (April to

October) and that recharge is at a maximum during high intensity rainfall events (Taylor and Howard

214

1996; 1998)). Coupled hydrology-geomorphology investigations have shown that the regolith and

subsurface bedrock are hydraulically-linked, that the regolith is more permeable than bedrock

aquifers, and that this weathered mantle hosts a more active hydrosphere than underlying fractured

bedrock systems (Taylor and Howard, 1996; 1998a; 2000). Annual groundwater recharge rates are

spatially variable. Paired watershed studies show that recharge at each catchment varies ten-fold with

recharge rates of 20 to 200 mm/year in each respective catchment (Taylor and Howard, 1998a).

Groundwater ages calculated using chlorofluorocarbons and tritium show that modern groundwater

with a mean age of less than 50 years exists at depths of more than 60 meters below ground level,

suggesting that certain shallow aquifers are well-flushed (Tindimugaya et al., 2007). Other isotope

based investigations have quantified over-lake evaporation from Lake Edward using a stable isotope

based approach (Russell and Johnson, 2006) and assessed geothermal activity (Kato, 2000; Bahati et

al., 2005).

The primary objective of this study is to quantify water fluxes into and out of Ugandan

surface waters via a stable isotope mass balance.


4.3.1 Sample collection and analysis

Samples of water were collected in high-density polyethylene bottles over a three week

sampling campaign in July of 2013. Water samples were collected from rivers, lakes, wetlands, springs

and groundwater wells throughout Uganda. Two tiers of samples were collected: tier 1, where waters

were sampled for analysis of 18O/16O and 2H/1H ratios, and tier 2, where waters were sampled for

18O/16O and 2H/1H ratios, concentrations of Ca2+, Mg2+, K+, Na+, Cl-, SO42- and 43 other solutes.

Tier 2 water samples (n = 45) were filtered through a 0.45 micron filter in the field. One sample was

preserved with ultrapure nitric acid (major cations, trace metals and uranium series elements) and

another was left without acidification (major anions). Chemical analyses were completed at the

215

University of New Mexico’s Analytical Chemistry Laboratory. Stable oxygen and hydrogen isotopic

compositions of water samples were analyzed at the University of New Mexico using a Picarro

L1102-i liquid water analyzer.

The three week field campaign led to the collection of 225 water samples. Because of the

short duration of our field trip we specifically targeted water bodies expected to integrate prolonged

residence times such as lakes (n = 36), groundwater wells (n = 75) and springs (n = 13). We also

sampled streams (n = 67), tap water (n = 22) and swamps (n = 12) throughout Uganda where

sampling opportunities arose.

4.3.2 Surface waters – evaporation modelling

We use the stable O and H isotopic data of lake waters to calculate evaporation losses from

each system. The approach taken has been described in numerous studies and readers are directed to

these earlier works for additional descriptions (Zuber, 1983; Gonfiantini, 1986; Gat et al., 1996;

Gibson, 2002; Gibson and Edwards, 2002; Gibson et al., 1996; 1998; 2002; Froehlich, 2000; Russell

and Johnson, 2006; Horita et al., 2008; Yi et al., 2008; Brock et al., 2009; Turner et al., 2010; 2014).

The calculation of lake water balances via a stable-isotope-based approach couples a hydrologic

(Equation 4.1) and isotopic (Equation 4.2) mass balance under an assumption of steady state:

𝐼 = 𝐸 + 𝑄 Equation 4.1

𝐼𝛿𝐼 = 𝐸𝛿𝐸 + 𝑄𝛿𝑄 Equation 4.2

where I, E and Q are the fluxes of water entering the lake (I), evaporation from the lake (E) and

liquid outflow from the lake via surface or groundwater discharges (Q), and δ denotes the isotopic

composition of each flux. Combining equations 4.1 and 4.2 yields an estimate of the evaporation flux

as a proportion of water inputs to each lake (i.e., evaporation/input ratio: E/I; Equation 4.3)

216

𝐸

𝐼=

𝛿𝐼−𝛿𝐿𝑎𝑘𝑒

𝛿𝐸−𝛿𝐿𝑎𝑘𝑒 Equation 4.3

assuming liquid outflows from the lake have the same isotopic composition as the bulk lake (i.e., well

mixed assumption: δQ = δLake). The isotopic composition of water inputs to lakes is estimated as the

intercept of a regression of lake O and H isotopic compositions (“local evaporation line: LEL;”

Figure 4-1) and a regression of Ugandan rainfall (“meteoric water line: MWL;” Figure 4-1). We find

that the isotopic composition of input waters to each lake is between δ18O values of −2.1 ‰ to +0.0

‰ and δ2H input values between −3.5 ‰ and +9.8 ‰ (maximum and minimum intercepts of 95th

percent confidence interval regressions of meteoric waters (MWL) and lake water (LEL)). Annual

evaporation rates (i.e., mm/year, rather than E/I ratios) can be calculated by rearranging equations

4.1 and 4.2 in cases where the liquid outflow from the lake is gauged (Jasechko et al., 2014):

𝐸 = 𝑄 ×𝛿𝐼−𝛿𝐿𝑎𝑘𝑒

𝛿𝐸−𝛿𝐼 Equation 4.4

The isotopic composition of evaporate is estimated applying an evaporation model (Craig

and Gordon, 1965):

𝛿𝐸 =(𝛿𝐿𝑎𝑘𝑒−[𝛼𝑙∙𝑣

∗−1])/𝛼𝑙∙𝑣∗−ℎ𝛿𝐴−(𝐶𝑘[1−ℎ])

1−ℎ+(𝐶𝑘[1−ℎ]) Equation 4.5

where 𝛼𝑙∙𝑣∗ represents a temperature-dependent equilibrium isotope fractionation factor (Horita and

Wesolowski, 1994), h represents the relative humidity near to the lake surface (derived from New et

al., 2002), δA represents the isotopic composition of the atmosphere (calculated as δA = δP – (𝛼𝑙∙𝑣∗−

1; Gibson et al., 2002) and CK is a constant that describes kinetic isotope effect during evaporation

(CK is 13.7 to 20.7 for δ18O-based calculations and 7.5 to 16.1 for δ2H based calculations; Jasechko et

al., 2014).

217

4.4 Results

4.4.1 Stable O and H isotopic composition of Ugandan waters

The isotopic composition of Ugandan surface- and ground-waters sampled in this study

range from −4.0 ‰ to +8.7 ‰ in δ18O, −13.2 ‰ to +55.5 ‰ in δ2H, and −16.7 ‰ to +21.9 ‰ in

deuterium excess (Figure 4-1; Table 4-1; Table 4-2). Monthly precipitation samples (n = 267)

collected at Entebbe (Uganda, n = 182), Soroti (Uganda, n = 11), Jinja, (Uganda, n = 27), Masaka

(Uganda, n = 20), Wobulenzi (Uganda, n = 8) and Kericho (western Kenya, n = 19) between 1960

and 2010 by the International Atomic Energy Agency (e.g., Araguás-Araguás et al., 2002) range from

−11.6 ‰ to +11.4 ‰ in δ18O, −81.2 ‰ to +69.0 ‰ in δ2H, and −22.1 ‰ to +27.1 ‰ in deuterium

excess, and plot along a regression – herein the “Ugandan meteoric water line” – of δ2H =

7.21(±0.13)×δ18O + 10.76(±0.40) (uncertainties are standard error of regression; Figure 4-1; Table

1). Water sampling locations are shown in Figure 4-2.

218

Figure 4-1. The O and H isotopic composition of Ugandan waters. Different symbols mark unique

water types, including groundwaters (squares), rivers (circles) and lakes (triangles). Black lines mark

linear regressions of meteoric waters (MWL) and lakes (LEL; grey funnel plot marks the 95th percent

confidence interval of regressions).

Lakes have the highest δ18O and δ2H values and the lowest deuterium excess values of each

of the sample groups. Lakes plot near to groundwater in some cases, but also plot along a trajectory

“beneath” meteoric waters in δ18O-δ2H space (Figure 4-1). A regression of the lake data gives a

δ2H/δ18O slope of 5.13±0.13, significantly shallower than a regression of meteoric waters (δ2H/δ18O

slope of 7.21±0.13).

River and wetlands have oxygen and hydrogen isotopic compositions that are similar to

Ugandan rainfall in most cases. A subset of river samples have deuterium excess values of less than

zero and plot close to lakes sampled in this study (10 of 67 river water samples, 15%). Some river

219

samples having deuterium excess values of less than zero were sampled downstream of large lakes

(e.g., outflows of Lakes Victoria and Lake Kachera). Perennial wetlands are common in central

Uganda near to Lake Kyoga. Isotopic analysis of wetland samples shows that most samples plot

along the Ugandan meteoric water line near to groundwater samples.

Groundwater samples collected from wells (n = 75) plot near to the Ugandan rainfall in

most cases in δ2H-δ18O space. Groundwater samples have deuterium excess values that are similar to

Ugandan rainfall (average deuterium excess values of 10.2 ‰ for groundwater and 12.3 ‰ for

rainfall). Six of our 75 groundwater samples (8 % of all groundwater samples) have a deuterium

excess value of less than zero, similar to Ugandan lakes (average deuterium excess of −1.3 ‰; Table

4-1).

Springs and tap waters have δ18O and δ2H values that fall within the range of precipitation in

Uganda. Springs have a deuterium excess value that is similar to rainfall (average of 15.5 ‰). The

lowest δ18O value observed in our dataset (−4.0 ‰) is a hot spring sample from the foothills of the

Rwenzori Mountains collected at an elevation of 1600 meters above sea level. The sources of tap

water were unknown in most cases. Tap waters are found to have stable O and H isotopic

compositions that fall within the range of groundwaters and surface waters collected in this study.

220

Table 4-1. The isotopic composition of Ugandan waters

Sample type n δ18O (‰) δ2H (‰) d-excess (‰)

Avg. s.d. Avg. s.d. Avg. s.d.

Groundwater 75 −0.5 1.9 +6.1 10.2 +10.2 6.0

Lakes 36 +3.4 2.6 +25.9 13.3 −1.3 7.6

Rivers 67 −0.5 2.4 +5.8 12.7 +10.0 7.6

Springs 13 −2.0 1.7 −0.3 9.3 +15.5 4.7

Swamp water 12 −0.3 2.0 +8.2 12.6 +10.5 6.5

Tap water 22 +0.1 2.5 +8.8 12.8 +7.9 7.5

Rainfall * 267 −2.0 2.4 −3.6 17.7 +12.3 5.3

* rainfall statistics from the combined precipitation datasets collected at Entebbe, Soroti, Jinja,

Masaka, Wobulenzi and Kericho; data obtained from the International Atomic Energy Agency’s

Water Resources Programme: www.iaea.org/water.

221

Figure 4-2. Locations of water samples collected in Uganda: groundwater (yellow squares), crater

lakes (red circles), other lakes (blue circles), rivers (blue triangles), springs (black triangles), wetlands

(green circles), tap waters (small dots).

222

Table 4-2. The isotopic composition and electrical conductivity of Ugandan water samples

ID Type Lat. [°]

Lon. [°]

Alt. [m]

EC [μs/cm]

T

[⁰C]

δ18O [‰ SMOW]

δ2H [‰ SMOW]

d−excess

T01 Tap 0.1 32.5 399 105 25.6 −0.24 5.9 7.9

T02 Tap 0.3 32.6 1187 132 26.3 1.88 16.6 1.6

T03 Tap 0.0 32.0 1166 −3.15 −9.9 15.4

T04 Tap −0.3 31.8 1250 −2.93 −10.2 13.2

T05 Tap 0.1 32.5 1188 3.53 26.7 −1.6

T06 Tap −0.6 31.0 1279 0.96 10.1 2.5

T07 Tap −0.5 30.7 1466 144 26.4 −1.64 0.6 13.7

T08 Tap −1.3 30.0 1888 265 17.5 3.94 27.9 −3.7

T09 Tap −0.2 30.0 980 −2.64 −3.5 17.6

T10 Tap 0.6 31.4 1286 173 29.4 0.05 6.9 6.5

T11 Tap 0.4 32.7 1196 129 24.9 3.16 25.3 0.1

T12 Tap 0.5 33.3 1195 103 26.3 3.54 26.9 −1.4

T13 Tap 0.8 33.7 1122 132 27.1 −1.83 −3.7 10.9

T14 Tap 1.1 34.2 1126 133 29.4 −1.57 2.5 15.1

T15 Tap 1.1 34.2 1126 130 26.3 −1.62 2.6 15.5

T16 Tap 0.4 32.7 1196 129 24.9 3.38 25.2 −1.9

T17 Tap 0.4 33.1 1245 106 25.0 4.06 28.8 −3.7

T18 Tap 2.3 31.6 636 943 32.1 −0.63 9.6 14.6

T19 Tap 1.4 32.3 1088 −2.17 −0.4 17.0

T20 Tap 1.6 31.7 1191 −1.42 2.1 13.5

T21 Tap 1.2 32.4 1084 53 26.3 −1.04 2.1 10.5

T22 Tap 0.2 30.1 964 −1.17 1.3 10.7

G01 Grdwtr. −0.4 31.5 1263 350 25.0 −0.64 4.7 9.8

G02 Grdwtr. −0.5 31.0 1285 258 25.1 3.44 28.1 0.6

G03 Grdwtr. −0.7 30.2 1504 71 18.0 −2.00 0.5 16.5

G04 Grdwtr. −1.0 30.2 1433 126 21.8 −1.64 −0.8 12.4

G05 Grdwtr. −0.6 29.8 1031 102 29.0 −0.98 3.6 11.4

G06 Grdwtr. −0.4 29.9 995 88 22.8 0.35 11.6 8.9

G07 Grdwtr. 0.5 30.1 1650 −2.50 −3.3 16.7

G08 Grdwtr. 1.2 34.2 1166 255 27.2 −1.32 2.0 12.5

G09 Grdwtr. 1.9 34.6 1146 726 28.4 0.21 13.8 12.1

G10 Grdwtr. 2.5 34.6 1259 652 25.5 0.08 9.5 8.8

G11 Grdwtr. 0.5 33.4 1148 239 26.4 −2.35 −5.1 13.6

G12 Grdwtr. 0.6 33.5 1144 116 27.6 3.95 29.1 −2.5

G13 Grdwtr. 0.8 33.6 1085 171 29.0 −1.79 −2.8 11.5

G14 Grdwtr. 1.2 34.3 1117 198 22.7 −1.45 2.1 13.8

223

ID Type Lat. [°]

Lon. [°]

Alt. [m]

EC [μs/cm]

T

[⁰C]

δ18O [‰ SMOW]

δ2H [‰ SMOW]

d−excess

G15 Grdwtr. 1.7 34.6 1144 723 28.1 −1.44 −0.4 11.1

G16 Grdwtr. 2.5 34.7 1407 718 23.6 −1.08 4.7 13.3

G17 Grdwtr. 2.4 34.5 1205 491 27.1 0.27 5.5 3.4

G18 Grdwtr. 2.1 34.2 1180 1060 26.4 −0.40 5.4 8.5

G19 Grdwtr. 2.0 34.1 1085 394 27.5 −0.23 7.0 8.9

G20 Grdwtr. 1.9 34.0 1060 619 27.5 −0.83 4.0 10.6

G21 Grdwtr. 1.9 34.0 1099 624 26.2 −0.68 6.3 11.7

G22 Grdwtr. 1.9 33.8 1054 187 27.9 −1.79 −2.3 11.9

G23 Grdwtr. 1.9 33.8 1078 129 27.3 3.87 23.0 −8.0

G24 Grdwtr. 1.8 33.5 1067 179 27.9 −1.15 4.0 13.2

G25 Grdwtr. 1.9 33.2 1098 233 26.8 −1.29 3.6 13.9

G26 Grdwtr. 2.0 33.1 1047 371 26.8 −0.11 7.6 8.4

G27 Grdwtr. 2.1 32.9 1066 147 27.3 0.12 10.1 9.2

G28 Grdwtr. 2.2 32.3 1041 100 27.2 1.02 24.1 15.9

G29 Grdwtr. 2.2 32.3 1045 189 28.3 −1.41 2.6 13.9

G30 Grdwtr. 2.3 34.3 1175 1390 26.1 −1.24 2.8 12.7

G31 Grdwtr. 1.9 34.0 1099 178 25.4 −1.47 −3.2 8.6

G32 Grdwtr. 1.8 33.6 1075 184 28.1 −1.51 −0.9 11.2

G33 Grdwtr. 1.9 33.3 1129 152 27.6 −1.71 −4.2 9.5

G34 Grdwtr. 1.9 33.1 1055 146 27.9 −3.09 −5.8 18.9

G35 Grdwtr. 2.0 33.0 1065 460 22.9 −0.94 5.0 12.5

G36 Grdwtr. 2.3 32.4 1055 101 27.2 −0.74 5.6 11.5

G37 Grdwtr. 2.5 32.4 1067 133 27.2 −0.96 3.0 10.6

G38 Grdwtr. 2.6 32.4 1073 188 26.5 −0.68 7.3 12.7

G39 Grdwtr. 2.7 32.3 1090 161 25.8 −0.75 5.3 11.4

G40 Grdwtr. 2.8 32.2 1080 293 25.1 1.12 2.5 −6.5

G41 Grdwtr. 2.6 31.8 959 160 27.2 −0.77 3.3 9.4

G42 Grdwtr. 2.8 32.2 1096 153 25.0 0.11 5.1 4.2

G43 Grdwtr. 2.7 32.2 1084 119 26.4 −1.26 −0.2 9.9

G44 Grdwtr. 2.6 32.0 985 162 26.7 −1.25 0.5 10.6

G45 Grdwtr. 2.6 31.9 962 144 24.0 −0.96 3.5 11.2

G46 Grdwtr. 2.6 31.6 854 232 28.5 −0.74 5.5 11.4

G47 Grdwtr. 1.7 31.3 1056 118 26.1 7.80 50.4 −12.1

G48 Grdwtr. 1.5 31.3 1128 62 24.4 5.03 32.7 −7.5

G49 Grdwtr. 2.2 31.5 719 372 27.2 3.35 25.8 −1.0

G50 Grdwtr. 1.6 31.3 1079 217 27.6 0.48 11.1 7.3

G51 Grdwtr. 1.6 31.3 1090 235 25.6 −0.27 8.9 11.1

G52 Grdwtr. 1.5 31.3 1231 34 24.7 −1.39 2.0 13.1

G53 Grdwtr. 1.6 31.8 1191 194 23.0 −1.42 2.1 13.5

224

ID Type Lat. [°]

Lon. [°]

Alt. [m]

EC [μs/cm]

T

[⁰C]

δ18O [‰ SMOW]

δ2H [‰ SMOW]

d−excess

G54 Grdwtr. 0.8 32.5 1117 277 26.2 2.67 25.9 4.6

G55 Grdwtr. 1.6 32.0 1053 327 25.3 −1.72 −1.9 11.8

G56 Grdwtr. 1.4 32.3 1088 82 26.2 −1.17 1.1 10.5

G57 Grdwtr. 0.1 32.5 1149 104 25.6 3.98 32.6 0.8

G58 Grdwtr. −0.6 30.6 1417 128 28.1 −1.42 3.2 14.5

G59 Grdwtr. −0.6 30.4 1469 454 25.1 −1.77 1.5 15.7

G60 Grdwtr. −1.1 29.9 1960 212 18.5 −2.17 0.7 18.0

G61 Grdwtr. −1.0 29.9 1715 305 25.7 −2.62 −2.7 18.3

G62 Grdwtr. 0.3 30.1 1202 1530 28.8 −2.03 −0.5 15.8

G63 Grdwtr. 0.5 31.1 1369 75 24.0 −1.07 4.9 13.4

G64 Grdwtr. 1.0 33.8 1079 911 28.5 −1.97 −0.7 15.0

G65 Grdwtr. 0.8 33.7 1132 −0.69 8.1 13.6

G66 Grdwtr. 1.4 34.3 1109 922 27.9 −0.78 4.8 11.0

G67 Grdwtr. 1.6 34.5 1100 1100 28.5 −2.15 −6.6 10.6

G68 Grdwtr. 2.4 34.4 1194 1000 26.3 −1.76 −1.0 13.1

G69 Grdwtr. 2.8 32.1 1068 587 25.0 −0.68 6.0 11.4

G70 Grdwtr. 2.1 31.5 664 485 31.2 −1.71 −2.9 10.8

G71 Grdwtr. 1.5 31.5 1146 74 25.7 −1.11 4.7 13.5

G72 Grdwtr. 1.4 32.3 1095 408 25.6 −1.45 0.7 12.3

G73 Grdwtr. 0.8 32.5 1113 206 25.7 −1.43 1.9 13.4

G74 Grdwtr. 0.2 30.1 1417 −1.27 4.4 14.5

G75 Grdwtr. 0.2 30.1 1417 −1.27 4.4 14.5

S01 Spring 0.2 32.5 1196 3.27 26.9 0.8

S02 Spring 0.5 30.1 1650 93 18.8 −2.11 −0.3 16.6

S03 Spring 0.5 30.1 1740 59 20.0 −2.82 −4.0 18.6

S04 Spring 0.4 30.2 1126 4100 24.8 −2.41 −1.7 17.6

S05 Spring 0.4 30.2 1126 4750 24.9 −2.41 −1.7 17.6

S06 Spring 0.4 30.2 1126 4750 24.9 −2.19 0.3 17.9

S07 Hot Spring −0.7 30.2 1632 760 54.0 −2.13 0.6 17.6

S08 Hot Spring −0.7 30.2 1632 761 54.0 −1.95 0.2 15.8

S09 Hot Spring −0.9 30.0 1382 1710 54.0 −3.15 −8.1 17.1

S10 Hot Spring 0.5 30.1 1650 1710 66.8 −4.03 −13.2 19.1

S11 Hot spring 2.8 31.9 1019 −2.23 −3.5 14.4

S12 Hot spring 2.8 31.9 1019 −2.23 −3.5 14.4

S13 Hot spring 2.8 31.9 1019 −2.12 −1.1 15.9

L01 Crater Lk. 0.4 30.2 1166 6750 28.1 7.51 46.6 −13.5

L02 Crater Lk. −0.4 30.3 1549 947 28.0 6.64 39.7 −13.4

L03 Crater Lk. 0.4 30.2 1169 1110 29.5 8.44 50.8 −16.7

L04 Crater Lk. −0.4 30.3 1533 376 26.8 −0.29 7.4 9.8

225

ID Type Lat. [°]

Lon. [°]

Alt. [m]

EC [μs/cm]

T

[⁰C]

δ18O [‰ SMOW]

δ2H [‰ SMOW]

d−excess

L05 Crater Lk. −0.4 30.3 1491 413 27.5 5.05 34.1 −6.3

L06 Crater Lk. −0.4 30.3 1574 451 23.6 1.17 15.0 5.7

L07 Crater Lk. −0.5 30.3 1645 320 29.6 4.68 30.3 −7.1

L08 Crater Lk. 0.5 30.3 1471 338 28.1 4.20 30.6 −3.0

L09 Crater Lk. −0.5 30.3 1471 4.20 30.6 −3.0

L10 Crater Lk. 0.5 30.3 1346 508 3.55 27.9 −0.4

L11 Crater Lk. 0.5 30.3 1281 434 3.81 29.4 −1.1

L12 Crater Lk. −0.3 30.1 1337 119 26.2 3.18 25.3 −0.1

L13 Crater Lk. −0.3 30.1 1335 152 26.4 2.74 22.4 0.5

L14 Crater Lk. −0.3 30.1 1344 119 24.9 3.59 26.8 −2.0

L15 Crater Lk. −0.3 30.1 1304 −1.57 −0.9 11.7

L16 Crater Lk. −0.3 30.1 1304 167 28.1 2.45 18.3 −1.2

L17 Crater Lk. −0.3 30.8 1317 343 28.2 5.61 36.1 −8.8

L18 Crater Lk. −0.3 30.1 1268 469 28.2 2.17 20.1 2.7

L19 Crater Lk. −0.3 30.1 1318 745 29.7 7.17 44.7 −12.7

L20 Crater Lk. −0.3 30.1 1318 7.17 44.7 −12.7

L21 Crater Lk. −0.2 30.1 1038 728 25.1 −1.08 4.8 13.4

L22 Crater Lk. −0.3 30.1 1387 298 4.56 28.8 −7.7

L23 Crater Lk. −0.3 30.1 1387 315 27.0 5.48 34.7 −9.1

L24 Lake −0.7 30.9 1259 0.08 8.8 8.2

L25 Lake −0.7 30.9 1259 0.08 8.8 8.2

L26 Lake −0.7 30.9 1259 0.08 8.8 8.2

L27 Lake −0.6 31.0 1281 126 0.57 9.2 4.6

L28 Lake 0.1 32.5 1149 122 25.7 3.62 31.4 2.4

L29 Lake −0.5 31.2 1268 374 26.2 2.91 22.4 −0.9

L30 Lake −1.3 29.9 2122 264 20.0 4.45 32.6 −2.9

L31 Lake −0.3 29.9 914 857 29.0 4.22 34.7 0.9

L32 Lake 0.4 32.0 1168 341 28.5 5.40 36.8 −6.3

L33 Lake −1.3 29.8 1905 2.11 17.4 0.5

L34 Lake −1.2 29.7 1907 1.23 13.8 3.9

L35 Lake −1.3 29.7 1815 1.29 15.0 4.7

L36 Lake 1.8 31.3 618 591 30.0 6.22 45.8 −4.0

Q01 Swamp 0.2 32.3 1178 105 19.7 −1.44 −1.6 9.9

Q02 Swamp 0.9 33.7 1056 139 23.0 −1.41 −1.2 10.1

Q03 Swamp 1.9 33.8 1067 352 25.7 −1.39 −0.3 10.8

Q04 Swamp 1.9 33.2 1090 379 27.7 0.46 11.1 7.4

Q05 Swamp 2.0 34.1 1066 139 21.9 −2.44 −3.5 16.0

Q06 Swamp 2.0 33.0 1036 160 27.9 1.51 31.2 19.1

Q07 Swamp 2.8 31.9 1019 −2.32 −5.1 13.5

226

ID Type Lat. [°]

Lon. [°]

Alt. [m]

EC [μs/cm]

T

[⁰C]

δ18O [‰ SMOW]

δ2H [‰ SMOW]

d−excess

Q08 Pond −0.6 30.3 1539 32 25.7 1.39 13.3 2.1

Q09 Pond −0.6 29.7 953 590 20.0 −1.16 0.4 9.7

Q10 Pond 2.2 34.3 1185 75 21.3 −0.24 11.7 13.6

Q11 Pond 2.3 34.4 1168 375 19.1 −1.89 1.0 16.1

Q12 Pond 0.2 32.4 1205 109 18.3 −1.13 1.2 10.2

R01 Major river 2.3 31.6 634 4.64 32.7 −4.5

R02 Major river 0.0 32.0 1197 117 21.1 −0.91 4.5 11.8

R03 Major river −1.4 30.0 1958 142 −1.41 −2.5 8.7

R04 Major river 2.5 31.5 624 2410 −2.56 −9.2 11.3

R05 Major river 1.7 32.1 1044 127 3.36 26.0 −0.9

R06 River −0.5 31.2 1265 3.00 14.7 −9.3

R07 River −0.5 30.9 1338 101 24.1 1.37 5.3 −5.7

R08 River −0.6 30.6 1417 620 26.1 −0.59 −0.6 4.1

R09 River −0.6 30.3 1524 690 24.3 −1.79 −0.4 13.9

R10 River −0.6 30.2 1515 540 25.2 −1.45 1.8 13.3

R11 River −1.3 30.1 1858 101 20.7 0.26 8.0 5.9

R12 River −1.1 29.9 1969 161 15.0 −2.56 −3.9 16.7

R13 River −1.0 29.9 1932 87 16.3 −2.75 −4.7 17.3

R14 River −0.9 30.0 1444 104 23.4 −1.21 2.1 11.8

R15 River −0.8 29.8 1372 185 24.5 −1.82 −0.7 13.8

R16 River −0.8 29.8 1449 91 24.4 −1.51 1.3 13.4

R17 River −0.7 29.9 1239 92 25.7 −1.61 0.2 13.1

R18 River −1.3 30.0 1907 168 16.2 −2.47 −5.7 14.1

R19 River −1.0 30.0 1722 385 20.9 −2.94 −7.4 16.1

R20 River −0.8 29.8 1372 286 22.5 −0.93 3.9 11.4

R21 River −0.8 29.8 1449 154 24.9 −1.49 1.8 13.7

R22 River −0.7 29.9 1170 243 25.1 −0.58 5.7 10.4

R23 River 0.0 29.8 1042 335 25.7 −1.31 −0.8 9.7

R24 River 0.0 29.9 1088 84 23.5 0.86 10.1 3.2

R25 River −0.6 29.7 969 216 23.6 0.92 14.0 6.6

R26 River −0.5 29.7 918 59 23.4 −2.04 −2.0 14.3

R27 River −0.3 29.9 986 151 23.4 −1.19 0.9 10.4

R28 River −0.2 30.0 963 334 19.1 −2.34 −2.5 16.2

R29 River −0.3 30.1 1304 85 20.9 −3.18 −6.8 18.6

R30 River 0.3 30.1 1107 86 20.0 −2.94 −4.7 18.8

R31 River 0.4 30.2 1067 −2.85 −4.9 17.9

R32 River 0.7 30.3 1520 761 20.6 −1.24 2.6 12.5

R33 River 0.6 30.4 1462 426 20.7 −1.70 0.6 14.2

R34 River 0.6 30.6 1364 −0.55 5.1 9.5

227

ID Type Lat. [°]

Lon. [°]

Alt. [m]

EC [μs/cm]

T

[⁰C]

δ18O [‰ SMOW]

δ2H [‰ SMOW]

d−excess

R35 River 0.4 32.0 1178 41 21.0 −1.64 −1.1 12.0

R36 River 2.4 34.6 1257 316 30.5 4.49 30.3 −5.6

R37 River 0.4 32.8 1120 42 20.0 −1.49 −1.6 10.3

R38 River 1.1 34.2 1126 322 24.9 1.35 16.4 5.6

R39 River 1.8 34.6 1228 294 33.6 −1.35 1.4 12.2

R40 River 2.4 34.6 1268 106 25.6 1.46 32.4 20.7

R41 River 1.9 33.3 1093 132 29.5 −0.90 6.7 13.9

R42 River 2.0 33.0 1065 233 29.1 −2.81 −3.3 19.2

R43 River 2.4 32.4 1063 170 26.8 −1.12 1.4 10.3

R44 River 2.5 34.7 1407 272 18.3 −1.27 1.3 11.5

R45 River 2.4 34.5 1198 180 18.3 3.69 28.4 −1.1

R46 River 1.8 33.5 1058 208 30.1 −2.33 −4.5 14.1

R47 River 1.9 33.4 1043 195 26.4 −1.61 1.7 14.5

R48 River 1.9 33.3 1118 172 27.7 3.23 26.2 0.4

R49 River 1.9 33.2 1084 173 28.3 3.56 25.1 −3.4

R50 River 2.2 32.9 1136 149 24.2 −0.91 4.3 11.6

R51 River 2.8 32.0 1043 63 21.0 −2.30 −6.0 12.4

R52 River 2.6 32.1 990 134 25.1 8.62 55.5 −13.5

R53 River 2.8 32.0 1042 64 20.7 −0.80 5.2 11.6

R54 River 2.3 31.7 716 4.63 32.8 −4.2

R55 River 1.9 31.4 634 108 25.7 −0.55 6.7 11.1

R56 River 1.9 31.5 634 151 25.9 −0.75 5.0 11.0

R57 River 1.7 31.4 991 324 25.5 −0.86 3.3 10.1

R58 River 1.6 31.6 1111 32.3 −0.60 7.0 11.9

R59 River 1.5 32.0 1045 −1.14 2.3 11.4

R60 River 1.6 31.8 1123 151 22.5 −0.39 4.8 7.9

R61 River 1.7 32.1 1044 127 25.1 −1.18 1.9 11.3

R62 River 1.0 32.5 1076 121 22.4 −3.20 −12.2 13.4

R63 River −0.6 30.9 1280 754 21.8 −2.32 −5.2 13.4

R64 River 0.2 30.0 1420 59 17.2 −2.94 −1.7 21.9

R65 River 0.5 30.1 1650 121 15.7 −3.04 −2.7 21.6

R66 River 2.2 32.2 1047 110 27.0 4.87 36.4 −2.6

R67 River −1.2 29.7 1949 −1.05 5.3 13.7

228

4.4.2 Ugandan hydrochemistry

The salinity of Ugandan waters sampled in this study ranges from total dissolved solids

values of 25 ppm to 3900 ppm. The highest salinities are observed in hot springs that have total

dissolved solids ranging between 248 mg/L and 3851 mg/L. The lowest salinities are observed in

rivers (n = 9) that have total dissolved solids of less than 1,000 mg/L. Crater lakes have the largest

variability in total dissolved solids, with a minimum of 80 mg/L and a maximum of 2100 mg/L.

Most samples are usually Ca-HCO3 water types (Figure 4-3), with the exception of hot springs that

have salinities dominated by Na+-K+ and Cl- and SO42- (Table 4-3).

Contaminants measured in this study include arsenic, fluoride and nitrate. The maximum

contaminant levels set by the Environmental Protection Agency are 10 ppb (arsenic), 4 mg/L

(fluoride) and 10 mg/L (nitrate as NO3-N). Most groundwater and river water samples meet the

drinking water standards. However, nitrate concentrations exceeding the maximum contaminant level

concentration were found in a subset of groundwater samples (e.g., 44 mg/L NO3-N). The origin of

the observed high nitrate concentrations is unknown.

229

Figure 4-3. A Piper diagram showing the major cation (lower left ternary) and major anion (lower

right ternary) projected onto a combined cation-anion diamond (top-most). Bicarbonate

concentrations were calculated using a charge balance because measurements in the field were not

possible.

230

4.4.3 Stable isotope-based evaporation fluxes

Our stable isotope based evaporation/inflow ratios range from near-zero (i.e., well-flushed

lakes) to near 100% (terminal lakes, where evaporation is the only water loss). The Bunyaruguru and

Kasenda crater lake systems (i.e., lakes with “B” and “K” in title, respectively) have a mixture of both

well-flushed lakes and lakes that are terminal (Figure 4-4). This approach, in spite of large

uncertainties, shows that terminal and well-flushed lakes can be distinguished on the basis of δ18O

and δ2H values.

Figure 4-4. Stable-isotope-based evaporation/input ratios for 24 Ugandan Lakes. Grey bars mark the

degree of flushing, with light grey being well-flushed lakes, and dark grey representing lakes having

the majority of water losses via evaporation. Lakes entitled with “B” are the Bunyaruguru crater lakes

system (south of Lake George) and lakes entitled with “K” are the Kasenda crater lakes system

(north of Lake George). The 18O/16O-based model results are shown here.

231

4.5 Discussion

Stable oxygen and hydrogen isotopic data reveal distinct hydrogeochemical processes across

Uganda. For example, stable oxygen isotopic data and electrical conductivity (a proxy for salinity)

show that the processes of mineral dissolution and evapo-concentration can be distinguished.

Mineral dissolution does not alter the oxygen isotopic composition of water and produces an

increase in the electrical conductivity of the water. Evapo-concentration, on the other hand, increases

both the electrical conductivity and the δ18O value of water (Figure 4-5). The process of

evapoconcentration also emerges when examining deuterium excess values and electrical

conductivity. The lake having the highest electrical conductivity also has the lowest deuterium excess

value, consistent with evapoconcentration. Groundwaters generally have a near-meteoric isotopic

composition and a large range of electrical conductivity values, implying that the source of salinity in

nearly all groundwaters is likely to be low-temperature mineral dissolution. Lakes, on the other hand,

have electrical conductivities that rise with increasing δ18O, highlighting that evapoconcentration of

input waters is an important control upon the salinity of certain lakes in Uganda. The process of

evapoconcentration is also evidenced by the relationship between deuterium excess and electrical

conductivity (Figure 4-6); the lake with the lowest deuterium excess (greatest evaporation/input ratio)

also has the highest electrical conductivity (Figure 4-6). The highest

232

Figure 4-5. Oxygen isotopic composition and electrical conductivity of Ugandan lakes (triangles) and

groundwaters (squares). Dashed lines mark schematic trajectories for mineral dissolution under low

temperatures and evapoconcentration of waters.

The deuterium excess of Ugandan river- and ground-waters is elevated for high latitude samples

(Figure 4-7), primarily collected in southwestern Uganda near to the Rwenzori Mountains. Although

the sampling site is different from source water elevations (due to streamflow and groundwater

advection), this finding suggests that precipitation in the Rwenzori mountains has a higher deuterium

excess than other Ugandan waters, perhaps due to moisture recycling in the Congo basin to the west

(Ndembo et al., 2007), or implicating that snowmelt comprises a portion of these samples because of

the known build-up of deuterium excess in snow (Gat et al., 1994).

233

Figure 4-6. Deuterium excess and electrical conductivity of Ugandan lakes (triangles) and

groundwaters (squares).

234

Figure 4-7. The deuterium excess and sample elevation of Ugandan Rivers (circles) and

groundwaters. High altitude samples (i.e., altitudes above 1,600 meters above sea level) have high

deuterium excess values, potentially related to kinetic isotope effects during snow formation or

moisture recycling from the Congo basin to the west.

235

Table 4-3. Major ion chemistry of Ugandan waters (units of ppm)

Sample Type Ca2+ K+ Mg2+ Na+ Si Cl- SO42-

L-19 Crater lake 11.0 71.6 21.4 83.1 18.2 14.6 0.7

R-63 River 58.5 4.5 23.7 43.8 21.0 19.8 321.1

GW-62 Groundwater 125.2 30.9 41.4 108.3 32.1 74.3 208.8

L-03 Crater lake 9.6 81.5 58.2 95.4 4.7 51.1 0.6

L-23 Crater lake 20.4 11.9 18.2 8.5 15.2 5.7 0.6

L-08 Crater lake 19.6 5.7 25.0 8.7 14.1 3.6 1.3

L-10 Crater lake 34.3 9.7 30.8 11.7 14.8 3.7 0.5

L-11 Crater lake 31.4 6.8 25.9 9.3 12.0 3.4 0.7

GW-64 Groundwater 49.9 10.1 12.7 97.5 29.0 199.3 22.0

GW-65 Groundwater 100.8 2.3 20.5 23.9 17.9 37.8 2.2

GW-66 Groundwater 104.8 4.6 18.7 54.3 17.0 3.9 1.1

GW-67 Groundwater bdl 13.1 bdl 244.0 8.4 11.7 5.4

GW-68 Groundwater 81.6 4.8 19.8 100.1 30.4 16.5 71.6

R-53 River 40.5 5.4 18.5 26.1 28.5 136.4 2.3

GW-46 Groundwater 8.3 34.3 8.4 35.2 31.2 2.5 1.1

R-66 River 6.0 3.0 3.3 9.8 2.4 4.3 0.9

GW-70 Groundwater 26.8 8.1 7.5 57.9 38.3 17.4 9.0

L-36 Lake 9.7 37.3 23.1 62.0 0.5 22.7 19.5

GW-71 Groundwater 3.1 0.6 1.8 6.1 6.3 4.9 1.0

GW-72 Groundwater 27.2 4.0 10.5 30.4 37.7 39.7 33.0

GW-73 Groundwater 13.0 3.0 3.7 18.7 34.9 2.2 1.8

R-04 Major river 32.7 12.4 9.8 340.6 16.5 585.0 n.a.

R-05 Major river 5.8 33.6 3.4 20.0 3.1 4.9 0.7

R-03 Major river 7.1 3.5 5.2 11.6 6.4 6.3 4.0

SP-10 Hot Spring 26.7 63.3 4.5 1439.0 24.5 786.8 1434.0

L-32 Lake 17.6 11.3 6.7 32.3 8.3 24.9 0.7

L-28 Lake 8.3 3.3 2.6 10.3 0.8 6.1 3.9

GW-57 Groundwater 5.0 2.6 2.4 9.1 0.8 10.6 11.1

SW-12 Pond 6.7 1.2 3.0 4.3 12.8 1.0 0.5

R-02 Major river 4.9 2.2 2.8 12.4 7.5 6.4 n.a.

L-29 Lake 22.1 9.9 11.3 33.5 10.2 49.3 12.7

GW-58 Groundwater 6.7 1.0 3.3 9.8 6.8 4.8 37.2

GW-59 Groundwater 17.6 12.4 9.2 39.1 16.5 69.2 15.9

SP-07 Hot Spring 33.1 11.0 0.3 185.1 32.2 83.8 344.7

SP-08 Hot Spring 33.2 11.1 0.2 185.7 32.4 115.9 342.4

L-30 Lake 18.2 4.3 8.6 16.3 0.4 26.2 1.6

236

Sample Type Ca2+ K+ Mg2+ Na+ Si Cl- SO42-

GW-60 Groundwater 15.7 2.0 10.6 6.6 14.2 6.4 12.6

GW-61 Groundwater 26.8 3.3 15.6 5.8 7.5 12.0 15.1

SP-09 Hot Spring 66.9 26.3 18.5 434.1 34.0 218.5 469.0

L-31 Lake 13.9 57.0 33.6 77.9 5.1 22.5 25.6

R-64 River 4.6 1.3 1.6 3.5 7.2 1.1 5.9

SP-04 Spring 323.4 70.5 86.7 410.3 37.9 227.8 634.4

R-65 River 9.1 1.5 2.1 13.2 8.1 6.0 12.9

GW-74 Groundwater 42.1 14.1 19.1 9.7 11.9 4.4 13.7

L-01 Crater lake 2.8 161.1 42.8 1229.0 3.4 483.3 178.6

SP-11 Hot spring 9.1 3.9 1.6 89.0 28.4 43.9 64.6

GW-63 Groundwater 2.4 2.1 1.3 5.5 15.6 3.0 2.6

Lake evaporation to input ratios can be derived from both isotopic tracers (i.e., 18O/16O and

2H/1H). Both isotopic tracers are conservative and should yield the same evaporation/inflow ratio if

all model parameters adequately represent reality. However, our results show that (i) the 2H/1H-

based model is more sensitive than the 18O/16O-based model, and (ii) that the 2H/1H-based model

yields higher evaporation/inflow ratios compared to results from the 18O/16O-based model (Figure

4-8).

237

Figure 4-8. Stable-isotope-based evaporation/input ratios computed using a 2H/1H-based model (y-

axis) and an 18O/16O-based model (x-axis).

Previous studies recognizing a mismatch between 2H/1H- and 18O/16O-based

evaporation/input ratios have multiplied α*l-v values by a constant (e.g. Bennett et al., 2008; Gibson

and Reid, 2014) or have only reported 18O-based results (e.g., Zuber, 1983) as 18O/16O-based

evaporation/input ratios are generally more reasonable (i.e., between 0 and 100 percent) than 2H/1H-

based evaporation/input ratios. In this study I report output from each tracer and acknowledge that

2H/1H- and 18O/16O-based results do not match. Next, I reanalyze modelled 2H/1H- and 18O/16O-

based evaporation/input ratios to test for the reasoning behind this discrepancy by modifying

multiple model input parameters: (i) relative humidity, (ii) kinetic fractionation coefficient, (iii)

atmospheric isotopic composition under changing deuterium excess, (iv) atmospheric isotopic

composition under constant deuterium excess.

First, the model input relative humidity of the atmosphere near to the lake surface was

modified to test if relative humidity could lead to convergence of 2H/1H- and 18O/16O-based

evaporation/input ratios. This analysis showed that modifying modelled relative humidity cannot

238

explain the observed mismatch of 2H/1H- and 18O/16O-based evaporation/input ratios. This finding

is consistent with expectations because changes to model input relative humidity simultaneously

impacts both 2H/1H- and 18O/16O-based evaporation/input ratios. Changing relative humidity does

not allow 2H/1H- and 18O/16O-based evaporation/input ratios to converge because the ratio of

CK(δ18O model) / CK(δ2H model) is close to one (see Equation 4.5).

Second, modifying the constant describing the kinetic evaporative isotope effect of an open

water body (CK) was able to converge 18O and 2H-based evaporation/input ratios when the ratio of

CK(δ18O model) / CK(δ2H model) was set to 0.20 to 0.45. To test to see if these CK(δ18O model) /

CK(δ2H model) ratios are reasonable I examined a compilation of empirically-based CK values for

δ18O and δ2H (Jasechko et al., 2014) that show CK(δ18O model) / CK(δ2H model) ratios to be

between 0.8 to 2.8. The CK(δ18O model) / CK(δ2H model) ratio required for convergence of 2H/1H-

and 18O/16O-based evaporation/input ratios (0.20 to 0.45) is lower than empirical CK(δ18O model) /

CK(δ2H model) ratios (0.8 to 2.8), suggesting that the constant describing the kinetic evaporative

isotope effect of an open water body (CK) is not the primary source of the observed difference

between 2H/1H- and 18O/16O-based evaporation/input ratios.

Third, modifying the modelled deuterium excess of the atmosphere converged the 2H/1H-

and 18O/16O-based evaporation/input ratios if the deuterium excess of δA is increased. However, the

modelled deuterium excess of atmospheric vapor must be increased to between +15 ‰ and +60 ‰

before the two evaporation/input ratios match. Although not conclusive, I propose that a build-up

of evaporated moisture over the lake surfaces may impact the isotopic composition of the

atmosphere during evaporation, thereby increasing the deuterium excess of δA and providing a

feedback into future evaporate. This build-up of deuterium excess has been discovered over large

lakes and semi-constrained seas (e.g., deuterium excess values of up to +85‰ observed downwind of

the North American Great Lakes: Machavaram and Krishnamurthy, 1995 other examples: Gat et al.,

239

1994; Bowen et al., 2012; Jasechko et al., 2014). An increase in the deuterium excess of near-surface

atmospheric vapor may explain part of the observed difference in 18O and 2H-based

evaporation/input ratios. However, the deuterium excess values required for convergence of 18O and

2H-based evaporation/input ratios (+15 ‰ and +60 ‰) are unreasonably high for some lakes in this

region, given the deuterium excess of regional precipitation (+12.3; Table 4.1), suggesting that

another model parameter must also be causing observed differences in 18O and 2H-based

evaporation/input ratios.

Fourth, modifying the modelled isotopic composition of the atmosphere under fixed

deuterium excess conditions (i.e., modifying δA under fixed deuterium excess) resulted in a

convergence of 2H/1H- and 18O/16O-based evaporation/input ratios if the offset between vapour-

precipitation was reduced to ~3.8‰ for δ18O and ~30‰ for δ2H. Model predictions of the isotopic

composition of the near-surface atmosphere using an equilibrium offset (Horita and Wesolowski,

1994) suggest higher offsets between atmospheric vapour and precipitation of ~9‰ for δ18O and

~70‰ for δ2H. This finding suggests that the near-surface atmospheric vapor δ18O and δ2H value is

higher than that of condensing vapor, conceptually consistent with known decreases in vapor δ18O

values with increasing height above the land surface (e.g., Strong, 2012). Indeed, Strong (2012) show

that atmospheric vapor δ2H values at >500 metres above the land surface are ~30‰ to ~150‰

lower than at the near surface. This finding shows that precipitation isotopic compositions are a poor

determinant of near-surface atmospheric vapor and that treating δA values determined by an

assumption of equilibrium with precipitation isotopic compositions provides a minimum value for

near surface atmospheric vapor δ18O and δ2H values.

The above discussion rules out changes to the modelled (i) relative humidity, (ii) kinetic

fractionation coefficient and (iii) atmospheric isotopic composition deuterium excess values as the

cause of the discrepancy between 2H/1H- and 18O/16O-based evaporation/input ratios. I show that

240

increasing model input δA under fixed deuterium excess conditions is able to constrain 2H/1H- and

18O/16O-based evaporation/input ratios. This increase is conceptually consistent with a decrease in

atmospheric vapor from the near surface to condensation altitudes. The magnitude of the increase in

model input δA values under fixed deuterium excess required to converge 2H/1H- and 18O/16O-based

evaporation/input ratios is consistent with observations (Strong, 2012). This research suggests that

stable isotope mass balances should use atmospheric vapor isotopic compositions derived from

equilibrium offset with precipitation (i.e., δA = δP – (𝛼𝑙∙𝑣∗− 1)) only as a minimum value.

Conclusions

The primary objective of this study were to quantify Ugandan lake water balances using a stable

isotope mass balance. The water balance of lakes throughout Uganda was indeed quantified using a

stable isotope mass balance and showed that well flushed (evaporation/inflow ratio approaching

zero) and terminal lakes (evaporation/input ratio approaching one) can be determined using a stable

isotope mass balance. Results from δ18O and δ2H mass balances were discovered to produce

inconsistent results with 2H-based evaporation/inflow ratios generally exceeding 18O-based

evaporation/inflow ratios. Further analysis of input parameters to the Craig-Gordon evaporation

model showed that the two tracers are synchronized when the modelled isotope composition of

atmospheric vapour is increased from original predictions that were based upon precipitation

isotopic compositions as a proxy for atmospheric vapor. This finding is conceptually consistent with

a decrease in atmospheric vapor δ18O and δ2H from the near surface to condensation altitudes. This

research suggests that stable isotope mass balances should use atmospheric vapor isotopic

compositions derived from equilibrium offset with precipitation (i.e., δA = δP – (𝛼𝑙∙𝑣∗− 1)) as a

minimum value.

241

4.6 References

Araguás-Araguás, L., Froehlich, K., and Rozanski, K. (2000), Deuterium and oxygen-18

isotope composition of precipitation and atmospheric moisture, Hydrological Processes 14, 1341–

1355.

Bahati, G., Pang, Z., Ármannsson, H., Isabirye, E. M., and Kato, V. (2005), Hydrology and

reservoir characteristics of three geothermal systems in western Uganda, Geothermics, 34, 568–591.

Bennett, K.E., Gibson, J.J., McEachern, P.M., 2008. Water yield estimates for critical

loadings assessment: comparisons of gauging methods versus and isotopic approach. Can. J. Fish.

Aquat. Sci., 65, 83–99.

Bowen, G. J., Kennedy, C. D., Henne, P. D., and Zhang, T. (2012), Footprint of recycled

water subsidies downwind of Lake Michigan, Ecosphere, 3, art53.

Brock, B. E., Yi, Y., Clogg-Wright, K. P., Edwards, T. W. D., Wolfe, B. B. (2009). Multi-year

landscape-scale assessment of lakewater balances in the Slave River Delta, NWT, using water isotope

tracers, Journal of Hydrology, 379, 81–91.

Craig, H., and Gordon, L.I. (1965), Deuterium and oxygen-18 variations in the ocean and

the marine atmosphere, In E. Tongiorgi, ed, Proceedings of a Conference on Stable Isotopes in

Oceanographic Studies and Paleotemperatures, Spoleto, Italy: 9–130.

Froehlich, K. (2000), Evaluating the water balance of inland seas using isotopic tracers: the

Caspian Sea experience, Hydrological Processes, 14, 1371–1383.

Gat, J. R., Bowser, C. J., and Kendall, C. (1994), The contribution of evaporation from the

Great Lakes to the continental atmosphere: estimate based on stable isotope data, Geophysical Research

Letters, 21, 557–560.

242

Gat, J. R., Shemesh, A., Tziperman, E., Hecht, A., Geogopoulos, D., and Basturk, O. (1996),

The stable isotope composition of waters of the Eastern Mediterranean Sea, Journal of Geophysical

Research, 101, 6441–6451.

Gibson, J. J. (2002), Short-term evaporation and water budget comparisons in shallow arctic

lakes using non-steady isotope mass balance, Journal of Hydrology, 264, 247–266.

Gibson, J. J., and Edwards, T. W. D. (2002), Regional water balance trends and evaporative-

transpiration partitioning from a stable isotope survey of lakes in northern Canada, Global

Biogeochemical Cycles, 16.

Gibson, J. J., and Reid, R. (2014), Water balance along a chain of tundra lakes: a 20-year

isotopic perspective, Journal of Hydrology, 519, 2148–2164.

Gibson, J. J., Edwards, T. W. D., and Prowse, T. D. (1996), Development and validation of

an isotopic method for estimating lake evaporation, Hydrological Processes, 10, 1369–1382.

Gibson, J. J., Reid, R., and Spence, C. (1998), A six-year isotopic record of lake evaporation

in the Canadian Subarctic, Hydrological Processes, 12, 1779–1792.

Gibson, J. J., Prepas, E. E., and McEachern, P. (2002), Quantitative comparison of lake

throughflow, residency, and catchment runoff using stable isotopes: Modelling and results from a

survey of boreal lakes, Journal of Hydrology, 262, 128–144.

Gonfiantini, R. (1986), Environmental isotopes in lake studies, In: Fritz, P., Fontes, J.Ch.

(Eds.), Handbook of Environmental Isotope Geochemistry, Elsevier, New York, 113–168.

Horita, J., and Wesolowski, D. J. (1994), Liquid-vapor fractionation of oxygen and hydrogen

isotopes of water from the freezing to the critical temperature, Geochimica et Cosmochimica Acta, 58,

3425–3437.

243

Horita, J., Rozanski, K. and Cohen, S. (2008), Isotope effects in the evaporation of water: a

status report of the Craig-Gordon model, Isotopes in Environmental and Health Studies, 44, 23–49.

Jasechko, S., Gibson, J. J., and Edwards, T. W. D. (2014), Stable isotope mass balance of the

Laurentian Great Lakes, Journal of Great Lakes Research, 40, 336–346.

Kato, V. (2000). Geothermal field studies using stable isotope hydrology: case studies in

Uganda and Iceland, United Nations University Reports, 10, 189–216.

Machavaram, M. V. and Krishnamurthy, R. V. (1995), Earth surface evaporative process: a

case study from the Great Lakes region of the United States based on deuterium excess in

precipitation, Geochemica et Cosmochimica Acta, 59, 4279–4293.

Ndembo, L., Travi, Y., Moyengo, L. M., and Wabakaghanzi, J. N. (2007), Isotopes in

precipitations of Kinshasa area: Moisture sources and groundwater tracing, in Advances in Isotope

Hydrology and its Role in Sustainable Water Resources Management (IHS—2007), 169–176.

New, M., Lister, D., Hulme, M. and Makin, I. (2002), A high–resolution data set of surface


Russell, J. M., and Johnson, T. C. (2006), The water balance and stable isotope hydrology of

Lake Edward, Uganda-Congo. Journal of Great Lakes Research, 32, 77–90.

Strong, M. (2012), Variations in the stable isotope compositions of water vapor and

precipitation in New Mexico : links to synoptic-scale weather, Ph.D. Dissertation, University of New

Mexico, Albuquerque, U.S.A., 266 pp.

Taylor, R. G., and Howard, K. W. (1996), Groundwater recharge in the Victoria Nile basin

of east Africa: support for the soil moisture balance approach using stable isotope tracers and flow

modelling. Journal of Hydrology, 180, 31-53.

244

Taylor, R. G., and Howard, K. W. (1998a), The dynamics of groundwater flow in the

regolith of Uganda. International Contributions to Hydrogeology, 18, 97–114.

Taylor, R. G., and Howard, K. W. F. (1998b), Post-Palaeozoic evolution of weathered

landsurfaces in Uganda by tectonically controlled deep weathering and stripping, Geomorphology, 25,

173–192.

Taylor, R. G., and Howard, K. W. (2000), A tectono-geomorphic model of the hydrogeology

of deeply weathered crystalline rock: evidence from Uganda, Hydrogeology Journal, 8, 279–294.

Tindimugaya, C., Taylor, R. G., Atkinson, T. C., Barker, J., and Kulkarni, K. M. (2007),

Assessment of groundwater dynamics in Uganda using a combination of isotope tracers and aquifer

hydraulics data, in Advances in Isotope Hydrology and its Role in Sustainable Water Resources

Management (IHS—2007), 169–176.

Turner, K. W., Wolfe, B. B., and Edwards, T. W. D. (2010), Characterizing the role of

hydrological processes on lake water balances in the Old Crow Flats, Yukon Territory, Canada, using

water isotope tracers, Journal of Hydrology, 386, 103–117.

Turner, K. W., Wolfe, B. B., Edwards, T. W., Lantz, T. C., Hall, R. I., and Larocque, G.

(2014), Controls on water balance of shallow thermokarst lakes and their relations with catchment

characteristics: a multi‐year, landscape‐scale assessment based on water isotope tracers and remote

sensing in Old Crow Flats, Yukon (Canada), Global Change Biology, 20, 1585–1603.

Yi, Y., Brock, B. E., Falcone, M. D., Wolfe, B. B., Edwards, T. W. D. (2008), A coupled

isotope tracer method to characterize input water to lakes, Journal of Hydrology, 350, 1–13.

Zuber, A. (1983), On the environmental isotope method for determining the water balance

of some lakes, Journal of Hydrology, 61, 409–427.

245

APPENDICIES

246

The stable isotopic composition of Earth’s large lakes

247

Lake δ18O

(SMOW) δ2H

(SMOW) Lake

δ18O (SMOW)

δ2H (SMOW)

Abhe 3.73 -3.5 Ladoga -9.53

Abiyata 10.00 64.2 Ladoga -10.45 -77.2

Abiyata 7.09 46.9 Ladoga -10.31 -75.6

Abiyata 8.36 Lucern -12.68

Abiyata 7.56 Malawi 1.70 10.6

Abiyata 7.52 Malawi 1.66 11.0

Abiyata 8.37 51.9 Malawi 1.60 11.7

Abiyata 7.99 56.1 Malawi 1.60 12.3

Afdera 6.61 29.0 Malawi 1.90 12.3

Afdera 6.62 27.9 Malawi 1.85 12.4

Afdera 5.90 23.9 Malawi 1.89 13.0

Afdera 6.28 29.0 Malawi 1.94 12.5

Afdera 6.49 29.4 Malawi 1.94 13.0

Afdera 6.66 29.2 Malawi 2.08 13.2

Afdera 6.71 28.5 Malawi 2.16 13.2

Afdera 6.87 28.8 Malawi 2.08 13.2

Afdera 6.79 28.3 Malawi 2.14 13.2

Afdera 6.57 27.3 Malawi 2.14 13.5

Afdera 5.28 25.3 Malawi 2.08 13.6

Albert 5.20 37.0 Malawi 2.02 14.0

Aral Sea 3.77 7.0 Malawi 2.09 13.4

Aral Sea 3.57 7.9 Malawi 2.07 13.4

Aral Sea 3.79 9.0 Malawi 2.13 13.5

Aral Sea 3.97 9.2 Malawi 2.06 13.6

Aral Sea 3.98 10.3 Malawi 2.00 13.2

Aral Sea 3.90 -0.1 Manasarovar -11.34 -83.7

Aral Sea 3.84 8.9 Manasarovar -9.36 -75.3






Aral Sea 3.25 3.9 Mar Chiquita 3.20 16.0


Aral Sea 1.80 -8.4 Mar Chiquita 3.20 17.0












248

Lake δ18O

(SMOW) δ2H

(SMOW) Lake

δ18O (SMOW)

δ2H (SMOW)

Aral Sea -5.30 -47.9 Mar Chiquita 2.90 18.0




Aral Sea -0.50 -19.4 Mar Chiquita 3.20 17.0






Athabasca -16.40 Mar Chiquita 3.10 18.0



Athabasca -15.30 -131.0 Mar Chiquita 2.90 18.0

Awasa 8.25 54.5 Mar Chiquita 3.30 18.0



Awasa 7.91 54.5 Mead -14.50 -113.5

Awasa 7.85 53.8 Mead -12.80 -100.6

Awasa 7.92 55.3 Mead -12.73 -101.7

Awasa 8.10 57.6 Mead -12.57 -99.3

Awasa 8.18 54.7 Mead -13.54 -107.3

Awasa 8.14 55.9 Mead -13.36 -106.9

Awasa 8.20 55.6 Mead -13.53 -107.5

Awasa 8.27 54.7 Mead -13.82 -108.5

Awasa 8.20 54.3 Mead -14.34 -112.4

Awasa 8.21 56.0 Mead -13.63 -107.8

Awasa 8.22 56.0 Mead -13.67 -106.9

Awasa 8.24 56.3 Mead -13.50 -107.7

Awasa 8.22 56.1 Mediterranean 2.19 8.4










Awasa 5.36 38.3 Mediterranean 10.3







249

Lake δ18O

(SMOW) δ2H

(SMOW) Lake

δ18O (SMOW)

δ2H (SMOW)


Baikal -15.90 -123.7 Mediterranean 1.51 8.7





























Baikal -15.80 -121.3 Mediterranean 10.3



Baltic Sea -8.20 -61.0 Mediterranean 2.42 8.6













250

Lake δ18O

(SMOW) δ2H

(SMOW) Lake

δ18O (SMOW)

δ2H (SMOW)























Baltic Sea -5.0 Mediterranean 1.39 7.7

Baringo 8.70 47.8 Mediterranean 1.62 7.6



Beysehir -1.60 -16.0 Mediterranean 1.43 8.4






Beysehir -3.70 Mediterranean 1.61 8.6





Biwa -6.79 -42.2 Mediterranean 1.49 6.5









251

Lake δ18O

(SMOW) δ2H

(SMOW) Lake

δ18O (SMOW)

δ2H (SMOW)







Black Sea -3.59 -27.7 Mediterranean 2.03 7.0


































Black Sea -2.60 -21.3 Michigan -5.70 -43.9






252

Lake δ18O

(SMOW) δ2H

(SMOW) Lake

δ18O (SMOW)

δ2H (SMOW)















































253

Lake δ18O

(SMOW) δ2H

(SMOW) Lake

δ18O (SMOW)

δ2H (SMOW)




Caspian Sea -2.00 -25.0 Michigan -5.85 -44.1

























Chad -0.89 -2.8 Naivasha 6.56 36.3

Chad 0.35 2.4 Naivasha 6.30 40.4

Chad 0.30 3.8 Naivasha 6.60 36.0

Chad 0.85 10.5 Naivasha 3.60 23.0

Chad 1.69 8.0 Naivasha 4.10 24.0

Chad 2.54 12.9 Naivasha 4.40 18.0

Chad 4.67 25.2 Naivasha 4.20 20.0

Chad 5.01 25.7 Naivasha 4.90 33.0

Chad 5.14 23.7 Naivasha 6.60 36.0

Chad 5.51 29.8 Nam Co -7.57 -73.0

Chad 6.40 31.8 Nam Co -7.03 -66.7

Chad 6.80 34.2 Nasser -1.17 1.2

Chad 6.97 41.9 Nasser -1.15 0.7

Chad 7.21 42.6 Nasser -1.11 -0.1

Chad 7.88 45.0 Nasser -0.99 1.1

Chad 8.06 43.8 Nasser -0.57 4.1

Chad 8.06 41.1 Nasser 0.06 10.5

Chad 8.60 54.1 Nasser 0.17 8.3

254

Lake δ18O

(SMOW) δ2H

(SMOW) Lake

δ18O (SMOW)

δ2H (SMOW)

Chad 10.61 57.5 Nasser -1.35 -0.1

Chad 12.37 64.9 Nasser -1.30 1.1

Chad 11.33 57.3 Nasser -1.20 2.1

Chad 9.52 47.7 Nasser -0.62 5.7

Chad 11.60 59.2 Nasser -1.22 0.6

Chad 12.69 69.8 Nasser -1.23 1.1

Chad 13.01 70.0 Nasser -1.27 -0.1

Chad 12.99 67.6 Nasser -1.24 0.3

Chad 13.11 73.7 Nasser -1.25 1.0

Chad 13.51 72.7 Nasser -1.41 1.1

Chad 13.88 69.8 Nasser -0.90 5.1

Chad 14.05 74.7 Nasser -0.76 4.4

Chad 14.97 76.7 Nasser -0.64 3.8

Chad 0.11 -1.3 Nasser -0.84 3.0

Chad 3.57 22.5 Nasser -0.85 3.5

Chad 4.09 25.7 Nasser -0.80 3.0

Chad 3.95 22.0 Nasser -0.78 3.2

Chad 4.09 22.7 Nasser -0.73 2.4

Chad 4.07 20.0 Nasser 0.69 11.9

Chad 4.59 23.9 Nasser 0.46 10.1

Chad 4.94 24.9 Nasser 0.27 9.5

Chad 5.46 26.6 Nasser 0.19 9.2

Chad 5.21 27.9 Nasser 0.34 9.4

Chad 5.63 31.1 Nasser 2.16 19.6

Chad 5.91 30.1 Nasser 2.11 18.8

Chad 5.93 31.5 Nasser 2.01 20.0

Chad 5.98 33.8 Nasser 2.12 21.0

Chad 5.85 33.8 Nasser 2.12 20.2

Chad 6.27 37.7 Nasser 2.11 20.1

Chad 6.35 36.7 Nasser 2.10 18.9

Chad 6.45 36.7 Nasser 2.11 20.8

Chad 6.65 36.7 Nasser 2.18 21.2

Chad 6.72 35.5 Nasser 1.56 15.8

Chad 6.82 38.7 Nasser 2.41 21.9

Chad 7.04 38.9 Ngangla Ringco -4.22 -56.6

Chad 7.32 36.2 Nicaragua -2.00 -9.0

Chad 7.72 35.7 Oahe -14.17 -112.6

Chad 7.67 37.7 Oahe -14.06 -113.5

Chad 7.84 39.1 Oahe -14.02 -113.1

Chad 7.09 40.6 Oahe -14.39 -116.7

Chad 7.29 44.6 Oahe -14.45 -117.4

Chad 7.39 43.8 Oahe -14.24 -116.8

Chad 7.54 44.3 Oahe -14.28 -116.0

Chad 7.96 43.1 Oahe -14.23 -115.8

Chad 8.11 42.6 Oahe -14.57 -117.4

Chad 8.36 42.1 Oahe -14.03 -115.5

255

Lake δ18O

(SMOW) δ2H

(SMOW) Lake

δ18O (SMOW)

δ2H (SMOW)

Chad 8.71 43.1 Oahe -14.21 -116.0

Chad 8.21 45.8 Okanagan -11.1 -104.0

Chad 7.98 49.5 Okanagan -12.0 -105.0

Chad 8.53 49.5 Okanagan -11.6 -105.0

Chad 8.90 52.9 Okanagan -10.6 -105.0

Chad 9.07 51.4 Okanagan -11.4 -103.0

Chad 9.12 50.9 Okanagan -10.7 -102.0

Chad 8.95 47.2 Okanagan -9.9 -101.0

Chad 9.10 47.7 Okanagan -11.5 -102.0

Chad 9.42 49.9 Okanagan -11.3 -101.0

Chad 9.22 55.8 Okanagan -11.6 -101.0

Chad 9.62 57.3 Okanagan -11.6 -102.0

Chad 9.54 56.1 Okanagan -11.8 -102.0

Chad 10.11 57.5 Okanagan -11.7 -101.0

Chad 10.24 50.7 Okanagan -11.2 -99.0

Chad 10.61 56.6 Okanagan -11.7 -108.0

Chad 10.74 54.8 Okanagan -10.7 -108.0

Chad 11.04 53.4 Okanagan -11.4 -108.0

Chad 10.91 57.0 Okanagan -11.9 -109.0

Chad 11.21 59.5 Okanagan -11.8 -105.0

Chad 11.55 62.7 Okanagan -10.7 -106.0

Chad 11.80 64.7 Okanagan -11.2 -104.0

Chad 12.02 70.6 Okanagan -10.7 -102.0

Chad 12.14 70.8 Okanagan -10.7 -98.0

Chad 12.29 68.6 Okanagan -11.8 -101.0

Chad 12.47 68.8 Okanagan -11.5 -101.0

Chad 12.08 58.7 Okanagan -11.8 -103.0

Chad 13.36 71.5 Okanagan -11.5 -101.0

Chad 13.88 75.4 Okanagan -11.9 -102.0

Chad 14.28 77.6 Okanagan -12.1 -104.0

Chad 14.95 79.9 Okanagan -11.9 -102.0

Chamo 8.54 50.4 Okanagan -11.8 -102.0

Chamo 6.55 45.1 Okanagan -11.7 -103.0

Chamo 6.63 45.2 Okanagan -11.3 -100.0

Chamo 6.60 45.6 Okanagan -11.9 -107.0

Chamo 7.59 49.5 Okanagan -11.7 -109.0

Chamo 7.46 50.1 Okanagan -11.6 -109.0

Chamo 8.12 50.9 Okavango Delta -4.72 -34.5







Dabusun -0.61 -45.9 Okavango Delta -2.31 -28.0

Dabusun -0.21 -43.5 Okavango Delta -0.85 -14.8

256

Lake δ18O

(SMOW) δ2H

(SMOW) Lake

δ18O (SMOW)

δ2H (SMOW)

Dabusun 0.17 -40.7 Okavango Delta -0.81 -11.9

Dabusun 0.54 -39.6 Okavango Delta -0.15 -3.3

Dabusun 0.56 -45.6 Okavango Delta 0.11 0.4

Dabusun 0.37 -47.8 Okavango Delta 0.56 -2.7



Dagze Co -6.38 -69.5 Okavango Delta 0.79 -4.2

Dead Sea 4.30 4.0 Okavango Delta 0.99 -4.2



Dead Sea 4.31 -1.7 Okavango Delta 1.36 -3.1


Dead Sea 4.46 -0.7 Okavango Delta 1.43 2.1

Dead Sea 5.00 2.0 Okavango Delta 2.06 4.4






Dead Sea -0.10 4.4 Okavango Delta 2.34 1.2

Dead Sea 0.10 4.4 Onega -10.94

Dead Sea -0.40 Onega -9.95

Dead Sea 0.10 4.7 Ontario -6.61 -49.2

Dead Sea -0.50 4.9 Ontario -6.37 -48.6

Dead Sea 0.70 4.5 Ontario -6.58 -48.9

Dead Sea -1.20 4.4 Ontario -6.42 -49.0

Dead Sea 0.10 4.5 Ontario -6.57 -48.8

Dead Sea 0.50 3.9 Ontario -6.50 -49.2

Dead Sea 0.20 4.5 Ontario -6.58 -49.4

Dead Sea 1.10 4.7 Ontario -6.67 -48.7

Dead Sea -0.50 4.5 Ontario -6.48 -49.1

Dead Sea -0.40 4.4 Ontario -6.53 -49.1

Dead Sea -1.90 4.5 Ontario -6.45 -49.2

Dead Sea -0.80 4.5 Ontario -6.58 -49.2

Edward 4.30 29.0 Ontario -6.62 -48.6

Edward 4.50 31.0 Ontario -6.56 -49.2

Edward 4.20 29.0 Ontario -6.68 -49.2

Edward 4.20 30.0 Ontario -6.48 -48.6

Egridir -1.90 -18.0 Ontario -6.59 -49.2

Egridir -1.60 -22.0 Ontario -6.57 -49.2

Egridir -2.30 -20.0 Ontario -6.65 -49.0

Egridir -1.30 -19.0 Ontario -6.68 -48.7

Egridir -2.30 -19.0 Ontario -6.58 -49.0

Egridir -2.70 -22.0 Ontario -6.64 -48.9

Egridir -2.90 -23.0 Ontario -6.50 -48.5

Egridir -3.20 -23.0 Ontario -6.63 -49.1

257

Lake δ18O

(SMOW) δ2H

(SMOW) Lake

δ18O (SMOW)

δ2H (SMOW)

Egridir -3.00 -25.0 Ontario -6.58 -49.1

Egridir -2.30 -22.0 Ontario -6.61 -49.4

Elephant Butte

-8.8 -73.0 Ontario -6.62 -49.3

Elephant Butte

-7.6 -67.0 Ontario -6.47 -48.8

Elephant Butte

-7.4 -66.0 Ontario -6.68 -49.0

Elephant Butte

-7.2 -65.0 Ontario -6.47 -49.2

Elephant Butte

-7.6 -68.0 Ontario -6.57 -49.0

Elephant Butte

-7.8 -68.0 Ontario -6.54 -49.6

Elephant Butte

-7.0 -65.0 Ontario -6.60 -51.6

Elephant Butte

-7.1 -64.0 Ontario -6.63 -49.7

Elephant Butte

-7.0 -65.0 Ontario -6.62 -49.7

Elephant Butte

-6.6 -63.0 Ontario -6.32 -47.9

Elephant Butte

-7.8 -64.0 Ontario -6.67 -49.5

Elephant Butte

-7.8 -65.7 Ontario -6.58 -49.1

Erie -6.47 -47.2 Ontario -6.62 -49.3

Erie -6.54 -47.6 Ontario -6.56 -48.8

Erie -6.42 -47.0 Ontario -6.67 -49.4

Erie -6.57 -47.8 Ontario -6.48 -49.1

Erie -6.47 -47.5 Ontario -6.59 -49.4

Erie -6.56 -47.0 Ontario -6.45 -48.6

Erie -6.70 -48.6 Ontario -6.57 -49.2

Erie -6.44 -47.2 Ontario -6.56 -49.2

Erie -6.71 -49.2 Ontario -6.59 -48.8

Erie -6.53 -47.5 Ontario -6.57 -49.1

Erie -6.75 -48.8 Ontario -6.62 -49.1

Erie -6.53 -47.4 Ontario -6.64 -49.2

Erie -6.43 -48.5 Ontario -6.48 -48.5

Erie -6.48 -47.8 Ontario -6.58 -49.2

Erie -6.53 -47.2 Ontario -6.65 -49.5

Erie -6.41 -47.4 Ontario -6.60 -49.1

Erie -6.55 -47.3 Ontario -6.61 -49.1

Erie -6.50 -47.3 Ontario -6.68 -49.4

Erie -6.37 -47.4 Ontario -6.45 -48.2

Erie -6.80 -44.6 Ontario -6.56 -49.1

258

Lake δ18O

(SMOW) δ2H

(SMOW) Lake

δ18O (SMOW)

δ2H (SMOW)

Erie -6.75 -46.6 Ontario -6.63 -49.0

Erie -6.38 -47.6 Ontario -6.53 -48.9

Erie -6.57 -49.2 Ontario -6.70 -49.3

Erie -6.43 -47.7 Ontario -6.55 -49.0

Erie -6.41 -47.3 Ontario -6.60 -49.3

Erie -6.63 -47.7 Ontario -6.70 -53.0

Erie -6.61 -47.9 Ontario -6.70 -51.0

Erie -6.85 -47.1 Ontario -6.60 -50.0

Erie -6.45 -47.6 Ontario -6.60 -51.0

Erie -6.48 -47.7 Ontario -6.60 -52.0

Erie -6.44 -47.9 Powell -14.61 -113.0

Erie -6.56 -48.0 Powell -14.82 -116.4

Erie -6.57 -48.0 Powell -15.46 -119.1

Erie -6.43 -47.5 Powell -15.02 -114.3

Erie -6.45 -47.7 Powell -15.12 -114.8

Erie -6.64 -47.4 Powell -14.94 -113.1

Erie -6.40 -47.8 Powell -14.92 -114.3

Erie -6.60 -47.1 Powell -15.14 -115.5

Erie -6.61 -47.8 Powell -14.87 -113.4

Erie -6.46 -47.8 Powell -14.72 -113.6

Erie -6.36 -47.8 Powell -15.35 -119.4

Erie -6.56 -47.5 Powell -15.44 -120.2

Erie -6.45 -48.0 Powell -15.36 -121.1

Erie -6.67 -48.3 Powell -15.37 -121.4

Erie -6.72 -47.9 Powell -15.37 -121.8

Erie -6.44 -47.7 Powell -15.44 -121.8

Erie -6.44 -48.2 Powell -15.29 -121.7

Erie -6.61 -48.0 Powell -15.33 -121.3

Erie -6.38 -47.5 Powell -15.47 -122.9

Erie -6.77 -48.2 Powell -15.23 -121.3

Erie -6.35 -47.5 Powell -15.61 -122.8

Erie -6.52 -47.6 Powell -15.64 -122.7

Erie -6.33 -47.5 Powell -15.58 -122.9

Erie -6.51 -47.3 Powell -15.63 -123.2

Erie -6.79 -50.8 Powell -15.52 -123.3

Erie -6.47 -48.0 Powell -15.45 -122.6

Erie -6.84 -50.6 Powell -15.46 -122.6

Erie -6.53 -47.6 Powell -15.51 -122.4

Erie -6.55 -47.5 Powell -15.45 -122.3

Erie -6.76 -51.3 Powell -15.60 -122.6

Erie -6.53 -46.6 Powell -15.57 -122.7

Erie -6.39 -48.4 Powell -15.54 -123.3

Erie -6.55 -47.9 Powell -15.29 -119.8

Erie -6.37 -48.5 Powell -14.76 -118.4

Erie -6.49 -46.7 Powell -14.82 -118.9

Erie -6.42 -48.4 Powell -14.92 -119.3

259

Lake δ18O

(SMOW) δ2H

(SMOW) Lake

δ18O (SMOW)

δ2H (SMOW)

Erie -6.61 -48.8 Powell -15.38 -120.9

Erie -7.48 -54.9 Powell -15.12 -120.1

Erie -6.57 -47.8 Powell -15.47 -121.3

Erie -7.37 -55.2 Powell -15.41 -121.3

Erie -6.55 -48.7 Powell -15.41 -121.6

Erie -7.37 -55.1 Powell -15.15 -121.2

Erie -7.11 -53.7 Powell -15.31 -121.6

Erie -6.51 -48.3 Powell -15.25 -120.3

Erie -6.54 -48.5 Powell -15.33 -121.1

Erie -7.22 -54.2 Powell -15.43 -121.1

Erie -6.63 -48.6 Powell -15.44 -121.2

Erie -7.18 -53.9 Powell -15.12 -120.0

Erie -7.24 -53.5 Powell -15.25 -120.2

Erie -6.88 -50.5 Powell -15.32 -120.8

Erie -6.92 -50.5 Powell -14.87 -119.4

Erie -7.18 -52.3 Powell -14.82 -116.9

Erie -7.20 -53.4 Powell -15.14 -117.7

Erie -6.91 -51.0 Powell -15.07 -117.7

Erie -7.06 -54.0 Powell -15.52 -120.2

Erie -6.90 -50.9 Powell -15.45 -120.9

Erie -7.07 -53.4 Powell -15.58 -121.1

Erie -6.96 -51.1 Powell -15.62 -121.7

Erie -7.15 -53.8 Powell -15.58 -121.9

Erie -6.86 -50.8 Powell -15.59 -121.4

Erie -6.37 -47.7 Powell -15.59 -121.5

Erie -6.31 -48.3 Powell -15.55 -121.0

Erie -6.31 -48.1 Powell -15.54 -120.7

Erie -6.48 -47.0 Powell -15.52 -120.7

Erie -6.45 -47.6 Powell -15.57 -121.3

Erie -6.41 -47.8 Powell -15.56 -121.0

Erie -6.35 -48.4 Powell -15.45 -120.7

Erie -6.54 -49.4 Powell -15.52 -120.5

Erie -6.50 -49.3 Powell -15.48 -120.8

Erie -6.33 -48.3 Powell -15.43 -120.5

Erie -6.46 -48.3 Powell -15.53 -120.9

Erie -6.52 -49.3 Powell -15.53 -121.0

Erie -6.41 -48.1 Powell -15.56 -120.9

Erie -6.56 -49.6 Powell -15.56 -120.9

Erie -6.47 -49.2 Powell -15.47 -120.5

Erie -6.42 -49.3 Powell -15.48 -120.9

Erie -6.42 -48.6 Powell -15.54 -120.9

Erie -6.43 -49.1 Powell -15.60 -120.7

Erie -6.65 -51.6 Powell -15.56 -121.3

Erie -7.21 -53.5 Powell -15.59 -120.5

Erie -6.72 -52.3 Powell -15.49 -121.3

Erie -7.23 -54.0 Powell -15.46 -120.9

260

Lake δ18O

(SMOW) δ2H

(SMOW) Lake

δ18O (SMOW)

δ2H (SMOW)

Erie -6.75 -51.8 Powell -15.48 -120.7

Erie -7.30 -54.7 Powell -15.35 -120.4

Erie -6.85 -52.0 Powell -15.43 -120.8

Erie -7.32 -54.1 Powell -15.37 -120.3

Erie -6.83 -52.5 Powell -15.35 -120.8

Erie -7.18 -54.4 Powell -15.06 -118.6

Erie -6.90 -52.2 Powell -15.31 -121.3

Erie -7.25 -53.6 Powell -15.37 -120.8

Erie -6.40 -48.8 Powell -15.42 -121.3

Erie -6.37 -49.2 Powell -15.28 -120.3

Erie -6.45 -49.0 Powell -15.35 -120.3

Erie -6.58 -49.4 Powell -15.28 -121.3

Erie -6.48 -49.3 Powell -15.43 -120.5

Erie -6.63 -50.3 Powell -15.25 -120.7

Erie -6.30 -48.0 Powell -15.37 -120.6

Erie -6.40 -52.0 Powell -15.37 -120.4

Erie -6.40 -47.0 Powell -15.19 -120.9

Erie -6.40 -46.0 Powell -15.33 -120.5

Erie -6.50 -50.0 Powell -15.23 -120.9

Erie -6.50 -46.0 Powell -15.34 -120.4

Erie -6.50 -48.0 Powell -15.21 -119.9

Erie -6.50 -50.0 Powell -14.99 -118.3

Erie -6.70 -52.0 Powell -14.88 -117.3

Erie -6.70 -49.0 Powell -15.17 -117.9

Erie -6.50 -50.0 Powell -14.95 -116.2

Erie -6.50 -51.0 Powell -14.93 -116.2

Erie -6.50 -48.0 Powell -15.12 -117.2

Erie -6.70 -49.0 Powell -15.16 -118.1

Erie -6.60 -45.0 Powell -14.86 -117.0

Erie -6.70 -51.0 Powell -14.89 -117.5

Erie -6.70 -52.0 Powell -15.20 -118.3

Erie -6.80 -53.0 Powell -15.32 -118.5

Erie -6.60 -49.0 Powell -14.86 -117.3

Erie -6.50 -48.0 Powell -15.24 -117.9

Erie -6.70 Powell -15.18 -118.2

Erie -6.60 -49.0 Powell -14.88 -117.1

Erie -6.80 -51.0 Poyang -10.61 -68.4

Erie -6.80 -56.0 Poyang -9.64 -57.4

Erie -6.10 -46.5 Poyang -9.28 -55.5

Garda -7.30 -55.1 Poyang -8.35 -49.2

Garda -7.20 -54.0 Poyang -7.85 -51.5

Garda -7.00 -53.4 Poyang -7.74 -43.9

Garda -7.40 -55.7 Poyang -6.83 -41.3

Garda -7.20 -55.0 Poyang -6.24 -42.2

Garda -9.20 -69.9 Poyang -6.42 -41.6

Garda -7.80 -59.0 Poyang -6.51 -38.5

261

Lake δ18O

(SMOW) δ2H

(SMOW) Lake

δ18O (SMOW)

δ2H (SMOW)

Garda -7.40 -56.2 Poyang -6.39 -38.1

Garda -7.30 -54.1 Poyang -6.28 -38.4

Garda -7.30 -55.0 Poyang -6.09 -34.1

Garda -7.20 -54.4 Poyang -6.16 -33.0

Garda -7.30 -55.5 Poyang -6.37 -29.9

Garda -7.20 -56.2 Poyang -6.48 -30.9

Garda -7.40 -56.9 Poyang -6.54 -32.3

Garda -7.30 -54.3 Poyang -6.53 -33.3

Garda -7.40 -54.8 Poyang -6.46 -33.7

Garda -7.40 -55.4 Poyang -6.30 -34.6

Garda -7.60 -56.4 Poyang -6.33 -33.3

Garda -7.30 -54.8 Poyang -6.48 -38.9

Garda -7.30 -54.3 Poyang -6.33 -39.7

Garda -7.40 -55.3 Poyang -6.30 -38.1

Garda -7.30 -54.2 Poyang -6.20 -33.0

Garda -7.00 -53.0 Poyang -6.37 -32.3

Garda -7.30 -54.3 Poyang -6.37 -33.6

Garda -7.30 -54.8 Poyang -6.45 -32.8

Garda -7.10 -53.0 Poyang -6.29 -32.6

Garda -7.40 -54.1 Poyang -6.46 -31.3

Garda -7.30 -54.4 Poyang -6.45 -33.9

Garda -7.30 -54.2 Poyang -6.27 -34.7

Garda -7.50 -54.7 Poyang -6.30 -32.0

Garda -7.30 -54.5 Poyang -6.50 -32.9

Garda -7.40 -55.3 Poyang -6.11 -33.6

Garda -7.20 -53.9 Qarhan Salt 6.63 -15.6

Garda -7.40 -55.1 Qianhai Hu 0.97 4.4

Garda -7.10 -53.8 Qianhai Hu 1.26 3.1

Garda -7.48 -55.0 Qianhai Hu 2.48 11.9

Garda -7.46 -55.0 Qianhai Hu 2.69 11.9

Garda -7.19 -54.8 Qianhai Hu 2.80 12.5

Garda -7.23 -54.3 Qianhai Hu 2.60 14.0

Garda -7.15 -53.9 Qianhai Hu 2.78 14.8

Garda -7.24 -53.5 Qianhai Hu 2.85 15.6

Garda -7.31 -54.2 Qianhai Hu 2.69 15.6

Garda -7.35 -55.3 Qianhai Hu 2.76 16.9

Garda -7.36 -54.2 Red Sea 0.98 5.4

Garda -7.30 -54.7 Red Sea 1.13 6.2

Garda -7.23 -53.9 Red Sea 1.33 8.2

Garda -7.26 -54.3 Red Sea 1.85 11.3

Garda -7.36 -55.0 Red Sea 1.95 11.5

Garda -7.34 -54.5 Red Sea 1.14 7.0

Garda -7.30 -55.1 Red Sea 1.16 7.1

Garda -7.32 -55.9 Red Sea 1.16 7.2

Garda -7.34 -54.9 Red Sea 1.19 7.7

Garda -7.33 -55.9 Red Sea 1.22 7.2

262

Lake δ18O

(SMOW) δ2H

(SMOW) Lake

δ18O (SMOW)

δ2H (SMOW)

Garda -7.26 -55.0 Red Sea 1.36 8.1

Garda -7.35 -55.3 Red Sea 1.38 7.7

Garda -7.27 -55.5 Red Sea 1.55 9.3

Garda -7.31 -55.1 Red Sea 1.57 9.0

Garda -7.38 -55.9 Red Sea 1.59 9.3

Garda -7.41 -55.5 Red Sea 1.62 9.5

Garda -7.38 -55.5 Rukwa 4.50 27.0

Garda -7.23 -54.7 Rukwa 4.30 27.0

Garda -7.38 -54.7 Rukwa 4.40 27.0

Garda -7.33 -54.4 Rukwa 4.10 23.0

Garda -7.21 -54.7 Sakakawea -15.35 -122.0

Garda -7.27 -55.6 Sakakawea -15.35 -122.5

Garda -7.45 -56.2 Sakakawea -15.60 -123.7

Garda -7.26 -55.9 Sakakawea -15.70 -123.3

Garda -7.43 -56.4 Sakakawea -15.75 -126.2

Garda -7.37 -55.4 Sakakawea -15.33 -123.1

Garda -7.47 -55.8 Sakakawea -15.22 -122.7

Garda -7.17 -54.6 Sakakawea -15.34 -125.6

Garda -7.39 -55.9 Sakakawea -15.37 -125.7

Garda -7.40 -56.1 Sakakawea -15.39 -124.1

Garda -7.42 -56.5 Sakakawea -15.46 -125.4

Garda -7.32 -54.7 Sakakawea -15.37 -122.6

Garda -7.12 -54.5 Sakakawea -15.63 -124.7

Garda -7.39 -55.1 Sakakawea -15.77 -126.0

Garda -7.29 -55.3 Sakakawea -15.53 -122.6

Garda -7.35 -55.8 Salton Sea -1.95 -43.0

Garda -7.35 -55.8 Salton Sea -5.30 -60.0

Garda -7.33 -55.9 Salton Sea -8.83 -82.7

Garda -7.32 -55.0 Sambhar Salt -5.50

Garda -7.29 -55.7 Sambhar Salt -1.00

Garda -7.31 -55.5 Sambhar Salt 4.50






Garda -7.18 -54.9 Shala 7.29 53.2

Garda -7.28 -55.4 Shala 7.92 55.1

Garda -7.33 -55.1 Shala 7.40 51.9

Garda -7.46 -56.2 Shala 6.22 45.7

Garda -7.46 -55.7 Shala 7.36 50.9

Garda -7.45 -55.8 Shala 7.52 48.4

Garda -7.10 -55.1 Shala 7.66

Garda -7.12 -54.3 Shala 7.49

Garda -7.13 -55.0 Shala 7.73

Garda -7.10 -54.9 Shala 7.77

263

Lake δ18O

(SMOW) δ2H

(SMOW) Lake

δ18O (SMOW)

δ2H (SMOW)

Garda -7.16 -54.5 Shala 5.36

Garda -6.99 -53.8 Shala 7.82

Garda -7.15 -54.0 Shala 7.23

Garda -7.08 -55.1 Shala 6.96

Garda -7.14 -54.6 Shala 8.30 54.4

Garda -7.36 -55.7 Shala 8.28 54.3

Garda -7.28 -55.2 Shala 8.49 54.5

Garda -7.29 -55.7 Shala 7.78 51.6

Garda -7.17 -55.0 Shala 7.05 52.5

Garda -7.33 -54.3 Superior -8.58 -65.6

Garda -7.27 -55.2 Superior -8.61 -66.8

Garda -7.21 -55.1 Superior -8.66 -66.7

Garda -7.30 -54.8 Superior -8.60 -65.9

Garda -7.31 -54.8 Superior -8.66 -66.9

Garda -7.37 -54.7 Superior -8.70 -66.8

Garda -7.34 -54.3 Superior -8.64 -65.6

Garda -7.34 -54.3 Superior -8.60 -66.7

Garda -7.43 -55.7 Superior -8.63 -65.1

Garda -7.47 -54.6 Superior -8.66 -66.8

Garda -7.30 -54.7 Superior -8.59 -65.9

Garda -7.38 -55.3 Superior -8.65 -67.2

Garda -7.46 -56.0 Superior -8.66 -65.6

Garda -7.42 -55.1 Superior -8.63 -66.8

Garda -7.29 -54.0 Superior -8.61 -66.6

Garda -7.24 -54.1 Superior -8.72 -65.0

Garda -7.19 -55.6 Superior -8.74 -66.8

Garda -7.39 -56.3 Superior -8.56 -65.3

Garda -7.30 -54.8 Superior -8.64 -66.8

Garda -7.30 -55.4 Superior -8.58 -66.8

Garda -7.32 -55.7 Superior -8.61 -64.6

Garda -7.44 -55.1 Superior -8.61 -67.4

Garda -7.41 -55.5 Superior -8.65 -64.6

Garda -7.54 -55.6 Superior -8.53 -67.4

Garda -7.51 -55.8 Superior -8.62 -65.3

Garda -7.58 -55.9 Superior -8.52 -67.2

Garda -7.44 -56.0 Superior -8.61 -65.4

Garda -7.41 -55.0 Superior -8.47 -67.0

Garda -7.35 -55.6 Superior -8.65 -64.4

Garda -7.39 -55.1 Superior -8.64 -66.7

Garda -7.43 -56.0 Superior -8.58 -66.5

Garda -7.40 -55.5 Superior -8.55 -64.7

Garda -7.25 -54.8 Superior -8.67 -64.8

Garda -7.26 -54.1 Superior -8.49 -67.4

Garda -7.37 -54.8 Superior -8.49 -67.0

Garda -7.51 -56.2 Superior -8.64 -64.9

Garda -7.47 -55.4 Superior -8.63 -67.0

264

Lake δ18O

(SMOW) δ2H

(SMOW) Lake

δ18O (SMOW)

δ2H (SMOW)

Garda -7.42 -56.1 Superior -8.65 -67.3

Garda -7.37 -54.4 Superior -8.69 -64.9

Garda -7.41 -55.7 Superior -8.55 -67.1

Garda -7.42 -54.5 Superior -8.60 -66.9

Garda -7.25 -54.5 Superior -8.74 -64.9

Garda -7.21 -53.5 Superior -8.60 -67.0

Garda -7.23 -54.4 Superior -8.71 -65.1

Garda -7.27 -55.0 Superior -8.52 -67.1

Garda -7.44 -56.1 Superior -8.65 -65.0

Garda -7.46 -55.8 Superior -8.55 -67.1

Garda -7.43 -54.8 Superior -8.55 -67.1

Garda -7.42 -55.5 Superior -8.59 -64.8

Garda -7.47 -55.5 Superior -8.62 -66.9

Garda -7.17 -54.6 Superior -8.61 -65.1

Garda -7.23 -55.2 Superior -8.53 -66.6

Garda -7.34 -55.1 Superior -8.60 -64.8

Garda -6.88 -54.5 Superior -8.51 -66.8

Garda -7.27 -55.6 Superior -8.67 -66.6

Garda -7.02 -54.0 Superior -8.57 -64.7

Garda -7.21 -55.3 Superior -8.74 -65.1

Garda -7.39 -55.3 Superior -8.56 -67.0

Garda -7.16 -55.5 Superior -8.56 -67.2

Garda -7.33 -55.9 Superior -8.61 -65.0

Garda -7.26 -55.6 Superior -8.57 -67.1

Garda -7.36 -54.8 Superior -8.63 -64.8

Garda -7.38 -56.0 Superior -8.69 -67.2

Garda -7.29 -55.2 Superior -8.55 -66.8

Garda -7.38 -54.9 Superior -8.68 -65.0

Garda -7.21 -55.5 Superior -8.60 -66.8

Garda -7.23 -55.9 Superior -8.62 -64.8

Garda -7.40 -56.2 Superior -8.54 -67.5

Geneva -12.38 -87.5 Superior -8.65 -64.9

Geneva -12.43 -88.5 Superior -8.58 -67.4

Geneva -12.23 -88.2 Superior -8.75 -64.9

Geneva -12.38 -85.2 Superior -8.62 -65.7

Geneva -12.17 -85.6 Superior -8.66 -67.2

Geneva -12.32 -88.5 Superior -8.65 -64.8

Geneva -12.41 -89.5 Superior -8.62 -66.8

Geneva -12.29 -86.2 Superior -8.50 -66.9

Geneva -12.21 -85.9 Superior -8.63 -65.1

Great Bear -17.90 Superior -8.73 -64.7

Great Bear -18.55 -157.6 Superior -8.72 -65.3





265

Lake δ18O

(SMOW) δ2H

(SMOW) Lake

δ18O (SMOW)

δ2H (SMOW)



























Great Salt -6.28 -72.8 Superior -8.65 -65.6




















266

Lake δ18O

(SMOW) δ2H

(SMOW) Lake

δ18O (SMOW)

δ2H (SMOW)













Great Slave -17.89 -140.1 Superior -8.80 -66.0






Great Slave -17.90 Superior -8.80

Huron -7.18 -52.9 Superior -8.80 -67.0

Huron -6.90 -54.3 Superior -8.80 -66.0

Huron -6.86 -54.1 Superior -8.80 -66.0

Huron -7.02 -52.1 Superior -8.70 -66.0

Huron -6.96 -54.0 Superior -8.70

Huron -7.00 -53.8 Superior -8.70 -66.0

Huron -7.04 -53.4 Superior -8.70 -65.0

Huron -6.97 -53.0 Superior -8.70 -65.0

Huron -7.04 -54.3 Superior -8.80 -65.0

Huron -7.07 -54.3 Superior -8.80 -67.0

Huron -7.03 -53.1 Superior -8.80 -68.0

Huron -6.92 -54.4 Superior -8.80 -67.0

Huron -7.08 -53.4 Superior -8.70 -68.0

Huron -7.01 -54.2 Tahoe -5.20 -59.0

Huron -7.00 -52.9 Tahoe -5.80 -59.0

Huron -7.05 -54.0 Tahoe -5.20 -56.0

Huron -7.09 -54.1 Tahoe -5.80 -56.0

Huron -7.06 -54.0 Tana 3.33 30.3

Huron -7.15 -53.9 Tana 4.15 33.5

Huron -7.12 -51.5 Tana 4.35 36.3

Huron -7.04 -54.0 Tana 4.57 36.6

Huron -7.06 -54.1 Tana 4.95 37.8

Huron -7.04 -52.4 Tana 5.11 38.4

Huron -6.97 -54.2 Tana 5.98 44.8

Huron -7.19 -53.4 Tana 5.32 42.8

Huron -6.94 -53.9 Tana 4.87 37.9

Huron -7.00 -53.5 Tana 4.88 38.0

267

Lake δ18O

(SMOW) δ2H

(SMOW) Lake

δ18O (SMOW)

δ2H (SMOW)

Huron -6.91 -54.1 Tana 4.87 37.8

Huron -7.07 -54.8 Tana 4.90 37.7

Huron -7.04 -53.2 Tana 4.11 35.8

Huron -7.10 -54.9 Tana 3.77 32.9

Huron -7.08 -53.2 Tana 3.93 34.5

Huron -6.90 -54.1 Tana 4.26 36.7

Huron -7.12 -52.8 Tana 6.30 46.0

Huron -6.93 -54.2 Tana 6.70 45.0

Huron -7.09 -53.7 Tana 6.80 50.0

Huron -7.01 -54.2 Tana 3.10 27.5

Huron -7.08 -54.0 Tana 3.20 29.0

Huron -7.05 -52.9 Tana 3.50 33.4

Huron -7.15 -53.9 Tana 3.85 32.5

Huron -7.06 -53.4 Tana 5.48 41.3

Huron -7.16 -53.7 Tana 5.72 43.1

Huron -7.07 -53.7 Tana 6.17 44.1

Huron -7.05 -53.8 Tana 6.52 48.7

Huron -7.10 -54.6 Tana 4.30 34.6

Huron -7.09 -53.4 Tana 3.74 29.5

Huron -7.02 -54.7 Tana 3.60 28.8

Huron -7.01 -54.4 Tana 3.46 28.8

Huron -7.01 -53.6 Tana 3.57 29.7

Huron -7.11 -53.3 Tana 3.55 29.8

Huron -7.06 -54.7 Tana 3.70 29.6

Huron -7.09 -54.0 Tana 3.68 30.7

Huron -7.05 -53.7 Tana 3.56 30.5

Huron -6.94 -54.7 Tana 3.67 30.8

Huron -7.00 -54.0 Tana 4.11 32.6

Huron -6.93 -53.9 Tana 3.67 32.1

Huron -7.18 -54.9 Tana 4.24 34.3

Huron -7.01 -54.4 Tana 4.27 35.1

Huron -7.08 -53.6 Tana 3.88 31.4

Huron -7.13 -54.7 Tana 4.30 34.0

Huron -7.10 -54.3 Tana 4.03 33.5

Huron -7.15 -54.3 Tana 4.05 34.2

Huron -7.03 -53.8 Tana 4.41 35.4

Huron -6.99 -55.2 Tana 4.31 36.2

Huron -7.08 -54.6 Tana 4.38 37.0

Huron -7.03 -54.3 Tana 5.16 37.8

Huron -7.06 -53.0 Tana 4.41 37.6

Huron -7.04 -53.9 Tana 3.91 35.4

Huron -6.96 -54.6 Tana 3.98 31.5

Huron -7.13 -53.5 Tanganyika 3.50 23.8




268

Lake δ18O

(SMOW) δ2H

(SMOW) Lake

δ18O (SMOW)

δ2H (SMOW)











































Huron -7.20 Tanganyika 3.68 24.8


Huron -7.30 -54.0 Taro Co -5.63 -68.5

Huron -7.30 -58.0 Taupo -5.31 -33.0

269

Lake δ18O

(SMOW) δ2H

(SMOW) Lake

δ18O (SMOW)

δ2H (SMOW)

Huron -7.40 -55.0 Titicaca -4.15 -52.0

Huron -7.40 -55.0 Titicaca -4.25 -53.0

Huron -7.20 -55.0 Titicaca -4.15 -50.4

Huron -7.20 -53.0 Titicaca -2.90 -47.3

Huron -7.10 -53.0 Titicaca -2.92 -47.3

Huron -7.10 -51.0 Titicaca -2.85 -47.1

Huron -7.30 -56.0 Titicaca -3.30 -46.3

Huron -7.40 -55.0 Titicaca -3.40 -48.5

Huron -6.40 -47.0 Titicaca -3.35 -46.4

Huron -7.50 -53.0 Titicaca -4.70 -54.8

Huron -8.60 Titicaca -4.60 -51.5

Huron -8.50 -62.0 Titicaca -4.70 -51.8

Huron -7.70 -58.0 Tonlé Sap -6.09

Huron -7.40 -53.0 Tonlé Sap -4.32

Huron -7.40 -58.0 Tonlé Sap -5.95

Huron -7.50 -52.0 Tonlé Sap -8.60

Huron -7.40 -53.0 Tonlé Sap -7.78

Huron -7.40 -53.0 Tonlé Sap -8.60

Huron -7.30 -54.0 Tonlé Sap -6.22

Huron -7.40 Tonlé Sap -3.61

Huron -7.40 Tonlé Sap -5.82

Huron -7.40 -55.0 Tonlé Sap -8.60

Huron -6.60 -53.6 Tonlé Sap -7.76

Issyk-Kul -0.97 -10.9 Tonlé Sap -8.47



Issyk-Kul -0.56 -8.9 Turkana 5.80 37.0




Jackson -17.97 -138.3 Turkana 5.80 40.0

Jackson -17.89 -137.5 Turkana 5.50 37.0

Kainji -17.5 Turkana 5.80 38.0



Kainji -2.2 Valencia 22.0

Kainji -2.9 Van 1.00 -6.6

Kainji -30.4 Van 0.90 -6.8

Kainji -31.9 Victoria 3.50

Kainji -29.9 Winnipeg -11.00

Kainji -28.8 Winnipeg -10.52 -79.8

Kainji -25.9 Winnipeg -10.40 -79.0

Kainji -23.8 Yamdruk-tso -5.48 -68.0

Kainji -20.0 Yellowstone -16.64 -134.5

Kainji -11.6 Zhari Namco -6.67 -75.2

Kainji -9.8 Ziway 6.70 49.0

270

Lake δ18O

(SMOW) δ2H

(SMOW) Lake

δ18O (SMOW)

δ2H (SMOW)

Kainji -8.6 Ziway 8.16 57.4

Kainji -12.8 Ziway 6.50 47.3

Kainji -12.8 Ziway 7.09 52.2

Kainji -25.8 Ziway 8.40 58.9

Kivu 0.11 10.0 Ziway 6.49 46.3

Kivu -0.29 10.8 Ziway 6.90 49.3

Kivu -0.19 12.3 Ziway 6.64 47.4

Kivu -0.15 12.1 Ziway 6.64 48.6

Kivu 0.00 12.2 Ziway 6.53 48.9

Kivu 0.47 10.8 Ziway 6.29 47.3

Kivu 0.68 11.5 Ziway 6.37 47.6

Kivu 0.70 13.6 Ziway 5.43 41.6

Kivu 0.59 16.4 Ziway 5.78 42.4

Kivu 1.36 19.9 Ziway 5.06 37.3

Kivu 1.56 20.3 Ziway 6.74 49.3

Kivu 1.85 21.5 Ziway 6.78 50.2

Kivu 2.50 24.9 Ziway 7.00 46.8

Kivu 2.51 22.0 Ziway 6.69 49.1

Kivu 3.05 24.3 Ziway 5.15 40.5

Kivu 3.15 25.4 Ziway 4.38 32.4

Kivu 3.27 24.8 Ziway 6.70 49.0

Kivu 3.47 25.7

Kivu 3.24 27.4

Kluane -21.13 -168.2

Kluane -22.51 -176.7

Kluane -22.94 -180.6

Kluane -22.86 -179.3

Kluane -22.95 -177.5

Kluane -22.86 -178.1

Kluane -22.58 -177.5

Kluane -22.81 -177.2

Kluane -22.79 -178.9

Kluane -22.84 -178.1

Kluane -22.52 -179.0

Kluane -22.47 -176.0

Kluane -22.49 -176.6

Kluane -22.54 -175.0

Kluane -22.16 -175.9

Continental-scale isotope hydrology - UNM Digital Repository

Documents

Transcript of Continental-scale isotope hydrology - UNM Digital Repository