THE INFLUENCE OF LARGE-SCALE CLIMATE DRIVERS ON THE

HYDROCLIMATOLOGY AND HYDROPOWER PRODUCTION IN SOUTHEASTERN

UNITED STATES

By

JOHANNA ENGSTRÖM

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL

OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2017

© 2017 Johanna Engström

To Ivar, my grandfather, who wanted a higher education but never got the chance

4

ACKNOWLEDGMENTS

There are many people I would like to thank for being part of this journey:

My dissertation committee, Dr. Peter Waylen, Dr. Christopher Martinez, Dr. Corene

Matyas and Dr. Joann Mossa, have shown me great support and encouragement. I am especially

thankful for Dr. Peter Waylen and Dr. Joann Mossa for encouraging me to apply to the PhD

program, and their continued mentoring of me, both as a researcher and instructor.

University of Florida and its Geography Department’s faculty and staff has supported me

both academically and professionally. The fellow geography graduate students have also played

a crucial role. Together we have shared worries, stress, crammed conference hotel rooms, and

endless laughter. Thank you for making an international student feel welcome, and for making

my years as a PhD student a great time!

Ryan Good and Qianyi Kuang, for housing me during my returns to Gainesville.

Cintia B. Uvo and Göran Loman, who were the ones that opened my eyes to the

fascinating worlds of teleconnections and renewable energies and remain true role models.

The U.S. Army Corps of Engineers personnel in Mobile and Savannah districts who’s

shared insights have been beneficial for the success of this project

My dear family: My brother Philip and his family, my mother, who introduced me to

hydropower and whose curiosity for science I have inherited, my father, who’s positive spirit I

always carry with me, and who once, in reference to my research, uttered the words “don’t quit

this, what you’re doing is important”, probably without having a clue how much those word

would come to mean to me, and David, who is both my greatest distraction and supporter.

Finally, I want to thank my wonderful friends. Many of you are far away, but your love

and support means everything, without it I would not be where I am today.

5

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...............................................................................................................4

LIST OF TABLES ...........................................................................................................................7

LIST OF FIGURES .........................................................................................................................8

LIST OF ABBREVIATIONS ........................................................................................................10

ABSTRACT ...................................................................................................................................11

CHAPTER

1 INTRODUCTION ..................................................................................................................13

Teleconnections ......................................................................................................................15

History of Hydropower in the Southeast ................................................................................16

2 DRIVERS OF LONG TERM VARIABILITY OF PRECIPITATION AND RUNOFF

IN THE SOUTHEAST ...........................................................................................................20

Data .........................................................................................................................................23 Teleconnection Indices ....................................................................................................23

Precipitation and Runoff Data .........................................................................................23

Methods ..................................................................................................................................24 Results.....................................................................................................................................25

PC Loadings ....................................................................................................................25

Identifying the Relationships ...........................................................................................27 Discussion ...............................................................................................................................29

Conclusion ..............................................................................................................................33

3 THE CHANGING HYDROCLIMATE OF THE SOUTHEAST UNITED STATES ..........46

Material and Methods .............................................................................................................47 Results.....................................................................................................................................50

Increased Precipitation ....................................................................................................52 Climate Change ...............................................................................................................54

Urbanization and Land Cover Change ............................................................................55 Conclusion ..............................................................................................................................56

4 HYDROPOWER IN THE SOUTHEAST: A HYDROCLIMATOLOGICAL

PERSPECTIVE ......................................................................................................................66

Data and Methods ...................................................................................................................67 Results and Discussion ...........................................................................................................70

Potential Production ........................................................................................................70

6

Influence of the Teleconnections .....................................................................................72

Discussion ...............................................................................................................................73 Conclusion ..............................................................................................................................75

5 SUMMARY AND CONCLUSIONS .....................................................................................86

LIST OF REFERENCES ...............................................................................................................89

BIOGRAPHICAL SKETCH .........................................................................................................99

7

LIST OF TABLES

Table page

1-1 Potential economic and environmental winnings. .............................................................19

1-2 Overview of the dams included in this project. .................................................................19

3-1 Resulting P-values from the Kolmogorow-Smirnov test. ..................................................57

8

LIST OF FIGURES

Figure page

1-1 The study area ....................................................................................................................18

2-1 Location of the uncontrolled river basins included in the analysis ....................................34

2-2 Seasonal variation of percentage of variability explained by P1, PC2 and PC3 ...............35

2-3 Spatiotemporal variation of basins in PC1 for precipitation. .............................................36

2-4 Same as Figure 2-3, but for runoff. ....................................................................................36

2-5 Lag correlation between PC1 (precipitation) for each season and the AMO index. .........37

2-6 Same as Figure 2-5 except lag correlation between PC1 (precipitation) and the AO. ......38

2-7 Same as Figure 2-5 except lag correlation between PC1 (precipitation) and the NAO. ...38

2-8 Same as Figure 2-5 except lag correlation between PC2 (precipitation) and the NAO. ...39

2-9 Same as Figure 2-5 except lag correlation between PC1 (precipitation) and ENSO. ........39

2-10 Same as Figure 2-5 except lag correlation between PC2 (precipitation) and ENSO. ........40

2-11 Same as Figure 2-5 except lag correlation between PC1 (precipitation) and the PNA. ....40

2-12 Same as Figure 2-5 except lag correlation between PC2 (precipitation) and the PNA. ....41

2-13 Same as Figure 2-5 except lag correlation between PC1( runoff ) and the AMO. ............41

2-14 Same as Figure 2-5 except lag correlation between PC2 (runoff ) and the AMO. ............42

2-15 Same as Figure 2-5 except lag correlation between PC1 ( runoff ) and the AO index. .....42

2-16 Same as Figure 2-5 except lag correlation between PC1 ( runoff ) and the NAO. ............43

2-17 Same as Figure 2-5 except lag correlation between PC1( runoff ) and the ENSO. ...........43

2-18 Same as Figure 2-5 except lag correlation between PC2 (runoff) and the ENSO. ............44

2-19 Same as Figure 2-5 except lag correlation between PC1 (runoff) and the PNA. ..............44

2-20 Same as Figure 2-5 except lag correlation between PC2 (runoff) and the PNA. ..............45

3-1 Basins included in the analysis ..........................................................................................58

3-2 Spatial patterns of estimated seasonal RCs across the study area. ....................................59

9

3-3 Annual standardized monthly maximum, median and minimum flows ............................60

3-4 Difference in the slope between precipitation and streamflow ..........................................61

3-5 Difference in the seasonal RCs before and after the identified breaks. .............................62

3-6 Visual depiction of identified break years. ........................................................................63

3-7 Cumulative distribution of break years ..............................................................................64

3-8 Tropical cyclone influence .................................................................................................65

4-1 Overview of the basins and hydroelectric dams included in the analysis. .........................77

4-2 Annual production anomaly in percent around the mean. .................................................78

4-3 Plots of average seasonal inflow and hydropower production 1988-2013 ........................79

4-4 Plots showing the influence of ENSO events. ...................................................................80

4-5 Example of lagged response. .............................................................................................81

4-6 Example of teleconnection interaction leads to amplified influences. ..............................81

4-7 Two examples form the ACT basin ...................................................................................82

4-8 Heat maps giving an overview of the seasonal influence of the teleconnections ..............83

4-9 Heat map showing the count of basins influenced by the teleconnections. .......................84

4-10 Generalized relationship between monthly reservoir inflow and hydropower prod..........85

10

LIST OF ABBREVIATIONS

ACE United States Army Corps of Engineers

ACF Apalachicola-Chattahoochee-Flint River Basin

ACT Alabama-Coosa-Tallapoosa River Basin

AMO Atlantic Multidecadal Oscillation

AO Arctic Oscillation

ENSO El Niño-Southern Oscillation

NAO North Atlantic Oscillation

PNA Pacific-North American Pattern

SAV Savannah River Basin

11

Abstract of Dissertation Presented to the Graduate School

of the University of Florida in Partial Fulfillment of the

Requirements for the Degree of Doctor of Philosophy

THE INFLUENCE OF LARGE-SCALE CLIMATE DRIVERS ON THE

HYDROCLIMATOLOGY AND HYDROPOWER PRODUCTION IN SOUTHEASTERN

UNITED STATES

By

Johanna Engström

May 2017

Chair: Peter Waylen

Major: Geography

This project takes a holistic view of the hydroclimatology of Southeastern United States,

analyzing what large-scale atmospheric patterns influence it, how the influence varies over time

and what this means for hydropower production. The study area consists of five states; Alabama,

Georgia, North Carolina, South Carolina and Tennessee, of which three (Alabama, North

Carolina and Tennessee) are among the top ten hydropower producing states in the U.S. Water

availability varies from year to year, which can partly be explained by the phase of various large-

scale atmospheric anomalies referred to as teleconnections. The teleconnections included in the

analysis are the Atlantic Multidecadal Oscillation (AMO), Arctic Oscillation (AO), El Niño-

Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), and the Pacific-North

American (PNA) pattern.

Through the application of Principal Components Analysis to historical precipitation and

natural streamflow records, and using multiple different time lags linking the leading modes of

variability to teleconnections, the AMO and ENSO are identified as the strongest drivers of

hydroclimatic variability, while the atmospheric-based patterns show more sporadic influences.

Using a Pettitt Change Point Analysis test, a breakpoint in the runoff coefficients of the

12

Southeast is identified. The shift to higher runoff coefficients throughout the region happened in

the late 1990s and is explained by increased hurricane impact on the region. Teleconnection

variability also affects hydropower production in the Southeast. Analyzing seasonal production

data from the Apalachicola-Chattahoochee-Flint, Alabama-Coosa-Tallapoosa and Savannah

River basins, the strongest and most widespread influence is asserted by the cold phase ENSO

that decrease both reservoir inflow and production throughout the region. Considering

teleconnection interaction also proved useful, as did the application of time lags, which makes

the findings useful for water resource and hydropower production forecasting.

13

CHAPTER 1

INTRODUCTION

The water resources of Southeastern U.S. (hereafter “the Southeast”) are dominated by

conflicting interests. The annual precipitation should not only satisfy the natural ecosystems, but

also the drinking water needs of rapidly growing metropolitan areas such as Atlanta and

Charlotte, recreational use, and power hydroelectric plants. The disputes stem in lingering water

resource scarcity, a result of rapid development, and include, but are not limited to, the “Tri-State

Water dispute” (between Alabama, Florida and Georgia) over the Apalachicola-Chattahoochee-

Flint and Alabama-Coosa-Tallapoosa rivers, the U.S. Supreme Court Case over water allocations

from the Catawba-Wateree River which is shared between North and South Carolina, and the

increased groundwater pumping around Memphis (Tennessee) that has led to dropping

groundwater levels in Mississippi (American Rivers, 2008; Bryan, 2008; Cameron, 2009;

Walton, 2011; Upholt, 2015). This study includes the states of Alabama, Georgia, South

Carolina, North Carolina and Tennessee, of which three (Alabama, Tennessee and North

Carolina) are among the top-ten hydropower producing states in the country (Figure 1-1)

(National Hydropower Association, 2013). As population pressure is increasing and the demand

for renewable energies is greater than ever before, there is a desperate need for a more profound

understanding of the hydroclimatology of this region in order to improve forecasting, reduce

conflicts and make the society more resilient. Climate drivers (teleconnections) can provide

valuable information for long term forecasting of water resources (Hamlet et al, 2002; Poveda et

al. 2003) and, as the climate is changing (IPCC, 2014), hydrological forecasts and projections

based on stationary historical records use assumptions that are no longer valid (Pal et al., 2015),

but need to be re-evaluated. The understanding of this changed relationship’s impact on water

availability might be even more important as a changing climate brings new normals

14

(Dominguez, Cañon, & Valdes, 2010), diverging from those that have been the dominant

patterns for decades to centuries and upon which modern water management infrastructure is

built.

This dissertation provides a holistic overview of the teleconnections influencing

the hydroclimatology of the Southeast. Unlike earlier research that focuses on one climate

driver’s influence on either precipitation or runoff, often during a limited time of the year, this

project includes multiple climate drivers and analysis of both precipitation and runoff throughout

the year. This way it adds to our understanding of the relative influences different climate drivers

have on the hydroclimatology on varying temporal scales. As a region that is facing increased

population pressure and urbanization it is imperative to investigate how available water resources

can be forecast in order to provide efficient distribution. This doctoral dissertation is a combined

study that considers how the climate and hydrologic variables interact and tries to quantify how

this interaction may have changed over time. The findings can be applied to many forms of water

resource management, but for this dissertation the application is limited to hydropower

production. Scholarly research binding hydroclimatology to hydropower has traditionally

focused on the hydropower intensive regions, such as Northwest U.S., neglecting the

hydroelectricity production of the Southeast. This project aims to fill that gap by advancing

knowledge of the complex hydroclimatology of the Southeast and its ties to hydroelectricity

generation.

In a similar study of the Columbia River Hamlet et al. (2002) found that including El

Niño-Southern Oscillation in water resource forecasting, to complement traditional forecasting

based on mountain snow pack, could increase the annual hydropower production of the

Columbia River by 9.6%. Considering federally installed dams on the Columbia River only, this

15

increase would lead to the production of an additional 5.5 million MWh of electricity and $153

million in increased revenues to the federal government per year.

This project aims to investigate what large scale atmospheric drivers influence the

hydrology and hydropower production in the Southeast, and hence would be useful in

forecasting. Table 1-1. shows how a 9.6% increase in production from the U.S. Army Corps of

Engineers’ dams in the region would benefit the environment and economy of the United States.

Teleconnections

Teleconnections are large-scale atmospheric anomalies that bring abnormal weather

conditions over vast geographic regions (Barnston & Livezey, 1987). These anomalies appear on

interannual, decadal and interdecadal timescales as a result of changed atmospheric pressure

patterns, such as the relation between the semi-permanent pressure centers Azores High and

Icelandic Low, which constitutes the North Atlantic Oscillation (NAO) index, influencing the

zonal flow over the North Atlantic (Hurrell et al. 2001, Visbeck et al. 2001). Another example

are the pressure centers over North America and East North Pacific that make up the Pacific-

North American Pattern (PNA), regulating the strength and location of the jet stream over North

America, where the negative (positive) phase is associated with a more zonal (meridional) flow

over the continent (Leathers et al. 1991). Other teleconnections are coupled ocean-atmospheric

patterns, such as El Niño-Southern Oscillation (ENSO), which is linked to changed ocean

circulation patterns and associated temperature anomalies in the Tropical Pacific Ocean. ENSO

events happens every two to seven years and the warm phase is referred to as El Niño while the

cold phase is called La Niña (Deser & Wallace 1987, Trenberth, 1997). The Atlantic

Multidecadal Oscillation (AMO) is a 60-80 year cycle of temperature variability in the North

Atlantic Ocean (Enfield et al. 2001). Such a large body of water changing temperature influences

the energy balance and hence the weather patterns of the surrounding continents.

16

As the teleconnections bring abnormal weather conditions, studies have shown that they

have a strong influence on global and regional water resources (Poveda et al. 2003, Tootle et al.

2005, Boening et al. 2012, Johnson et al. 2014) and therefore can be useful to consider in water

resources planning and management.

History of Hydropower in the Southeast

Hydropower covers approximately 7% of the electricity needs of the U.S. (U.S. Energy

Information Administration, 2013). Although hydropower has been associated with

environmental and societal concerns such as river bed erosion (Graf, 2006) changed wildlife

habitats (Young et al., 2011; Harnish et al. 2014), greenhouse gas emissions (Rudd et al, 1993;

Teodoru et al. 2012) and loss of human lands and livelihoods (Jackson & Sleigh, 2000; Plateau,

2006; Ziv et al. 2012), it still remains the most available, affordable and reliable option of all the

renewable energies in the U.S. Despite the local environmental concerns, the use of hydropower

significantly decreases electricity production related emissions of carbon dioxide and particles,

which have an adverse effect on both climate and people’s health. Recognizing the benefits of

hydropower and as the U.S. is a net importer of electricity, it is of interest to the nation to further

increase hydropower production, and decrease non-domestic electricity dependence. Work on

investigating the potential for and promoting the development of hydropower has been of great

interest in the U.S. during the last few years:

The Department of Energy has started a Water Power Program promoting the

development of hydro, wave and tidal energy extraction with the goal that 15 % of the

nation’s electricity needs should be generated using these energy sources by 2030 (U.S.

Department of Energy, 2013).

In 2014 Oak Ridge National Laboratory released a report on the potential for further

developing hydropower in the United States in rivers where there are none, or only a

limited number, of hydropower stations (“New Stream-reach Development: A

17

Comprehensive Assessment of Hydropower Energy Potential in the United States”),

showing that there is potential to almost double the current annual hydropower

production in the Southeast. This report is a follow up to the “Non-powered Dam

Resource Assessment” (2011) which evaluated the potential of hydroelectric installation

in currently non-powered dams (U.S. Department of Energy, 2013)

In 2013 the “Hydropower Regulatory Efficiency Act” was signed by President Barack

Obama. This act is meant to promote and facilitate the development of hydroelectric

power in the U.S. by streamlining the permission process and raising the small-hydro

license exemption from 5 to 10 MW installed capacity.

In 2016 the Hydropower Vision was released. This is an analysis of current and future

potential hydropower production in the U.S., created by the U.S. Department of Energy’s

Wind and Water Power Technologies Office in cooperation with non-federal agencies,

concluding that installed hydropower capacity could be increased by 50% by 2050 (U.S.

Department of Energy, 2016).

In the Southeast (Alabama, Georgia, North Carolina, South Carolina and Tennessee)

hydropower produces about 11% of the total electricity. This is more than the national average,

but in-depth analysis of how the hydropower production in this region is affected by climate

variability has been neglected in favor of more hydropower intensive regions, such as the Pacific

Northwest.

Approximately 50% of the dams in the Southeast are federally owned and operated (U.S.

Department of Energy, 2016) by the U.S. Army Corps of Engineers (ACE) and the Tennessee

Valley Authority. Among the non-federal there are numerous agencies of which Southern

Company and Duke Energy are the largest cooperations. In this study the focus will be on the

federal dams operated by ACE.

Most ACE dams are multipurpose projects, originally designed to improve navigation

and provide flood control and hydropower. In more recent times purposes have been expanded to

include water supply, recreation, and fish and wildlife management. There were plans to build

dams in the Southeast already in the early 1940s, plans that were put on hold due to World War

II. After the war construction started on the Allatoona dam in the northern part of the ACT basin,

18

which was completed in 1950 and it was soon followed by others, many of which were

authorized by the Flood Control Acts of 1941 and 1944 or Rivers and Harbors Act of 1945,

1946. (U.S. Army Corps of Engineers, 2017). Commencement years for the dams included in the

analysis can be found in Table 1-2.

The possibility to reach full production within minutes is an advantage that sets

hydropower apart from both renewable and traditional energy sources. The ACE’s hydropower

plants in the Southeast are sometimes referred to as “peaking” plants meaning they are operated

to meet peak electricity demands, typically occurring in the middle of the day during weekdays.

Figure 1-1. The study area, including the states of Alabama, Georgia, North Carolina, South

Carolina and Tennessee, is outlined in black. In the insert map the top-ten

hydropower producing states in the U.S. are highlighted in blue and the geographic

location of the study area marked with a black box.

19

Table 1-1. Potential economic and environmental winnings based on a 9.6% increase in annual

production.

Table 1-2. Overview of the dams included in this project. The bottom three are found in the

analyzed basins but excluded.

Dam Operation start Installed Cap (MW) River basin

Buford Dam 1958 126 ACF

Walter F George Dam 1963 168 ACF

West Point Dam 1975 87 ACF

Allatoona 1950 85 ACT

Millers Ferry Dam 1970 90 ACT

Robert F Henry Dam (Jones Bluff) 1975 82 ACT

Hartwell 1963 42 SAV

J. Strom Thurmond Dam (Clarks Hill) 1952 380 SAV

Jim Woodruff Dam 1957 43.35* ACF

Carters Dam 1975 280** ACT

Richard B Russel 1985 300** SAV

* No storage

** Does not include installed capacity for reversed pumping

Annual benefits from a 9.6% increase in

annual production

8 dams included in

this analysis

All Army Corps of

Engineers’ Dams in SE

US****

Increased production (MWh)

9.6% of average annual production

244,705 569,952

Additional number of households that could be

powered (based on an electricity use of 12 MWh/year)

20,392 47,496

Decreased CO2 emissions (metric tons)*

(Coal 40%, Natural gas 40%, Fossil free 20%)**

145,844 339,691

Increased revenue ($) *** 8,148,692 18,979,402

* This number does not include emissions resulting from extraction and transport of fuel

** Average electricity mix for the study area (EIA, 2016)

*** Based on average 2014 electricity prices (Army Corps of Engineers, 2015)

**** Mobile, Nashville and Savannah Districts.

20

CHAPTER 2

DRIVERS OF LONG TERM VARIABILITY OF PRECIPITATION AND RUNOFF IN THE

SOUTHEAST

The water resources of the Southeast are dominated by conflicting interests. The average

annual precipitation of 1,000-1,500 mm (Kunkel et al. 2013) is required to not only satisfy the

natural ecosystems, but also the agriculture and drinking water needs of the rapidly growing

metropolitan areas such as Atlanta and Charlotte, recreational use, and hydroelectricity power

plants. In response to increasing population and pressures to switch to renewable energy sources,

this research is part of a broader project investigating climate drivers of hydropower output in the

Southeast. The current study investigates interannual variability in precipitation inputs to, and

runoff outputs from, eighteen basins in the region and whether that variability can, in part, be

explained by the influence of various ocean-atmosphere teleconnections. The study area includes

the states of Georgia, South Carolina, Alabama, North Carolina and Tennessee, of which the last

three are among the top-ten hydropower producing states in the country (National Hydropower

Association 2013).

The form, as well as the spatial and temporal variability of precipitation in the region can

be attributed to three main factors: distance to the sea (Atlantic or Gulf of Mexico), elevation,

and latitude (Kunkel et al. 2013). Areas located closest to the Atlantic and Gulf of Mexico

receive the bulk of their annual precipitation during the summer months as a result of convection,

sea breezes and tropical cyclones (Giemeno et al. 2010; Kunkel et al. 2013; Brun and Barros

2014). Further inland the difference between summer and winter precipitation diminishes. The

influence of summertime sea breezes and tropical cyclones decreases and that of the passage of

precipitation-bearing cold fronts increases (Kunkel et al. 2013).

Latitude and elevation influence the form of precipitation during the winter months.

Snow is common at higher altitudes in Tennessee and North Carolina, while falling sparsely in

21

continental portions of Alabama, Georgia and South Carolina (Kunkel et al. 2013). The

likelihood of precipitation falling as snow during the winter season controls the quantity of snow

storage. Subsequent melt is a determinant of the timing and quantity of flows in spring and

seasonal losses to evapotranspiration (Vanrheenen et al. 2004; Carmona et al. 2014).

While the general intra-annual pattern of spatial and temporal variability of precipitation

regime can be explained by the physical features described above, prior research has indicated a

number of potential external drivers (teleconnections) of hydroclimatic variability in the region.

In their extensive mapping of the influence of Pacific Ocean-based El Niño-Southern Oscillation

(ENSO) on the streamflow in the continental United States Kahya and Dracup (1993) and

Dracup and Kahya (1994) indicate that ENSO’s influence is relatively weak in the Southeast

(excluding Florida and the coast of the Gulf of Mexico), with warm phase ENSO events leading

to higher flows and the cold phase to lower flows. Several authors (see for example, Hastenrath

1990) posit that the analysis of streamflow rather than precipitation records is a preferable means

of detecting such long-run drivers of climatic variability. The drainage basin acts as spatial filter

of the noise inherent in precipitation data and the non-linear nature of the hydrologic system and

comparative consistency of losses through evapotranspiration (Bonan 2008, p. 171; Peel et al.

2010) may amplify the signal of climate drivers in the streamflow record.

There is limited research showing linkages between the North Atlantic Oscillation (NAO)

and streamflow in the study area. Coleman and Budikova (2013) conclude that the influence of

the NAO is strongest in the Northeastern United States, but positive relationships also can be

found between the NAO and summer streamflow in parts of the Southeast. Temperatures, which

may play a key role in determining the type of precipitation (Kang et al. 2016) and controlling

22

evapotranspiration, have been shown to be associated with both the NAO and ENSO in the

Southeast (Rogers 1984).

Katz et al. (2003) identified the Pacific-North American (PNA) pattern as an additional

control of winter precipitation, confirming the earlier findings by Serreze et al. (1998), and

foreshadowing those of Sen (2012). PNA is negatively correlated with wintertime temperatures

throughout the Southeast, while its direct negative influence on wintertime precipitation is

limited to the continental portion of the region (Katz et al., 2003). These associations between

climate drivers and winter precipitation and temperatures highlight the need to incorporate the

use of lag-times in regional studies linking teleconnections to seasonal flows. It has been

suggested (Enfield et al. 2001) that the Atlantic Multidecadal Oscillation (AMO) displays no

significant connection to streamflow in the Southeast (excluding Florida). Subsequently Ortegren

et al. (2011, 2014) found that warm phase AMO is associated with summer droughts in the

Southeast, a finding that was further confirmed by Johnson et al. (2013) that detected AMO

signals in the flow of the Apalachicola–Chattahoochee–Flint river (draining parts of western

Georgia, eastern Alabama and northwestern Florida).

The Arctic Oscillation/Northern Annular Mode index (AO) impacts the location and

strength of the Polar Jetstream (Thompson and Wallace 1998; Wallace 2000, Feldstein and

Franzke 2006). Less well known, and arguably indistinguishable from the NAO index (Deser,

2000; Feldstein and Franzke 2006), the AO has been more or less excluded in the study of the

climatology of the United States, in favor of the longer established NAO (Walker and Bliss

1932). Since it is considered by some to be the dominant mode of variability in the extratropics

(Higgins et al. 2000; Wallace 2000; Feldstein and Franzke 2006) and has been linked to

temperature variations throughout the Northern Hemisphere (Thompson and Wallace 1998) the

23

AO is also included in this analysis. Labosier and Quiring (2013) take a holistic view of the

regional hydroclimatology of the Southeast, concluding that variability in regional

hydroclimatology is controlled by ENSO, the Pacific-North American pattern, the strength and

location of the Bermuda High and tropical cyclones. All of these teleconnections are explicitly

included as possible drivers of seasonal precipitation and runoff in the Southeast with the

exception of the Bermuda High, which can be considered as represented by the NAO, and

tropical storms, which are important local meteorological phenomena, driven by larger scale

variability governed in part by the various teleconnection indices (Goldenberg et al., 2001;

Elsner 2003; Xie et al., 2005).

Data

Teleconnection Indices

The following teleconnection indices are included in this analysis:

The NAO and PNA indices, derived by the methods described by Barnston and Livezey,

(1987) are obtained from National Oceanographic and Atmospheric Administration

(NOAA) (2013a, 2013b). The AO index is also obtained from NOAA, but emerges when

an Empirical Orthogonal Function (EOF) is applied to the 1000 hPa height anomalies 20-

90°N, using the standard deviation of the 1979-2000 monthly index as the base period

(NOAA, 2005).

ENSO is represented by the Bivariate ENSO Timeseries (BEST), a combination of the

Southern Oscillation Index (SOI), and the Niño 3.4 index (Smith and Sardeshmukh

2000).

The AMO index consists of the 121 months smoothed version of the Kaplan SST V2 data

(Kaplan et al. 1998; Enfield et al. 2001; NOAA 2002)

Precipitation and Runoff Data

Estimates of historic precipitation inputs to the basins of interest are derived from the

Parameter-elevation Regressions on Independent Slopes Model (PRISM) 4x4 km gridded

dataset. The eighteen basins included (Figure 2-1.) range in size from 300 to 7300 km2, thus due

24

to their large size, influence of any highly localized geographical differences may be minimized.

PRISM interpolates precipitation data from almost 13,000 stations in the continental United

States by applying a climate-elevation regression (Daly et al. 1994) and has been widely used in

long term climate variability research (Hamlet et al. 2005; Kennedy et al. 2009; Latysh and

Wetherbee 2011; Yang and DelSole 2012; Ciancarelli et al. 2014; Nag et al 2014). Monthly

streamflow data are available from the United States Geological Survey (USGS) (2013). The

gage sites and basins are all part of the Hydro-Climatic Data Network Streamflow Data Set

(Slack et al. 1993).

Methods

The Southeast could be divided into a number of different climate regions depending on

the scope of the research (Kunkel et al. 2013; Ortegren et al. 2014). However, the focus here is

on longer-term (seasonal) influences of teleconnections on the overall water resource of the

region, and a number of generalizing and smoothing methods are employed.

The gridded PRISM data are converted to time series of monthly precipitation inputs

(mm) to each basin, and discharge data converted to runoff (mm) to facilitate comparisons

between variables and basins (Figure 2-1.). Both variables are converted to seasonal averages by

creating 3-month running mean time series, which are entered into a Principal Components

Analysis (PCA). The PCA identifies spatial patterns of variability of all the time series

(eigenvectors), their variation in time (orthogonal time series of the PCA) and their rank

(modes), where the first mode explains the largest fraction of the variance (Lorenz 1956,

Bretherton et al. 1992, Wilks 2011). The sizes of the basins, the 3-month running mean and the

PCA work together to reduce local geographical and short-term temporal variability to make any

findings useful for long-term forecasting.

25

The new time series of the PCA are entered in a Spearman rank correlation to determine

any relationship between the precipitation/runoff and the teleconnections, employing a number

of different time lags, recognizing that the influence of a teleconnection on the hydroclimatology

need not be synchronous but can be lagged up to several months (Ropelewski and Halpert 1986,

Kahya and Dracup 1993, Dracup and Kahya 1994, Hamlet et al. 2002, Su et al. 2005, Engström

and Uvo 2015).

Many different philosophies exist regarding how many PCs should be considered and

included in further analysis. One alternative is to consider all PCs, starting with the highest

ranked, until together they explain a predetermined percentage of the variance of the original

dataset. Another approach is to construct scree-plots, which portray the ranks of the PCs on the

x-axis and their associated eigenvalues on the y-axis and to include all those PCs with an

eigenvalue >1 meaning that they explain more of the variance in the dataset than any single input

vector of data.

In this study only the first two PCs are examined, since the PC3 in this case typically

explains less than 10 % of the variance of the original dataset. Such small variances explained

are not considered useful in correlating the PCs with the teleconnection indices. The influence of

the teleconnection on precipitation/ streamflow is the product of the explained variance of the PC

and r2. For example, PC3 would typically explain <1% of the hydroclimate variability, and was

therefore considered too small to be useful for water management purposes.

Results

PC Loadings

Figure 2-2. shows the temporal variability of variance explained by the first three

Principal Components (PC1, PC2 and PC3) for precipitation and runoff. PC1 explains more of

the variability in precipitation than in runoff, but similar patterns of seasonality appear in both

26

graphs. In general there is less homogeneity in both precipitation and runoff during the summer

months, which is reflected by a smaller fraction of the variability of the datasets explained by the

PC1 during this part of the year. PC2 compensates for this by explaining a larger fraction of the

variability during the same part of the year. The most uniform behaviors are found during the

cold part of the year where PC1 explains 60-70% of the variability in precipitation and 60-65%

of the variability in runoff. PC3 is illustrated in Figure 2-2. only as reference and is not included

in further analyses.

Figure 2-3. depicts the spatial patterns of the significant loadings of the PC1, and

complements Figure 2-2. by illustrating spatiotemporal variability. The radar plot in the middle

of Figures 2-3 and 2-4 shows how many basins (of eighteen) are significantly correlated to PC1

on a seasonal basis. The surrounding maps describe the locations of those basins throughout the

year. Those significantly correlated with PC1 are represented by a filled point, while those

related to PC2 and PC3 are marked with an empty point. Emerging from Figures 2-3 and 2-4 is a

“heartland” of basins that dominate PC1. This central cluster is surrounded by a couple of

clusters of stations that are better explained by PC2 during parts of the year. One such cluster on

the precipitation maps (Figure 2-3) appears in the northwest. These basins show little to no

association with PC1 during the winter, as they receive significantly more precipitation during

this part of the year than the other basins in the analysis (Figure 2-1.). During the summer

another cluster of basins appears in the northeast. Not unexpectedly a similar pattern emerges in

the patterns of runoff and PC1 (Figure 2-4), with small clusters behaving differently appearing

seasonally in the northwest and northeast. Runoff in the Satilla basin in the southeastern corner

of the study area is also markedly different from most basins with no specific seasonality (its

double mass curve is displayed in Figure 2-1., bottom row, second from the right). This basin is

27

dominated by marshland, in which hydrologic storage might produce differing lagged responses

to seasonal precipitation.

Identifying the Relationships

The complete results (correlation coefficients and associated significance levels) of the

Spearman rank correlations between teleconnection indices and precipitation and streamflow

respectively, are shown as correlation charts in Figures 2-5 – 2-20. The AMO’s association with

variations in precipitation (Figure 2-5) is limited to the late fall through spring seasons, where a

positive phase of the AMO leads to decreased precipitation (r ≥-0.34), supporting the findings of

Enfield et al. (2001). The influence of the AMO persists for much longer than that of any other

teleconnection, with significant time lags longer than a year. This can be explained by the

AMO’s long cycles (typically 60-70 years) of variability. Significant correlations between

precipitation and the AMO are mainly prevalent in PC1 of the precipitation data, while the

second shows a limited relationship with precipitation.

Significant positive correlations between AO and precipitation appear sporadically

throughout the year (Figure 2-6) but are concentrated in the late fall-winter, with a time lag of

about one year, when the phase of the AO during the previous fall and winter exhibits a high

correlation (r≤0.52). The teleconnection’s influence on PC2 is more limited and not displayed.

Despite earlier indications that the NAO influences the temperature and hydrology of the

Southeast (Rogers 1984, Katz et al. 2003), only a temporally scattered influence on precipitation

is revealed here. The association is sometimes stronger (r ≤ 0.4) but shows no consistency of

seasonality or “direction” in association in the first nor the second mode of the PCA (Figure 2-7,

2-8).

Variability in fall and winter precipitation can be explained partially by ENSO, which is

positively correlated with the first mode of the precipitation PCA from OND through JFM,

28

reflecting the common observation that a warm phase ENSO leads to increased precipitation

(and vice-versa) in the Southeast (Figure 2-9). The second mode of the PCA (Figure 2-10) is also

significantly correlated with ENSO, but in contrast to the first mode the sign of the correlation

varies, and several consecutive months of negative correlations appear during the second half of

the year. This is a result of the perpendicular relationship between the PC1 and PC2, but might

also indicate that on rare occasions the warm phase ENSO leads to decreased precipitation.

Further, this could be the consequence of other contemporary large scale patterns, perhaps the

noted association between tropical cyclones in the North Atlantic basin and the phase of ENSO

(Gray, 1984; Goldenberg et al., 2001). This would also explain the lack of consistency during the

spring, in which the PCA does not manage to identify any dominant pattern of influence. Just

like the impact of the AMO, the influence of ENSO events persist for relatively long periods as

the El Niño/La Niña events occur over many months, and therefore have a long-lasting influence

on the regional hydroclimatology.

The association of the PNA and regional precipitation is scattered. In the first mode of the

PCA it appears as negative (r ≥ -0.3) in the early summer through winter seasons (Figure 2-11).

The second mode of the PCA displays a less coherent association, concentrated into the late

winter-spring seasons (Figure 2-12).

Compared with precipitation, associations of teleconnections with streamflow are

generally stronger. AMO shows up as the strongest driver, affecting the runoff during more than

half of the year, its influence ranging from contemporary to up to 24 months’ time lag in the first

mode of the PCA (Figure 2-13) and is a negative correlation, suggesting that a warmer Atlantic

Ocean correspond to periods of reduced regional runoff. The influence of the AMO in the second

mode is limited (Figure 2-14).

29

The AO has restricted positive association to the winter runoff (Figure 2-15) and its

association is not synchronous being linked to the phase of the AO in the previous year, similar

to the association detected with precipitation (Figure 2-6.). Few and scattered significant

correlations appeared with the time series from the second mode of the AO PCA.

Despite earlier research linking the NAO and regional hydroclimatology, this analysis

suggests a more complex set of interactions (Figure 2-16). The association of the teleconnection

with runoff is lagged. Of particular note are the positive correlations of runoff at the beginning of

the year (r ≤ 0.41) with the teleconnection’s phase during the following summer and early fall.

ENSO exhibits an association with runoff in both the first and second mode of the PCA,

but of differing signs. The first mode is positively correlated with ENSO (r ≤ 0.42), indicating

that a warm ENSO phase leads to more runoff in accordance to the observations concerning

precipitation (Figure 2-9). The influence of ENSO can be detected in the winter months in lags

ranging from the preceding summer through to the following May-June (Figure 2-17). The

second mode of the PCA, displayed in Figure 2-18, matches the second mode of the precipitation

shown in Figure 2-10, indicating a negative correlation (r ≥ -0.53).

The PNA is mainly negatively correlated with river discharge in the Southeast. In the first

mode of the PCA (Figure 2-19), the summer-fall PNA has a slight dampening effect on the

wintertime river discharge the coming year. These patterns are similar to the PNA’s influence on

precipitation (Figure 2-11), indicating that the teleconnection mainly influence precipitation

amounts, while having little effect on temperatures. In the second mode of the PCA the influence

of the PNA is limited to the early spring (Figure 2-20).

Discussion

Association between teleconnection indices and observed streamflow is useful from a

water resource management perspective. Analysis of the phase of these indices can give an

30

indication of expected hydroclimatic conditions months ahead, which facilitates water resource

distribution and decreases the risk of water related conflicts.

The basins included in the analysis are all part of the Hydroclimate Data Network (Slack

at al. 1993). The authors realize that the basin flows might have been altered since the

publication of Slack et al. (1993), but as long-term time series analysis is not part of this paper,

removal of basins displaying a changing hydrologic behavior leads to an incomplete depiction of

the actual water balance (Rice et al. 2015) as there is widespread human influence in the region.

As suggested in earlier research (Hastenrath 1990), the teleconnection signals often

appear more strongly in streamflow than precipitation data. From a holistic water resource

perspective, the teleconnections considered in this analysis have limited value in forecasting

precipitation. However, the results can be usefully incorporated into surface water forecasting for

those industries and organizations that depend on that resource for their activities.

The ocean-atmosphere based teleconnections ENSO and AMO have the strongest and

most persistent influences on the hydroclimatology in the Southeast based on these findings.

During the warm-phase of ENSO an enhanced subtropical jetstream causes southwesterly winds

to dominate over the Gulf of Mexico bringing warm, moist air in over land which leads to above

normal precipitation in the Southeast. During the cold phase the wind shifts to the northwest,

bringing drier air and below normal precipitation (Ropelewski & Halpert 1986, Katz et al. 2003).

The warmer Atlantic sea surface temperatures associated with the warm phase of the AMO

offers more favorable conditions for tropical cyclone development and to an increased number of

tropical cyclone landfalls along the U.S. east coast, and hence more precipitation (Goldenberg et

al., 2001). Despite this, the general pattern of influence of the AMO is that the warm phase leads

to relatively dry conditions in the Southeast. Analyzing the 500 hPa geopotential heights

31

anomalies in the warm vs the cold phase of AMO, Enfield et al. (2001) find that the winter ridge-

trough pattern over Northern U.S. is flattened during the warm AMO phase. The weaker ridge of

the Pacific Northwest leads to an increased number of winter cyclones, while the North Central-

East region gets a reduced number of cyclones. The cold phase AMO shows the opposite with a

strengthened ridge in the Northwest and a deepened trough in the North Central-East. The

southwestern region gets increased 500 hPa heights during the warm phase while the Southeast

experiences decreased geopotential heights, leading to increased precipitation. Considering the

very long cycles of variability of the AMO, the usefulness of including this index in intra-annual

forecasts is questionable. It is however an important teleconnection to consider when planning

for future water availability in the longterm and construction of infrastructure as its influence is

strong and long lasting.

Associations to ENSO events vary on a shorter term basis than the AMO, but typically

persist from fall through early spring. For this reason it is more useful from an intra-annual water

resource planning perspective. As a warm (cold) phase ENSO event is predicted or appears in the

central equatorial Pacific during late boreal summer, water managers can conclude with greater

certainty that the winter and early spring will bring above (below) normal precipitation and

runoff and can thus plan accordingly.

Atmosphere-based teleconnection indices included in this analysis display scattered

influences on the hydroclimatology of the region. The PNA, which through its variability

influences the location and height of atmospheric ridges and troughs over the United States, and

hence has direct influence on wintertime temperatures (Wallace and Gutzler, 1981), shows a

negative correlation with both precipitation and runoff. The PNA’s influence shows varying time

32

lags and little consistency and its usefulness in water resource forecasting is therefore considered

limited.

Researchers debate whether the AO and NAO are the same or separate atmospheric

phenomena. In a general sense the NAO can be described as partly controlling the strength and

location of the midlatitude jet stream over the North Atlantic. During positive conditions North

Atlantic cyclones are tunneled towards Northwestern Europe that gets excess amounts of

precipitation. During the negative phase the North Atlantic storm track is shifted slightly south,

causing increased precipitation throughout the Mediterranean. On the western side of the

Atlantic basin the Southeast show a similar relationship to the NAO as Northwest Europe with

positively correlated winter temperatures and precipitation anomalies (Barnston and Livezey,

1987, Hurrell 1995). The AO could be described as an expanded NAO with oscillating pressure

centers on multiple longitudes around the Arctic, where a positive phase leads to stronger

subpolar westerlies (Thompson and Wallace, 2001), which could be expected to have a similar

influence on the Southeast as the positive phase of the NAO. Results suggest that their relative

associations are different to the hydroclimatology of the Southeast, primarily because of

differences in the periods over which the indices are seen to vary. Secondly, the AO shows a

stronger association with precipitation, and the NAO with runoff. The implication is that AO

changes precipitation, but that change is not transmitted as markedly to the rivers. Thompson and

Wallace (1998) showed that phases of the AO are positively correlated to warmer temperatures

throughout the Northern Hemisphere. This observation, in combination with the timing of the

strong correlation between AO and precipitation (SON-DJF), suggests that increases/decreases in

precipitation may be offset by the opposite change in evapotranspiration, thereby dampening any

signal in the rivers.

33

By contrast to the AO, positive phase of the NAO leads to more winter time runoff,

despite lack of association with increased precipitation. Approximately the same amount of

precipitation is leading to more runoff, which would imply that winter time temperatures would

be higher during the positive phase of the NAO, decreasing snow and ice storage, which is

confirmed by Katz et al. (2003) as well as Thompson and Wallace (1998). It is also noteworthy

that the NAO shows an inverse lead/lag relation to the runoff, meaning that the winter and spring

runoff are correlated with the phase of the NAO later in that same year. This fact implies that the

rivers in the Southeast experience some kind of precursor of the NAO, before the index itself

actually changes. Similar patterns of inversed responses have been observed in conjunction to

other teleconnections, such as ENSO (Ropelewski & Halpert, 1987; Waylen & Caviedes, 1996).

Conclusion

This paper confirms the significant influence of various teleconnections upon the

hydroclimate of southeastern United States. The most significant external contributors to

variability in the region are the ocean-atmosphere based teleconnections AMO and ENSO, which

strongly influence the hydroclimate in the fall through spring with several months’ time lag. The

atmospheric-based teleconnections AO, NAO and PNA show more sporadic influences. In

general, the teleconnection signal can be more clearly identified in streamflow than precipitation

records. This fact, in combination with the temporal lag between the teleconnection indices and

the hydroclimatic response make teleconnection indices useful parameters to include in surface

water forecasts.

34

Figure 2-1. Location of the uncontrolled river basins included in the analysis. Surrounding

diagrams of monthly mean (Jan-Dec) double mass curves (with a 1:1 line for

reference) for the rivers A) Buffalo, B) Emory, C) French Broad, D) Deep, E)

Conecuh, F) Sweetwater, G) Satilla, H) Lynches, give an idea about the varying

hydrology of the region. The precipitation and runoff have been converted to monthly

average millimeters (mm) for ease of comparison.

A D C B

E H G F

35

Figure 2-2. Seasonal variation of percentage of variability explained by P1, PC2 and PC3 (outer,

middle and inner polygons respectively).

36

Figure 2-3 Spatiotemporal variation of basins in PC1 for precipitation. Basins significantly

correlated with PC1 are represented by a filled point, while those related to PC2 and

PC3 are marked with an empty point.

Figure 2-4 Same as Figure 2-3, but for runoff.

37

Figure 2-5. Lag correlation between PC1 (precipitation) for each season and the AMO index.

The labels along the x-axis indicate the season (monthly triad) considered and the

strength of only significant correlations for that season on a scale ranging from -0.7 to

0.7 (for clarity the first digit (zero) of the correlation coefficient has been removed,

hence the number 5 indicates a correlation coefficient of 0.5). The nature of each

significant correlation is indicated with a grey bar for negative correlation and black

for positive. Filled bars indicate a correlation with a p-value of <0.05, while empty

bars display correlations with p-values of <0.1. The y-axes show the triads in which

the corresponding teleconnection index is computed. Alternating triads are printed on

the right and left of the chart. The diagonal line of boxes indicates periods of

contemporary precipitation/runoff and teleconnection index. As an example, at the far

left, the box is centrally located, indicating a negative correlation of synchronous JFM

values of precipitation and the AMO index. Precipitation is also negatively correlated

with the AMO index in the twelve preceding months (vertically above the box) and

with that in eleven subsequent months (below the box), acknowledging the fact that

the association of a teleconnection with precipitation/runoff need not be

instantaneous. This type of figure not only highlights the specific strength of the

correlations, but also indicates temporal extents of the association to the

teleconnections.

38

Figure 2-6. Same as Figure 2-5 except lag correlation between PC1 (precipitation) and the AO

index.

Figure 2-7. Same as Figure 2-5 except lag correlation between PC1 (precipitation) and the NAO

index.

39

Figure 2-8. Same as Figure 2-5 except lag correlation between PC2 (precipitation) and the NAO

index.

Figure 2-9. Same as Figure 2-5 except lag correlation between PC1 (precipitation) and the ENSO

index (BEST).

40

Figure 2-10. Same as Figure 2-5 except lag correlation between PC2 (precipitation) and the

ENSO index.

Figure 2-11. Same as Figure 2-5 except lag correlation between PC1 (precipitation) and the PNA

index.

41

Figure 2-12. Same as Figure 2-5 except lag correlation between PC2 (precipitation) and the PNA

index.

Figure 2-13. Same as Figure 2-5 except lag correlation between PC1( runoff ) and the AMO

index.

42

Figure 2-14. Same as Figure 2-5 except lag correlation between PC2 (runoff ) and the AMO

index.

Figure 2-15. Same as Figure 2-5 except lag correlation between PC1 ( runoff ) and the AO index.

43

Figure 2-16. Same as Figure 2-5 except lag correlation between PC1 ( runoff ) and the NAO

index.

Figure 2-17. Same as Figure 2-5 except lag correlation between PC1( runoff ) and the ENSO

index.

44

Figure 2-18. Same as Figure 2-5 except lag correlation between PC2 (runoff) and the ENSO

index.

Figure 2-19. Same as Figure 2-5 except lag correlation between PC1 (runoff) and the PNA index.

45

Figure 2-20. Same as Figure 2-5 except lag correlation between PC2 (runoff) and the PNA index.

46

CHAPTER 3

THE CHANGING HYDROCLIMATE OF THE SOUTHEAST UNITED STATES

Hydroclimatological variability in the Southeast can be partly explained by the phase of

various teleconnections (Dracup & Kahya, 1994; Johnson et al., 2013; Kahya & Dracup, 1993;

Katz et al., 2003; Labosier & Quiring, 2013; Ortegren et al. 2014). However, the hydroclimate

should not be treated as a static, but rather a constantly evolving system (Milly et al. 2008;

Schindler & Hilbron, 2015). Groisman et al. (2001) find a general trend of increased runoff

during the twentieth century throughout the U.S. Instead of calling it a longterm trend, McCabe

& Wolock (2002) describes this change as an abrupt shift in the early 1970s, a pattern that show

up significantly mainly in sites located in Eastern U.S. The authors speculate that this increase is

linked to increased Fall season precipitation reported by Karl & Knight (1998). Wang & Hejazi

(2011), using the same temporal breakpoint, argue that human impacts play a more important

role in reported streamflow changes than have yet been acknowledged. They only identify minor

streamflow increases in the Southeast that could be attributed to a changed climate, while finding

a just as strong opposite pattern of decreased streamflow linked to human impacts. While the

above-mentioned research consider the continental U.S., this paper focuses solely on the

Southeast part of the country (Alabama, Georgia, North Carolina, South Carolina and

Tennessee), a region that thus far has been underrepresented in hydroclimatic research. By

limiting the geographical area of study, a deeper analysis and subregionalization is possible,

leading to more detailed results that are useful and applicable to regional water management.

No obvious warming trend has been identified in the Southeast (Misra et al., 2012; Pan,

2004; Portmann et al., 2009), yet identified hydroclimatological changes include weak decreases

in the number of snowcovered days in the Southeast (Groisman et al. 2001). Seager et al. (2009)

on the other hand find that there have been no significant changes to the hydroclimate of the

47

Southeast during the period of anthropogenic forcing of climate, but that climate change may

lead to increased precipitation and evaporation, resulting in higher risk of drought. In their study

of the hydroclimatology of the U.S. Sankarasubramanian & Vogel (2003) find that for most of

the Southeast a 1 % increase in precipitation leads to 1.5-2.5% increase in runoff, while some

areas in the Southeast of our study area get up to 4% increase in streamflow, implying less

evaporation and/or storage capacity.

Evidently, there are many different aspects and contradictory findings regarding trends in

the regional hydroclimatology of the Southeast U.S. In this paper we aim to investigate if and

how the translation of precipitation to streamflow has changed over time by examining historical

data. The non-stationarity in the translation between precipitation and runoff is likely to be more

pronounced as a changing climate brings new normals that diverge from the historically

dominant patterns upon which modern water management infrastructure is built. The findings of

this paper would be of greatest interest to water managers in a region that is used to intense

summer rainstorms, winter snowfall, spring floods and water related conflicts.

Material and Methods

The approach employed is based on a simple seasonal water balance equation where

Precipitation = Runoff + Evapotranspiration +/- Storage. Mean seasonal runoff and estimates of

mean seasonal precipitation are available from historic records. The residual is assumed to

represent losses to evapotranspiration and or changes in basin storage. Streamflow data from the

Hydro-Climatic Data Network (Lins 2012; Slack et al, 1993) are supposedly minimally affected

by human interventions and are as such frequently used in hydrologic analyses (Frei et al, 2015;

Henn et al, 2015; Sagarika et al. 2014; Stewart et al. 2005). For this paper historic monthly

discharge observations (1952-2014) from 18 gauges (Figure 3-1.) drawn from the U.S.

Geological Survey’s Hydro-Climatic Data Network are analyzed. The extent of the basins are

48

defined by a 100 m resolution Digital Elevation Model (DEM) (U.S. Geological Survey, 2014).

Basins range in size from as little as 299 to 7370 km2, although it should be noted that the second

and third largest basins are only half the size of the largest. Discharge data are converted to depth

units (mm) to facilitate comparison to precipitation data by normalizing by watershed area.

Estimates of historical precipitation input (mm) to the basins of interest are taken from the

Parameter-elevation Regressions on Independent Slopes Model (PRISM) 4x4 km gridded data

(Daly et al, 1994), a dataset that is widely used in hydroclimatic research (Kennedy et al, 2009;

Nag et al, 2014; Yang & DelSole, 2012). The introduction of the 100 m resolution DEM to

create the outlines of the basins based on the location of the gage leads to some generalizations.

However, a comparison between the basin sizes reported by U.S. Geological Survey and the

sizes defined in this analysis shows a maximum difference of 4 %, while the average difference

is less than 0.5%. This is recognized as a limitation of the analysis, but since the size is constant

through time it is not considered as being an influential factor upon the findings of this research.

The approach taken assumes a simple, seasonally varying linear relationship between

precipitation and runoff described by the slope of a fitted regression line (the runoff coefficient,

RC). While accepting that this assumption runs contrary to the basic non-linear nature of rainfall-

runoff relationships, at this level of temporal aggregation and within this environment, empirical

evidence suggests that the assumption is a reasonable first step in such a spatio-temporal

analysis. The improvement in fit resulting from the use of second order functions was generally

small and most apparent during the summer and fall seasons in years of very low precipitation

input when baseflow-dominated runoff appeared more insensitive to changes in precipitation

input. Given the small changes in goodness of fit and the associated loss of meaningful

interpretation of results, the RC derived from the slope of the first order fit is preferred.

49

A Pettitt Change Point Analysis is employed to identify any changes in seasonal time

series of RCs viewed through a 30 year moving windows of precipitation and runoff. The

window is used to smooth any interannual variability that might arise through the influence of

atmospheric drivers such as El Niño-Southern Oscillation, The North Atlantic Oscillation and

Pacific North American Pattern, all found to significantly change seasonal precipitation and

runoff in southeastern U.S. (Engström & Waylen, 2017; Sen 2012; Katz et al. 2003), while

detecting inter-decadal scale changes in rainfall-runoff relations. The coefficients give an

indication of the proportion of the precipitation exported as runoff. The moving window is

constructed as such that the estimated coefficient is assigned to the last calendar year of the 30

year period. The Petitt’s Change Point Problem (Pettitt, 1979) is similar to the Kruskal Wallis

non-parametric test comparing populations, but the Pettitt test employs a different approach,

identifying the year in which a data series breaks, leaving the data series before and after that

break-year significantly different. The null hypothesis of the Pettitt test is that there is no

breakpoint. The test has successfully been employed to detect breakpoints in both rainfall and

streamflow data (Alghazali & Alawadi, 2014; Gao et al. 2011; Harrigan et al. 2014; Ma et al.

2008; Salarijazi et al. 2012)

Following Mallakpour & Villarini (2015), a sensitivity analysis is performed on the

Pettitt test. The test is run 100 times on constructed time series with means and standard

deviations from two of the basins included in the analysis. The spread of the break years

identified varies by season with the most spread in the summer months and the least spread in

fall, but the correct break year was identified the majority of the time in all seasons and the

Pettitt test is therefore considered to have a high sensitivity to breakpoints in time series of

means, as confirmed by Mallakpour & Villarini (2015).

50

Results

Figure 3-2. shows the seasonal variation of RCs in the study area. In DJF the lowest RCs

are found in the mountains, where there some years is a significant snow storage (Keighton et al.

2009; Sugg et al. 2014). The rest of the region has relatively high RCs during this season due to

the limited evapotranspiration. The coefficients are highest in MAM following snowmelt and as

temperatures are still relatively low, limiting evaporation and vegetation growth. In JJA RCs are

at their minimum due to high temperatures accompanied with intense vegetation growth. The

mountain region stands out in this season as having slightly higher RCs, which can be explained

by the higher elevations experiencing milder temperatures, hence evapotranspiration is less

pronounced in this region. SON shows a north-south pattern where the northerly and continental

basins pick up the first signals of the approaching colder season, which is reflected in higher

RCs, while the southern and eastern basins still have high evapotranspiration levels.

Following the approach of McCabe & Wolock (2002), Figure 3-3. depicts annual

standardized min, median and max flow for the 18 basins included in this analysis. The reported

break in 1970-71 (McCabe & Wolock, 2002; Wang & Hejazi, 2011), is not visually apparent.

Rather Figure 3-3. shows that the basins experience considerable interannual variability in these

three particular streamflow characteristics.

This paper focuses on longer term changes than those identified as being associated with

teleconnections. The Pettitt test identifies any such significant breaks in the pattern of RCs in all

seasons. Estimated coefficients before and after the breaks are compared and displayed in Figure

3-4. clearly indicating higher RCs after the breaks. In only 17 of the72 cases (24%) are increases

not experienced. The pattern is most pronounced during the summer (JJA) and fall (SON).

Figure 3-5. displays the observed changes in RC geographically. Each season is

represented separately by an arrow, emanating from the gauge site. The shade and the length of

51

the arrow indicate the nature (positive/negative) and magnitude of the observed change in RC

before and after the break. The summer and fall stand out as displaying the greatest change in RC

in the mountains and in some of the eastern coastal basins. Spring shows the least difference pre-

and post-break, while winter shows increased RCs of varying strength throughout the study area.

The years of the breaks (Figure 3-6.) are represented by pie charts for easier identification

of spatial clusters of basins exhibiting similar timings. DJF displays no distinguishable spatial

patterns. In MAM two trends emerge with the majority of the station records breaking in the late

1980-early 1990s, while a couple of stations show changes in the early 2000s. With few

exceptions most stations break earlier in MAM than in other seasons. In JJA the western stations

indicate a shift in the late 1990s, while the central mountainous region tends to elicit a change a

little later. East coast basins show a lot of variability. SON returns the most uniform pattern of

break years and the majority of the basins experience a change in the late 1990s. The

southernmost basins stand out as breaking in the early 2000s. The diversity in the observed break

years reflects the hydroclimatic heterogeneity of the region as recognized by Carmona et al.

(2014).

The 30-year averages entered into the Pettitt analysis are constructed such that a break in

1970-71, as reported by (McCabe & Wolock, 2002), would appear in 1999-2000 (in the end of

the 30 year time series). Figure 3-7. displays the observed cumulative frequency of break years

by season. 50 percent of the basins have already experienced their break in JJA RCs by the year

suggested by these authors; 65% of MAM, 75% of DJF and 90% of SON. Only coefficients for

JJA show any large number of changes 1998-2002. A Kolmogorow-Smirnov test comparing the

empirical cumulative distributions of the seasonal breaks reveals that the spring season is

significantly different from all the other seasons (0.05 level). The spring season break years are

52

typically earlier (average year 1997); while the other seasons break in 1998-1999 (Table 3-1.).

Due to the nature of how the 30 year averages are constructed, a reported change in the late

1990s could also mean that the actual change was in the beginning of that 30 year time window,

which would be in the late 1960s, close to the break identified by McCabe & Wolock (2002). To

ensure the timing of the actual breakpoints the analysis replicated using 20 year moving

averages. If the actual breakpoints were in the beginning of the moving window (late 1960s),

these changed settings would lead to that the reported break year would be 10 years earlier than

previously reported. No such changes appeared.

Identified changes in seasonal runoff coefficients and slopes potentially suggest increased

(decreased) proportions if seasonal water being released from (input to) seasonal hydrologic

stores and/or decreased losses to evapotranspiration. Several different explanations for this

change in runoff coefficient are possible, the principle ones of which are analyzed below.

Increased Precipitation

Seager et al. (2009) project a trend of increasing precipitation and evapotranspiration in

the Southeast Kunkel et al (2013) on the other hand, write that the regional annual precipitation

has not changed significantly over the period 1895-2011, but that there has been a shift in the

timing of the precipitation with a smaller fraction falling during the summer and increased

precipitation in the fall months.

Considering the geographic location of the study area and the observed seasonality of

changes in the precipitation-runoff relationship, tropical storm frequency was investigated as

possible driver of observed changes. Generally the occurrence of tropical storms not only

increases the seasonal precipitation total, but it also tends to increase the proportion of both

precipitation input and runoff output to and from the basin in just a few days, thereby amplifying

the essential non-linerar nature of the relationship between precipitation and runoff, which is

53

generally linearized by taking seasonal totals. Application of a 500 km radius of influence

(Barlow, 2011; Brun & Barros, 2014; Zick & Matyas, 2015) around North Atlantic tropical

storm track data (IBTrACS by Knapp et al. 2010) revealed that there has been a significant

increase in the number of tropical storms exerting a potential influence on the basins before and

after the breaks identified by the Pettitt test. Considering all seasons, sixteen of the eighteen

basins intersected more frequently with the “circle of influence” around tropical storms after the

break in the time series. The average increase in frequency was 21%. Klotzbach et al. (2015)

show that the number of Atlantic hurricanes has decreased since 2012. If only data up to year

2011 (1952-2011) are considered all eighteen basins report increased frequencies of potential

interactions with tropical storms averaging 36% more. Considering the JJA and SON seasons as

the main hurricane season twelve of the eighteen basins get more hurricanes after the break. On

average all the basins get hit 18% more often during JJA and SON after the break when the full

record is included. If the record is shortened, excluding 2012-2014, the basins get hit by

hurricanes on average 41% more often (Figure 3-8.).

An increase in the number of tropical storms in the North Atlantic basin, following a

change in the phase of the Atlantic Multidecadal Oscillation (AMO) in 1995, has been reported

by Goldenberg et al. (2001) and Webster et al. (2005). The warmer phase of the AMO

constitutes more favorable conditions for tropical cyclone development, while the cold phase has

the opposite effect. The influence of the AMO is most obvious when studying landfalls along the

U.S. east coast and in the Caribbean, and less so around the Gulf of Mexico, where the sea

surface temperatures do not show the same type of interannual or interdecadal variability as

those in the North Atlantic (Goldenberg et al., 2001). During the warm phase of the AMO, when

tropical storms are more likely to form and grow stronger, there is also a higher risk of them

54

moving further inland, as a stronger tropical storm survives longer over land than a weak. The

difference in storm impact frequency is therefore larger inland than in coastal areas (Figure 3-8.),

even though the coastal areas always get hit by a higher number of tropical storms than the more

continental basins.

Xie et al (2005) applied a Principal Components Analysis to a hurricane track density

function, confirming the findings of Goldenberg et al. (2001) as the first mode of variability was

significantly correlated with the AMO as well as El Niño-Southern Oscillation. The second

Principal Component on the other hand showed significant correlation with the AMO, Arctic

Oscillation and North Atlantic Oscillation; hence the AMO is identified as one of the strongest

drivers of tropical storm variability. The AMO returned to its cool phase in 2012, which has led

to a decrease in tropical storm activity (Klotzbach et al. 2015; McCarty et al. 2015). This is

reflected as a distinct decrease in the number of tropical storms impacting the study basins

during the last two years of the historical time record. In the SON seasons of 2013 and 2014 no

storms came within 500 km of the basins – an occurrence unique in observations of consecutive

years throughout the current historic data.

Climate Change

Groisman et al. (2001) found a weak declining trend in the number of days with snow cover in

the Southeast during the last century, which might be an indicator of warmer temperatures. A

Mann-Kendall test is performed on seasonal PRISM mean temperature data to identify increased

temperatures, which would explain changes in RCs as a result of changes in hydrologic storage

(snow and snowmelt) during DJF and MAM and changes in evapotranspiration losses. No

significant temperature change trends appeared in SON, DJF and MAM, while a couple of basins

in the southeastern section of the study region show a warming trend through JJA. These

findings concur with the literature, which identifies the Southeast as part of what Pan (2004)

55

termed “the Warming Hole”, - a region of the U.S. that show less, or no, warming connected to

climate change than the rest of the country. Later studies confirm that the Southeast thus far is

one of the few places where an obvious warming temperature trend related to climate change

cannot be detected (Misra et al., 2012; Portmann et al., 2009).

Urbanization and Land Cover Change

The study region underwent substantial land cover changes during the last century. The natural

forest cover was largely denuded for agriculture in the late 19th and early 20th centuries.

Following the Great Depression of the 1930s much of the farmland was abandoned and forest

returned through natural restoration, silviculture and a lucrative forestry industry. Today some

areas are experiencing rapid urbanization and have done so for several decades (Liu, 2011;

Sampson, 2004). Despite the fact that the watersheds included in this analysis are supposedly

minimally affected by man in terms of flow regulation structures, it must be recognized that the

basin landscapes may have been modified during the time period studied. Changes in land cover

could influence the translation of precipitation to runoff as urbanization typically involves

removal of vegetation and the installation of impervious surfaces and storm drains. Hence, in the

absence of any remedial measures increasing runoff and decreasing infiltration, transpiration and

interception might be expected. Further such changes can alter local and regional surface energy

fluxes through changed albedos and decreased latent heat exchange, which in turn affect

evapotranspiration. However, most of these basins are sufficiently large that the urbanization still

only constitutes a small portion of the basin area, and the requirement that they be free of major

flow regulation also increases the probability of occupying more rural settings. Moreover, if

urbanization would be the answer to why the precipitation-runoff slopes of the study region are

changing one would expect to see the similar changes all seasons. No such pattern emerges.

56

Conclusion

This study finds that there has been a significant change in the hydroclimatology of the Southeast

U.S. during the last half-century. A general pattern of increased streamflow per unit of

precipitation occurs, especially in JJA-SON, but also in DJF, indicating decreased precipitation

storage. Changed temperatures, urbanization and land cover change, and increased hurricane

generated precipitation were factors analyzed as potential drivers of the observed change in the

precipitation-runoff relationship. Considering the timing of both the seasonal and long-term

observed changes, and the fact that the frequency at which the basins are hit by hurricanes

increase post observed breaks, hurricane generated precipitation is concluded to be the major

driver of the changed hydroclimatology of the southeast US. It is however important to note that

the changed frequency in land falling hurricanes is tightly linked with the phase of the Atlantic

Multidecadal Oscillation, which hence is a useful indicator in long-term water resource

forecasting in the region.

57

Table 3-1. Resulting P-values from the Kolmogorow-Smirnov test comparing the cumulative

distributions in breaks identified in the seasonal series of runoff coefficients by the

Pettitt test. Significant differences at 0.05 level are bolded.

DJF MAM JJA SON

DJF - - - -

MAM 0.016 - - -

JJA 0.43 0.044 - -

SON 0.64 0.016 0.47 -

58

Figure 3-1. Basins included in the analysis. The location of the USGS gauging stations are

indicated by black dots. The basin sizes in the table to the right are defined using a

DEM.

59

Figure 3-2. Spatial patterns of estimated seasonal RCs across the study area.

60

Figure 3-3. Annual standardized monthly A) maximum, B) median and C) minimum flows

averaged over the eighteen study basins.

A

C

B

61

Figure 3-4. Difference in the slope between precipitation and streamflow (RCs) before and after

the identified breaks in seasonal relationships.

62

Figure 3-5. Difference in the seasonal RCs before and after the identified breaks. Black arrows

represent a positive difference, indicating increased runoff per unit of precipitation

after the identified break. Grey arrows represent decreased runoff. The magnitude of

the change is indicated by the length of the arrows.

63

Figure 3-6. Visual depiction of identified break years. The pie charts are filled according to their

break years, a key is given on the left side of the figure. Basins where no significant

break was identified are represented with a black dot. The most uniform break year

pattern emerges in SON, which is also the only season in which all basins show

significant breaks, while the other seasons show more regional variations.

64

Figure 3-7. Cumulative distribution of break years. Basins typically break the earliest in MAM

and the latest in JJA. The break in 1970-71 identified by McCabe & Wolock (2002) is

marked with a line and an asterisk.

65

Figure 3-8. Comparisons of how frequently tropical cyclone tracks and zones of influence

intersect with the basins before and after the identified breaks in the time series. A)

includes the full time series and the full North Atlantic hurricane season and is

considered “the base case”. In this record the more continental basins show an

increase in hurricane frequency, while the coastal show a slight decrease. The map in

B) shows the 1952-2011 (excluding the three last years after the AMO phase change)

record relative to the base case.

A B

66

CHAPTER 4

HYDROPOWER IN THE SOUTHEAST: A HYDROCLIMATOLOGICAL PERSPECTIVE

Water allocation for hydropower in the Southeast competes with several conflicting

interest for the region’s limited water resources. This competition could be expected to become

more severe in the face of burgeoning demands from an increasing population for domestic,

industrial, and agricultural consumption, contemporary to a growth in interest in cheap

renewable energy sources.

All these societal changes are to be played out against the backdrop of a climate that is

facing extended periods of drought, such as 1986-1988, 1998-2002 and 2006-2009 (Pedersen et

al. 2012, Seager et al. 2009) and excess precipitation brought by warm El Niño-Southern

Oscillation (ENSO) events (Johnson et al., 2013; Dracup & Kahya, 1994; Kahya & Dracup,

1993) or tropical storms (Brun & Barros, 2014; Ortegren & Maxwell, 2014).

The Southeast is a region not typically associated with hydropower, but three out of the

nation’s top 10 hydropower-producing states are located within: Alabama, North Carolina and

Tennessee (National Hydropower Association, 2013). As seen in Figure 4-1., the three

watersheds included in the analysis drain Alabama, Georgia and minor parts of North Carolina,

South Carolina and Tennessee. They contain multiple hydroelectric dams, of which a portion is

managed by the Army Corps of Engineers (ACE). The ACE dams commenced operation

between the early 1950s-mid 1970s and are operated to provide peak power, i.e. meet peaks in

demand with short notice and operate during parts of the day when the demand typically is the

highest. On average hydropower produces approximately 11% of the regions’ electricity, but it

exhibits great year-to-year variability (Figure 4-2.).

This paper examines how hydropower in the Southeast responds to large scale climate

drivers (teleconnections). Through this analysis we add to the research on the utility of including

67

teleconnections in forecasting of hydropower and water resources. Recognition of

teleconnections as important drivers of water availability and market behavior could benefit

hydropower production, and would allow it to make up a larger proportion of the energy mix in

the Southeast. This could lead to the two-fold benefits for the U.S. as a country as more

hydroelectric production by the ACE leads to increased revenue for the Department of the

Treasury. At the same time increased hydroelectric production would allow decreases in the

production of electricity using traditional resources such as oil, coal and gas, which would

benefit the health of both people and the environment.

Linking hydropower production to large-scale atmospheric drivers (teleconnections) is a

new field of study in the Southeast, but similar studies have been completed elsewhere. The

NAO has been identified as the dominant driver of the hydropower in Scandinavia (Cherry et al.

2005, Engström and Uvo, 2015), and a valuable index for the electricity companies to monitor to

have a good idea of expected water resources. Poveda et al. (2003) found that if the phase of

ENSO would have been considered, the country of Colombia could have saved $10 million/year

in energy costs between the years of 1977-1994. Hamlet et al. (2002) suggest that large

economic gains can be made if teleconnections were to be included in the water resource

forecasting of the Columbia River in the Pacific Northwest of the United States. If ENSO and the

Pacific Decadal Oscillation were to be included in the water resource forecasting the annual

electricity production could be increased by 9.6% (Hamlet et al. 2002). Table 1-1. provides an

overview of what a 9.6% increase of hydropower production would mean for the ACE dams in

the Southeast.

Data and Methods

The motivation for the inclusion of teleconnections included in this analysis are based on the

comprehensive overviews of teleconnections influencing the hydrology of the Southeast

68

described by Labosier and Quiring (2013) and Engström and Waylen (2017). The influence of

ENSO in the Southeast is well documented (Kahya & Dracup, 1993, Dracup & Kahya, 1994,

Kunkel et al. 2013, Labosier & Quiring, 2013). Typically the warm phase brings increased

winter precipitation to the west part of the region due to strong south westerly winds over the

Gulf of Mexico. This pattern of increased precipitation is less marked along the east coast as the

strong southwesterly flow tends to keep moisture-bringing tropical cyclones off-shore,

amplifying the tendency for greater vertical shear over the North Atlantic basin to produce fewer

such events. The cold phase of ENSO is generally associated with drier conditions. In this study

the Bivariate ENSO Timeseries (BEST), a combination of the Niño 3.4 index and the Southern

Oscillation Index (SOI), is used to represent ENSO (Smith and Sardeshmukh 2000).

Due to its cycle of variability being 60-80 years, the Atlantic Multidecadal Oscillation

(AMO) has a longer-lasting influence on the hydroclimate of the Southeast relative to other

teleconnections. Its positive phase brings drier conditions (Tootle et al. 2005, Johnson et al.

2013), especially during the summer (Enfield et al. 2001, Ortegren et al. 2011, 2014), while the

negative phase is associated with increased precipitation and streamflow. The AMO index

utilized here is the smoothed version of the Kaplan SST V2 data (Kaplan et al. 1998; Enfield et

al. 2001; National Oceanographic and Atmospheric Administration, 2002).

There is limited research showing linkages between the NAO, precipitation and

streamflow in the study area. Coleman and Budikova (2013) conclude that its influence is

strongest in the Northeastern United States, but positive relationships can also be found in

summer streamflow in parts of the Southeast. Surface temperatures, which play a key role in

determining the type of precipitation (Kang et al. 2016) and controlling evapotranspiration, have

been shown to be associated with both the NAO and ENSO throughout the Southeast (Rogers,

69

1984; Katz et al. 2003). In this study we use the NAO index as it is defined by Barnston and

Livezey (1987).

Katz et al (2003) also found significant relationships between the SO, NAO, and Pacific-

North American pattern (PNA) and the wintertime precipitation across the Southeast. The

positive link between the PNA and the wintertime snowfall in the Appalachians identified by

Serreze et al. (1998) highlights the need for time lags when analyzing the effect of

teleconnections on water resources in the Southeast. In this study we use the PNA index as it is

defined by Barnston and Livezey (1987).

Hydrologic data consists of seasonal water availability (reservoir inflow and outflow) and

hydroelectric power generation for eight dams managed by the ACE. The dams are located in

three different watersheds: Alabama–Coosa–Tallapoosa (ACT, three dams), Apalachicola–

Chattahoochee–Flint (ACF, three dams) and Savannah River (SAV, two dams) (Figure 4-1.). As

dam operation downstream relies heavily on discharge from upstream reservoirs the analysis is

made at the watershed level, including all ACE hydroelectric dams on the river. Plants in which

water is sometimes pumped back into the reservoir (so called pumped storage) to meet peak

demands excluded from the analysis as the re-use of the water resource is less dependent on

water resource availability and the relationship with production is instead mainly driven by

demand.

The original data contains some extreme outliers that are excluded, as they tend to distort

the further statistical analysis. Hurricane related precipitation data are obtained from Zhou &

Matyas (2017), however, exceptionally high seasonal flows couldn’t be linked to tropical

cyclones alone. Instead outliers are defined as being more than two standard deviations from the

mean inflow (the standard deviation was inflated due to a few extreme outliers and therefore two

70

standard deviations are used instead of the more common practice of using three). Seasonal

inflow and hydropower production data are sorted depending on the phase of the various

teleconnections. The variables are associated with a teleconnection if they occur in the upper

tercile of the teleconnection index range included in this analysis (1988-2013). Teleconnection

interactions are also considered as, depending on season and teleconnection, the teleconnections

sometimes amplify or cancel out the effect of each other (Enfield et al. 2001, Tootle et al. 2005).

To account for interactions between teleconnections, the analysis includes all inflow and

hydropower production data that match the set criteria (ex. positive AMO and positive NAO

coincide).

A total 45 different combinations of teleconnections are considered. Due to the rare

occurrence of certain teleconnections, 31 teleconnections and combinations are entered into the

analysis. T-tests determine if the seasonal inflow and/or hydropower production that are

influenced by any of the teleconnections are significantly different from those that aren’t

influenced by that same teleconnection. A t-test is chosen as it allows the populations to be

analyzed separately, hence acknowledging that the variables might not respond the same way

under teleconnection influence.

Results and Discussion

Potential Production

Figure 4-3. shows the seasonal average inflow and production for the three basins over

the study period. Production is strongly associated with inflow in the ACF and SAV basins,

indicating that long-term storage is limited. This observation is further confirmed by a simpler

analysis of outflow data (not displayed). In the ACT basin MAM and DJF production does not

correspond as well to the inflow. Maximum monthly potential production is shown as an insert in

each diagram of Figure 4-3. This number is computed as the product of the installed capacity

71

(MW) and the number of hours in a 30 day month. At most the ACF basin produces 56% of its

potential monthly production, the ACT basin 57% and SAV 90%. Figure 4-3a suggests the ACF

basin production is closely linked to inflow. Based on these data, it appears that a lack of

sufficient water resource may be a contributing factor, the maximum production only reaches

56% of its potential, i.e. there is overcapacity in the dams. The ACT basin plot (Figure 4-3b)

suggests that the production is capped in some way around 110,000 MWh, despite its maximum

potential production of 185,000 MWh. There is also significant variability in production during

the periods of higher inflow, indicating that the inflow-production relationship at these volumes

is far more complex than what is seen at lower flow volumes and in the other basins. These high

inflows occur mainly in the winter and spring. In the ACF and SAV basins the electricity to

inflow ratio is slightly higher during the summer and fall, indicating that more electricity is

produced than with equivalent inflow volumes during the winter and spring. This might be an

indicator of short-term storage during the cooler part of the year and/or higher electricity demand

during the summer and fall seasons.

Two different approaches are taken to estimate potential production based on inflow for

the ACT basin. The first, more conservative approach, applies the average summer inflow-

production ratio onto the inflow. Considering that the powerplants may have to close down for

scheduled maintenance, etc. a max powerplant availability of 90% is applied. This modification

leads to a 46% increase in annual production. The greatest changes are found in the winter and

spring seasons, when production would increase by 82%. A second, more optimistic approach

uses the ten highest inflow-production ratios and applies those to the ACT inflow. This results in

a 67% increase in annual production, and a 101% and 99% increases respectively in winter and

spring seasons. In both approaches, the ACT basin reaches 90% of maximum potential

72

production multiples times, an indication that if the water volumes alone controlled production

there would be room for additional installed capacity.

Influence of the Teleconnections

Nineteen out of the 38 teleconnections and their combinations significantly altered inflow

and/or hydropower production.

ENSO, identified as one of the main driver of hydrologic variability in the region,

significantly alters both inflow and hydropower production in its cool phase, while a warm phase

signal only appears to have an impact in the ACT basin (Figure 4-4.).

Results indicate that the influence of teleconnections is sometimes lagged, for example

Figure 4-5. illustrates how ENSO phases in MAM significantly change inflow and hydropower

production in following JJA in the SAV basin.

The interaction of teleconnections sometimes leads to amplified responses in the basins.

Figure 4-6. indicates that neither warm phase AMO nor cool phase PNA alone, exert any

significant influence on the SAV summer inflow or production, however when they coincide

both inflow and the production decreases significantly.

Figure 4-7. shows examples of different responses in inflow and hydropower production,

indicating that management is not always linked to water availability. Significantly higher

hydropower production, despite lack of significant change in inflow, as depicted in Figure 4-7a,

could be an indication that stored water resources are used to meet the market demands, while

Figure 4-7b indicates significantly lower hydropower production, despite abundant water

availability. An analysis of inflow and total outflow through the basin does not indicate any long-

term storage.

The heat maps in Figure 4-8. summarize the influence of the nineteen significant

teleconnections and combinations per basin. The strongest influence across all basins and all

73

seasons of inflow and hydropower production is brought by the wintertime cold phase ENSO,

which significantly lowers both inflow and production (top row of Figure 4-8.). Cold phase

ENSO events occurring in the fall, have the second strongest influence, bringing lower inflow

and production throughout the fall and winter. Fall cold phase events also affects the summer

production (but not inflow), which might be an indicator that water management operates the

powerplants to conserve water following information on expected dry conditions from the

National Weather Service. The third strongest influence and most pervasive is brought by the

combination of a year of warm phase AMO (AMO Y+) and a year with negative NAO (NAO Y-

), conditions that bring lower inflow and production during the cold seasons and higher rates in

the summer, a response that is particularly pronounced in the ACT basin. As can be seen in

Figure 4-8. many of the teleconnections show up in the inflow and production record with a

lagged response. Good examples are the influence of springtime ENSO events on the SAV basin

as well as the coupled influence of summertime AMO and cold phase ENSO in the ACF basin.

This kind of lagged behavior is useful in terms of water resources planning. Figure 4-9.

summarizes the heat maps of Figure 4-8., giving an overview of the overall influence of the

teleconnection on the region.

Discussion

Results show that the ACF and ACT basins in general behave more similar to each other

than the SAV basin. This can be explained by the geography of the basins, both the ACF and the

ACT originate from the southern end of the Appalachian Mountains and run southwards,

draining into the Gulf of Mexico. SAV on the other hand drains from the southeastern flank of

the Appalachians and discharge into the Atlantic Ocean. It is hence more affected by easterly

atmospheric flows than ACF and ACT that are strongly influenced by south-westerly winds,

regulating the northwards transport of moisture from the Gulf of Mexico. In some cases, west-

74

east geographical trends can be seen in the inflow and hydropower production response to the

teleconnections in Figure 4-8. One example is the influence of springtime warm phase ENSO on

the basins (row 7 in Figure 4-8.). The teleconnection phase brings slightly higher inflow and

production to the ACT basin, the response is somewhat stronger in the ACF basin and the

strongest and longest lasting effect is found in the SAV basin that is furthest to the east.

Conversations with ACE Water Managers in the region revealed that despite general

knowledge of teleconnections, they are not accounted for in water resources planning. Instead,

production planning is based on seven-day weather forecasts, which are updated daily. Overall

monthly production goals are set annually by the Southeastern Power Administration, that sell

ACE hydropower electricity, in response to anticipated demand from their customers. In this

paper we find that, disregarding other constraints, hydropower production at the plants included

in this analysis could be increased. This is based on insights from ACE personnel who stated that

even during non-flood conditions water is sometimes let through the spillways instead of going

through the turbines. Data on how much water is discharged like this, with what frequency it

happens, and how much potential electricity is lost is not available.

Even without considering the influence of teleconnections the powerplants in the ACT

basin could significantly increase their production, providing the region with cleaner energy and

the nation with additional revenues. The way the ACT basin behaves at the moment, there seems

to be a “cap” of some sort in the production. Splitting Figure 4-3a up by its three sub-basins

(powerplants) reveals that the production of the first powerplant on the river (Allatoona) has a

similar response to inflow as the ACF and SAV basins (high inflow= high production).

However, the two downstream powerplants R.F. Henry and Millers Ferry (that the authors note

have six non-federal dams in between them and Allatoona) show a very different behavior as

75

production at these dams do not peak at high inflows, but instead production decreases at times

of high inflows (Figure 4-10.). This kind of response cannot be explained by the power curve of

the turbine (Sammartano et al. 2014) but is possibly due to management strategies. The author

speculates that the decrease in production at high inflows might be due to the monthly

production goals set by the Southeast Power Administration. When there are ample water

resources throughout the entire basin the set production goal is met by maximizing production at

the uppermost dam and capping it downstream once the set goal is achieved. One could argue

that the additional electricity that could be produced could still be sold to bring extra income to

the Department of Treasury and decrease the use of fossil fuels. However, there are thirteen

hydropower dams in the ACT basin (U.S. Army Corps of Engineers, 2017), hence at high flows

the total hydropower output is high and might exceed market electricity demand, i.e. there might

not be need for additional electricity and since reservoir storage is limited, the water is

discharged to prevent flooding and/or dam failure. Following along the same line of high water

levels and high hydropower production throughout the basin, it is possible that the limiting factor

is not the market, but rather the electric grid that might not have the capacity of transporting

simultaneous maximized production from all dams in the basin, and hence production levels are

decreased downstream.

Teleconnections also show strong linkage to inflow and/or hydropower production during

parts of the year. Acknowledging this linkage would allow water managers to make more long-

term plans for the water resources and would also allow hydropower to become a more

competitive electricity alternative in this region.

Conclusion

Inflow and production analysis revealed that based on water resource availability,

hydropower production in the ACT basin could be increased 46-67% per year. For the ACF

76

basin, available water resources was the limiting factor for increased hydropower production.

The SAV basin reaches 90% of its maximum production.

The teleconnections AMO, ENSO, NAO and PNA proved to be significantly linked to

variations in inflow to hydropower reservoirs as well as hydropower production in the Southeast.

The strongest influence is asserted by wintertime and fall cold phase ENSO events, that bring

significantly drier conditions to all basins in the study area. The influence of El Niño events was

more limited but still significant. Considering teleconnection interactions as well as time lags

proved useful.

The findings highlight the potential to incorporate teleconnections in water resources

planning and forecasting. What teleconnections to consider varies by basin and season, but the

AMO, ENSO, NAO and PNA are all significantly influencing the water resources of the

Southeast. Teleconnections have potential to inform water resource forecasting and improve the

efficiency of hydropower production.

77

Figure 4-1. Overview of the basins and hydroelectric dams included in the analysis. Pumped

storage dams managed by the ACE are no included in the analysis but displayed here

in lighter colors. For readers unfamiliar with the region a small scale map in the upper

left corner shows the location of the study area.

78

Figure 4-2. Annual production anomaly in percent around the mean. In an average year the eight

dams included in this analysis produce 2,500 GWh of electricity, enough to supply

over 200,000 households with their total electricity needs.

79

Figure 4-3. Plots of average seasonal inflow and hydropower production 1988-2013 in

geographical order (west to east) for the a) ACT, b) ACF, and c) SAV basins. Seasons

are represented by different colored dots and the maximum potential monthly

production is noted on the right-hand side of each diagram.

A A

B

C

80

Figure 4-4. Plots showing the influence of ENSO events. Inflow is displayed on the x-axis and

hydropower production on the y-axis. ENSO years of desired phase are indicated as

filled dots. The upper row shows the influence of warm phase ENSO in the A) ACT

basin, B) ACF basin, and C) SAV basin. The bottom row shows the influence of cold

phase ENSO on the D) ACT basin, E) ACF basin, and F) SAV basin. Influence

leading to significantly different behavior is highlighted with a red circle.

ENSO – (DJF)

ENSO + (DJF)

Pro

du

ctio

n S

AV

(D

JF)

ENSO – (DJF) ENSO – (DJF)

Pro

du

ctio

n A

CT

(DJF

)

Pro

du

ctio

n A

CF

(DJF

)

ENSO + (DJF) ENSO + (DJF)

Pro

du

ctio

n A

CF

(DJF

)

Pro

du

ctio

n A

CT

(DJF

)

Pro

du

ctio

n S

AV

(D

JF)

Inflow SAV (DJF)

Inflow SAV (DJF)

Inflow ACT (DJF)

Inflow ACF (DJF)

Inflow ACF (DJF)

Inflow ACT (DJF)

A

F E D

C B

81

Figure 4-5. Example of lagged response. SAV summer inflow and hydropower production is

significantly altered by both A) La Niña, and B) El Niño, spring events.

Figure 4-6. Example of how teleconnection interaction leads to amplified influences. Alone the

A) annual (Y) positive phase of AMO and B) negative phase of PNA do not change

the summertime behavior of the SAV basin, but C) shows that when they coincide

inflow and hydropower production decrease significantly.

+ =

ENSO – (MAM) ENSO + (MAM)

Pro

du

ctio

n S

AV

(JJ

A)

Pro

du

ctio

n S

AV

(JJ

A)

Inflow SAV (JJA) Inflow SAV (JJA)

Inflow SAV (JJA) Inflow SAV (JJA) Inflow SAV (JJA)

Pro

du

ctio

n S

AV

(JJ

A)

Pro

du

ctio

n S

AV

(JJ

A)

Pro

du

ctio

n S

AV

(JJ

A)

AMO + (Y) PNA - (Y) AMO + (Y), PNA - (Y)

A B

C B A

82

Figure 4-7. Two examples form the ACT basin where only one of the variables is significantly

changed under the influence of teleconnections. In plot A) production is increased,

despite lack of significantly higher inflow. In plot B) production is significantly low,

despite ample water resources.

Inflow ACT (MAM) Inflow ACT (MAM)

Pro

du

ctio

n A

CT

(MA

M)

Pro

du

ctio

n A

CT

(MA

M)

AMO -, ENSO+ (DJF) AMO +, NAO+ (Y)

B A

83

Figure 4-8. Heat maps giving an overview of the seasonal influence of the nineteen

teleconnections on the inflow and hydropower production of the three basins. Every

row represents a teleconnection phase or combination and its season, as displayed to

the left. The columns represents the seasonal inflow and hydropower production. The

heat maps are displayed in the geographical order of the basins, from west to east for

easier identification of spatial patterns. A key to the colors is shown to the right.

ACT ACF SAV

MA

M I

nflow

MA

M P

roduction

JJA

Inflow

JJA

Pro

duction

SO

N I

nflow

SO

N P

roduction

DJF

Inflow

DJF

Pro

duction

ENSO DJF - 0 0 1 0 1 1 0 1

ENSO SON - 0 0 0 1 1 1 1 1

AMO Y + NAO Y - 0 1 3 2 0 0 0 1

AMO + ENSO MAM - 0 0 0 0 1 1 0 0

AMO + PNA Y - 0 1 1 0 0 0 0 0

AMO + ENSO JJA - 0 0 0 0 0 1 0 0

ENSO MAM + 1 1 0 0 0 0 0 0

ENSO MAM - 0 0 0 0 0 0 0 0

AMO - ENSO DJF + 1 0 0 0 0 0 0 0

AMO + ENSO DJF - 0 0 0 0 1 0 0 0

ENSO DJF + 0 0 0 0 2 2 0 0

NAO Y - 0 0 0 0 0 1 1 0

AMO - ENSO SON + 0 0 0 0 0 0 0 0

AMO + ENSO SON - 0 0 0 0 1 0 0 0

AMO + ENSO JJA + 0 0 0 0 0 0 0 0

AMO Y + NAO Y + 1 0 0 0 0 0 0 0

ENSO JJA - 0 0 0 0 0 0 0 0

ENSO JJA + 1 0 0 0 0 0 0 0

ENSO SON + 0 0 0 0 0 0 0 0

MA

M I

nflow

MA

M P

roduction

JJA

Inflow

JJA

Pro

duction

SO

N I

nflow

SO

N P

roduction

DJF

Inflow

DJF

Pro

duction

ENSO DJF - 0 1 0 0 1 1 1 1

ENSO SON - 0 0 0 1 1 1 0 1

AMO Y + NAO Y - 1 1 0 1 0 0 0 1

AMO + ENSO MAM - 0 0 0 0 1 1 1 0

AMO + PNA Y - 1 1 0 0 0 0 0 0

AMO + ENSO JJA - 0 0 0 0 1 1 1 1

ENSO MAM + 2 2 0 0 0 0 0 0

ENSO MAM - 1 0 1 0 0 0 1 0

AMO - ENSO DJF + 0 0 2 2 2 2 0 0

AMO + ENSO DJF - 0 0 0 0 1 1 1 1

ENSO DJF + 0 0 0 0 0 1 2 1

NAO Y - 0 0 0 0 1 1 0 0

AMO - ENSO SON + 0 0 2 0 1 0 0 0

AMO + ENSO SON - 0 0 0 0 0 1 0 1

AMO + ENSO JJA + 0 0 1 1 0 0 0 0

AMO Y + NAO Y + 0 0 0 0 0 0 0 0

ENSO JJA - 0 0 0 0 0 0 0 1

ENSO JJA + 0 0 0 0 0 0 0 0

ENSO SON + 0 0 0 0 0 0 1 0

MA

M I

nflow

MA

M P

roduction

JJA

Inflow

JJA

Pro

duction

SO

N I

nflow

SO

N P

roduction

DJF

Inflow

DJF

Pro

duction

ENSO DJF - 1 1 0 1 1 1 0 1

ENSO SON - 0 0 0 1 1 1 1 1

AMO Y + NAO Y - 0 0 0 0 0 0 0 1

AMO + ENSO MAM - 1 0 1 1 1 0 0 0

AMO + PNA Y - 1 1 1 1 1 1 0 0

AMO + ENSO JJA - 1 0 0 0 0 1 0 0

ENSO MAM + 2 2 2 1 0 0 0 0

ENSO MAM - 1 0 1 1 0 0 0 0

AMO - ENSO DJF + 0 0 0 1 0 0 0 0

AMO + ENSO DJF - 0 0 0 0 0 0 0 0

ENSO DJF + 0 0 0 0 0 0 0 0

NAO Y - 0 0 0 0 0 0 1 0

AMO - ENSO SON + 2 0 2 0 0 0 0 0

AMO + ENSO SON - 0 0 0 0 0 0 0 0

AMO + ENSO JJA + 0 0 0 0 0 0 0 0

AMO Y + NAO Y + 0 0 0 0 0 0 0 0

ENSO JJA - 0 0 0 0 0 0 0 0

ENSO JJA + 0 0 0 0 0 0 0 0

ENSO SON + 0 0 0 0 0 0 0 0

+ >100%

+ 75-99%

+ 50-74%

+ 25-49%

+ 0-24%

No signifiant change

- 0-24%

- 25-49%

- 50-74%

- 75-100%

Key

84

Figure 4-9. Heat map showing the count of basins influenced by the teleconnections.

MA

M I

nflow

MA

M P

roduction

JJA

Inflow

JJA

Pro

duction

SO

N I

nflow

SO

N P

roduction

DJF

Inflow

DJF

Pro

duction

ENSO DJF - -1 -2 -1 -1 -3 -3 -1 -3

ENSO SON - 0 0 0 -3 -3 -3 -2 -3

AMO Y + NAO Y - -3 -1 1 2 0 0 -1 -3

AMO + ENSO MAM - -1 -1 -1 -1 -3 -2 -1 0

AMO + PNA Y - -2 -3 -2 -1 -1 -1 0 0

AMO + ENSO JJA - -1 0 0 0 -2 -3 -1 -1

ENSO MAM + 3 3 1 1 0 0 0 0

ENSO MAM - -2 -1 -2 -1 0 0 -1 0

AMO - ENSO DJF + 1 0 1 2 1 1 0 0

AMO + ENSO DJF - 0 0 0 0 -2 -1 -1 -1

ENSO DJF + 0 0 0 0 1 2 1 1

NAO Y - 0 0 0 0 -1 -2 -2 0

AMO - ENSO SON + 1 0 2 0 1 0 0 0

AMO + ENSO SON - 0 0 0 0 -1 -1 0 -1

AMO + ENSO JJA + 0 0 -1 -1 0 0 0 0

AMO Y + NAO Y + -1 0 0 0 0 0 0 0

ENSO JJA - 0 0 0 0 0 0 0 -1

ENSO JJA + 1 0 0 0 0 0 0 0

ENSO SON + 0 0 0 0 0 0 1 0

Key

3 basins increase

No significant change

3 basins decrease

3

2

1

0

-1

-2

-3

85

Figure 4-10. Drawings of generalized relationship between monthly reservoir inflow and

hydropower production at the three ACT basins. Actual production is marked with an

A, potential production with a P.

Allatoona

Inflow

A

R.F. Henry

Inflow

P A

Millers Ferry

Inflow

P A

86

CHAPTER 5

SUMMARY AND CONCLUSIONS

For decades the water resources in the Southeast has been a topic of controversy. The

water should not only satisfy natural ecosystems, but also the drinking water needs of growing

urban areas, agricultural needs, and in the case of the Southeast, hydroelectric production. The

hydroclimate in the region exhibits great variability, giving rise to additional political tensions in

times of drought as well as floods. Through the studies presented in this dissertation, some light

is shed on the drivers of natural variability of precipitation, runoff and hydropower production.

The analyses are made on a seasonal basis, recognizing that influences and natural processes

might vary throughout the year, as does the demand for the water resource. Knowledge of

influential hydroclimatological drivers and the relationship between precipitation and runoff are

some of the most fundamental ingredients for successful water resource planning and

management.

The first study, presented in Chapter 1, confirms the significant influence of various

teleconnections upon the hydroclimate of the Southeast. The most significant external

contributors to variability in the region are the ocean-atmosphere based teleconnections AMO

and ENSO, which strongly influence the hydroclimate in the fall through spring with several

months’ time lag. The atmospheric-based teleconnections AO, NAO and PNA show more

sporadic influences. The findings of this paper also add to the ongoing debate on whether the AO

and NAO are one or separate phenomena by showing that the teleconnections’ relative influence

on the hydroclimate of the Southeast differs both in seasonal timing and the form of the influence

(AO influence precipitation, NAO streamflow), suggesting that they in fact are separate

phenomena. In general all teleconnection signals appear more clear in streamflow than

precipitation records.

87

In the second study, disregarding teleconnections, a significant change to the

hydrological cycle in the Southeast is identified. Results show that the relationship between

precipitation and runoff has changed during the last half-century. A general pattern of increased

streamflow per unit of precipitation occurs, especially in JJA-SON, but also in DJF, indicating

decreased hydrological storage. Changed temperatures, urbanization and land cover change, and

increased hurricane generated precipitation were factors analyzed as potential drivers of the

observed change in the precipitation-runoff relationship. Considering the timing of both the

seasonal and long-term observed changes, and the fact that the frequency at which the basins are

hit by hurricanes increase post observed breaks, hurricane generated precipitation is concluded to

be the major driver of the changed hydroclimatology of the Southeast. It is however important to

note that the changed frequency in land falling hurricanes is tightly linked with the phase of the

AMO, which hence is a useful indicator in long-term water resource forecasting in the region, a

fact that is further confirmed by the findings of studies number one and three (Chapter 2 and 4).

Chapter 4 investigates the influence of teleconnections on the hydropower production in

the Southeast. Earlier, similar, studies of other geographic regions have indicated that

hydropower production could be increased if teleconnection indices are considered in the

production forecast. In the current study several different linkages were established between

hydropower reservoir inflow, production and teleconnections:

In some cases a time lag is observed between the teleconnection indices and the

reservoir/production response. This proves that teleconnections can be useful in seasonal

forecasting.

Considering the simultaneous phase of multiple teleconnections showed that the

combined influence is sometimes stronger than that asserted by the teleconnections

separately.

Sometimes a response was found in the reservoir inflow but not in the production, and

vice versa. The behavior was especially pronounced in the ACT basin.

88

The decreased inflow and production associated with a cold phase ENSO is more

pronounced than the increases associated with ENSO’s warm phase.

There is hence many types of existing relationships between the teleconnections and

hydropower, indicating that incorporating them in forecasts could be useful. Further, it is found

that based on available water resources alone, and disregarding teleconnections, the annual

production of the ACT basin could be increased by up to 67%. The increased production would

mainly occur during the winter and spring, as this is when high inflows are observed and the

inflow-production ratio of the ATC basin decreases before reaching maximum production,

The findings highlight the potential to incorporate teleconnections in water resources

planning and forecasting. What teleconnections to consider varies by basin and season, but the

AMO, ENSO, NAO and PNA are all significantly influencing the water resources of the

Southeast. By understanding the natural drivers that influence hydropower production, reservoir

management and electricity production can be optimized. Decreasing uncertainties of future

water availability and demand offer potential for further development of hydropower that may

ultimately decrease dependence on non-renewable resources and energy imports.

Although the findings of this project show great potential to help improve water resource

management in the Southeast, further directions include incorporating teleconnections into

streamflow forecasting models. There is also potential to connect their variation to electricity

pricing and possibly also the economy of the region. Water quality is also a challenge throughout

the Southeast, and as teleconnections affect the water quantity, they might also indirectly

influence the water quality.

89

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BIOGRAPHICAL SKETCH

Johanna Cecilia Louise Engström is born and raised in the small fishing village Lomma,

just north of Malmö, in southern Sweden. This is where she went to elementary school for nine

years before she started high school, specializing in social science and business, at ProCivitas

Private Gymnasium in Malmö. After high school graduation Johanna continued to study business

at Lund University for one semester before moving to Florence, Italy, to study Italian.

Geography always being Johanna’s favorite subject in school and having a passion for

nature, made Johanna apply to the Bachelor of Science in Physical Geography and Ecosystem

Analysis at Lund University in 2006. She received her Bachelor of Science in 2009 and Master

of Science in 2011. Johanna has a long ranging interest in renewable energies and during her

university studies she had an internship and later on also a part-time job at E.ON Wind, Malmö,

which gave her valuable insights in the opportunities and challenges associated with wind power

development. During her master’s program she also got the chance to study abroad for one

semester and chose to go to University of Florida. After graduating from Lund University

Johanna worked for one year as a consultant for Sweco Position, Malmö, before moving to the

U.S. in 2012 to start the Ph.D. program in University of Florida’s Geography Department.

Apart from doing research, Johanna has also been the instructor of Geography for a

Changing World, Geography of Europe and Physical Geography at University of Florida. She

has presented her research at multiple national conferences, published five peer-reviewed journal

articles and taken on the role as the Geography Department’s graduate student representative.

The Geography Department honored Johanna’s departmental service, teaching and academic

excellence by awarding her the Ryan Poehling Award in 2015.

Johanna hopes to continue her career within the field of water, climate and renewable

energies after graduation.