1

Hydroclimatology: The Missing Component of

PUB

Praveen KumarFrancina Dominguez, Geremew Amenu

Institute for Sustainability of Intensively Managed Landscapes

Department of Civil & Environmental Engineering

University of IllinoisUrbana, Illinois 61801

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“Hydrologists spend too much time thinking about water” Ciaran Harman

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Global Water Vapor

4

a)

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Mon

thly

PW

Ano

mal

y (m

m)

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

Mon

thly

T A

nom

aly

(oC

)NVAP R-2 T

b)

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

Ann

aul P

W A

nom

aly

(mm

)

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

Ann

ual T

Ano

mal

y (

oC

)

NVAP R-2 T

c)

R2 = 0.6615

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

Monthly NVAP Anomaly (mm)

Mon

thly

R-2

Ano

mal

y (m

m)

d)

0.3

0.4

0.5

0.6

0.7

0.8

0 1 2 3 4 5

Time Lag (months)

Cro

ss-C

orre

lati

on

NVAP & T

R-2 & T

Amenu, G.G. and P. Kumar, BAMS, 86(2), pp. 245-256, 2005.

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Underlying Question

• How do the atmospheric and terrestrial moisture, and vegetation interact to produce the observed variability in the water cycle?

• How is this variability altered by anthropogenic influences?– Energy Cycle– Water Cycle

E

Pm

6

Near surface processes

Atmospheric Anomalies

Ocean Ocean

Enhancement or Dissipation of Anomalies through Feedback

Feedback between near- and deep-surface processes

Feedback between atmosphere and deep layer processes?

Ocean-atmosphere feedback

Deep layer processes

Land

7

Hypothesis• Regional atmospheric

moisture transport is governed by both the large scale forcing as well as local recycling, and their relative contributions have important implications in the inter-annual variability of the hydrologic cycle.

• The deep layer terrestrial moisture and energy storage modulate the dynamics of the near-surface layer thereby influencing the land-atmosphere interaction.

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Precipitation Precipitation RecyclingRecycling

Motivation:

• Changes in land surface hydrology will affect regional climate variables (eg. precipitation).

• Humans affect land surface, e. g. deforestation in Amazon.

Araca River, Brazil. Landsat 7 WRS http://landsat.gsfc.nasa.gov/earthasart/araca.html

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PEy

wv

x

wu

t

w

)()(

The principle for all analytic recycling models is the conservation of atmospheric mass

All previous recycling models have assumed negligible change of storage of atmospheric water vapor.

Analytic Models that assume negligible dw/dt:

-Budyko (1974)

-Drozdov and Grigor’eva (1965)

-Lettau et al. (1979)

-Brubaker et al. (1993)

-Eltahir and Bras (1994)

-Savenije (1995)

-Burde and Zangvil (2001)

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While the storage term is smaller than the advection terms, it is nonegligible at a daily timescale

Storage term is small compared to advection at a monthly timescale.

At the daily level, it is approximately 50% as large as the advection terms.

- Using NCEP/NCAR Reanalysis II data from 1979-2001.

Daily

Monthly

y

wv

x

wu

t

w

)()(

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Consequently, older models are not applicable at smaller time scales.

Recycling can be now studied at daily, weekly, and longer timescales.

Decade MonthSeasonYear Week Day Hour Minute

Important Land - Atmosphere Interactions occur at this timescale.

Assumption: Well mixed atmosphere.

Dominguez, F. and P. Kumar, “Impact of Atmospheric Moisture Storage on Precipitation Recycling,” Journal of Climate, 19 (8), pages 1513–1530, 2006.

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Correlation between daily recycling ratio and precipitation shows different sign in the east/west.

Soil Moisture climatology presents a similar pattern.

-.5 -.43 -.37 -.3 -.23 -.17 -.1 -.03 .03 .1 .17 .23 .3 .37 .43 .5

Legendmpdsirmnt<VALUE>-0.5 - -0.43

-0.42 - -0.37-0.36 - -0.3-0.29 - -0.23-0.22 - -0.17-0.16 - -0.1-0.09 - -0.03-0.02 - 0.030.04 - 0.100.11 - 0.170.18 - 0.230.24 - 0.30.31 - 0.370.38 - 0.430.44 - 0.5

Correlation of Monthly Summer (JJA) 1979-2000 Recycling Ratio and Corresponding PDSI

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Understanding the physical mechanisms that drive recycling variability at the Daily to Intraseasonal Timescale

We choose two regions because of their contrasting land surface response to precipitation.

Semi-arid North American Monsoon Region.

Moisture-abundant Midwestern United States.

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The Dynamical Recycling Model is used to calculate the recycling ratios

The NARR data provides land-atmosphere variables

Multichannel Singular Spectrum Analysis (M-SSA)

MODE 1

MODE 2

MODE 3

MODE 4

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CEC Level II Ecoregions of NA

10.4

10.2

13.1

13.2

12.1

14.3

The NAMS region is composed of a variety of ecoregions, from deserts (lightest green) to tropical dry forests (darkest green).

Sonoran Desert

Upper Gila Mountains

Western Pacific Coastal Plain Hills And Canyons

Western Sierra Madre

Western sierra MadrePiedmont

Chihuahuan Desert

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In the NAMS region, evapotranspiration significantly contributes to precipitation after monsoon onset.

At the peak of the season, an average of 15% of precipitation comes from evapo-transpiration, although some days it can be as high as 25%.

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gmons86q9v2Value

High : 1.500000 Low : 0.000000

gmons86q9v3Value

High : 0.500000 Low : 0.000000

grdndvi89aug1Value

High : 7000.000000 Low : 0.000000

gmons86q9v4Value

High : 0.900000 Low : 0.000000

Recycled Precipitation (mm/day)

Evapo-transpiration (mm/day)

Precipitation (mm/day)

NDVI

0 1.5 0 0.7

07.2

0 4

Precipitation, evapotranspiration and vegetation greenness is higher in the southwest of the region

Evapotranspiration is transported north and east where it falls as precipitation of recycled origin.

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The three longest monsoons in the 1985-1995 period are characterized by two precipitation peaks.

Precipitation recycling peaks during the intermediate dry period (midsummer drought - also called veranillo or canicula).

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Recycling Ratio Evapo-transpiration (mm/day)

Precipitation (mm/day) NDVI

Low Mid-Summer Precip

No effect on NDVINo effect on

Evaporation

Recycling continues during dry season.

During long monsoons, even when precipitation decreases, ET continues to provide moisture to the overlying atmosphere.

Region 14.3, Seasonally Dry Tropical Forest

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The region faces sever damage to existing vegetation health and consequently evapotranspiration regimes.

The seasonally dry tropical forests of Mexico have an annual deforestation rate of 1.4% (Trejo and Dirzo, 2004).

Our results show that this could have important consequences for precipitation regimes.

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22

Hydraulic Redistribution by Plant Roots:

A Mechanism of Interaction between Moisture Reservoir of

Deep-Soil, the Near-Surface Soil, and the Atmosphere

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Global Datasets•••

•••Ground Observations:

Very few locations, with poor spatial and temporal coverage

Illinois dataset is exceptionally unique dataset available

Remote Sensing:Only near-surface

soil moisture is measured

No long-term data

L1

L2

L3

L10

L11

10 cm

20 cm

20 cm

20 cm

10 cm

20 cm

each

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0

10

20

30

40

50

60

0.001 0.01 0.1 1Frequency (cycles/month)

Pow

er (

norm

aliz

ed)

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50

60

0.001 0.01 0.1 1Frequency (cycles/month)

Pow

er (

norm

aliz

ed)

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60

0.001 0.01 0.1 1Frequency (cycles/month)

Pow

er (

norm

aliz

ed)

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50

60

0.001 0.01 0.1 1Frequency (cycles/month)

Pow

er (

norm

aliz

ed)

Layer1 0-10 cm

Layer 5 70-90 cm

Layer 7 110-130 cm

0

10

20

30

40

50

60

0.001 0.01 0.1 1Frequency (cycles/month)

Pow

er (

norm

aliz

ed)

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10

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50

60

0.001 0.01 0.1 1Frequency (cycles/month)

Pow

er (

norm

aliz

ed) Layer 3

30-50 cm

Layer 9 150-170 cm

Layer 11 190-200 cm

Soil Moisture Power Spectra

Amenu, G.G., and P. Kumar, “Deep‑Layer Terrestrial Memory and Mechanisms of Its Influence on Land-Atmosphere Interaction,” Journal of Climate, 18, 5024 – 5045, 2005.

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Singular Spectrum Analysis (SSA) is used to extract the dominant modes

0

10

20

30

40

50

60

0.00 0.02 0.04 0.06 0.08 0.10 0.12

Frequency (cycles/month)

Pow

er (n

orm

aliz

ed)

Quasi-Quadrennial

(QQ) ENSO

T ≈ 5 yrs

Quasi-Biennial

(QB) ENSO

T ≈ 2.8 yrs (4/3)

ENSO

T ≈ 1.5 yrs

Annual

Cycle

T = 1 yr

The (4/3) ENSO is a result of nonlinear interaction (sub-harmonic frequency locking) between the Quasi-Quadrennial and the Annual modes.

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Aclimatization Strategies by Vegetation

Type of Stress Acclimatization Strategies

Water Stress

♦ Deep rooting ♦ Water uptake patterns ♦ Hydraulic lift ♦ C4 and CAM photosynthetic pathways ♦ Resource storage ♦ Smaller leaf sizes ♦ Leaf shading ♦ Increase of cell elasticity

Nutrient Stress ♦ Deep rooting ♦ Resource storage

Thermal Stress

♦ Reproducing only during cooler months ♦ leaf abscission (falling) ♦ Steep leaf angles/ vertically hanging leaves ♦ Leaf folding/rolling ♦ Light-colored leaves (increased albedo) ♦ Heat convection

CO2 Stress

CO2 Abundance♦ C4 photosynthetic pathways♦ Decreased stomatal conductance

Water-Logging Stress ♦ Root thickening ♦ Reduced respiration rate

Salinity Stress ♦ Limiting the amount of toxic ions entering root ♦ Exuding the excess salt out of the plant

Competition ♦ Resource partitioning ♦ Exuding the excess salt out of the plant

≈ 2 m

≈ 5 m

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0

5

10

15

20

25

30

Arizon

a

Califo

rnia

Color

ado

Florid

a

Iowa

Miss

ouri

Nebra

ska

New M

exico

New Y

ork

Orego

n

S. Car

olina

Texas

Virgin

ia

Was

hingt

on

Max

. Roo

t D

epth

(m

)

Observed Max. Rooting Depths

for USA

53 m 61 m

0

10

20

30

40

50

60

70

Tropic

al Eve

rgree

n For

est

Tropic

al Dec

iduo

us F

ores

t

Tropic

al Gras

sland

/Sav

ana

Deser

t

Shrub

s

Tempe

rate G

rassl

and

Tempe

rate D

ecid

uous

For

est

Tempe

rate C

onife

rous

For

est

Cropl

and

Borea

l For

est

Tundr

a

Roo

t D

epth

(m

)

Maximum Root Depth

Average Root depth

Approximate Maximum Rooting Depth for

Current Climate Models

Based on the study by Canadell et al. (1996) & Kleidon and Heimann (1998)

Plant roots are much deeper than traditionally thought in our modeling approaches. In particular, this is true for water-limited environments

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Hydraulic Redistribution is the passive transport of soil water via plant roots from wet soil layers to dry soil layers.

During day: the greatest water potential gradient in the plant exists between the roots and the leaf stomata. As a result of this gradient, water moves from the roots and exits the transpiring leaves.

During night: the stomata closes, but water continues to flow into

the deeper taproots. These results in turgor pressure that increases water potential within the plant body, and finally start to efflux

from the roots into the drier soil.

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Water Uptake Pattern

Rambal (1984) identified four patterns of water uptake in Quercus coccifera during a dry summer season. In late spring, water loss occurred exclusively from the top 0-50 cm soil layer. In early summer, peak water uptake occured between 2 and 2.5 m depth. Late summer, the deepest soil layers were contributing much of the water. In early fall at the end of the dry period, all the layers were depleted.

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Evidences of Hydraulic Redistribution

SOURCE PLANT SPECIES LOCATION

Mooney et al. (1980) Shrubs Atacama Desert, Chile

Baker & van Bavel (1988) Cotton Lab Experiment

Caldwell & Richards (1989) Sagebrush, Wheatgrass Utah, USA

Dawson (1993) Sugar Maples New York, USA

Wan et al. (1993) Broom Snakeweed Texas, USA

Emerman & Dawson (1996) Sugar Maples New York, USA

Burgess et al. (1998) Silky Oak, Eucalyptus tree Kenya, Western Australia

Yoder & Nowak (1999) Shrubs, Grasses Mojave Desert, Nevada, USA

Burgess et al. (2000) Proteaceous tree Western Australia

Millikin & Bledsoe (2000) Blue Oaks California, USA

Song et al. (2000) Sunflower Lab Experiment, Kansas

Wan et al. (2000) Maize Lab Experiment

Brooks et al. (2002) Ponderosa pine, Douglas-fir Oregon, Washington, USA

Ludwig et al. (2003) Woody trees (savanna) Tanzania

Moreira et al. (2003) Savanna Brazil

Espeleta et al. (2004) Oaks, bunch grass South Carolina, USA

Hultine et al. (2004) Leguminous tree Arizona, USA

Leffler et al. (2005) Cheatgrass Rush Valley, Utah, USA

Oliveira et al. (2005) Amazon trees Brazil

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Xylem

Phloem

Water flow the plant system

zkq

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Flow in the root xylem

SoilRoot

Flow in the soil pores

Flow into the root from the

soil

Hydraulic Redistribution

Hydraulic Redistribution by plant roots can be modeled by coupling water flow within the soil media and the root media, where flow in both media is governed by water potential gradient and hydraulic conductivity of the system.

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Fresno

Boundary of study site

0

20

40

60

80

100

120

140

160

180

Jan

Feb Mar Apr

May Ju

n Jul

Aug Sep OctNov Dec

Pre

cipi

tati

on (

mm

)

CASE STUDY SITE:

The major PFTs at the site include C3 grasses (52%), needle leaf evergreen temperate trees (46%), and broadleaf deciduous temperate trees (2%)

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Application Example

0.01

0.1

1

10

-0.5 0 0.5 1 1.5 2 2.5

Water Uptake (10-5 mm/s)

Soil d

epth

(m

)

During Day

Day-1

Day-120

0.01

0.1

1

10

-0.5 0 0.5 1

Water Uptake (10-5 mm/s)

Soil d

epth

(m

)

During Night

Day-1

Day-120

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Application Example

0.01

0.1

1

10

-40 -20 0 20 40 60 80

Total Water Uptake (mm)

Soil d

epth

(m

)

Net Uptake

Uptake

Release

36

0 50 100 150 200 250 3000

25

50

75

100

125

150Et

(W/m

2 )[Case1] without HR [Case2] with HR

0 50 100 150 200 250 300-20

0

20

40

60

80

100

Chan

ge in E

t (W

/m2 )

Effect of HR: [Case2-Case1]

2 4 6 8 10 120

25

50

75

100

125

Et

(W/m

2 )

1 2 3 4 5 6 7 8 9 10 11 12-20

0

20

40

60

80

Time (months)

Chan

ge in E

t (W

/m2)

Effect of HR

1 2 3 4 5 6 7 8 9 10 11 12-50

0

50

100

150

200

Chan

ge in E

t (%

)

Effect of HR

Moisture Flux Through Plant Leaves (Transpiration)

without HRwith HR

0 50 100 150 200 250 3000

4

8

12

16

20

A (

mol

/m2s)

0 50 100 150 200 250 300-5

0

5

10

15

20

Chan

ge in A

(m

ol/m

2s)

Effect of HR: [Case2-Case1]

2 4 6 8 10 120

3

6

9

12

15

18

A (

mol

/m2s)

1 2 3 4 5 6 7 8 9 10 11 12-3

0

3

6

9

12

Time (months)

Chan

ge in A

(m

ol/m

2s)

Effect of HR

1 2 3 4 5 6 7 8 9 10 11 12-100

0

100

200

300

400

Chan

ge in A

(%

)

Effect of HR

[Case1] without HR [Case2] with HR

Carbon Assimilation (Photosynthesis)

without HRwith HR

0 50 100 150 200 250 3000

2

4

6

8

10

WU

E

Water-Use-Efficiency (WUE)

0 50 100 150 200 250 300-0.2

0

0.2

0.4

0.6

0.8

1

Chan

ge in W

UE Effect of HR: [Case2-Case1]

2 4 6 8 10 120

1

2

3

4

WU

E

1 2 3 4 5 6 7 8 9 10 11 120

0.1

0.2

0.3

0.4

0.5

Time (months)

Chan

ge in W

UE Effect of HR

1 2 3 4 5 6 7 8 9 10 11 120

50

100

150

200

Chan

ge in W

UE (

%)

Effect of HR

[Case1] without HR [Case2] with HR

with HR

without HR

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Are hydroclimatology and ecohydrology two sides of the

same coin?• Optimality Hypothesis: evolutionary selection

pressures drive ecosystems towards a state of maximum utilization of available light, water and nutrient resources for the production of biomass

• Acclimatization Hypothesis:Vegetation form and function from the canopy to the ecosystem scale are a reflection of the acclimatization strategies adopted by the vegetation to maximize CO2-assimilation in the presence of the spatial and temporal variability of the controlling factors

• Complexity Hypothesis: Co-evolution of the eco-hydrologic environment and vegetation patterns and functioning, in the presence of complex non-linear feedbacks results in self-organization

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So

il Ca

rbo

n

Soil Moisture

Atmospheric Moisture

Temperature

Atmospheric CO2

Photosynthesis

Biomass

Respiration

Stomatal Conductance

Transpi-ration

Sensible Heat

Instability

Boundary Layer Conductance

Cloud Formation

Precipitation

Longwave Radiation

Shortwave Radiation

Evapo-ration

Streamflow

River CO2

Root Water Uptake

Glo

bal O

cean

and

Atm

osph

ere

- C

limat

e S

cale

Root Growth

Shoot Growth

Albedo

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Basic Characteristics of Water Cycle

• Natural systems associated with the water cycle are under continual evolution.

• Water is both a “driver” (through hydrologic variability) and a “medium” of interaction for a variety of natural processes.

– The two roles are fundamentally different.

• Water cycle consists of a network of cycles that interact with each other, that is, the water cycle is a hypercycle.

– The interaction between these cycles provides a mechanism for the dynamic stability of the water and energy cycle in the presence of positive and negative feedback cycles.

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On the Role of Hydrologic Variability

• “Hydrologic variability” plays a role as important as the variability of energy in driving all global systems

• This independent (but linked) role suggests that global systems (ecological, biogeochemical, …) are as vulnerable to the anthropogenic changes in the water cycle as they are to the changes in the energy cycle

Water CycleEnergy Cycle

Sustainability and Environment

42

ConclusionsClimate and ecology interact to define atmospheric pathways that are at the heart of hydrologic variability.

Vegetation should be seen as an adaptively and actively modifing the water cycle.

Water is both a driver and a medium of interaction for open dissipative systems – water cycle is A hypercycle.