Applications of GRACE data to estimation of the water budget of large U.S. river basins

Huilin Gao, Qiuhong Tang, Fengge Su, Dennis P. Lettenmaier

Dept. of Civil and Environmental Engineering, University of Washington

GRACE hydrology workshop

Nov. 4th, 2009U N I V E R S I T Y O F

WASHINGTON

Outline

1. Background and motivation2. Research strategy3. Evaluation of remotely sensed precipitation, evapotranspiration (ET), and terrestrial water storage (TWS)4. Testing the ability to close the water budget solely from remote sensing 5. Further evaluation of GRACE terrestrial water storage change (TWSC) over the west coast6. Conclusions

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Background

1. Importance for understanding water budget at continental scale2. Limitations of observations and modeling3. The opportunities brought by remotely sensed water budget terms, especially GRACE TWS (Rodell et al., 2004; Tang et al., 2009)4. Challenges to remote sensing products (Sheffield et al., 2009)

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∆S = P –R– ET

Background

1. Importance for understanding water budget at continental scale2. Limitations of observations and modeling3. The opportunities brought by remotely sensed water budget terms, especially GRACE TWS (Rodell et al., 2004; Tang et al., 2009)4. Challenges to remote sensing products (Sheffield et al., 2009)

Motivation Over major river basins across the CONUS, how well can estimates of terrestrial water budget terms derived entirely from remote sensing be used to close the terrestrial water budget?

Which remotely sensed terms have the largest/least uncertainty, and is it possible to close the water balance by selecting a suite of satellite products with superior performance?

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∆S = P –R– ET

Research Strategy

R (observed) = P – ∆S – ET (remote sensing)

Research Domain – Continental U.S.

Precipitation ET ΔS Runoff

Remote sensing TMPACMORPHPERSIANN

MODIS based by UM, PU,

UW

GRACE by CSR; GFZ; JPL

Inferred

Observed/Modeled

Gridded gauge data

*VIC output *VIC output Observed runoff

*VIC output: Variable Infiltration Capacity model forced by gridded gauge precipitation

High quality precipitation from gridded gauge measurements - help evaluate P

LSM outputs using quality forcings - help evaluate ΔS and ET

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Arkansas-Red (AR) East Coast (EA) Lower Mississippi (LM) Rio Grande (RG)California (CA) Great Lakes (GL) Upper Mississippi (UM)Colorado (CO) Great Basin (GB) Missouri (MO)Columbia (CB) Gulf (GU) Ohio (OH)

Hydrological Regions and River Basins in the U.S.

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Annual Precipitation(2003~2006)

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Seasonal Precipitation

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• Orographic effects are poorly represented by the remote sensing products

• Remotely sensed precipitation is biased high over the central CONUS

•TMPA precipitation performs the best among the three

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Annual Evapotranspiration(2003~2006)

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Seasonal Evapotranspiration

• It is difficult to validate remotely sensed ET at the continental scale

• Over most regions, UM ET tends to provide the smallest values, and UW ET is closest to VIC estimate

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•Remotely sensed ET accounts for irrigation contribution

Dynamic Range of TWS(2003~2006)

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Seasonal TWS

• GRACE products from different data centers are similar in their differences with VIC

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• Dynamic range of VIC TWS is larger than GRACE over the western hydrologic regions

• Dynamic range of VIC TWS is smaller than GRACE estimates in much of the Mississippi basin

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R (observed) = P – ∆S – ET (remote sensing)

Inferred Runoff v.s. Observed Runoff (I)3×3×3=27 ensemble members

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R (observed) = P – ∆S – ET (remote sensing)

Inferred Runoff v.s. Observed Runoff (II)3×3=9 ensemble members

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mean of the three GRACE datasets maximum and minimum of the three

Amplitude of Seasonal TWS

Are the biases from VIC or GRACE?

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KL03 KL04

Tonzi Ranch Vaira Ranch

Blodgett

Satellite/Observation based TWSC

∆S = P –R– ET

PRISM gaugeSatellitevalidatedreliable

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PRISM: Parameter-elevation Regressions on Independent Slopes Model

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Observed and estimated ETday at flux tower KL04 (irrigated site)

• Flux towers• METRIC (Mapping Evapotranspiration at high Resolution and

with Internalized Calibration) Landsat estimates

ET ValidationKL03, KL04

AmeriFlux

(Details about this ET algorithm and its applications are available through Tang et al., JGR, 2009)

(a) (b)

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TWSC intercomparisonsT

WS

C (

mm

)

Conclusions• Water budget closure at the scale of large continental river basins is not currently possible on the basis of satellite data alone, even with a combination of the best products;

• Among the remotely sensed budget terms, precipitation has the largest error;

• ET estimation errors are the second most important, and notwithstanding their coarse spatial resolution, GRACE TWSC errors are of smaller magnitude than the other two sources;

• GRACE water storage change appears to be underestimated along the west coast.

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Thanks!!!Questions?