1. 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, 2009 U N I V E R S I T Y O F WASHINGTON

2. Outline 1. Background and motivation 2. Research strategy 3. 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 coast 6. Conclusions 1 U N I V E R S I T Y O F WASHINGTON

3. Background 1. Importance for understanding water budget at continental scale 2. Limitations of observations and modeling 3. 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 ) 2 U N I V E R S I T Y O F WASHINGTON ∆S = P –R– ET

4. Background 1. Importance for understanding water budget at continental scale 2. Limitations of observations and modeling 3. 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? 2 U N I V E R S I T Y O F WASHINGTON ∆S = P –R– ET

5. Research Strategy R (observed) = P – ∆S – ET (remote sensing ) Research Domain – Continental U.S. PrecipitationETΔSΔSRunoff Remote sensing TMPA CMORPH PERSIANN MODIS based by UM, PU, UW GRACE by CSR; GFZ; JPL Inferred Observed/ Modeled Gridded gauge data *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 3 ?

6. 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. 4 U N I V E R S I T Y O F WASHINGTON

7. Annual Precipitation (2003~2006) 5 U N I V E R S I T Y O F WASHINGTON

8. Seasonal Precipitation 6 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 U N I V E R S I T Y O F WASHINGTON

9. Annual Evapotranspiration (2003~2006) 7 U N I V E R S I T Y O F WASHINGTON

10. 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 8 U N I V E R S I T Y O F WASHINGTON Remotely sensed ET accounts for irrigation contribution

11. Dynamic Range of TWS (2003~2006) 9 U N I V E R S I T Y O F WASHINGTON (acknowledgement to Dr D.P. Chambers for smoothing method)

12. Seasonal TWS GRACE products from different data centers are similar in their differences with VIC 10 U N I V E R S I T Y O F WASHINGTON 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

13. U N I V E R S I T Y O F WASHINGTON R (observed) = P – ∆S – ET (remote sensing ) Inferred Runoff v.s. Observed Runoff (I) 3×3×3=27 ensemble members 11 ?

14. U N I V E R S I T Y O F WASHINGTON R (observed) = P – ∆S – ET (remote sensing ) Inferred Runoff v.s. Observed Runoff (II) 3×3=9 ensemble members 12 ?

15. mean of the three GRACE datasets maximum and minimum of the three Amplitude of Seasonal TWS Are the biases from VIC or GRACE? U N I V E R S I T Y O F WASHINGTON 13

16. KL03 KL04 Tonzi Ranch Vaira Ranch Blodgett Satellite/Observation based TWSC ∆S = P –R– ET PRISM gauge Satellite validated reliable 14 PRISM: Parameter-elevation Regressions on Independent Slopes Model

17. 15 U N I V E R S I T Y O F WASHINGTON Observed and estimated ET day at flux tower KL04 (irrigated site) Flux towers METRIC (Mapping Evapotranspiration at high Resolution and with Internalized Calibration) Landsat estimates ET Validation KL03, KL04 AmeriFlux (Details about this ET algorithm and its applications are available through Tang et al., JGR, 2009) (a)(b)

18. 16 U N I V E R S I T Y O F WASHINGTON TWSC intercomparisons TWSC (mm)

19. 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. 17

20. Thanks!!! Questions?