Estimating Continental-Scale Water Balance through Remote Sensing Huilin Gao 1, Dennis P. Lettenmaier 1 Craig Ferguson 2, Eric F. Wood 2 1 Dept. of Civil.

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Presentation transcript:

Estimating Continental-Scale Water Balance through Remote Sensing Huilin Gao 1, Dennis P. Lettenmaier 1 Craig Ferguson 2, Eric F. Wood 2 1 Dept. of Civil and Environmental Engineering, University of Washington 2 Dept. of Civil and Environmental Engineering, Princeton University 2008 Fall AGU meeting U N I V E R S I T Y O F WASHINGTON PRINCETON UNIVERSITY

Motivation 1.Importance for understanding water budget at continental scale 2.Limitations of observations and modeling 3.Advantages of remote sensing 4.Challenges of remote sensing ∆S = P –R– ET Research questions:  how closely can the water budget be estimated solely using remote sensing data?  What are the major error sources?  What is the role of reservoir in water storage change? 1

Research Strategy R (observed) ?=? P – ∆S – ET (remote sensing ) Research Domain – Continental U.S. PrecipitationETΔSΔSRunoff Remote sensing TRMM 3B42-RT MODIS by Princeton GRACE by CSR; GFZ; JPL By difference Observed/ Modeled Gridded gauge data *VIC output Observed runoff High quality precipitation from gridded gauge measurements - help evaluate P Variable Infiltration Capacity (VIC) model outputs using good forcings - help evaluate ΔS and ET 2

1. Arkansas-Red5. East Coast9. Lower Mississippi 13. Rio Grande 2. California6. Great Lakes10. Upper Mississippi 3. Colorado7. Great Basin11. Missouri 4. Columbia 8. Gulf12. Ohio Major River Basins within the U.S. Study period: 2003 ~ 2006 Grid resolution: 0.5 deg; Temporal resolution: hourly, daily, monthly

Methodology Tair (inst) (AIRS) Net Longwave Albedo (MODIS) Downward Solar (GOES) Net Shortwave Net Radiation Precipitation (TRMM) Rainfall Snow ET (inst) (MODIS) ET ΔS (GRACE) Snowmelt RunoffObs. Runoff Tair > 0 Tair < 0 EF Calibration, interpolation Tair (hourly) Gridded gauge data Model output 4

Seasonal Precipitation TRMM real time product has significant errors in some basins Precipitation from remote sensing needs to be corrected for orographic effect 5

Seasonal Evapotranspiration It is difficult to validate remotely sensed ET at the continental scale Remotely sensed and modeled ET are seasonally consistent 6

Seasonal Storage Change GRACE products from different data centers are similar GRACE products over the west coast suffer from “signal leakage” Range offset VIC Max-Min ΔS (mm) GRACE Max-Min ΔS (mm) California Columbia 7 ΔSΔS ΔSΔS ΔSΔS

Remote Sensing Capability by Basin Good Arkansas California Colorado Columbia East Coast Great Lakes Great Basin Lower MissiUpper Missi Missouri Gulf Ohio Rio Grande Correlation Coeff. MAE(mm/mo) Range Offset(mm) Precip ETΔSΔS 8

Good Precip ET ΔSΔS TRMM real time precipitation has the largest error among the three ET has the best seasonal representation, but it is biased over some basins GRACE water storage change is biased low over the west coast Remote Sensing Capability over All Basins 9

Seasonal Runoff It is difficult to close water budget by solely using remote sensing data 10

dams major rivers (Graf, 2006) Large Dams (storage > 1.2 km 3 ) in the United States Reservoir Impacts on Water Storage Change 11 15mm 5mm GRACE

( Remote Sensing of Reservoir Storage 12

Summary Accuracy towards closing the water budget at the continental scale from remote sensing heavily depends on precipitation quality; GRACE water storage change tends to be biased low over the west coast; Remotely sensed ET over the 13 basins is consistent with VIC output; Reservoir storage is a significant component for understanding terrestrial water storage. 13

Thanks!!! Questions?