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
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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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Estimating Continental-Scale Water Balance through Remote Sensing
Huilin Gao1, Dennis P. Lettenmaier1
Craig Ferguson2, Eric F. Wood2
1 Dept. of Civil and Environmental Engineering, University of Washington2Dept. of Civil and Environmental Engineering, Princeton University
2008 Fall AGU meetingU N I V E R S I T Y O F
WASHINGTONPRINCETONUNIVERSITY
Motivation
1.Importance for understanding water budget at continental scale2.Limitations of observations and modeling3.Advantages of remote sensing4.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?
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Research StrategyR (observed) ?=? P – ∆S – ET (remote sensing)
Research Domain – Continental U.S.
Precipitation ET ΔS Runoff
Remote sensing TRMM 3B42-RT MODIS by Princeton
GRACE by CSR; GFZ; JPL
By difference
Observed/Modeled
Gridded gauge data
*VIC output *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
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1. Arkansas-Red 5. East Coast 9. Lower Mississippi 13. Rio Grande2. California 6. Great Lakes 10. Upper Mississippi 3. Colorado 7. Great Basin 11. Missouri4. Columbia 8. Gulf 12. Ohio