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Estimating Continental-Scale Water Balance through Remote Sensing

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

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Estimating Continental-Scale Water Balance through Remote Sensing

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  1. 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 Washington 2Dept. of Civil and Environmental Engineering, Princeton University 2008 Fall AGU meeting PRINCETON UNIVERSITY U N I V E R S I T Y O F WASHINGTON

  2. Motivation • Importance for understanding water budget at continental scale • Limitations of observations and modeling • Advantages of remote sensing • 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

  3. Research Strategy R (observed) ?=?P – ∆S – ET (remote sensing) Research Domain – Continental U.S. • 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

  4. Major River Basins within the U.S. 4 11 6 Study period: 2003 ~ 2006 Grid resolution: 0.5 deg; Temporal resolution: hourly, daily, monthly 10 7 12 2 1 5 3 13 8 9 1. Arkansas-Red 5. East Coast 9. Lower Mississippi 13. Rio Grande 2. California 6. Great Lakes 10. Upper Mississippi 3. Colorado 7. Great Basin 11. Missouri 4. Columbia 8. Gulf 12. Ohio 3

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

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

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

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

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

  10. Remote Sensing Capability over All Basins Good Good Good Precip ET ΔS • 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 9

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

  12. Reservoir Impacts on Water Storage Change 15mm 5mm GRACE 11 Large Dams (storage > 1.2 km3) in the United States major rivers dams (Graf, 2006)

  13. Remote Sensing of Reservoir Storage 12 (http://www.legos.obs-mip.fr/en/soa/hydrologie/hydroweb/)

  14. 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

  15. Thanks!!! Questions?

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