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?

1

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

2

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

Major River Basins within the U.S.

Study period: 2003 ~ 2006Grid resolution: 0.5 deg; Temporal resolution: hourly, daily, monthly

123

4

5

6

7

89

10

11

12

13

3

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

Runoff Obs. Runoff

Tair > 0

Tair < 0

EF

Calibration, interpolation

Tair(hourly)

Grid

ded

gaug

e da

ta

Model output

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)

GR

AC

E M

ax-M

in Δ

S (

mm

)

California

Columbia

7

ΔSΔSΔS

Remote Sensing Capability by Basin

Good

Good

Good

Ark

ansa

sC

alifo

rnia

Col

orad

oC

olum

bia

Eas

t Coa

stG

reat

Lak

esG

reat

Bas

in

Low

er M

issi

Upp

er M

issi

Mis

sour

i

Gul

f

Ohi

oR

io G

rand

e

Cor

rela

tion

Coe

ff.

MA

E(m

m/m

o)R

ange

Off

set(

mm

)

Precip ET ΔS

8

Good

Good Good

PrecipETΔS

• 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

damsmajor rivers

(Graf, 2006)Large Dams (storage > 1.2 km3) in the United States

Reservoir Impacts on Water Storage Change

11

15mm

5mm

GRACE

(http://www.legos.obs-mip.fr/en/soa/hydrologie/hydroweb/)

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?