matt rodell hydrological sciences branch, nasa gsfc enhancing the value of grace for hydrology matt...

Matt RodellMatt Rodell

Hydrological Sciences Branch, NASA GSFCHydrological Sciences Branch, NASA GSFC

Enhancing the Value of GRACEEnhancing the Value of GRACEfor Hydrologyfor Hydrology

Matt Rodell1, Jay Famiglietti2, and Ben Zaitchik1,3

1 Hydrological Sciences Branch, NASA Goddard Space Flight Center2 Earth System Science, University of California, Irvine

3 Earth System Science Interdisciplinary Center, University of Maryland



• Demonstrate that the value of GRACE data can be enhanced by synthesizing them with other observations and models

• Describe a few hydrological applications

MotivationMotivation



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GRACE GroundwaterMississippi River basin

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Soil moisture from a land surface model (top) or in situ observations (bottom) can be used to isolate groundwater from GRACE derived TWS variations

GRACE groundwater estimate

Groundwater well observations

GRACE groundwater estimates (smoothed)

Rodell et al., Hydrogeology, 2006

Yeh et al., WRR, 2006

Matt RodellMatt RodellNASA GSFCNASA GSFC

Isolating Groundwater from GRACE TWSIsolating Groundwater from GRACE TWS



Evapotranspiration (ET) estimated using a terrestrial water budget:

SQPET

Observation based precipitation product

River runoff observations

From GRACE

ET as a Water Balance ResidualET as a Water Balance Residual



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Catchment LSM GRACE/3B42 Time Period

Comparison of ET Estimates Over the Comparison of ET Estimates Over the Mississippi River BasinMississippi River Basin

Updated from Rodell et al., GRL, 2004



Comparison of ET Estimates Over the Comparison of ET Estimates Over the Mississippi River BasinMississippi River Basin

[mm/day] GRACE/3B42

GRACE/CMAP

NOAA/GDAS

ECMWF AFWA GLDAS/Noah

GLDAS/CLM2

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NLDAS/Noah

Catchment LSM

Mean 1.53 1.40 2.53 1.99* 1.98 1.64 1.34 1.75 2.22* 1.84

Bias -0.13 1.00 0.47 0.46 0.12 -0.19 0.22 0.44 0.32

Corr. Coef. 0.99 0.90 0.91 0.91 0.92 0.92 0.92 0.97 0.89

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Catchment LSM GRACE

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Catchment LSM GRACE/3B42 Time Period

• (P-Q) determines long term average ET; GRACE ΔS enables generation of ET time series

• High bias in modeled ET is a known issue

• Potential application is improvement of land surface and atmospheric models



• Offline simulations of the Catchment LSM using GLDAS forcing data

• 10 year spin-up under 2002 forcing• 20-member ensemble simulations for

open loop (OL) and data assimilation (DA)

• Monthly GRACE anomalies: CSR/GFZ/JPL mean, Jan 2003 - May 2006

• Ensemble Kalman smoother DA

Assimilation of GRACE TWS DataAssimilation of GRACE TWS Data



Results have higher resolution than GRACE alone, better accuracy than model alone.

GRACE TWS anomalyJanuary 2003 – June 2006

GRACE Assimilating Catchment LSM TWS anomaly, mm

January 2003 – June 2006

From scales useful for water cycle and climate studies…

To scales needed for water resources and agricultural

applications




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Modeled Water Storage

Model-GRACE Assimilation

GRACE Water Storage

Models produce continuous time series.

Mississippi River sub-basins

Daily estimates are critical for

operational applications

Monthly GRACE data anchor

model results in reality




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Models separate snow, soil moisture, and groundwater; GRACE ensures accuracy.

Mississippi River basin


Catchment LSM TWS

GRACE-Assimilation TWS

From a global, integrated observationTo application-specific water storage components

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Snow Water Equivalent

Soil Moisture

Groundwater

Observed Groundwater

GRACE Total Water




Statistically significant improvement of groundwater estimates

r RMSE r RMSE skillMississippi 0.59 23.5 0.69 18.7 0.20

Ohio-TN 0.78 62.8 0.82 41.1 0.35Upper Miss. 0.29 42.6 0.29 40.1 0.06

Red-Ark. / L.M. 0.69 30.9 0.72 26.5 0.14Missouri 0.41 24.5 0.66 19.7 0.20

OL GRACE DA



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River Discharge OL GRACE DA OL GRACE DA

Kanawha 537 0.41 0.42 0.52 0.52Wabash 1,001 0.55 0.62 0.18 0.18

Illinois 527 0.68 0.72 0.30 0.29

Minnesota 160 0.61 0.69 0.35 0.36

Arkansas 240 0.19 0.29 0.20 0.22

Ouachita 83 0.37 0.35 0.04 0.04

Yellowstone 212 0.24 0.26 0.35 0.42Kansas 107 0.4 0.49 0.55 0.59

rTWS rR


Some improvement of runoff estimates



• More sophisticated error estimates

• Evaluation in other large basins

• Implement Routing Model

• Application: Drought monitoring

• Application: Seasonal prediction systemsApril 2005

-2.5 2.5-0.7 0.7 -14 14-3.8 3.8

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A BSoil Moisture Latent Heat Flux

% W m-2

GRACE data assimilation influences other modeled variables as well

Assimilation of GRACE DataAssimilation of GRACE Data



Application to Drought MonitoringApplication to Drought Monitoring

June 2005

April 2006

GRACE Obs GRACE Assimilation US Drought Monitor



• GRACE data have enabled many innovative scientific studies, but we must also begin to apply GRACE for socially relevant applications

• The value of GRACE data can be enhanced by merging them with information from other sources

- auxiliary observations

- data assimilating models

• Creativity is the key

☆ GLDAS output are now available from: http://disc.gsfc.nasa.gov/hydrology/index.shtml

SummarySummary

matt rodell hydrological sciences branch, nasa gsfc enhancing the value of grace for hydrology matt...

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