1. Enhancing the Value of GRACE for Hydrology
Matt Rodell1, Jay Famiglietti2, and Ben Zaitchik1,3
1 Hydrological Sciences Branch, NASA Goddard Space Flight Center
2 Earth System Science, University of California, Irvine
3 Earth System Science Interdisciplinary Center, University of Maryland

2. Motivation
Demonstrate that the value of GRACE data can be enhanced by synthesizing them with other observations and models
Describe a few hydrological applications

3. Isolating Groundwater from GRACE TWS
Mississippi River basin
GRACE groundwater estimate
Groundwater well observations
GRACE groundwater estimates (smoothed)
Rodell et al., Hydrogeology, 2006
GRACE’s observations of changes in the total amount of water stored in the land are valuable for global climate studies, but for applications such as water management and agriculture, we need to know about where within the land surface the water is stored: on the land as snow or surface water, in the shallow subsurface as soil moisture, or deeper as groundwater. Thus scientists are beginning to isolate individual components, such as groundwater, using data from other observing systems or computer models. The figures on the left show that seasonal variations in groundwater estimated from GRACE observations compare favorably with estimates from groundwater wells on the ground, first in the Mississippi River basin and then in the state of Illinois. These results give us confidence that we can use GRACE to monitor changes in groundwater in regions of the world where we don’t have groundwater well data, due to a lack of observations or political boundaries, such as northern Africa and the Middle East.
Illinois
Yeh et al., WRR, 2006
Soil moisture from a land surface model (top) or in situ observations (bottom) can be used to isolate groundwater from GRACE derived TWS variations
Matt Rodell
NASA GSFC

4. ET as a Water Balance Residual
Evapotranspiration (ET) estimated using a terrestrial water budget:
Observation based precipitation product
River runoff observations
From GRACE

5. Comparison of ET Estimates Over the Mississippi River Basin
Updated from Rodell et al., GRL, 2004

6. Comparison of ET Estimates Over the Mississippi River Basin
[mm/day]
GRACE/3B42
GRACE/CMAP
NOAA/GDAS
ECMWF
AFWA
GLDAS/Noah
GLDAS/CLM2
GLDAS/Mosaic
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
0.92
0.97
0.89
(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

7. Assimilation of GRACE TWS Data
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 May 2006
Ensemble Kalman smoother DA

8. Assimilation of GRACE TWS Data
Results have higher resolution than GRACE alone, better accuracy than model alone.
GRACE Assimilating Catchment LSM TWS anomaly, mm
January 2003 – June 2006
GRACE TWS anomaly
January 2003 – June 2006
From scales useful for water cycle and climate studies…
To scales needed for water resources and agricultural applications

9. Assimilation of GRACE TWS Data
Models produce continuous time series.
Monthly GRACE data anchor model results in reality
Missouri
Upper Mississippi
Mississippi River sub-basins
column water (mm)
column water (mm)
GRACE Water Storage
Modeled Water Storage
Model-GRACE Assimilation
Daily estimates are critical for operational applications
column water (mm)
column water (mm)
Lower Miss-Red-Arkansas
Ohio-Tennessee

10. Assimilation of GRACE TWS Data
Models separate snow, soil moisture, and groundwater; GRACE ensures accuracy.
Catchment LSM TWS
Mississippi River basin
GRACE-Assimilation TWS
From a global, integrated observation
To application-specific water storage components

11. Assimilation of GRACE TWS Data
Statistically significant improvement of groundwater estimates

12. Assimilation of GRACE TWS Data
Some improvement of runoff estimates

13. Assimilation of GRACE Data
GRACE data assimilation influences other modeled variables as well
More sophisticated error estimates
Evaluation in other large basins
Implement Routing Model
Application: Drought monitoring
Application: Seasonal prediction systems
Soil Moisture
Latent Heat Flux %
W m-2
April 2005

14. Application to Drought Monitoring
June 2005
April 2006
GRACE Obs
GRACE Assimilation
US Drought Monitor

15. Summary
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: