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

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

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

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

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

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

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 2003 - May 2006 Ensemble Kalman smoother DA

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

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

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

Assimilation of GRACE TWS Data Statistically significant improvement of groundwater estimates

Assimilation of GRACE TWS Data Some improvement of runoff estimates

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

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

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: http://disc.gsfc.nasa.gov/hydrology/index.shtml