GRACE in the Murray-Darling Basin: integrating remote sensing with field monitoring to improve hydrologic model prediction Kevin M. Ellett Department of Civil and Environmental Engineering, University of Melbourne, Australia and USGS WRD Sacramento, California Colleagues: Jeffrey Walker, Rodger Grayson, Adam Smith and Matt Rodell (NASA-GSFC) and Matt Rodell (NASA-GSFC)

Outline  Motivation for Research  Contributions from GRACE  Research Approach  Preliminary Results

Motivation for Research  Modeling hydrological processes at the catchment-scale  Soil moisture is a key component in the terrestrial water and energy balance  Primary controls on soil moisture distribution  climate, soils, vegetation, topography  Scaling of soil moisture and hydrological processes? Western and Grayson, 1998

Motivation for Research  Current policy initiatives on sustainable water resource management in Australia –Murray-Darling Basin (MDB)  Land clearing has resulted in devastating impacts from salinity –Long-term increase in terrestrial water storage –Re-vegetation to reverse this trend

GRACE Contributions in the MDB  Measuring the trend in storage change –Re-vegetation –Limited 5 year lifespan  Assessment of regional-scale hydrological models –Water balance closure –Model bias  Can GRACE help to improve modeling at the catchment-scale? –Scaling

Objectives 1)GRACE “validation” (comparison) from an observational network 2)Examine the utility of GRACE observations at the catchment-scale  Downscaling

Approach 1)Installation of a ground-based measurement network for monitoring changes in gravity and terrestrial water storage  Nested catchment and grid-based designs provide data at 4 different scales using 46 total sites 2)Development of a modelling framework for the downscaling and assimilation of GRACE data into a catchment-based land surface model 3)Assessing the utility of GRACE by comparing model results with and without GRACE data assimilation to the measurement network  Results will depend on downscaling approach, model physics, data assimilation, and observations- uncertainty in each component a)Testing of alternative model with simple water balance parameterization allowing automated calibration b)Testing of alternative downscaling schemes

Murray-Darling Basin and the Murrumbidgee Catchment

MDB and Murrumbidgee Average Annual Precipitation (mm)

MDB and Murrumbidgee Annual Actual Evapotranspiration (mm)

MDB and Murrumbidgee Precipitation minus Evapotranspiration (mm)

MDB and Murrumbidgee Surface Water Storages

Murrumbidgee Monitoring Network Irrigation Areas Yanco Study Area (50km x 50km Grid) Kyeamba Ck. Study Area

Monitoring Site Instrumentation Gravity monitoring with CG-3M on stable platform Logger CS616 Backfilled soil T107 Raingauge Piezometer (water level and neutron probe) TDR Star pickets (2.5 m length) Schematic diagram of instrumentation installed at monitoring sites

Gravity Network Tied to Canberra SG

Preliminary Results Observed average monthly dS = 13.5 mm Annual amplitude approx. 50 mm

Conclusions  Murray-Darling Basin is a reasonable candidate for GRACE validation/comparison –Signal dominated by soil moisture component –Magnitude?  Modeling framework for testing the utility of GRACE is currently being developed –Catchment-based LSM [Koster et al., 2000] –Assimilation scheme for GRACE and AMSR-E –Development of alternative downscaling schemes and simple “bucket” model calibrated