GRACE in the Murray-Darling Basin: integrating remote sensing with field monitoring to improve hydrologic model predicti

1. 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)

2. Outline • Motivation for Research • Contributions from GRACE • Research Approach • Preliminary Results

3. Western and Grayson, 1998 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?

4. 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

5. 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

6. Objectives • GRACE “validation” (comparison) from an observational network • Examine the utility of GRACE observations at the catchment-scale • Downscaling

7. Approach • 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 • Development of a modelling framework for the downscaling and assimilation of GRACE data into a catchment-based land surface model • 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 • Testing of alternative model with simple water balance parameterization allowing automated calibration • Testing of alternative downscaling schemes

8. Murray-Darling Basin and the Murrumbidgee Catchment

9. MDB and MurrumbidgeeAverage Annual Precipitation (mm)

10. MDB and MurrumbidgeeAnnual Actual Evapotranspiration (mm)

11. MDB and MurrumbidgeePrecipitation minus Evapotranspiration (mm)

12. MDB and MurrumbidgeeSurface Water Storages

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

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

15. Gravity Network Tied to Canberra SG

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

17. 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