An NSF Science and Technology CenterSAHRA Potential of Distributed GRACE Measurements to Estimate Spatially Variable Terrestrial Water Storage Changes.

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Presentation transcript:

An NSF Science and Technology CenterSAHRA Potential of Distributed GRACE Measurements to Estimate Spatially Variable Terrestrial Water Storage Changes in the Colorado River Basin Peter A. Troch and Matej Durcik HWR-SAHRA, University of Arizona Shaakeel Hasan, Remko Uijlenhoet and Ruud Hurkmans Wageningen University

An NSF Science and Technology Center SAHRA 2 Support and Acknowledgment NSF EAR-Hydrologic Sciences: Sustainability of Semi-arid Hydrology and Riparian Areas (SAHRA; PIs: Jim Shuttleworth, Juan Valdés, Kathy Jacobs) US Dept. of the Interior – Bureau of Reclamation: Enhancing Water Supply Reliability in the Lower Colorado River (PI: Kathy Jacobs, Peter Troch) WSP-UA (TRIF FY07-11): Tracing Arizona’s Water Reserves from Space Andy Wood, 3Tier and Univ. Washington Dennis Lettenmaier, Sonia Seneviratne

An NSF Science and Technology Center SAHRA 3 Focus Area: Colorado River Basin Important water resource in semi-arid southwest of US; Global change imposes enormous stress on the basin’s hydrology; Assessing climate variability impacts on stream flow requires accurate estimates of terrestrial water storage variability.

An NSF Science and Technology Center SAHRA 4 What is terrestrial water storage (TWS)? TWS is the total amount of water stored (surface and subsurface), at a given time, in a river basin; TWS is an important indicator of the hydrologic state and defines the basin’s response to atmospheric forcing, e.g. –Snow pack defines (potential) spring runoff –Groundwater storage defines baseflow In Colorado River basin, surface reservoir inflow (e.g. Lake Powell) depends on TWS, hence accurate estimates of TWS can help water management & planning

An NSF Science and Technology Center SAHRA 5 Terrestrial Water Storage (TWS) Dynamics Troch et al., 2007 (EOS) Wet years Dry years E S R W P CqCq

An NSF Science and Technology Center SAHRA 6 VIC Simulated Natural Flow

An NSF Science and Technology Center SAHRA 7 Near real-time TWS monitoring system

An NSF Science and Technology Center SAHRA 8 GRACE data processing We used Release 04 (RL04) of monthly gravity field for ; GRACE estimates of surface mass anomalies for the Colorado basin are obtained from the University of Colorado website: More info on RL04 gravity coefficients conversion into 1-degree maps of equivalent water thickness can be found at (Chambers, 2006). Averages of three data sets (JPL, CSR, GFZ) of solutions up to degree and order 40 are used. GRACE estimates of TWS anomalies at 1-degree resolution are compared to VIC estimates and in-situ data of SWE

An NSF Science and Technology Center SAHRA 9 Results Basin-average intercomparison between VIC simulated TWS anomalies and GRACE derived mass anomalies; Correlation coefficients are generally high, except during 2 nd half of 2004.

An NSF Science and Technology Center SAHRA 10 Results (2): 1-degree correlation maps Total StorageSoil MoistureSnow StorageGroundwater Total Storage

An NSF Science and Technology Center SAHRA 11 Results (3): In-situ correlation maps a) Pixels containing surface reservoirs; b) Pixels containing groundwater wells; c) Pixels containing stream flow gauges; d) Pixels containing Snotel sites; Numbers refer to time lag of maximum correlation.

An NSF Science and Technology Center SAHRA 12 Results (4): Effects of Topographic Surface Roughness a) Slope of regression relationship between GRACE and VIC TWS anomalies (>1: VIC overestimates TWS anomalies); b) Relationship between slope of regression line and topographic surface roughness (left y-axis) and standard deviation of elevation (right y-axis).

An NSF Science and Technology Center SAHRA 13 Conclusion and Outlook Distributed GRACE measurements show great potential to estimate spatially variable terrestrial water storage anomalies in the Colorado River basin; Current research efforts focus on using TWS information to improve seasonal to interannual stream flow predictions conditioned by climate variability indicators (See Matt Switanek’s talk at 2:15PM in Room 129B).