Anticipated SWOT Observations of Human Impacts on the Water Cycle

1. H13F-1413 Elizabeth A. Clark1,Konstantinos Andreadis2, Delwyn Moller3, and Dennis P. Lettenmaier1 From:1Department of Civil and Environmental Engineering, University of Washington, Seattle, WA; 2Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA; 3Remote Sensing Solutions, Pasadena, CA AGU Fall Meeting, San Francisco 3-7 December 2012 Introduction Results SWOT design • Satellite estimates of time-varying varyingPreviousstudies (Gao et al., 2012) • The anticipated Surface Water and Ocean Topography mission will provide an unprecedented opportunity to measure water stored in manmade reservoirs on a global scale. 31 cycles (~23 Months) 41 cycles (~30 Months) 61 cycles (~45 Months) 11 cycles (~8 Months) 81 cycles (~60 Months) Bonny Belle Fourche Anticipated SWOT Observations of Human Impacts on the Water Cycle Objectives Here we investigate the plausibility of estimating the bathymetric properties of ten such reservoirs from SWOT-like synthetic measurements of water surface elevation and water extent, dependent on the number of observation cycles available. Altus Conclusions Methods • Assuming a linear fit to reservoir hypsometry could produce large errors in estimates of area from water level or of water level from area. • SWOT observations of reservoirs … Tom Steed • We used previously published bathymetry data to convert observations of reservoir storage to raster maps of water depth and surface elevation. • Based on simple orbital model corresponding to an anticipated 22-day repeat cycle and 78° inclination orbit, we determined the ground-track and timing of SWOT observations over each reservoir for a 5-year observation period. • Spatially uncorrelated Gaussian white noise with a standard deviation of 0.5 m over each pixel errors was applied to estimate elevation errors, and errors in observed area were estimated as a function of compactness index following Lee et al. (2010). • In cases where the water body is not completely sampled during a given overpass, pixels that were observed to have water at a lower elevation than the current water surface elevation were added to the observed surface area. Foss Anderson Ranch References Gao, H., C. Birkett, and D.P. Lettenmaier, 2012: Global monitoring of large reservoir storage from satellite remote sensing. Water Resour. Research 48, W09504, doi: 10.1029/2012WR012063. Leeet al., 2010. Characterization of surface water storage changes in Arctic lakes using simulated SWOT measurements, Int. J. Remote Sens., 31: 14, 3931-3953. Moller et al., 2010. A Virtual Mission to estimate discharge using assimilation of high-resolution simulated SWOT data: Initial results over the Ohio River, in Sensors, Systems, and Next-Generation Satellites XIV, Proc. SPIE, 7826, 782617-782617-10. Bathymetric data sources: USBR (http://www.usbr.gov/pmts/sediment/projects/ReservoirSurveys/index.html) ; Texas Water Development Board (http://www.twdb.state.tx.us/hydro_survey/); and Belew and Cross (2003) USGS Open-File Report 03-320. Ray Hubbard Elevation of reservoir beds Increasing shoreline complexity Elephant Butte Acknowledgments Funding for this research was provided by a NASA Graduate Student Research Program fellowship. Increasing shoreline complexity Lake Mead Futher Information Elizabeth Clark University of Washington Civil and Environmental Engineering, Box 352700 Seattle, WA 98195 http://www.hydro.washington.edu/~eclark eclark@hydro.washington.edu Sam Rayburn