Benchmarking Optical/Thermal Satellite Imagery for Estimating Evapotranspiration and Soil Moisture in Decision Support Tools1. (17th December 2015)

• Record Type: Journal Article
• Authors: Hendrickx, Jan M.H.; Allen, Richard G.; Brower, Al; Byrd, Aaron R.; Hong, Sung‐ho; Ogden, Fred L.; Pradhan, Nawa Raj; Robison, Clarence W.; Toll, David; Trezza, Ricardo; Umstot, Todd G.; Wilson, John L.
• Is Part Of: Journal of the American Water Resources Association. Volume 52:Number 1(2016:Feb.)
• Page Start: 89
• Page End: 119
• Publication Date: 2015-12-17
• Subjects: soil moisture -- evapotranspiration -- GSSHA -- SEBAL -- METRIC -- DPWM -- distributed hydrologic modeling -- optical/thermal satellite imagery -- Landsat -- MODIS -- groundwater recharge -- water management -- hydrograph; Water-supply -- Periodicals; Hydrology -- Periodicals; Water resources development -- Periodicals; Water resources development -- Environmental aspects -- Periodicals; 333.9100973
• DOI: 10.1111/1752-1688.12371
• Languages: English
• ISSNs: 1093-474X

Abstract

Abstract: Generally, one expects evapotranspiration (ET) maps derived from optical/thermal Landsat and MODIS satellite imagery to improve decision support tools and lead to superior decisions regarding water resources management. However, there is lack of supportive evidence to accept or reject this expectation. We "benchmark" three existing hydrologic decision support tools with the following benchmarks: annual ET for the ET Toolbox developed by the United States Bureau of Reclamation, predicted rainfall‐runoff hydrographs for the Gridded Surface/Subsurface Hydrologic Analysis model developed by the U.S. Army Corps of Engineers, and the average annual groundwater recharge for the Distributed Parameter Watershed Model used by Daniel B. Stephens & Associates. The conclusion of this benchmark study is that the use of NASA/USGS optical/thermal satellite imagery can considerably improve hydrologic decision support tools compared to their traditional implementations. The benefits of improved decision making, resulting from more accurate results of hydrologic support systems using optical/thermal satellite imagery, should substantially exceed the costs for acquiring such imagery and implementing the remote sensing algorithms. In fact, the value of reduced error in estimating average annual groundwater recharge in the San Gabriel Mountains, California alone, in terms of value of water, may be as large as $1 billion, more than sufficient to pay for one new Landsat satellite.