Improvement of hydrologic model soil moisture predictions using SEBAL evapotranspiration estimates

Jan M. H. Hendrickx; Nawa R. Pradhan; Sung-ho Hong; Fred L. Ogden; Aaron R. Byrd; David Toll

doi:10.1117/12.819780

4 May 2009 Improvement of hydrologic model soil moisture predictions using SEBAL evapotranspiration estimates

Jan M. H. Hendrickx, Nawa R. Pradhan, Sung-ho Hong, Fred L. Ogden, Aaron R. Byrd, David Toll

Proceedings Volume 7303, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIV; 730311 (2009) https://doi.org/10.1117/12.819780
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

Abstract

Soil moisture conditions influence practically all aspects of Army activities and are increasingly affecting its systems and operations. Regional distributions of high resolution soil moisture data will provide critical information on operational mobility, penetration, and performance of landmine and UXO sensors. The US Army Corps of Engineers (USACE) developed the Gridded Surface/Subsurface Hydrologic Analysis (GSSHA), which is a grid-based two-dimensional hydrologic model that has been effectively applied to predict soil moisture conditions. GSSHA computes evapotranspiration (ET) using the Penman-Monteith equation. However, lack of reliable spatially-distributed meteorological data, particularly in denied areas, makes it difficult to reliably predict regional ET and soil moisture distributions. SEBAL is a remote sensing algorithm that computes spatio-temporal patterns of ET using a surface energy balance approach. SEBAL has been widely accepted and tested throughout the world against lysimeter, eddy-covariance and other field measurements. SEBAL estimated ET has shown good consistency and agreement for irrigated fields, rangelands and arid riparian areas. The main objective of this research is to demonstrate improved GSSHA soil moisture and hydrological predictions using SEBAL estimates of ET. Initial results show that the use of SEBAL ET and soil moisture estimates improves the ability of GSSHA to predict regional soil moisture distributions, and reduces uncertainty in runoff predictions.

Citation Download Citation

Jan M. H. Hendrickx, Nawa R. Pradhan, Sung-ho Hong, Fred L. Ogden, Aaron R. Byrd, and David Toll "Improvement of hydrologic model soil moisture predictions using SEBAL evapotranspiration estimates", Proc. SPIE 7303, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIV, 730311 (4 May 2009); https://doi.org/10.1117/12.819780

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