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a NASA–GSFC Hydrologic Sciences Branch, Code 974, Greenbelt, MD 20771 USA
b USDA–SWRC, 2000 E. Allen Road, Tucson, AZ 85719 USA
dhymer{at}hydro4.gsfc.nasa.gov
Studies show that it may be possible to combine satellite-derived soil water maps with soil–vegetation–atmosphere transfer (SVAT) models to obtain spatially distributed, temporally continuous information on vadose zone water contents. However, before this method can be instituted, it is essential to determine the ability of a SVAT model to simulate vadose zone soil water contents. A study was designed to evaluate the simultaneous heat and water (SHAW) model by comparing its soil water predictions with measured soil water contents collected by electrical resistance sensors (ERS) during the Monsoon '90 multidisciplinary field experiment. ERS collected hourly soil water measurements at 5-, 15-, and 30-cm depths in a shrub-dominated site [Larrea tridentada (Sessé & Moc. ex DC.) Coville] with large bare interspace areas. Data collected by the ERS were calibrated to time domain reflectometer (TDR) sensor measurements placed adjacent to the ERS using an in situ calibration technique. Results indicated that the SHAW model overestimated soil water at each depth by 0.02 m3 m-3 under bare soil and underestimated soil water at each depth under shrub cover by 0.02 m3 m-3. The ability of the model to simulate ERS water content values gives it the potential to be periodically updated with remotely sensed data to predict vadose zone soil water content over large areas at high temporal resolutions.
Abbreviations: CI, confidence interval • ERS, electrical resistance sensors • MBE, mean bias error • MPD, mean percentage difference • RMSE, root mean square error • SAR, synthetic aperture radar • SHAW, simultaneous heat and water [model] • SVAT, soil–vegetation–atmosphere transfer [model] • TDR, time domain reflectometer • 
v, volumetric water content
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