Soil water evaluation using a hydrologic model and calibrated sensor network

ABSTRACT to automated dataloggers, however, can record tempo-rally continuous data with little maintenance over longStudies show that it may be possible to combine satellite-derived time periods. Like many soil water sensors, ERS requiresoil water maps with soil–vegetation–atmosphere transfer (SVAT) models to obtain spatially distributed, temporally continuous informa- a calibration to convert a measured signal to volumetric tion on vadose zone water contents. However, before this method water content. Seyfried (1993) documented two difficul-can be instituted, it is essential to determine the ability of a SVAT ties with calibrating these sensors: (i) They exhibit large model to simulate vadose zone soil water contents. A study was de- variability among individual sensors; and (ii) They are signed to evaluate the simultaneous heat and water (SHAW) model sensitive to variations in soil properties and tempera-by comparing its soil water predictions with measured soil water ture. This same study, however, demonstrated that a contents collected by electrical resistance sensors (ERS) during the field calibration of ERS to TDR sensors accounted forMonsoon ’90 multidisciplinary field experiment. ERS collected hourly site-specific soil conditions and yielded reasonable esti-soil water measurements at 5-, 15-, and 30-cm depths in a shrub-mates of soil water (60.05 m3 m23).dominated site [Larrea tridentada (Sesse ́ & Moc. ex DC.) Coville] Hydrologic models can also be used to estimate soilwith large bare interspace areas. Data collected by the ERS were calibrated to time domain reflectometer (TDR) sensor measurements water at various spatial and temporal resolutions. In par