Hydrologic Downscaling of Soil Moisture Using Global Data Sets without Site-Specific Calibration

Abstract

Numerous applications require fine-resolution (1–3 m) soil moisture patterns, but most satellite remote sensing and land-surface models provide coarse-resolution (9–6 km) soil moisture estimates. The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model downscales soil moisture using fine-resolution topography, vegetation, and soil data, but it requires specification of 16 parameters. In previous applications, the parameters have been calibrated using detailed in situ soil moisture data, but very few regions have such data. This study aimed to evaluate EMT+VS model performance when the parameters are estimated from global data sets instead of site-specific calibration. Methods were developed to estimate key parameters from the data sets, and the global model (without site-specific calibration) was applied to six study sites. The global model results were compared with the results of locally calibrated models and to in situ soil moisture observations. The use of global data sets decreases EMT+VS downscaling performance and reduces the spatial variability in the fine-resolution soil moisture patterns. Overall, however, the global model provides more reliable soil moisture estimates than simply using the coarse-resolution moisture.