Mapping High-Resolution Catchment-Scale Root Zone Soil Moisture Content with Ground-Based Sensor and Satellite Data.

Mapping root zone (0-100 cm) soil moisture (RZSM) content distribution across the landscape is essential for drought and flood predictions, irrigation planning, weather forecasts, and many other applications.  Satellites such as the recently launched NASA Soil Moisture Active Passive (SMAP) can estimate near-surface (0-5 cm) soil moisture content globally at coarse spatial resolutions (SMAP: 9 km). We developed a hierarchical Ensemble Kalman Filter (EnKF) data assimilation modeling system to downscale AMSRE near-surface soil moisture and to estimate RZSM content across the Shale Hills Catchment at 1-m resolution in combination with ground-based soil moisture sensor data.  A simple infiltration model within the EnKF-model was parameterized for six soil-terrain units to forecast daily RZSM content in the catchment. LiDAR-derived terrain variables defined intra-unit RZSM variability using a novel covariance localization technique.  This method also allowed us to map uncertainty with our RZSM estimates for each time-step.  We found that daily RZSM storage varied most within concave hillslopes and least within planar hillslopes over a 395-day period. The valley was consistently wetter than the rest of the catchment, and RZSM storage within the valley varied the most during spring and fall seasons.  Portions of the catchment had RZSM content reach the wilting level during summer months.  A catchment-wide satellite-to-surface downscaling parameter, which nudges the satellite measurement closer to in situ near-surface data, was also calculated for each time-step.  We found a significant seasonal variation in the downscaling parameter across all soil-terrain units.  Developing an EnKF-model system that downscales satellite data and predicts RZSM content is especially important and timely, given the anticipated release of SMAP surface moisture data in 2015.