The Value of Coarse-Scale Soil Moisture Observations for Regional Surface Energy Balance Modeling

Abstract

Using high-resolution (1 km) hydrologic modeling of the 575 000-km2 Red?Arkansas River basin, the impact of spatially aggregating soil moisture imagery up to the footprint scale (32?64 km) of spaceborne microwave radiometers on regional-scale prediction of surface energy fluxes is examined. While errors in surface energy fluxes associated with the aggregation of soil moisture are potentially large (>50 W m?2), relatively simple representations of subfootprint-scale variability are capable of substantially reducing the impact of soil moisture aggregation on land surface model energy flux predictions. This suggests that even crude representations of subgrid soil moisture statistics obtained from statistical downscaling procedures can aid regional-scale surface energy flux prediction. One possible soil moisture downscaling procedure, based on an assumption of spatial scaling (i.e., a power-law relationship between statistical moments and scale), is demonstrated to improve TOPmodel-based Land?Atmosphere Transfer Scheme (TOPLATS) prediction of grid-scale surface energy fluxes derived from coarse-resolution soil moisture imagery.