A Unified Data‐Driven Method to Derive Hydrologic Dynamics From Global SMAP Surface Soil Moisture and GPM Precipitation Data

Abstract

AbstractData sets provided by the Soil Moisture Active Passive (SMAP) and the Global Precipitation Measurement (GPM) satellite missions contain rich information about land surface hydrologic processes.In this study, a unified regression method is proposed and applied to these global data sets to investigate surface soil moisture (SSM) dynamics. Two forms of regressors are implemented: 1) the linear regressors of SSM and precipitation flux and 2) the linear regressors of SSM and precipitation flux with an additional interaction term. Regression results based on 3 years of global SMAP and GPM data show that the unified regression method can identify the SSM characteristics found by several recent studies, including the SSM exponential decay rate, the fraction of precipitation retained in the surface soil layer, and the effective depth of hydrologic storage. Additionally, including the interaction regressor provides a novel way to derive the sensitivity of infiltration/runoff partitioning to antecedent SSM without the need for streamflow observations. These SMAP/GPM regression results are compared with those derived from a global SSM data set simulated by the variable infiltration capacity model. Relative to the satellite data, variable infiltration capacity retains moisture longer in the top layer, retains too much precipitation input in that layer, and exhibits levels of sensitivity of runoff/infiltration partitioning to top‐layer soil moisture that generally match SMAP especially in humid regions. This study demonstrates that the regression‐based method can recover useful process‐level insight from SMAP SSM retrievals and is a viable tool for evaluating the representation of surface processes in hydrologic models.