Integrating satellite-based surface soil moisture and rainfall accumulation products for improved hydrologic modeling over large portions of the globe, water resource management and hydrologic…

This $266,328 federal grant was awarded by the National Aeronautics and Space Administration (NASA) Shared Services Center to the University of Washington to develop a data assimilation approach that integrates precipitation accumulation data from the Global Precipitation Mission (GPM) with surface soil moisture retrievals from the Soil Moisture Active/Passive (SMAP) mission. The goal is to enhance the utility of these satellite-based products for improved hydrologic modeling and water resource management applications. The project leverages the Soil Moisture Analysis Rainfall Tool (SMART) and a data assimilation system to simultaneously enhance the characterization of antecedent soil moisture and within-storm rainfall estimation. This grant does not have a set-aside designation.

Integrating satellite-based surface soil moisture and rainfall accumulation products for improved hydrologic modeling over large portions of the globe, water resource management and hydrologic prediction applications are hampered by a lack of adequate rain gauge (and/or ground-based radar) coverage. This shortcoming is a key motivator for the development of satellitebased precipitation products in general and the global precipitation mission (GPM) in particular. However, remaining concerns about the accuracy of satellite-based precipitation accumulation products over land have tempered expectations concerning the expected utility of GPM products for hydrologic applications. These concerns are mirrored by a similar set of obstacles faced by the NASA soil moisture active/passive (smap) mission. Like GPM, smap is commonly presumed to contribute to hydrologic prediction applications. However, past studies have failed to conclusively demonstrate that existing satellite-based soil moisture products contribute significant utility to hydrologic modeling. Given the (potential) concurrent operation of GPM and smap (from late 2014 onward), and the challenges faced by each in contributing to key hydrologic applications, this proposal seeks to develop a data assimilation approach to optimally merge precipitation accumulation information acquired from GPM with surface soil moisture retrievals from smap in order to maximize the eventual utility of both missions for hydrologic (i.e., stream flow) prediction applications. Our approach is based on the integration of two existing parts. First, the soil moisture analysis rainfall tool (smart) is used to enhance satellite liquid precipitation accumulation estimates using remotely-sensed surface soil moisture retrievals. Second, a data assimilation system is applied to integrate remotely-sensed surface soil moisture retrievals into a hydrologic model to improve its representation of pre-storm, antecedent soil moisture. By intelligently integrating these two components, we seek to create a system in which smap and GPM observations are leveraged to simultaneously enhance both pre-storm characterization of antecedent soil moisture and within-storm estimation of rainfall accumulation.