The management of water resources within the San Joaquin Valley of Central California is a balancing act between temporally and spatially limited sources and competitive needs, which can often exceed the supply. There has yet to be a single system that integrates the entire range of technically practical information, physical processes, user community's needs and political or legal constraints. Thus NASA's Earth Science - Applied Science Program put forth an effort to develop a prototype hardware/software infrastructure to improve water management in the San Joaquin River watershed in California, while still being scalable to other regions. As a result, the NEXRAD Rainfall Estimation Processing System (NREPS), which uses weather radars to estimate rainfall for water resources management and initially designed for use in the Tennessee Valley watershed with the collaboration of NASA MSFC and the Tennessee Valley Authority, was further developed to take on some of the challenges involved with operational radar mapping of precipitation over an area as climatologically and topographically diverse as that of Central California. Key NREPS algorithm components such as the non-precipitation mitigation, partial beam-blockage correction and vertical profile of reflectivity (VPR) correction will be discussed. Performance of the NREPS hourly rainfall estimates relative to rain gauges during a four month period during the cool season (15 Nov 2010 – 15 Mar 2011) will be presented. The biggest improvement to NREPS was the implementation of a means to correct for the effects of NEXRAD's sampling of the vertical profile of precipitation (i.e., VPR), which resulted in a 30% reduction of the radar estimate error relative to rain gauges across Central California. In order to further reduce local biases in the radar estimates, existing rain gauge networks were used to locally tune the radar estimates. The resultant hourly rainfall products output by NREPS were ingested by a distributed hydrologic model for use in NASA's Terrestrial Observation and Prediction System, which can be used for monitoring crop conditions in near real-time.