On the Use of Rainfall Remote Sensing Information in Hydrologic Modeling: Data Accuracy Assessment and Potential Improvements

This study focuses on validation assessment of accuracy of rainfall remote sensing information for improving hydrologic predictions. The multi-sensor precipitation products (MPE) of the National Weather Service (NWS) River Forecast Centers (RFC) are considered in this study. Multi-sensor data is provided at hourly temporal resolution and over a national Hydrologic Rainfall Analysis Project (HRAP) grid having a nominal size of 4 square kilometers. The analysis was done over a three-year period (2004–2006) using 13 independently operated rain gauges located within an area of ~30 km² in south Louisiana. The study will examine the impact of MPE uncertainties on runoff simulations in a mid-size experimental watershed in south Louisiana. The physically-based hydrologic model (Gridded Surface Subsurface Hydrologic Analysis, GSSHA) will be driven by two scenarios of rainfall forcing: rainfall data from a dense gauge network over the watershed, which represents a rather ideal scenario of rainfall sampling, and finally rainfall data from MPE products. Differences between MPE and dense gauge simulations will shed light on uncertainties in MPE and their impact on runoff predictions. The study will have implications for validation of multi-sensor estimates as a technique for improvement of radar-gauge merging algorithms and further enhancing accuracy of hydrologic forecasting.