The utility of satellite precipitation products for hydrologic prediction in topographically complex regions: The Chehalis River Basin, WA as a case study

Abstract:

Accurate measurements of precipitation are of great importance in hydrologic predictions especially for floods, which are a pervasive natural hazard. One of the primary objectives of Global Precipitation Measurement (GPM) mission is to provide a basis for hydrologic predictions using satellite sensors. A major advance in GPM relative to the Tropical Rainfall Measuring Mission (TRMM) is that it observes atmospheric river (AR) events, most of which have landfall too far north to be tracked by TRMM. These events are responsible for most major floods along the U.S. West Coast. We address the question of whether, for hydrologic modeling purposes, it is better to use precipitation products derived directly from GPM and/or other precipitation fields from weather models that have assimilated satellite data. Our overall strategy is to compare different methods for prediction of flood and/or high flow events by different forcings on the hydrologic model. We examine four different configurations of the Distroibute Hydrology Soil Vegetation Model (DHSVM) over the Chehalis River Basin that use a) precipitation forcings based on gridded station data; b) precipitation forcings based on NWS WSR-88D data, c) forcings based from short-term precipitation forecasts using the Weather Research and Forecasting (WRF) mesoscale atmospheric model, and d) satellite-based precipitation estimates (TMPA and IMERG). We find that in general, biases in the radar and satellite products result in much larger errors than with either gridded station data or WRF forcings, but if these biases are removed, comparable performance in flood predictions can be achieved by Satellite-based precipitation estimates (TMPA and IMERG).