Assessing the Efficacy of High-Resolution Satellite-Based PERSIANN-CDR Precipitation Product in Simulating Streamflow

AbstractThis study aims to investigate the performance of Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) in a rainfall–runoff modeling application over the past three decades. PERSIANN-CDR provides precipitation data at daily and 0.25° temporal and spatial resolutions from 1983 to present for the 60°S–60°N latitude band and 0°–360° longitude. The study is conducted in two phases over three test basins from the Distributed Hydrologic Model Intercomparison Project, phase 2 (DMIP2). In phase 1, a more recent period of time (2003–10) when other high-resolution satellite-based precipitation products are available is chosen. Precipitation evaluation analysis, conducted against stage IV gauge-adjusted radar data, shows that PERSIANN-CDR and TRMM Multisatellite Precipitation Analysis (TMPA) have close performances with a higher correlation coefficient for TMPA (~0.8 vs 0.75 for PERSIANN-CDR) and almost the same root-mean-square deviati...

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