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Hamed Ashouri, Phu Nguyen, Andrea Thorstensen, Kuo-lin Hsu, Soroosh Sorooshian, and Dan Braithwaite

Abstract

This 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 deviation (~6) for both products. TMPA and PERSIANN-CDR outperform PERSIANN, mainly because, unlike PERSIANN, TMPA and PERSIANN-CDR are gauge-adjusted precipitation products. The National Weather Service Office of Hydrologic Development Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) is then forced with PERSIANN, PERSIANN-CDR, TMPA, and stage IV data. Quantitative analysis using five different statistical and model efficiency measures against USGS streamflow observation show that in general in all three DMIP2 basins, the simulated hydrographs forced with PERSIANN-CDR and TMPA have close agreement. Given the promising results in the first phase, the simulation process is extended back to 1983 where only PERSIANN-CDR rainfall estimates are available. The results show that PERSIANN-CDR-derived streamflow simulations are comparable to USGS observations with correlation coefficients of ~0.67–0.73, relatively low biases (~5%–12%), and high index of agreement criterion (~0.68–0.83) between PERSIANN-CDR-simulated daily streamflow and USGS daily observations. The results prove the capability of PERSIANN-CDR in hydrological rainfall–runoff modeling application, especially for long-term streamflow simulations over the past three decades.

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