Analysis of Spatial Distribution of Precipitation Using Hydrological Modeling of Watersheds

Abstract One of the significant challenges in hydrological modeling is the spatial variability of rainfall; even a slight change in rainfall patterns can lead to fundamental differences in runoff responses, affecting the accuracy of predictions. Moreover, the complexity of rainfall patterns, influenced by topography, increasingly leads to mispredictions. Therefore, this study investigates and analyzes the spatial distribution of rainfall and the role of the dispersion of raindrop data and its impact on rainfall-runoff modeling using raindrop data from two gridded precipitation products, PERSIANN-CDR and ERA5, at daily and monthly scales with the semi-distributed SWAT model. For this purpose, raindrop data from four stations—Talezang, Tang-e Panj Bakhtiari, Sepid Dasht Sezar, and the Dez Dam Rain gauge stations and Tele-Zang hydrometry station —from 2008 to 2019 were utilized. The results of the simulations compared with the observed data indicate that due to the lack of a suitable raingage network and spatial distribution of rainfall, the SWAT rainfall-runoff simulation model was unable to establish a correspondence between the rainfall amounts and their corresponding discharge values. Consequently, an appropriate modeling with the observed data could not be achieved, as evidenced by the NSE coefficient values of 0.52 and -0.01 for daily calibration and validation periods, respectively, and 0.76 and 0.82 for monthly periods. Additionally, it was observed that in the daily hydrological evaluation, the performance of the PERSIANN-CDR dataset was better, with NSE values of 0.29 and 0.59 for the calibration and validation periods, respectively. In contrast, the ERA5 dataset displayed superior capabilities in monthly hydrological evaluation for rainfall-runoff modeling, with calibration and validation coefficients of 0.77 and 0.82, respectively.