| Summary: | In recent years, rain floods caused by abnormal rainfall precipitation have caused several damages in various part of Russia. Precise forecasting of rainfall runoff is essential for both operational practice to optimize the operation of the infrastructure in urbanized territories and for better practices on flood prevention, protection, and mitigation. The network of rain gauges in some Russian regions are very scarce. Thus, an adequate assessment and modeling of precipitation patterns and its spatial distribution is always impossible. In this case, radar data could be efficiently used for modeling of rain floods, which were shown by previous research. This study is aimed to simulate the rain floods in the small catchment in north-west Russia using radar- and ground-based measurements. The investigation area is located the Polomet’ river basin, which is the key object for runoff and water discharge monitoring in Valdai Hills, Russia. Two datasets (rain gauge and weather radar) for precipitation were used in this work. The modeling was performed in open-source Soil and Water Assessment Tool (SWAT) hydrological model with three types of input data: rain gauge, radar, and gauge-adjusted radar data. The simulation efficiency is assessed using the coefficient of determination R2, Nash–Sutcliffe model efficiency coefficient (NSE), by comparing the mean values to standard deviations for the calculated and measured values of water discharge. The SWAT model captures well the different phases of the water regime and demonstrates a good quality of reproduction of the hydrographs of the river runoff of the Polomet’ river. In general, the best model performance was observed for rain gauge data (NSE is up to 0.70 in the Polomet’river-Lychkovo station); however, good results have been also obtained when using adjusted data. The discrepancies between observed and simulated water flows in the model might be explained by the scarce network of meteorological stations in the area of studied basin, which does not allow for a more ...
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