Abstract
Precipitation is one of the essential variables in rainfall-runoff modeling. For hydrological purposes, the most commonly used data sources of precipitation are rain gauges and weather radars. Recently, multi-satellite precipitation estimates have gained importance thanks to the emergence of Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG GPM), a successor of a very successful Tropical Rainfall Measuring Mission (TRMM) mission which has been providing high-quality precipitation estimates for almost two decades. Hydrological modeling of mountainous catchment requires reliable precipitation inputs in both time and space as the hydrological response of such a catchment is very quick. This paper presents an inter-comparison of event-based rainfall-runoff simulations using precipitation data originating from three different sources. For semi-distributed modeling of discharge in the mountainous river, the Hydrologic Engineering Center-Hydrologic Modelling System (HEC-HMS) is applied. The model was calibrated and validated for the period 2014–2016 using measurement data from the Upper Skawa catchment a small mountainous catchment in southern Poland. The performance of the model was assessed using the Nash–Sutcliffe efficiency coefficient (NSE), Pearson’s correlation coefficient (r), Percent bias (PBias) and Relative peak flow difference (rPFD). The results show that for the event-based modeling adjusted radar rainfall estimates and IMERG GPM satellite precipitation estimates are the most reliable precipitation data sources. For each source of the precipitation data the model was calibrated separately as the spatial and temporal distributions of rainfall significantly impact the estimated values of model parameters. It has been found that the applied Soil Conservation Service (SCS) Curve Number loss method performs best for flood events having a unimodal time distribution. The analysis of the simulation time-steps indicates that time aggregation of precipitation data from 1 to 2 h (not exceeding the response time of the catchment) provide a significant improvement of flow simulation results for all the models while further aggregation, up to 4 h, seems to be valuable only for model based on rain gauge precipitation data.
Highlights
Precipitation being one of the key variables of the water cycle plays a vital role in the rainfall-runoff modeling in hydrology [1,2,3]
It has been found that the applied Soil Conservation Service (SCS) Curve Number loss method performs best for flood events having a unimodal time distribution
Different spatial and temporal scales of data acquisition make it difficult to compare different precipitation datasets in hydrological applications. Considering these aspects is essential in rainfall-runoff modeling of mountainous catchments where hydrological response is very sensitive to the timing and spatial distribution of rainfall
Summary
Precipitation being one of the key variables of the water cycle plays a vital role in the rainfall-runoff modeling in hydrology [1,2,3]. The principal instruments used for measuring precipitation are rain gauges, weather radars and satellite sensors. Nowadays the rain gauge and weather radar data are considered the best precipitation data sources for catchment modeling [1] whereas satellite data. Different spatial and temporal scales of data acquisition make it difficult to compare different precipitation datasets in hydrological applications. Considering these aspects is essential in rainfall-runoff modeling of mountainous catchments where hydrological response is very sensitive to the timing and spatial distribution of rainfall. Estimating precipitation over small mountainous catchments is a significant challenge due to a small scale topographic variability and orographic effects [4]
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