The ability to adequately and continually assess the hydrological catchment response to extreme rainfall events in a timely manner is a prerequisite component in flood-forecasting and mitigation initiatives. Owing to the scarcity of data, this particular subject has captured less attention in Rwanda. However, semi-distributed hydrological models have become standard tools used to investigate hydrological processes in data-scarce regions. Thus, this study aimed to develop a hydrological modeling system for the Nyabarongo River catchment in Rwanda, and assess its hydrological response to rainfall events through discharged flow and volume simulation. Initially, the terrain Digital Elevation Model (DEM) was pre-processed using a geospatial tool (HEC-GeoHMS) for catchment delineation and the generation of input physiographic parameters was applied for hydrological modeling system (HEC-HMS) setup. The model was then calibrated and validated at the outlet using sixteen events extracted from daily hydro-meteorological data (rainfall and flow) for the rainy seasons of the country. More than in other events, the 15th, 9th, 13th and 5th events showed high peak flows with simulated values of 177.7 m3
, 171.7 m3
, 169.9 m3
, and 166.9 m3
, respectively. The flow fluctuations exhibited a notable relation to rainfall variations following long and short rainy seasons. Comparing the observed and simulated hydrographs, the findings also unveiled the ability of the model to simulate the discharged flow and volume of the Nyabarongo catchment very well. The evaluated model’s performance exposed a high mean Nash Sutcliffe Efficiency (NSE) of 81.4% and 84.6%, with correlation coefficients (R2
) of 88.4% and 89.8% in calibration and validation, respectively. The relative errors for the peak flow (5.5% and 7.7%) and volume (3.8% and 4.6%) were within the acceptable range for calibration and validation, respectively. Generally, HEC-HMS findings provided a satisfactory computing proficiency and necessitated fewer data inputs for hydrological simulation under changing rainfall patterns in the Nyabarongo River catchment. This study provides an understanding and deepening of the knowledge of river flow mechanisms, which can assist in establishing systems for river monitoring and early flood warning in Rwanda.
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