FLOOD RISK ASSESSMENT AND HYDROLOGICAL MODELING USING HEC-HMS: SEBEYA CATCHMENT , RWANDA

Abstract

Flooding remains one of the most devastating natural disasters globally, affecting nearly 80% of the population and causing widespread economic, social, and environmental damage. This study aims to support flood risk management efforts in Rwanda through a comprehensive flood risk assessment of the Sebeya Catchment, using the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS). A continuous hydrological model was developed in HEC-HMS to estimate runoff in the catchment using appropriate methods tailored to the local conditions. The model was calibrated using meteorological and hydrological data from 2014 to 2018, and validated with daily data from 2019 to 2021. The simulation results demonstrated good agreement between observed and modeled hydrographs, with performance metrics indicating satisfactory accuracy (Nash Sutcliffe Efficiency (NSE) = 0.66, R² = 0.76, and RMSE = 0.6). In parallel, a social assessment was conducted through questionnaires to capture local community perceptions and experiences related to flooding. Respondents reported common impacts such as loss of life, destruction of agricultural land, damage to infrastructure, and losses in livestock, homes, and commercial properties. A significant proportion of the population resides within 100 to 500 meters of rivers due to limited financial capacity to relocate, exposing them to high flood risk. Government interventions identified include relocation efforts, construction of dams, retaining walls, gabions, and use of sandbags. Flood hazard mapping was carried out using ArcGIS to visualize flood characteristics including water depth, flow velocity, surface elevation, and inundation extent. The analysis showed that flood extent increases with return period, corresponding with higher peak discharges. Compared to the 5-year return period, inundated areas increased by 1.4%, 2.1%, and 3.3% for return periods of 10, 20, and 50 years, respectively. The most flood-prone areas were identified as Mahoko, Nyundo, Pfunda, Kanama, Rugerero, Nyakiriba, and Karambo. Overall, the findings provide critical insights into runoff behavior, flood dynamics, and community vulnerability, offering valuable guidance for integrated watershed management and future flood mitigation planning in the Sebeya Catchment.