(Anyeko, P. 2023.) Characterizing flood risk in the Aswa catchment, upper Nile water management zone of Uganda

Abstract

According to recent estimates, more than 1.18 billion people worldwide are directly at risk from floods, and more than 85% of them reside in developing nations like Uganda. The dynamics of flood risk in the Upper Nile Water Management Zone are poorly understood despite observations and numerous flood studies conducted over Uganda. This is due to the fact that studies have only used GIS hazard mapping, leaving a gap in knowledge of the hydrodynamic features of local floods. This study's overall goal was “To contribute to a better understanding of flood dynamics in the Aswa catchment in the Upper Nile management zone of Uganda.” The study's specific objectives were (i) to establish the spatial-temporal characteristics of floods in the Aswa catchment, (ii) to analyze the Aswa catchment's spatial-temporal flood risk levels, and (iii) to evaluate the Aswa catchment's effects on elements at risk. Remote sensing, GIS, and semi-structured questionnaires were used to collect primary and secondary data. The steps in the methodology used were as follows: (i) preparation of a DEM based on SRTM data; (ii) delineation of a watershed and drainage network using Arc hydro; (iii) analysis of flood frequency using the observed discharge data; (iv) creating geometric data using HEC-GeoRAS; (v) using HEC-RAS to analyze a variety of potential flow scenarios corresponding to various flood return-period; and (vi) creating floodplain maps with GIS. Utilizing an index-based methodology, the household's exposure, sensitivity, and capacity for adaptation to flooding were evaluated. Results indicate that Log Pearson III distribution was the best fit distribution. Areas near the river in Pader, Alebtong, Otuke, Abim, and Lira districts experience more flooding than any other districts in the study area. The vulnerability indices in the districts of Alebtong, Lira, Otuke, and Pader ranged from 0.18, -0.20, 0.18, 0.44 respectively. Pader recorded the highest vulnerability index at the household level, while Lira recorded the lowest. In conclusion, using remote sensing GIS and flood model HEC RAS to apply flood modeling in low-data environments helps to better understand the hydrodynamic characteristics of floods in the area, and using an index-based approach makes it simpler to identify and rank the areas of the region that are most vulnerable to flooding. In order to improve future model simulation, the paper suggests that future studies include a hydrological simulation prior to the hydraulics simulations in order to better understand the hydrological response units of the Aswa catchment.