ASSESSMENT OF FLOOD RISK USING ANALYTICAL HIERARCHY PROCESS AND GEOGRAPHIC INFORMATION SYSTEM WITHIN THE RIBB RIVER WATERSHED, SOUTH GONDAR, ETHIOPIA

Abstract

Floods are most frequent and the most widely experienced catastrophic geologic hazards and the cause the many damage of all natural disasters. It perhaps takes the greatest toll in terms of human lives and property damage throughout the world. The river channel is directly related to the quantity of water that it usually carries, if they overflow its banks, it causes the flood. Ethiopia experiences two types of floods: flash floods and river floods. This study was carried out in Ribb River watershed in South Gondar, Amhara Region, Ethiopia, where flood occurs on regular basis. The main aim of this study is to assess the effect of flood risk related to Ribb River watershed. The study used different types of data such as toposheet, ALOS PALSA DEM (12.5m), Sentinel-2L-A sensor data, soil, and precipitation data, Gross population data; all these data were processed with the help of GIS tools. To prepare the flood risk mapping, remote sensing and GIS tools were used to integrate eleven parameters such as slope, elevation, soil, rainfall intensity, distance to the main river, topographic wetness index, normalized difference vegetation index, drainage density, lithology, land use/land cover and population density. The analytic hierarchy process (AHP) was applied to derive factor weights using Excel with reasonable consistency ratio (CR=0.04). In addition to analytic hierarchy process analysis and GIS, flood frequency analysis was calculated by using Gumbel’s method for 5, 10, 50, 100 and 150 years return period and the flood magnitude is estimated about 7.50 m, 7.92m, 8.85m, 9.25m and 9.49m respectively. The produced risk map indicates that most of the study area is highly susceptible to flood risk. Finally from generated risk map, 20 % (300.36 Km2) in very high flood risk level, 36 % (559.27km2) in high risk level, 38 % (588.46 km2) in moderate flood risk level, 5.5 % (71.23 km2) in low flood risk level and 0.5 % (0.24 Km2). The resultant map can aid as baseline data for decision makers in disaster management system, better land use planning and future preventive measures