Flood is one of the natural hazards that causes widespread destruction such as huge infrastructural damages, considerable economic losses, and social disturbances across the world in general and in Ethiopia, in particular. Dega Damot is one of the most vulnerable districts in Ethiopia to flood hazards, and no previous studies were undertaken to map flood-prone areas in the district despite flood-prone areas identification and mapping being crucial tasks for the residents and decision-makers to reduce and manage the risk of flood. Hence, this study aimed to identify and map flood-prone areas in Dega Damot district, northwestern Ethiopia, using the integration of Geographic Information System and multi-criteria decision-making method with analytical hierarchy process. Flood-controlling factors such as elevation, slope, flow accumulation, distance from rivers, annual rainfall, drainage density, topographic wetness index, land use and land cover, Normalized Difference Vegetation Index, soil type, and curvature were weighted and overlayed together to achieve the objective of the study. The result shows that about 86.83% of the study area has moderate to very high susceptibility to flooding, and 13.17% of the study area has low susceptibility to flooding. The northeastern and southwestern parts of the study area dominated by low elevation and slope, high drainage density, flow accumulation, topographic wetness index, and cropland land use were found to be more susceptible areas to flood hazards. The final flood susceptibility map generated by the model was found to be consistent with the historical flood events on the ground in the study area, revealing the method’s effectiveness used in the study to identify and map areas susceptible to flood.

Soil erosion by water is the major form of land degradation in Chereti watershed, Northeastern Ethiopia. This problem is exacerbated by high rainfall after a long period of dry seasons, undulating topography, intensive cultivation, and lack of proper soil and water conservation measures. Hence, this study aimed to estimate the 23 years (1995-2018) average soil erosion rate of the watershed and to identify and prioritize erosion-vulnerable subwatersheds for conservation planning. The integration of the revised universal soil loss equation (RUSLE), geographic information system, and remote sensing was applied to estimate the long-term soil loss of the watershed. The RUSLE factors such as rainfall erosivity ( R), soil erodibility ( K), topography ( LS), cover and management ( C), and support and conservation practices ( P) factors were computed and overlayed to estimate the soil loss. The result showed that the annual soil loss rate of the watershed ranged up to 187.47 t ha−1 year−1 in steep slope areas with a mean annual soil loss of 38.7 t ha−1 year−1, and the entire watershed lost a total of about 487 057.7 tons of soil annually. About 57.9% of the annual watershed soil loss was generated from 5 subwatersheds which need prior intervention for the planning and implementation of soil conservation measures. The integrated use of RUSLE with GIS and remote sensing was found to be indispensable, less costly, and effective for the estimation of soil erosion, and prioritization of vulnerable subwatersheds for conservation planning.