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.

Climate change affects ecosystems, agriculture, human health, forestry, and water resource availability. This study is mainly aimed at assessing the climate change effect on the water resources of the Kessie Watershed in the Upper Blue Nile Basin, Ethiopia. The updated Coupled Model Intercomparison Project Phase 6 (CMIP-6) data outputs were used. The three climate model outputs: ACESS_ESM1-5, FGOALS_g3, and GFDL_ESM4 with two shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5) scenarios, were used. The climate model output rainfall and temperature data were downscaled to the station level through bias correction. The catchment hydrology was represented by the SWAT—Soil and Water Assessment Tool—through calibration and validation. Future temperatures and rainfall change were evaluated by the Mann–Kendall trend test and Sen’s slope estimator. Future climate change trend analysis and streamflow simulation were done on two time horizons: the 2050s (2041–2070) and the 2080s (2071–2100). The baseline streamflow data (1985–2014) were used as a reference. The global climate model projection data indicated mean annual precipitation and temperatures show a slight increase for the future in both scenarios for all climate model outputs. According to the SSP2-4.5 and SSP5-8.5 scenarios, respectively, mean annual precipitation is expected to increase by 5% and 4.89% in the 2050s and 10.13% and 6.8% in the 2080s based on ACCESS_ESM1-5; 4.7% and 3.8% in the 2050s and 4.3% and 4.84% in the 2080s based on FGOALS_g3; and 4.67% and 3.81% in the 2050s and 4.67% and 3.81% based on GFDL_ESM4 models data. Yearly average maximum temperature may increase by 3.62 °C and 1.87 °C in the 2050s and 3.31 °C and 2.99 °C in the 2080s based on ACCESS ESM1-5, 1.76 °C and 1.25 °C in the 2050s and 3.44 °C and 2.61 °C in the 2080s based on FGOALS-g3, and 2.15 °C and 3.83 °C in the 2050s and 1.37 °C and 2.66 °C in the 2080s based on GFDL-ESM4 model data. Similarly, the mean annual minimum temperature is also expected to increase by 2.73 °C and 1.90 °C in the 2050s and 5.63 °C and 4.52 °C in the 2080s based on ACCESS ESM1-5, 3.04 °C and 2.43 °C in the 2050s and 3.55 °C and 4.36 °C in the 2080s based on FGOALS-g3, and 2.31 °C and 3.29 °C in the 2050s, and 3.16 °C and 3.87 °C in the 2080s based on GFDL-ESM4 model data. The streamflow is also expected to increase. In the 2050s, simulated annual streamflow is expected to increase from 12.1 to 21.8% and 9.8 to 15.4% in SSP2-4.5 and SSP5-8.5, respectively, whereas in the 2080s, the change is expected to increase from 15.14 to 24.08% and 13.08 to 41% in SSP2-4.5 and SSP5-8.5, respectively. Future water resource potential of the case study watershed seems able to support irrigation and other projects.

Scientific findings indicated there is climate change that affects given hydrology and, hence, water availability worldwide. To quantify its impact on a specific catchment scale, since spatial and temporal variability of climate change impact, this study was carried out at Ribb catchment, Lake Tana basin, Ethiopia. The catchment hydrology was represented by the Soil and Water Analysis Tool (SWAT) through using historical observed data. Regional Climate Model (RCM) projection data set for Nile Basin studies at Representative Concentration Pathway (RCPs) (RCP4.5 and RCP8.5) were used for future streamflow generation on three-time horizons; 2020s (2011–2040), 2050s (2041–2070), and 2080s (2071–2098). A baseline period (1976–2005) was used as a reference. SWAT was calibrated (R2 = 0.83 and NSE = 0.74) and validated (R2 = 0.72 and NSE = 0.71). The analysis was done based on the changes from the baseline period to the 2080s. Temperature showed an increasing trend but rainfall is decreasing. The mean annual streamflow could potentially reduce from 42.78 m3/s to 40.24 m3/s and from 42.78 m3/s to 37.58 m3/s based on RCP4.5 and RCP8.5 scenarios, respectively. On a monthly time scale, decreases in streamflow were found from March to August whereas there was a slight increase from September to February. Concerning individual months, June flows were found to have maximum impact in both scenarios (63.3% at RCP8.5 and 55.45% at RCP4.5 scenarios). The least impacted month was August based on the RCP8.5 scenario which is decreased by 6.64% and April based on the RCP4.5 scenario which is reduced by 1.21%. Looking at total volume, July showed a maximum decrease in both scenarios which is reduced by 21.08 m3/s at the RCP4.5 scenario and 51.22 m3/s at the RCP8.5 scenario. The maximum increase was found in October with 10.31 m3/s and 11.26 m3/s at RCP4.5 and RCP8.5 scenarios respectively. The future streamflow of Ribb River has decreased annually and monthly due to increasing temperature and reduction of rainfall.

Climate model ensembles and proper bias correction enhance climate projections; however, most previous predictions focused on a single model. This study aimed to project the climate of the Kessie watershed in the Ethiopian Abay Basin using best-fitting model ensembles, bias-corrected with relatively outperformed methods for the study area. The performance of the following methods was evaluated: power transformation, linear scaling, local intensity scaling, and distribution mapping for rainfall; and linear scaling, variance scaling, and distribution mapping for temperature. The method’s performance was assessed by its ability to correct errors in the model’s historical predictions, using observational time series from 1984 to 2014 as a reference. The capability of climate models to capture the case study area’s climate was evaluated using bias-corrected historical model predictions, from which various model ensembles were developed. The performance of individual and model ensembles was re-evaluated to select outperforming models for climate projections. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used to rank models and methods based on their performance, as measured by statistical metrics. The selected models’ future simulations were bias-corrected using the outperforming methods, and the resulting data were used to project the climate of the case-study area. It was done at the 2030s (2015–2044), the 2060s (2045–2074), and the 2090s (2075–2100), under four Shared Socioeconomic Pathway scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP4-8.5. All methods reduced errors in model simulations, with the power transformation (variance scaling) comparatively excels for rainfall (temperature). ACCESS-CM2, MIROC6, MRI-ESM2-0, and BCC-CSM2-MR models relatively outperform for rainfall; ACCESS-ESM1-5, GFDL-ESM4, and NorESM2-LM for maximum temperature, and UKESM1-0-LL, ACCESS-CM2, and NorESM2-LM for minimum temperature. However, all model ensembles consistently outperform individual models for both rainfall and temperature. The long-term trend (1986 to 2100) showed significant increases ( p < 0.05 ) in rainfall and temperature across all scenarios. Across different time periods, rainfall is expected to increase, except under SSP1-2.6 in the 2060s (decrease) and the 2090s (remains unchanged). However, none of these changes is statistically significant ( p > 0.05 ) except in the 2030s under SSP2-4.5. Both maximum and minimum temperatures are expected to increase significantly across all time periods, except that maximum temperature remains unchanged in the 2060s and 2090s under SSP1-2.6, and minimum temperature remains unchanged in the 2090s under SSP1-2.6 and increases, but not statistically significantly, in the 2090s under SSP4-8.5. The rainfall projections exhibit mixed and inconsistent trends, whereas temperature projections show a statistically significant upward trend across most scenarios and time periods. The study’s results suggest the need for climate change adaptation strategies to support sustainable development in the region.

Ethiopia is characterized by observed rainfall and runoff data scarcity and consequently, still nowadays the rational formula is commonly applied for estimating the design peak discharge.In this contribution we would like to verify if more advanced hydrological tools can be applied in such challenging conditions and we provide a comparative assessment of event based and continuous modeling approaches in estimating the design hydrographs for small and ungauged basins.The event based approach is applied using the freeware software named EBA4SUB. It is a classical approach, specifically tailored for ungauged conditions, that consists in selecting a design rainfall event based on the Depth-Duration-Frequency (DDF) curves’ parameters, estimating the rainfall excess, and transforming it into the direct hydrograph. The continuous approach is applied using the freeware software named STORAGE and COSMO4SUB. STORAGE is designed for generating long and high resolution synthetic rainfall timeseries based on scarce raingauge observations, like daily rainfall amount and DDF parameters, while COSMO4SUB is the continuous rainfall–runoff module.Results seem promising, in particular we verified that the Ethiopian small and ungauged watersheds can be modeled using advanced approaches that offer more complete design information for the practitioner as respect to the rational formula.