Land use/land cover change evaluation and prediction using spatiotemporal data are crucial for environmental monitoring and better planning and management of land use. The main objective of this study is to evaluate land use/land cover changes for the time period of 1991–2022 and predict future changes using the CA-ANN model in the Upper Omo–Gibe River basin. Landsat-5 TM for 1991, 1997, and 2004, Landsat-7 ETM+ for 2010, and Landsat-8 (OLI) for 2016 and 2022 were downloaded from the USGS Earth Explorer Data Center. A random forest machine learning algorithm was employed for LULC classification. The LULC classification result was evaluated using an accuracy assessment technique to assure the correctness of the classification method employing the kappa coefficient. Kappa coefficient values of the classification indicate that there was strong agreement between the classified and reference data. Using the MOLUSCE plugin of QGIS and the CA-ANN model, future LULC changes were predicted. Artificial neural network (ANN) and cellular automata (CA) machine learning methods were made available for LULC change modeling and prediction via the QGIS MOLUSCE plugin. Transition potential modeling was computed, and future LULC changes were predicted using the CA-ANN model. An overall accuracy of 86.53% and an overall kappa value of 0.82 were obtained by comparing the actual data of 2022 with the simulated LULC data from the same year. The study findings revealed that between 2022 and 2037, agricultural land (63.09%) and shrubland (5.74%) showed significant increases, and forest (−48.10%) and grassland (−0.31%) decreased. From 2037 to 2052, the built-up area (2.99%) showed a significant increase, and forest and agricultural land (−2.55%) showed a significant decrease. From 2052 to 2067, the projected LULC simulation result showed that agricultural land (3.15%) and built-up area (0.32%) increased, and forest (−1.59%) and shrubland (−0.56%) showed significant decreases. According to the study’s findings, the main drivers of LULC changes are the expansion of built-up areas and agricultural land, which calls for a thorough investigation using additional data and models to give planners and policymakers clear information on LULC changes and their environmental effects.

It is imperative to assess and comprehend the hydrological processes of the river basin in light of the potential effects of land use/land cover and climate changes. The study’s main objective was to evaluate hydrologic response of water balance components to the projected land use/land cover (LULC) and climate changes in the Omo–Gibe River Basin, Ethiopia. The study employed historical precipitation, maximum and minimum temperature data from meteorological stations, projected LULC change from module for land use simulation and evaluation (MOLUSCE) output, and climate change scenarios from coupled model intercomparison project phase 6 (CMIP6) global climate models (GCMs). Landsat thematic mapper (TM) (2007) enhanced thematic mapper plus (ETM+) (2016), and operational land imager (OLI) (2023) image data were utilized for LULC change analysis and used as input in MOLUSCE simulation to predict future LULC changes for 2047, 2073, and 2100. The predictive capacity of the model was evaluated using performance evaluation metrics such as Nash–Sutcliffe Efficiency (NSE), the coefficient of determination (R2), and percent bias (PBIAS). The bias correction and downscaling of CMIP6 GCMs was performed via CMhyd. According to the present study’s findings, rainfall will drop by up to 24% in the 2020s, 2050s, and 2080s while evapotranspiration will increase by 21%. The findings of this study indicate that in the 2020s, 2050s, and 2080s time periods, the average annual Tmax will increase by 5.1, 7.3, and 8.7%, respectively under the SSP126 scenario, by 5.2, 10.5, and 14.9%, respectively under the SSP245 scenario, by 4.7, 11.3, and 20.7%, respectively, under the SSP585 scenario while Tmin will increase by 8.7, 13.1, and 14.6%, respectively, under the SSP126 scenario, by 1.5, 18.2, and 27%, respectively, under the SSP245 scenario, and by 4.7, 30.7, and 48.2%, respectively, under the SSP585 scenario. Future changes in the annual average Tmax, Tmin, and precipitation could have a significant effect on surface and subsurface hydrology, reservoir sedimentation, hydroelectric power generation, and agricultural production in the OGRB. Considering the significant and long-term effects of climate and LULC changes on surface runoff, evapotranspiration, and groundwater recharge in the Omo–Gibe River Basin, the following recommendations are essential for efficient water resource management and ecological preservation. National, regional, and local governments, as well as non-governmental organizations, should develop and implement a robust water resources management plan, promote afforestation and reforestation programs, install high-quality hydrological and meteorological data collection mechanisms, and strengthen monitoring and early warning systems in the Omo–Gibe River Basin.

Reliable seasonal forecasting of water resources variability may be of great value for agriculture and energy management in Ethiopia. This work aims to develop statistical forecasting of seasonal total water storage (TWS) anomalies in Ethiopia using sea-surface temperature and sea-level pressure indices. Because of the spatial and temporal variability of TWS over the country, Ethiopia is divided into four regions each having similar TWS dynamics. Periods of long-term water deficit observed in GRACE TWS products for the region are found to coincide with periods of meteorological drought. Multiple linear regression is employed to generate seasonal forecasting models for each region. We find that the skill of the resulting models varies from region to region, with R ² from 0.33 to 0.73 and correlation from 0.27 to 0.77 between predicted and observed values (using leave-one-out cross-validation). The skill of the models is better than the climatology in all regions.