Regional climate models (RCMs) that produce good outputs in one region or for specific variables may underperform for others. Thereby, assessing the performance of various model simulations and their corresponding mean ensemble is critical in identifying the most suitable models. In this regard, a study was conducted to evaluate the performance of ten RCMs against observations from multiple ground-based stations in the East African Transboundary Omo Gibe River Basin, Ethiopia, during the baseline period of 1986–2005. The study evaluated the models' ability to replicate various aspects of climatic variables and their corresponding statistical indicators. The results confirmed that RCMs have varying abilities to reproduce climatic conditions across the basin. The ensembles and RACMO22T (EC-EARTH) were better at replicating the average annual precipitation distribution. Meanwhile, the CCLM4-8-17 (MPI) together with the ensembles better captured the measured precipitation annually, despite the discrepancies in the actual magnitudes. All RCMs were able to simulate the seasonal precipitation patterns effectively, with RACMO22T (EC-EARTH), CCLM4-8-17 (CNRM), RCA4 (CNRM), CCLM4-8-17 (MPI), and REMO2009 (MPI) models captured superior, excluding the maximum value. Interannual and seasonal rainfall pattern variations were more significant than variations in air temperature. Additionally, a better correlation was observed between actual and simulated precipitation at multiple separate monitoring places. The RCA4 (MPI) and CCLM4-8-17 (MPI) demonstrated reasonable minimum and maximum temperatures. The RCA4 (MIROC5) model was more effective in reproducing extreme precipitation events. However, all RCMs and their ensembles tended to overestimate the return periods of these events. In general, the research highlights the importance of selecting reliable RCMs that better replicate observed climatic settings and employing the ensemble mean of top-performing models following systematic bias adjustment for a specific application.

Land Use/Land Cover (LULC) change has been a substantial environmental concern, hindering sustainable development over the past few decades. To that end, comprehending the past and future patterns of LULC change is vital for conserving and sustainably managing land resources. This study aimed to analyze the spatiotemporal patterns of landscape dynamics from 1986 to 2022 and predict situations for 2041 and 2058, considering a business-as-usual (BAU) scenario in the Wabe River Catchment. The historical land use image classification employed a supervised technique using maximum likelihood algorithms in ERDAS Imagine, and identified six major land cover classes. For future projections of LULC changes in 2041 and 2058, multilayer perceptron neural network and cellular automata-Markov chain algorithms were utilized, incorporating various driving factors and independent spatial datasets. The findings revealed significant and ongoing LULC dynamics in the catchment, with persistent trends expected. Notably, woodland, built-up areas, and agriculture experienced substantial net increases by 0.24%, 1.96%, and 17.22% respectively, while grassland, forest, and agroforestry land faced notable decreases of 4.65%, 3.58%, and 11.20% respectively from 1986 to 2022. If the current rate of change continues, built-up and agricultural lands will expand by 1.28% and 5.07%, while forest and agroforestry land will decline by 2.69% and 3.63% respectively by 2058. However, woodland and grassland cover will exhibit divergent patterns, with a projected decrease of 0.57% in woodland and an anticipated increase of 0.54% in grassland cover. Overall, the observed changes indicated a shift towards intensive agriculture, built-up area expansion, and potentially adverse environmental consequences such as soil degradation, biodiversity loss, and ecosystem decline. To mitigate these consequences and promote sustainable development, immediate action is necessary, including environmentally friendly conservation approaches, sustainable land management practices, habitat protection, and reforestation efforts, ensuring the long-term resilience and viability of the catchment’s ecosystems.

Using regional climate models (RCMs) and ensembles of multiple model simulation outputs without assessing their modeling performance did not always ensure the best agreement between observed and modeled climate variables. To this end, assessing the modeling performance of regional climate models (RCMs) is indispensable in selecting the most effective model to use for climate change impact studies. In this study, the performance of ten Coordinated Regional Climate Downscaling Experiments (CORDEX) in Africa was examined against observational datasets from 1986 to 2005 across the entire Omo Gibe River Basin (OGRB). The output of RCMs was evaluated based on their ability to reproduce the magnitude and pattern of monthly, seasonal, and annual precipitation and air temperature, precipitation characteristics, and statistical metrics. The results confirm the difference between RCMs in capturing climate conditions at both spatial and temporal scales. The spatial pattern of mean annual precipitation was better reproduced by the ensemble mean and RACMO22T (EC-EARTH). CCLM4-8-17 (MPI) and the ensemble mean reproduced the annual patterns of observed precipitation, even though the amounts were different. Except for peak precipitation, all RCMs simulated seasonal precipitation, and the pattern was reasonably captured by RACMO22T (EC-EARTH), CCLM4-8-17 (CNRM), RCA4 (CNRM), CCLM4-8-17 (MPI), and REMO2009 (MPI). The interannual and seasonal variability of precipitation was higher than the variability of air temperature. It was found that observed and RCM precipitation simulations using CCLM4-8-17 (MPI), REMO2009 (MPI), and RCA4 (CNRM) showed better agreement at several individual stations in the Omo Gibe River Basin (OGRB. Likewise, RCA4 (MPI) and CCLM4-8-17 (MPI) were superior in capturing minimum and maximum air temperatures. The cumulative distribution of extreme precipitation was better captured by RCA4 (MIROC5), and all RCMs, including their ensemble mean, overestimated the return period. Overall, the study emphasizes that the selection of robust RCMs that better reproduce observed climate conditions and the use of multi-model ensembles of models with the best performance after systematic bias correction are fundamentally necessary for any study of climate change impacts and adaptation in the OGRB.

Understanding how the quality of the soil parameters differ across different land use categories is essential for enhancing soil fertility, productivity, and environmental sustainability. This study evaluated the soil quality in the Shapa watershed under three nearby land use systems: grassland, enset, and maize. For each land use, soil samples were taken from the surface (0–20 cm depth), and 20 soil quality parameters were investigated. The effect of different land uses on soil quality measures was assessed using a one-way analysis of variance (ANOVA). A minimum data set of 10 indicators chosen from physical and chemical factors was used to generate a soil quality index (SQI). With weights generated from Principal Component Analysis (PCA), the SQI was utilized to evaluate the impact of land use categories on soil quality parameters. The outputs showed significant differences in SQI among the different land uses, with key indicators including bulk density, total porosity, pH, percent base saturation (PBS), available phosphorus, organic carbon (OC), and exchangeable Mg⁺². The SQI values were 0.61 for enset land, 0.56 for grassland, and 0.51 for the maize field. Enset and grasslands were classified as intermediate soil quality, while the maize field was categorized as low soil quality, highlighting the need for immediate remedial actions to promote sustainable natural resource management. The study suggests that enhancing soil quality in this research watershed might be realized through improved land use practices that consider farmers’ traditional resource management practices.