Background: Soil erosion is one of the major threats in the Ethiopian highlands. In this study, soil erosion in the Muga watershed of the Upper Blue Nile Basin (Abay) under historical and future climate and land use/land cover (LULC) change was assessed. Future LULC was predicted based on LULC map of 1985, 2002, and 2017. LULC maps of the historical periods were delineated from Landsat images, and future LULC was predicted using the CA–Markov chain model. Precipitation for the future period was projected from six regional circulation models. The RUSLE model was used to estimate the current and future soil erosion rate in Muga watershed. Results: The average annual rate of soil erosion in the study area was increased from about 15 t ha⁻¹ year⁻¹ in 1985 to 19 t ha⁻¹ year⁻¹ in 2002, and 19.7 t ha⁻¹ year⁻¹ in 2017. Expansion of crop cultivation and loss of vegetation caused an increase in soil erosion. Unless proper measure is taken against the LULC changes, the rate of soil loss is expected to increase and reach about 20.7 t ha⁻¹ year⁻¹ in 2033. In the 2050s, soil loss is projected to increase by 9.6% and 11.3% under RCP4.5 and RCP8.5, respectively, compared with the baseline period. Thus, the soil loss rate is expected to increase under both scenarios due to the higher erosive power of the future intense rainfall. When both LULC and climate changes act together, the mean annual soil loss rate shows a rise of 13.2% and 15.7% in the future under RCP4.5 and RCP8.5, respectively, which is due to synergistic effects. Conclusions: The results of this study can be useful for formulating proper land use planning and investments to mitigate the adverse effect of LULC on soil loss. Furthermore, climate change will exacerbate the existing soil erosion problem and would need for vigorous proper conservation policies and investments to mitigate the negative impacts of climate change on soil loss.

Ecosystem service changes caused by land use and land cover change (LULCC) is an important indictor and early warning of ecological changes. However, few attempts have been made to evaluate the effects of LULCC on ecosystem services in the Afroalpine highlands of Northwestern Ethiopia. Therefore, this study aimed to analyze the impacts of LULCC on ecosystem services values in the afro-alpine area of Guna Mountain, Northwestern Ethiopia. Image classification was carried out using Landsat imageries of 1995, 2008, and 2020 following Random Forest algorithm with Google Earth Engine(GEE) based on filtered sample points. A modified benefit transfer method was used to evaluate ecosystem service value (ESV) changes in response to LULCC. The results revealed that the most notable feature of LULCC in the afro-alpine area of Guna Mountain was the expansion of cropland and built-up areas at the expense of grassland, forest, and shrubland. The overall ESV of the study site was estimated at USD 46.97 × 10⁶ in 1995, USD 36.77 × 10⁶ in 2008, and USD 37.19 × 10⁶ in 2020. The net ESVs of the study site declined by USD 9.78 × 10⁶ between 1995 and 2020. The regulating service values accounted for the greatest share, accounting for over 42% in all periods, followed by provisioning and supporting service values, which accounted for over 29% and 13%, respectively, while cultural services accounted for the smallest amount of the total ESV. The ecosystem service value of food production experienced the highest increase. However, the values of the remaining 16 types of ecosystem services declined with varying degrees of reduction over the study periods. The results of this study is necessary for land-use planners and decision-makers who require site-specific information on impacts of LULCC on ecosystem service.

Ethiopia is widely acknowledged to be extremely vulnerable to climatic variability and change. Its agricultural sector, which is particularly susceptible to risks associated with rainfall variability, represents a major source of vulnerability. Household livelihood vulnerability varies across different agroecological zones (AEZs). This research aimed to investigate farmers' susceptibility to climate variability and change in northwestern Ethiopia. A total of 405 households from three AEZs (lowland, highland, and midland) were chosen through simple random sampling. Focus group discussions and key informant interviews were also conducted to complement and validate the numerical data. To assess the household vulnerability levels, the social, economic, and environmental indicators were aggregated, and the LVI and LVI-IPCC index were employed. The study's findings indicated significant variation in livelihood vulnerability indices, profiles, and indicators across agroecological zones (AEZs). The midland AEZ exhibited low exposure, higher adaptive capacity, and low vulnerability, while the lowland AEZ showed higher vulnerability, weaker adaptive capacity, and greater exposure compared to the midland and highland AEZs. The LVI-IPCC analysis corroborated these findings, with values of 0.128 for the midland AEZ, 0.168 for the highland, and 0.180 for the lowland. The variation is due to differences in agroecological and socioeconomic factors among households. The results indicate the necessity of implementing adaptation strategies specifically tailored to the agroecological systems in the study area. Such an approach is essential for effectively reducing vulnerability among households engaged in mixed crop-livestock agriculture. Adaptation measures developed from these assessments are both applicable and relevant to local conditions, as they are grounded in the community’s long-term realities.

Soil erosion in Ethiopia harms agricultural production, water quality, and overall ecosystem health. Effective soil loss estimation can help minimize these outcomes. The implementation of the Revised Universal Soil Loss Equation (RUSLE) in Ethiopia has not been sufficiently evaluated. Thus, the main objective of this review is to systematically review the comprehensive strengths and limitations of the RUSLE model applications for soil loss estimation. Specifically, it evaluates the specific parameters used, the type of input data, the validation approaches, and the role of GIS and remote sensing in the computation process of the RUSLE Model. An extensive search method was employed to classify suitable research papers about applying the RUSLE model in estimating soil erosion in Ethiopia. The review underscores that different approaches have been used to compute the five parameters of the RUSLE model. Key strengths of the RUSLE model include its widespread acceptance and adaptability to various environmental conditions; however, notable limitations are evident, regarding the validation and calibration processes. This review highlights that the scale of study areas varies widely, from 18.33 to 1,119,683 km². Soil loss estimates across the reviewed studies ranged from 12.94 to 576 t ha⁻¹ yr⁻¹. The findings of this review presented here are critical for using the RUSLE model to estimate soil loss integrating it with other process-based models and the advanced machine learning technologies. Ultimately, this review aids in improving soil resource management and ecological sustainability in Ethiopia.