Imran Ahmad

Soil erosion poses a major threat to sustainable land use in Ethiopia's highland regions, where steep terrain and expanding agriculture accelerate environmental degradation. This study assesses erosion susceptibility in the Gubalafto district using a spatially adaptive modeling approach-Geographically Weighted Regression (GWR)-integrated with remote sensing and GIS techniques. High-resolution terrain data (12.5 × 12.5 m) were used to derive key topographic and hydrological indicators, including slope, LS factor, curvature, valley depth, channel network base level, channel network distance, and wetness index. The GWR model revealed significant spatial variation, with erosion hotspots concentrated in steep, sparsely vegetated areas near drainage networks. Validation metrics confirmed the model's reliability and spatial sensitivity. The resulting erosion susceptibility map offers a decision-support tool for targeted watershed interventions. It identifies priority zones for terracing, reforestation, and land-use regulation, enabling more efficient allocation of conservation resources. The key finding is that slope, LS factor, and proximity to drainage channels are the dominant predictors of erosion, providing a spatially precise framework for sustainable land management in vulnerable highland ecosystems.

Land degradation caused by water erosion is the most critical challenge in Ethiopia’s highlands, leading to excessive sediment load in reservoirs and landscape desertification. To prevent land degradation, the government of Ethiopia has been implementing integrated watershed management practices in the northern highlands. The Megech integrated watershed management was part of the intervention aimed at restoring the degraded watershed towards reducing sediment delivery to the downstream Megech reservoir and Lake Tana while at the same time improving the livelihoods of the community within the watershed. Therefore, the objective of this study was to evaluate the performance of biophysical soil and water conservation measures (SWC) and their technical viability. Primary and secondary data were collected on the impact, suitability, and sustainability of biophysical SWC measures evaluated using detailed field surveys, interviews, focus group discussions, key informant interviews, field measurements, observation, and laboratory analysis. Technical viability and performance of implemented SWC measures evaluated on different slope classes and land uses. In some parts of the sub-watersheds, almost a meter of soil depth has been accumulated, and the area is restored to its previous natural state. In the sub-watersheds, the implemented biophysical SWC measures have resulted in a positive impact on soil erosion reduction, forest cover, water availability, and improved soil fertility. Some of the constructed gullies and stone bunds failed, and further scoring was aggravated in some of the sub-watersheds. Therefore, care has to be taken when designing and regular monitoring. Maintenance would be complemented at the planning stage for sustainable watershed management efforts.

Drought is a complex and severe hazard, particularly in Africa, where precipitation underpins livestock and agriculture—the core of the continent’s economy. This study maps drought vulnerability across Africa using a Geographic Information System (GIS)–based Fuzzy Logic Framework, incorporating temperature, precipitation, wind speed, water vapor pressure, and solar radiation factors. Semivariogram modeling was employed to understand the spatial variability of these climatic factors. Semivariogram modeling was chosen for its ability to quantify spatial dependencies, ensuring accurate variability assessment over large regions. Unlike deterministic methods, it captures continuous spatial autocorrelation, enhancing precision in drought vulnerability mapping. Fuzzy membership scores, scaled from 0 to 1, were allocated according to the relative contribution of each variable to drought vulnerability. The final output was classified into five categories: very severe, moderate, mild, slight, and no drought. An explanatory regression analysis was then performed to determine the optimal model for quantifying the influence of climatic variables on drought vulnerability. Model 30 emerged as the optimal fit, demonstrating an adjusted R² value of 0.9777 and the lowest Akaike's Information Criterion (AICc) value of 4428.887. Despite high accuracy, data resolution constraints and regional climate variability may introduce biases, limiting predictive adaptability across diverse ecosystems and future climate shifts. Additionally, the spatial autocorrelation tool Moran's I was utilized to verify the uniform distribution of standard residuals, ensuring the model's reliability. The study effectively presents the spatial extent of drought vulnerability across various African countries, highlighting the regions most at risk. This comprehensive analysis provides valuable insights into the geographic distribution of drought vulnerability, offering a robust framework for future research and practical applications in drought management and mitigation strategies. The results emphasize the significance of incorporating advanced GIS methodologies and fuzzy logic to improve the understanding of drought dynamics and to guide targeted measures aimed at reducing their impacts. Furthermore, the proposed model contributes to the development of early warning systems, precision drought interventions, and policy-driven resilience planning.