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Quantification of soil is crucial for maximizing the advantages of land resources while minimizing the negative consequences of land degradation in the long term. It will also make it possible to identify locations that need immediate soil erosion management. The present study was carried out in the Kulfo river catchment, Rift valley, Southern Ethiopia. The Revised Universal Soil Loss Equation (RUSLE) method was utilized to estimate the mean yearly soil loss in the research region using remote sensing, other collateral data. The RUSLE model inputs were mapped and integrated into the ArcGIS software, and the results show that 0 and 1211 t ha−1year−1 are the minima and maximum soil loss in the present study area. Soil erosion-prone regions were divided into three categories: 0-42 t ha−1year−1 (low), 43-128 t ha−1 year−1 (medium), and > 128 t ha−1 year−1 (high). And the average rate of soil erosion is 68.47 t ha−1year−1. Low, medium, and high soil erosion areal extent and area percentages in the current research area is 270 km2 (77 %), 61 km2 (17 %), and 19 km2 (6%), respectively. A high rate of soil erosion was found where high steep slope, barren land, and high precipitation occurred in the present study area. The current study's outcomes were confirmed by comparing soil loss estimates in the same geo-environmental conditions found in Ethiopia's highlands. The outcome of this study is important for decision-makers and policymakers.

When groundwater quality is good, it may be a substantial water supply for various applications. However, no systematic research on hydrogeochemistry and water quality features for drinking and irrigation has been undertaken in the present study area. As a result, the current study looked at hydrogeochemical variables and groundwater quality for drinking and irrigation in Tercha district, Dawuro Zone, Southern Ethiopia. Forty-seven groundwater samples were collected and tested to satisfy the required target for various physicochemical properties. The hydrogeochemical features of the groundwater in the study region were assessed using in-situ testing and laboratory analysis of physicochemical parameters. Groundwater samples from the research region were slightly acidic to slightly basic, with the principal cations and anions decreasing in sequence: Na+ > Ca2+ > Mg2+ > K+ and HCO3-> Cl-> SO42-. The hadrochemical facies of the studied region evolved from mildly mineralized dominant highland Ca-HCO3 water types to moderately mineralized mixed Ca-Na-HCO3 water types to highly mineralized deep rift floor Na-HCO3 water types. Additionally, the World Health Organization and the Ethiopian Standard Agency were utilized to compare the drinking water quality. Except for NO3- (4.25 %), Fe (8.51 %), and F- (2.12%), all groundwater samples from the research region were determined to be within permitted limits and appropriate for drinking. According to the Water Quality Index, about 80.86% of groundwater samples are excellent, and 19.14% are good drinking water. Sodium absorption ratio (SAR), sodium (Na) percentage, residual sodium carbonate RSC, permeability index (PI), and magnesium hazard were among the irrigation water quality indicators calculated (MH). The great majority of groundwater samples are suitable for agricultural use.

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The flood is one of the frequently occurring natural hazards within the sub-basin of Lake Tana. The flood hazard within the sub-basin of Lake Tana causes damage to cropland, properties, and a fatality every season. Therefore, flood susceptibility modeling in this area is significant for hazard reduction and management purposes. Thus, the analytical hierarchy process (AHP), bivariate (information value [IV] and frequency ratio [FR]), and multivariate (logistic regression [LR]) statistical methods were applied. Using an intensive field survey, historical document, and Google Earth Imagery, 1,404-flood locations were determined, classified into 70% training datasets and 30% testing flood datasets using a subset within the geographic information system (GIS) environment. The statistical relationship between the probability of flood occurrence and 11 flood-driving factors was performed using the GIS tool. The flood susceptibility maps of the study area were developed by summing all weighted aspects using a raster calculator. It is classified into very low, low, moderate, high, and very high susceptibility classes using the natural breaks method. The accuracy and performance of the models were evaluated using the area under the curve (AUC). As the result indicated, the FR model has better performance (AUC = 99.1%) compared to the AHP model (AUC = 86.9%), LR model (AUC = 81.4%), and IV model (AUC = 78.2%). This research finds out that the applied methods are quite worthy for flood susceptibility modeling within the study area. In flood susceptibility modeling, method selection is not a serious challenge; the care should tend to the input parameter quality. Based on the AUC values, the FR model is comparatively better, followed by the AHP model for regional land use planning, flood hazard mitigation, and prevention purposes.