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Gully erosion is the most intensive type of water erosion and it leads to land degradation across the world. Therefore, analyzing the spatial occurrence of this phenomenon is crucial for land management. The objective of this research was to predict gully erosion susceptibility in the Kakia-Esamburmbur catchment in Narok, Kenya, which is badly affected by gully erosion. GIS and ensemble techniques using weight of evidence (WoE) and logistic regression (LR) models were used to map the susceptibility to gully erosion. First, 130 gullies were detected in the study area and portioned out 70:30 for training and validation, respectively. Nine gully erosion conditioning factors were selected as predictors. The relationships between the gully locations and the factors were identified and quantified using WoE, LR and WoE–LR ensemble models. The results show that land use/cover, distance to road, sediment transport index (STI) and topographic wetness index (TWI) are the factors that have the most influence on gully occurrence in the catchment. Additionally, the WoE–LR model performed better than the WoE and LR models, producing an AUC value of 0.88, which was higher than that of the WoE model, 0.62 and the LR model, 0.63. Therefore, the WoE–LR ensemble model is useful in gully erosion susceptibility mapping and is of help to decision makers in land-use planning. [ABSTRACT FROM AUTHOR]

Knowledge of design peak flow is crucial in various hydrology, hydraulics, and water resources management applications. However, obtaining design peak flow is challenging, especially in ungauged basins lacking discharge observations, a circumstance that does not allow the calibration of advanced hydrological models. Recently, a new method called EBA4SUB (event-based approach for small and ungauged basins) was introduced to estimate the design peak flow and hydrograph in ungauged basins. The current study used the EBA4SUB model to evaluate the design peak flow in the Kakia and Esamburumbur sub-watersheds, Narok town, Kenya. The study investigated the link between rainfall frequency analysis and flood frequency analysis, showing the reliability of this approach to model at event scale the selected watersheds. Furthermore, the coefficient of determination between the rainfall-based return period and the flow-based return period in both the Kakia and Esamburumbur sub-watersheds highlighted the strong dependency between design rainfall and design peak flow. [ABSTRACT FROM AUTHOR]