Soil loss results from the hydrological and hydromechanical responses. The soil loss intensity theoretically relates to the soil water storage and the suction stress. This work contributes to exhibiting the close relationship between erosion intensity in the bed and on the slope and the soil water storage and suction stress. The results of this work highlight that antecedent precipitation should be considered in predicting soil loss in the permanent gully rather than solely from rainfall amount.

This study explores how deep learning can improve our understanding of groundwater levels, using an approach that combines climate data and physical characteristics of aquifers. By focusing on different types of groundwater levels and employing techniques like clustering and wavelet transform, the study highlights the importance of targeting relevant information. This research not only advances groundwater simulation but also emphasizes the benefits of different modelling approaches.

In this study, we implement a climate, water, and crop interaction model to evaluate current conditions and project future changes in rainwater availability and its yield potential, with the goal of informing adaptation policies and strategies in Ethiopia. Although climate change is likely to increase rainfall in Ethiopia, our findings suggest that water-scarce croplands in Ethiopia are expected to face reduced crop yields during the main growing season due to increases in temperature.

Snow water equivalent (SWE) is an environmental variable that represents the amount of liquid water if all the snow cover melted. This study evaluates the potential of the Weather Research and Forecasting (WRF) model to estimate the daily values of SWE over the mountainous South Saskatchewan River Basin in Canada. Results show that high-resolution WRF simulations can provide reliable SWE values as an accurate input for hydrologic modeling over a sparsely monitored mountainous catchment.

This study develops an integrated framework based on the novel Driving index for changes in Precipitation–Runoff Relationships (DPRR) to explore the controlling changes in precipitation–runoff relationships in non-stationary environments. According to the quantitative results of the candidate driving factors, the possible process explanations for changes in the precipitation–runoff relationships are deduced. The main contribution offers a comprehensive understanding of hydrological processes.