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.

This work demonstrates that seasonal forecasts of soil moisture are a valuable resource for groundwater management in the areas of the Central Mediterranean where longer memory timescales are found. In particular, they show significant correlation coefficients and forecast skill for the deepest soil moisture at 289 cm depth. Wet and dry events can be predicted 6 months in advance, and, in general, dry events are better captured than wet events.

A serious game experiment is presented which assesses the interplay between hazard, exposure, and vulnerability in a flash flood event. The results show that participants' use of information to make decisions was based on the severity of the situation. Participants used precipitation forecast and exposure to make correct decisions in the first round, while they used precipitation forecast and vulnerability information in the second round.

Here we propose a new mathematically robust and cost-effective model to improve glacial lake water storage estimation. We have also provided a dataset of measured water storage in glacial lakes through field depth measurements. Our model incorporates an automated calculation process and outperforms previous ones, achieving an average relative error of only 14 %. This research offers a valuable tool for researchers seeking to improve the risk assessment of glacial lake outburst floods.

Rainfall and snowfall spatial estimation for hydrological purposes is often compromised in cold mountain regions due to inaccessibility, creating sparse gauge networks with few high-elevation gauges. This study developed a framework for quantifying gauge network uncertainty, considering elevation to aid in future gauge placement in mountain regions. Results show that gauge placement above 2000 m is the most cost-effective measure to decrease gauge network uncertainty in the Canadian Rockies.

Scientists and engineers need better indices to frame the hydrologic effects of land development. Existing approaches are not able to reflect the interactions due to the spatial arrangement of distinct land patches, which affect how much runoff is generated and how fast it can travel downstream, impacting flood response. Our novel, GIS-based modeling framework explicitly considers these aspects and is applicable to a wide range of problems, including peak-flow predictions in ungauged basins.

Owing to differences in the existing published results, we conducted a detailed analysis of the runoff components and future trends in the Yarlung Tsangpo River basin and found that the contributions of snowmelt and glacier melt runoff to streamflow (both ~5 %) are limited and much lower than previous results. The streamflow in this area will continuously increase in the future, but the overestimated contribution of glacier melt could lead to an underestimation of this increasing trend.

This study reconstructs daily runoff in Switzerland (1962–2023) using a deep-learning model, providing a spatially contiguous dataset on a medium-sized catchment grid. The model outperforms traditional hydrological methods, revealing shifts in Swiss water resources, including more frequent dry years and declining summer runoff. The reconstruction is publicly available.

Stream temperature sensitivity in northern regions responds to many of the same environmental controls as in temperate regions, but the presence of annually frozen ground (permafrost) influences catchment hydrology and stream temperature regimes. Permafrost can have positive and negative influences on thermal regimes. The net effect of northern environmental change on stream temperature is complex and uncertain, but permafrost will likely play a role through its control on cold region hydrology.

A new drought feature, the duration of the drought onset, is proposed to describe how quickly droughts develop. The effects of high temperatures before and after droughts on drought occurrence are quantified. High temperatures affect droughts differently in regions with different vegetation conditions.

There is a perceived mismatch between the spatial scales on which global climate models can produce data and those needed for water management decisions. However, poor communication of specific metrics relevant to local decisions is also a problem. We assessed the credibility of a set of water management decision metrics in the Community Earth System Model v2 (CESM2). CESM2 shows potentially greater use of its output in long-range water management decisions.

Precipitation data from an automated observational network in eastern Canada showed a temperature interval where rain and snow could coexist. Random forest models were developed to classify the precipitation phase using meteorological data to evaluate operational applications. The models demonstrated significantly improved phase classification and reduced error compared to benchmark operational models. However, accurate prediction of mixed-phase precipitation remains challenging.

Non-asymptotic probability distributions of block maxima (BM) have been proposed as an alternative to asymptotic distributions from classic extreme-value theory. We show that the non-asymptotic models are unnecessary and redundant approximations of the corresponding parent distributions, which are readily available, are not affected by serial dependence, have simpler expression, and describe the probability of all quartiles of the process of interest and not only the probability of BM. 

Models help to understand natural systems and are used to predict changes based on scenarios (e.g., climate change). To simulate water temperature and deduce impacts on water quality in lakes, 1D lake models are often used. There are several such models that differ regarding their assumptions and mathematical process description. This study examines the performance of four such models on a global dataset of 73 lakes and relates their performance to the model structure and lake characteristics.

Future extreme rainfall events are influenced by changes in both absolute and relative humidity. The impact of increasing absolute humidity is reasonably well understood, but the role of relative humidity decreases over land remains largely unknown. Using hourly observations from France and the Netherlands, we find that lower relative humidity generally leads to more intense rainfall extremes. This relation is only captured well in recently developed convection-permitting climate models.