This study compares two prominent hydrometeorological datasets across 47 shared watersheds in the United States to assess their compatibility. While daily temperature and precipitation data showed notable discrepancies, agreement improved at monthly, seasonal, and annual scales. The variability between the datasets is comparable to the inherent variability within each individual dataset, and our findings validate the results of previous research employing either dataset. 

In the boreal Krycklan Catchment, 40 years of warming has notably altered hydrological patterns, with increases in winter runoff and decreases in summer flows. Winter climate indices effectively predicted minimum winter flows, while summer runoff variability was influenced by temperature extremes across seasons. Isotope data revealed a growing contribution of precipitation to winter runoff, indicating potential challenges for catchment water storage under continued warming.

Modeling reservoir outflow and storage is challenging due to limited publicly available data and human decision-making. For 100 reservoirs in the US, we examined how calibrating reservoir algorithms against outflow and storage-related variables affects performance. We found that calibration notably improves storage simulations, while outflow simulations are more influenced by the quality of inflow data. We recommend using remotely sensed storage anomalies to calibrate reservoir algorithms.

The fate of liquid water from melting snow in winter and spring is difficult to understand in the mountains. This work uses a multi-instrumental network to accurately monitor the dynamics of snowmelt and infiltration at different depths in the ground and at different altitudes. The results show that melting snow quickly infiltrates into the upper layers of the soil but is also quickly transferred through the soil along the slopes towards the river.

To address challenges related to unreliable hydrological simulations, we present an enhanced hydrological simulation with a refined climate model and a more comprehensive hydrological model. The model with the two parts outperforms that without, especially in migrating bias in peak flow and dry-season flow. Our findings highlight the enhanced hydrological simulation capability, with the refined climate and lake module contributing 24 % and 76 % improvement, respectively.

Turbidity is a key factor for water quality monitoring. Here, an image-based procedure is tested in a full-scale river monitoring experiment using digital cameras. This approach can enhance our understanding of the real-time status of waterbodies, overcoming the spatial and temporal resolution limitations of existing methods. It also facilitates early-warning systems, advances water research through increased data availability and reduces operating costs.

Continuous and high-quality meteorological datasets are crucial to study extreme hydro-climatic events. We here conduct a comprehensive spatio-temporal evaluation of precipitation and temperature for four climate reanalysis datasets, focusing on mean and extreme metrics, variability, trends, and the representation of droughts and floods over Switzerland. Our analysis shows that all datasets have some merit when limitations are considered, and that one dataset performs better than the others.

Our study focuses on the rising concern of pesticides damaging aquatic ecosystems, which puts drinking water, the environment, and human health at risk. We provided more accurate estimates of how pesticides break down and spread in small water systems, helping to improve pesticide management practices. Using unique chemical markers in our analysis, we enhanced the accuracy of our predictions, offering important insights for better protection of water sources and natural ecosystems.

Rain falling on snow (RoS) can accelerate snowmelt and affect runoff, potentially causing severe flooding. We assessed the regional and seasonal variations in the occurrence of RoS in mountainous catchments in central Europe under different perturbations of future climate. Our results showed that RoS changes driven by climate change vary greatly among regions, across elevations, and within the cold season. However, most projections suggested a decrease in RoS events and RoS-driven runoff.

This study combines the global hydrological model PCRaster Global Water Balance with the World Food Studies crop model to analyze feedbacks between hydrology and crop growth. It quantifies one-way and two-way interactions, revealing patterns in crop yield and irrigation water use. Dynamic interactions enhance understanding of climate variability impacts on food production, highlighting the importance of two-way model coupling for accurate assessments.

Groundwater can be replenished by rainfall that percolates from the surface to the water table. The amount of rainfall that is needed to generate this groundwater recharge is hard to measure. We determined this rainfall amount by identifying recharge events as water percolates from the surface, through a cave. During our monitoring, an intense fire occurred above the cave, and we were able to quantify any change in the amount of rainfall necessary to generate recharge before and after the fire.

Balancing exploration and exploitation in conventional inversion algorithms is challenging. We evaluated the effectiveness of tandem neural network architecture (TNNA) in addressing this challenge. Inversion problems with different heterogeneous scenarios were designed to compare TNNA with four metaheuristic algorithms. Results show that TNNA significantly improves computational efficiency and accuracy, offering a promising framework for developing robust inversion algorithms.

Understanding groundwater flow is crucial for both environmental and economic reasons. In our study, the dynamic interaction of the forces driving groundwater flow is presented, partly in synthetic models and partly in a real deep geothermal reservoir. We point out that ignoring certain driving forces can lead to oversimplification of groundwater flow and even misinterpretation of the phenomena, causing environmental problems or economic losses.

Simulations of peatland rewetting in Sweden under various conditions of climate, local hydrology, and rewetting practices showed insubstantial changes in landscape flow extremes due to mixing with runoff from various landcovers. The impacts on local hydrological extremes are governed by groundwater levels prior to rewetting and reduced tree cover; hence wetland allocation and management practices are crucial if the purpose is to reduce flow extremes in peatland streams.

This study explores how to improve models predicting water flow in southern Vietnam's Saigon and Dongnai rivers, where data are scarce. By testing three different methods to adjust the river model using river water level and river discharge measurements, we found ways of better predicting river behavior. These findings can help manage water resources more effectively and aid in decision-making for flood protection and environmental conservation.