When water infiltrates into a snowpack, it alters the thermal state of the system. This work presents a first-of-its-kind multi-phase heat transfer model for local thermal non-equilibrium scenarios of water infiltration into an existing snowpack, such as during rain-on-snow events. The model can be used to calculate the formation of ice layers, as well as partial melting of the snow. Hence, it can support hazard assessment for flash floods and snow avalanches.

Coastal urban areas are highly prone to flooding from rainfall, storm surge, and the combination of both. We improve a coastal model and use it to quantify flooding from Hurricane Ida in the Jamaica Bay watershed of New York City (NYC), creating a flood map and flooded area estimation. Experiments with shifted storm tracks and rainfall timing at high tide show that Ida, already the worst rainfall in NYC, could have been worse. This highlights the area's vulnerability and the need for thorough flood risk analysis.

Hydrometeorological forecasting is crucial for managing water resources and mitigating extreme weather events, yet current long-term forecast products are often embedded with uncertainties. We develop a deep-learning-based modelling framework to improve 30 d rainfall and streamflow forecasts by combining advanced neural networks and physical models. With the flow forecast error reduced by up to 33 %, the framework has the potential to enhance water management and disaster prevention.

Hydrological droughts affect ecosystems and socioeconomic activities worldwide. Despite the fact that they are commonly described with the Standardized Streamflow Index (SSI), there is limited understanding of what they truly reflect in terms of water cycle processes. Here, we used state-of-the-art hydrological models in Andean basins to examine drivers of SSI fluctuations. The results highlight the importance of careful selection of indices and timescales for accurate drought characterization and monitoring.

This paper presents a method to solve a certain type of equation controlling the storage of water in hydrological models. This equation is often solved with complex numerical methods that may lead to slow runtimes. The method, called the Quadratic Solution of the Approximate Reservoir Equation (QuaSoARe), is both fast and applicable to any equation of this kind regardless of its complexity. The method reduces runtime by a factor of 10 to 50 depending on the model.

This study explores using drone-acquired digital terrain models (DTMs) for flood modeling of mountainous urban rivers. Terrain analysis indicates that a DTM resolution of 1 m–5 m is optimal for accurate flood modeling. In larger cities with non-extreme discharge volumes, a resolution of 5 m–10 m can suffice for modeling inundation areas, although it may slightly overestimate flood depth. This provides a balance between model accuracy and processing costs.

Accurate early-warning systems are crucial for reducing the damage caused by flooding events. In this study, we explored the potential of long short-term memory networks for enhancing the forecast accuracy of hydrologic models employed in operational flood forecasting. The presented approach elevated the investigated hydrologic model’s forecast accuracy for further ahead predictions and at flood event runoff.

Water infiltration systems in peat meadows commonly reduce groundwater level. Groundwater level fluctuations induce soil volume decreases and increases in both the saturated and unsaturated zone, causing yearly vertical soil movement dynamics of up to 10 cm. Multiyear subsidence rates are of the order of millimeters per year. Such research is vital to increase knowledge of subsidence processes and develop effective measures to reduce land subsidence and greenhouse gas emissions.

Common intuition holds that higher input data resolution leads to better results. To assess the benefits of high-resolution data, we conduct simulation experiments using data with various temporal resolutions across multiple catchments and find that higher-resolution data do not always improve model performance, challenging the necessity of pursuing such data. In catchments with small areas or significant flow variability, high-resolution data is more valuable.

A warming climate alters the freshwater availability over land, and, due to related tree cover change and potential forestation, this availability can be further enhanced or negated. We find that large-scale change in tree cover may counteract climate-driven changes on a global scale, whereas, regionally, the climate and tree cover impacts can differ extensively. Current ecosystem restoration projects should account for the effects of (re-)forestation on (non-)local water availability.

This study examined how water availability, climate dryness, and plant productivity interact at the catchment scale. Using various indices and statistical methods, we found a 0–2-month lag in these interactions. Strong correlations during peak-productivity months were observed, with a notable hysteresis effect in vegetation response to changes in water availability and climate dryness. The findings help better understand catchment responses to climate variability.

This study takes a data-driven approach to evaluate long-term groundwater nitrate concentration trends in Wallonia, Belgium, over ~20 years following the implementation of regional nitrogen management policies. Results highlight a persistently negative impact of agricultural land use, the importance of sustained policies and long-term monitoring to mitigate nitrogen legacy effects, and the need for improved datasets to better capture controlling factors. 

Potato farmers in Switzerland are facing drier conditions and water restrictions. We explored how improving soil health and planting early-maturing potato varieties might help them to adapt. Using a computer model, we simulated potato yields and irrigation water needs under water scarcity. Our results show that earlier-maturing potato varieties reduce the reliance on irrigation but result in lower yields. However, improving soil health can significantly reduce yield losses.

This research explores the use of a new isotope tracer, ¹⁷O excess, to better understand how hydrological processes drive large seasonal water level changes in small lakes in semiarid regions. The study shows that triple oxygen isotopes offer a more detailed understanding of these changes compared to traditional methods. These findings are valuable for reconstructing past climates and predicting how climate change, influenced by human activity, will affect small lakes in these dry areas.

Root water uptake strongly affects plant development and soil water balance. We use novel upscaling methods to develop land surface and crop models from detailed mechanistic models. We examine the mathematics behind this upscaling, pinpointing where errors occur. By simulating different crops and soils, we found that the accuracy loss varies based on root architecture and soil type. Our findings offer insights into balancing model complexity and accuracy for better predictions in agriculture.