Deep learning models achieve strong results in hydrological simulations but often lack links to physical processes. We integrate the principles of the Xinanjiang rainfall–runoff model into a recurrent neural network layer, then combine it with long short-term memory layers. This design improves accuracy while keeping the model explainable, showing small errors in flood peaks and timing.

With glacier retreat, barren areas are exposed to life settlement. Biofilms, surface attached colonies of microbes, are amongst the first colonizers. In low flow streams, they grow into millimeter thick mats gluing fine sediments together. We studied how such bio-clogging enhance water availability on lateral terraces, suffering from water scarcity. Mat permeability was quantified from streamside flume experiments and an idealized terrace model was conceived to estimate stream elongation.

We show that geological maps with varying levels of detail may influence the identification of geology–streamflow relationships across European catchments at multiple scales. At the large scale, controls varied between basins, with no map consistently superior. At the intermediate and small scales, however, higher geological detail consistently strengthened correlations, particularly for baseflow signatures, with the regional map highlighting controls more consistent with process understanding.

In drylands, rainfall mainly falls during short-lived and localised storms, with the rainfall characteristics of these storms key in controlling how water moves through the landscape. But most climate models cannot represent dryland storms and their characteristics accurately. By using a simple hydrological model at four sites in the Horn of Africa (HOA), we show that using a model that can represent these storms results in higher soil moisture for plants and groundwater for humans.

Water use is often estimated with coarse data that overlook spatial heterogeneity, limiting effective water planning. This study proposes a framework to simulate water use at multiple spatial scales across China, combining a grid-based approach and uncertainty analysis. It finds that both the model structure and spatial scale affect. The framework reveals detailed patterns in water use and can guide smarter water resources management.

Mixing of fresh groundwater and circulating seawater below beaches triggers water-rock chemical reactions and may affect coastal water quality. The subsurface of so-called high-energy beaches that are exposed to high tides, waves and storm floods are understudied as monitoring under these conditions is difficult. For the first time, this study quantifies the subsurface flow and mixing processes of a high-energy beach with the help of computer simulations based on an extensive set of field data.

Process representation in hyper-resolution large-scale hydrological models (LHMs) limits model performance, particularly in mountain regions. Here, we update mountain process representation in an LHM and compare different meteorological forcing products. Structural and parametric changes in snow, glacier, and soil processes improve discharge simulations, while meteorological forcing remains a major control on model performance. Our work can guide future development of LHMs.

Tropical forests supply the water needs of millions of people around the world. Hydrological intermittency, defined as the cessation of river and stream flow, reduces the ability of these forests to provide continuous flow throughout the year. However, there is a lack of knowledge about the factors that cause hydrological intermittency in tropical forests. The results reveal that geology is a key factor causing hydrological intermittency in the Ecuadorian forest of the Chocó-Darién ecoregion.

Our research addresses the challenge that bioclogging poses to the operation of Soil Aquifer Treatment (SAT) systems. A conceptual model that considers the dynamic interactions between microbial activity, water flow, and soil clogging is presented and studied to optimize SAT operation. Simulations show that tweaking wet and dry periods enhance water infiltration and SAT performance. Additionally, a link between the biomass spatial distribution and the wet and dry cycles was discovered.

How does climate change impact floods in France? Using simulations for 3727 rivers with climate projections, results show that flood trends vary depending on the region. In the north, floods may become more severe, but in the south, the trends are mixed. Floods from intense rainfall are becoming more frequent, while snowmelt floods are strongly decreasing. Overall, the study shows that understanding what causes floods is key to predicting how they are likely to change with the climate.

This study improves how we measure the slow, steady flow of water in rivers, known as baseflow, which is vital for ecosystems and water supply. By combining chemical tracers with data-filtering techniques, the research offers a more accurate way to separate baseflow from fast runoff. This approach helps scientists better track water movement and manage water resources, especially during dry seasons and in changing climates.

This study presents a method to make the spatial resolution of global Water Storage Compartments (WSCs) compatible with terrestrial water storage (TWS) data from Gravity Recovery And Climate Experiment (GRACE) missions. The method compares the spatial structure of the WSCs and TWS by considering the correlation between neighboring grid cells. An isotropic Gaussian filter with an optimal filter width of 250 km is found to be the most suitable, ensuring compatibility for consistent comparison with GRACE data in hydrological applications.

Our study presents a method to estimate high-resolution snow water equivalent (HR-SWE) using low-resolution climate data (LR-CD). By using a data-driven approach, we analyze historical weather patterns from LR-CD to generate HR-SWE maps. Machine learning and statistical relationships between LR-CD and HR-SWE enable estimation for dates without HR-SWE data. This method enhances water resource management and climate impact assessments, especially in data-scarce regions.

Water resources face more challenges from climate change and human activities. We improved global water modeling by developing a high-resolution system using Soil and Water Assessment Tool (SWAT+), using automated reproducible workflow. This approach simplifies tracking the progress of global impact assessment modelling efforts. The global model will further help assess water stress hot-spots and inform sustainable water management as further improvements come.

Groundwater in agricultural regions can transport nutrients and contaminants into rivers, affecting water quality. This study examines nutrient flux in the German Elbe River using multi-environmental tracers. Groundwater takes a few decades to reach the river, mostly infiltrating after 1985. This means that massive nutrient inputs from past fertilization have peaked and will decline in the future. These findings guide management strategies to reduce eutrophication and protect aquatic ecosystems.

An improved understanding of land-atmosphere coupling processes during flash (rapid-onset) droughts is needed to aid the development of forecasts for these events. We use satellite observations to investigate the surface energy budget during flash droughts globally. The most intense events show a perturbed surface energy budget months before onset. In some regions, vegetation observations 1–2 months before onset provide information on the likelihood of heat extremes during an event.

This study explores how combining different model types improves streamflow predictions, especially in data-sparse scenarios. By integrating two highly accurate models with distinct mechanisms and leveraging multiple meteorological datasets, we highlight their unique strengths and set new accuracy benchmarks across spatiotemporal conditions. Our findings enhance the understanding of how diverse models and multi-source data can be effectively used to improve hydrological predictions.

In this study we evaluate the performances of observation-based and climate model-based evapotranspiration estimations and project future changes for evapotranspiration in Australia. Our results show that climate models can provide reasonably accurate estimations, compared to observation-based estimations. This study offers new insights into future water loss and demand changes in Australia with implications for agriculture production, water security, and environmental management.

Using global and regional datasets, we compared attention-based models and Long Short-Term Memory (LSTM) models to predict hydrologic variables. Our results show LSTM models perform better in simpler tasks, whereas attention-based models perform better in complex scenarios, offering insights for improved water resource management.