Spatial Pattern Regression (SPR) is a new way to reconstruct daily weather fields in regions with few measurement stations. Our approach combines information from past high-resolution simulations with available observations to produce more accurate maps of precipitations and temperature. Tests on both synthetic and real data show clear improvements over common methods, especially when stations are sparse, helping support better hydrological and climate studies.

Groundwater drought impacts depend on rainfall deficits, aquifer storage, and human activities. We analyzed rainfall and groundwater data from three aquifers to compare drought timing, duration, recovery, and spatial patterns. The deepest aquifer had the longest deficits and showed no signs of recovery during the study period. Our framework can support groundwater monitoring and management under combined climate and human pressures.

This study evaluates the ability of the convection-permitting regional climate model AROME (Applications of Research to Operations at Mesoscale) to reproduce precipitation extremes and their trends, at daily and hourly scales in France, over 1959–2022. Overall, the results highlight the value of the explicit-convection model for extreme precipitation studies, while also highlighting its limitations for convective extremes.

We studied how well simple bucket-type models can reproduce observed river flow using large data sets from many regions in the world. Model performance varies widely depending on local conditions, so fixed performance thresholds are misleading. To better judge model performances, we propose lower and upper benchmarks. These benchmarks help to better understand what level of model performance is achievable and, thus, enable us to compare models across different catchments.

Climate change strongly modifies runoff in alpine regions like Switzerland, altering water supply and affecting both human sectors and natural ecosystems. We test whether a model that learns only from past observations can credibly project future runoff under climate scenarios. It captures major expected shifts and reveals known limits for extremes. Its speed and ability to generalize across catchments make it a promising tool for assessing climate impacts on water resources.

Snow and rainfall measurements in mountain regions often underestimate the true amounts. We developed a method to correct Austrian precipitation data using weather stations and terrain features. The corrected data greatly improves runoff, snow and glacier modeling. This work shows that considering measurement errors is important for accurately assessing water resources, which is increasingly relevant as climate change affects alpine water systems supplying hydropower and downstream communities.

We used machine learning to explore what causes streamflow droughts across the U.S. We found that different regions are influenced by different factors like temperature, snow, and rainfall. Our new method can also predict droughts in areas without streamflow data, helping improve water resource planning.

Rainfall-runoff event identification can be highly affected by parameter-driven uncertainty and event definitions that do not always reflect hydrological processes. We introduce RVEIM, a parsimonious method for event identification that improves both robustness and physical plausibility. Applied across 467 Australian catchments, RVEIM reveals systematic shifts in event runoff coefficient distribution, demonstrating how event-scale rainfall-runoff relationships vary with climate regime.

This study presents a new high-resolution precipitation dataset covering North America over the past 45 years. Unlike most reanalysis products that rely mainly on weather models, it directly incorporates ground-based weather station observations. This results in more reliable precipitation estimates than the previous version and commonly used reanalyses, especially in regions with few measurements, supporting studies of water resources, floods, droughts, and climate impacts.

This paper applies a coupled 3D surface–subsurface model to a degraded fen peatland using long-term field data on evapotranspiration, groundwater, vegetation, and ditch water levels. By explicitly representing layered peat degradation and vegetation-controlled evapotranspiration, it enables consistent quantification of all water-balance components. The results provide process-based insights relevant for peatland rewetting under climate variability.

Groundwater plays a crucial role in maintaining baseflow in rivers and ensuring water supply, particularly in alpine regions where the freeze–thaw cycle exerts a strong influence. This study takes the Qinghai Lake basin as a case example to quantify the dynamic characteristics of groundwater recharge sources and pathways during the FT periods. Our research demonstrates that in alpine permafrost regions, freeze-thaw processes regulate water storage and transport.

A HydroGeoSphere model which represents surface and groundwater is used to assess trends from 2002–2019 in water resources in Alberta, Canada and the driving factors behind these changes. Satellite-derived gravity data is compared to HydroGeoSphere model results; a strong correlation is identified. Components of water storage are assessed, namely groundwater, soil moisture, surface water, and snow. Fluctuations in water storage in Southern Alberta are linked to global climatic indices.

We sought to test and improve the modeling tools used to quantify evapotranspiration (ET) in urban bioretention basins, where plant size and shade vary widely. Using direct measurements and spatially detailed data from a basin in Philadelphia, we found that common models often produce ET estimates that differ substantially from empirical estimates. Simple corrections to standard models greatly improve agreement, helping cities better predict green stormwater infrastructure performance.

How plants cope with water stress is relevant when studying plant water sources in soil. We associated two techniques for measuring the content in water stable isotopes in the stem of sunflower plants and for locating where they take up water in a non-destructive manner. We highlight the role of stem water as a source of water to transpiration flux.