This paper introduces a method to identify irrigated areas by combining hydrology models with satellite temperature data. Our method was tested in the Rhine basin and aligns well with official statistics. It performs best in regions with large farms and less well in areas with small farms. Observed differences to existing data are influenced by data resolution and methods.

We investigate the freeze–thaw cycles of a rock glacier located in Switzerland and their influence on subsurface hydrology. By analyzing aerial pictures, we estimate the evolution of its creeping velocity on an inter-annual scale. We use geochemical tracers measured at springs to identify the mixing of meltwater and deep groundwater on seasonal to diurnal timescales. This study provides new insights into the cryo-hydrogeological processes that regulate water fluxes in mountain regions.

Extreme floods are more common than expected. Knowing where these floods are likely to occur is key for risk management. Traditional methods struggle with limited data, causing uncertainty. We use common streamflow dynamics to indicate extreme flood propensity. Analyzing data from Atlantic Europe, northern Europe, and the US, we validate this novel approach and unravel intrinsic linkages between regional geographic patterns and extreme flood drivers.

This study determines the value of subsurface water for ecosystem services' supply in an agricultural watershed in Ontario, Canada. Using a fully integrated water model and an economic valuation approach, the research highlights subsurface water's critical role in maintaining watershed ecosystem services. The study informs on the sustainable use of subsurface water and introduces a new method for managing watershed ecosystem services.

We consider how well one can identify hydraulic conductivity that varies from place to place by using only measurements obtained at a finite number of groundwater monitoring wells. In particular, we relate how accurately features (meaning connected high- or low-conductivity regions) are identified to their size and to well spacing, and we examine which kinds of information are most valuable. When feature size exceeds 4 times the well spacing, better-than-random identification is possible.

Our research compares two techniques, bias correction (BC) and data assimilation (DA), for improving river flow forecasts across 316 UK catchments. BC, which corrects errors after simulation, showed broad improvements, while DA, adjusting model states before forecast, excelled under specific conditions like snowmelt and high baseflows. Each method's unique strengths suit different scenarios. These insights can enhance forecasting systems, offering reliable and user-friendly hydrological predictions.

Our study projects how climate change will affect the drying of river segments and stream networks in Europe, using advanced modelling techniques to assess changes in six river networks across diverse ecoregions. We found that drying events will become more frequent and intense and will start earlier or last longer, potentially turning some river sections from perennial to intermittent. The results are valuable for river ecologists for evaluating the ecological health of river ecosystem.

Deforestation has a significant impact on climate, yet its effects on drought remain less understood. This study investigates how deforestation affects drought across various climate zones and timescales. Findings indicate that deforestation leads to drier conditions in tropical regions and wetter conditions in arid areas, with minimal effects in temperate zones. Long-term drought is more affected than short-term drought, offering valuable insights into vegetation–climate interactions.

We studied how soil and weather data affect land model simulations over Africa. By combining soil data processed in different ways with weather data of varying time intervals, we found that weather inputs had a greater impact on water processes than soil data type. However, the way soil data were processed became crucial when paired with high-frequency weather inputs, showing that detailed weather data can improve local and regional predictions of how water moves and interacts with the land.

We generate operational forecasts of daily maximum stream water temperature for 32 consecutive days at 54 stations in Switzerland with our best-performing data-driven model. The average forecast error is 0.38 °C for 1 d ahead and increases to 0.90 °C for 32 d ahead given the uncertainty in the meteorological variables influencing water temperature. Here we compare the skill of several models, how well they can forecast at new and ungauged stations, and the importance of different model inputs.

The quantification of precipitation into evaporation and runoff is vital for water resources management. The Budyko framework, based on aridity and evaporative indices of a catchment, can be an ideal tool for that. However, recent research highlights deviations of catchments from the expected evaporative index, casting doubt on its reliability. This study quantifies deviations of 2387 catchments, finding them minor and predictable. Integrating these into predictions upholds the framework's efficacy.

In this article, we show that by taking the optimal parameters calibrated with a semi-lumped model for the discharge at a catchment's outlet, we can accurately simulate runoff at various points within the study area, including three nested and three neighboring catchments. In addition, we demonstrate that employing more intricate melt models, which better represent physical processes, enhances the transfer of parameters in the simulation, until we observe overparameterization.

Long short-term memory (LSTM) networks have demonstrated state-of-the-art performance for rainfall-runoff hydrological modelling. However, most studies focus on predictions at a daily scale, limiting the benefits of sub-daily (e.g. hourly) predictions in applications like flood forecasting. In this study, we introduce a new architecture, multi-frequency LSTM (MF-LSTM), designed to use inputs of various temporal frequencies to produce sub-daily (e.g. hourly) predictions at a moderate computational cost.

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.

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.