This study employs the Darcy-Brinkman-Stokes equations to characterize the coupled seepage process in porous media of the karst aquifer and the free flow process in karst conduit and stream. Results show that the impact of different water retention models on the interaction process is quite significant. The degree of change in water level of the stream also varies, and this change can affect the ease with which different strata media in the karst aquifer recharge the stream.

This study explores how climate change affects river temperatures under hydropower influence using numerical modeling. While average warming is similar to natural rivers, hydropower both increases vulnerability in low-flow areas and helps limit extreme temperatures through cold lake releases in summer. This research helps adapt hydropower production to protect aquatic species in a changing climate.

Environmental changes can cause hydrological model biases that vary over time (nonstationarity). We demonstrate a new calibration framework to detect and correct nonstationary streamflow biases after a large wildfire, which reduces predictive uncertainty and constrains parameter equifinality.

This study assesses how small on-farm reservoirs (OFRs) can impact surface hydrology using a new framework combining remote sensing and hydrological modeling. Findings show that reservoirs can reduce annual flow by 14–24 % and peak flow by 43–60 %, with effects varying by location and reservoir's capacity. This is relevant as the number of OFRs is expected to increase globally as an adaptation to climate change under severe drought conditions.

By coupling PLUS-InVEST models under 24 climate-land scenarios, we constructed a water supply-demand risk (WSDR) assessment framework to quantify impacts of climate change and anthropogenic activities on water resource allocation patterns and associated risks. Results demonstrate that significant cultivated land expansion drives a surge in water demand. The root cause lies in frequent anthropogenic perturbations (land use change), which intensify conflicts between water demand-supply capacity.

By involving soil oxygen dynamics, we explore tile drainage impacts on the coupled hydrology–biogeochemistry–crop system. We find that soil oxygen dynamics is the key mediator of tile–system dynamics. Tile drainage lowers soil water content and improves soil oxygen levels, helping crops grow during wet springs. The developed roots also help mitigate drought stress in dry summers. Overall, tile drainage increases crop resilience to climate change, making it a valuable future agricultural practice.

We developed a new method to improve hydrological modeling by jointly using soil moisture and groundwater level data from field sensors in a catchment in Germany. By updating the model separately for shallow and deep soil zones, we achieved more accurate predictions of soil water, groundwater depth, and evapotranspiration. Our results show that combining both data types gives more balanced and reliable outcomes than using either alone.

We describe a stream monitoring method using low-cost cameras and a deep learning model. It produces a relative hydrograph (0–100%). We applied the method to 11 cameras at 8 sites and found model performance sufficient to describe floods and droughts. The models were trained on image pairs annotated by people. We examined how well people performed annotations and how many annotations were needed. We concluded this method can be used to gain new insights into under-monitored small streams.

Using a sediment core and hydroclimatic data, this study assesses how past environmental changes contribute to the growing vulnerability of hydropower in southern France.

Climate change is altering the water cycle in mountain regions as glaciers melt, but slower-degrading rock glaciers remain influential. This study examines how a rock glacier in Yukon, Canada, interacts with a riverbed, using a combination of remote sensing and hydrochemical methods. It shows that rock glaciers shape river channels, affect groundwater flow, and encourage ice formation in winter.

Using machine learning techniques and remotely sensed reservoir data, we develop a workflow to derive reservoir storage bounds. We put these bounds in a global hydrologic model, PCR-GLOBWB 2, and evaluate the difference between generalized operations (the schemes typically in global models) and this data derived method. We find that modelled storage is more accurate in the data derived operations. We also find that generalized operations over estimate storage and can underestimate water gaps.

Some streams respond to rainfall with flow that peaks twice: a sharp first peak followed by a broad second peak. We analyzed data from a catchment in Luxembourg to better understand the processes behind this phenomenon. Our results show that the first peak is mostly driven directly by rainfall, and the second peak is mostly driven by rain that infiltrates to groundwater. We also show that the relative importance of these two processes depends on how wet the landscape is before the rain falls.

This research explores different modelling approaches within a distributed and physically based hydrological model, emphasizing configurations that integrate groundwater recharge and dynamics. The study demonstrates that precise adjustments in model calibration can enhance the accuracy and representation of hydrological variables, which are crucial for effective water management and climate change adaptation strategies.

Measurements of water isotopes are often used to estimate water transit time distributions and aquifer storage thickness in catchments. However, laboratory-scale measurements show that water isotopes exhibit transport behavior identical to that of inert chemical tracers rather than of pure water. The measured mean tracer and apparent mean water velocities are not necessarily equal; recognition of this inequality is critical when estimating catchment properties such as aquifer storage thickness.

Commercial microwave links (CMLs), part of mobile phone networks, transmit comparable signals as instruments specially designed to estimate evaporation. Therefore, we investigate if CMLs could be used to estimate evaporation, even though they have not been designed for this purpose. Our results illustrate the potential of using CMLs to estimate evaporation, especially given their global coverage, but also outline some major drawbacks, often a consequence of unfavourable design choices for CMLs.

The Atlantic Meridional Overturning Circulation (AMOC) moderates the European climate. The AMOC is a tipping element and may collapse to a substantially weaker state under climate change. Such an event induces global and regional climate shifts. The European hydroclimate becomes drier under an AMOC collapse, this response is not considered in the standard hydroclimate projections. Our results indicate a considerable influence of the AMOC on the European hydroclimate.

We propose a method to estimate the reservoir water level–storage volume (WLSV) curve based on the capacity loss induced by sediment accumulation and assess the potential negative impact caused by outdated design WLSV curve on flood regulation risks. The findings highlight that when storage capacity is considerably reduced, continued use of design WLSV curves may significantly underestimate, thus posing potential safety hazards to the reservoir itself and downstream flood protection objects.

This study proposes a framework combining robustness and adaptiveness for long-term flood planning. Applied to Shanghai, it shows that the most cost-effective option may not meet long-term goals, and a combination of green spaces, drainage, and tunnels outperforms alternative options. The findings emphasize that flexibility and adaptability are critical for developing robust, long-term adaptation pathways and minimizing future risks in other urban areas.

During summer drought, a clear sub-daily cycling of atmospheric water vapour isotopes (δ_v) and plant xylem water isotopes (δ_xyl) was observed. δ_v daytime depletion was driven by evaporation and local atmospheric factors (entrainment). δ_xyl daytime enrichment was consistent with high vapor pressure deficit and stomatal regulation of transpiration. This sub-daily dataset provides unique insights on sub-daily cycling of stable water isotopes and can help constrain ecohydrological models.

Dams and reservoirs in karst areas often struggle with significant leakage, making construction both challenging and costly. This study uses a numerical model to show how karst aquifers in water divide regions evolve to form low-karstified rock-blocks (LKB). It also explores how and when these LKBs can significantly reduce leakage across the water divides if a reservoir is built on one side.

We improved the widely used SWAT (Soil and Water Assessment Tool) model to better predict how pesticides move through the environment. We added a new process that considers how plants take-up chemicals from the soil. Testing this updated model in two catchments showed very good prediction capabilities and a reduction of chemicals in river water by up to 17 % due to the plant uptake. The enhanced model offers a valuable tool for assessing the environmental impacts of agricultural management.

On 29 October 2024 Valencia (Spain) was struck by torrential rainfall, triggering devastating floods in this area. In this study, we quantify and describe the spatial and temporal structure of this rainfall event using personal weather stations (PWSs). These PWSs provide near real-time observations at a temporal resolution of ~5 min. This study shows the potential of PWSs for real-time rainfall monitoring and potentially flood early warning systems by complementing dedicated rain gauge networks.

In hydrology, domain dependence (DD) of spatial Principal Component patterns is a rather unknown feature of the widely applied Principal Component Analysis. It easily leads to wrong hydrological interpretations. DD reference patterns enable to differentiate from the effect. Here, we (1) explore the DD effect, (2) present two methods to calculate DD reference patterns and (3) discuss considering DD. Scripts with an introduction to the DD effect and an implementation of both methods are provided.

Glaciers are vital water sources, especially in alpine regions. Using the Glacier Evolution Runoff Model (GERM), we examined how forcing data and model resolution impact glacio-hydrological model results. We find that precipitation biases greatly affect results, and coarse resolutions miss critical small-scale details. This highlights the trade-offs between computational efficiency and model accuracy, emphasizing the need for high-resolution data and precise calibration for reliable predictions.

We studied a severe compound drought and heatwave event in the Adige River basin in May 2022 and found that similar events are now hotter and drier due to current warming. These changes worsen water stress and river drying. We show that timing matters: events in June are now more critical than in April, as the snowmelt contribution to streamflow in June has become much lower than in the past. However, many climate models still fail to capture these changes.