Seasonal streamflow forecasts for snowmelt-dominated catchments often rely on snowpack data, which are not always available and are prone to errors. Our study evaluates near-real-time global snow estimates and climate oscillation indices for predictions in the data-scarce mountains of central Asia. We show that climate indices can improve prediction accuracy at longer lead times, help offset snow data uncertainty, and enhance predictions where streamflow depends on in-season climate variability.

Karst springs respond quickly to environmental changes, making them crucial to understanding climate impacts on groundwater. This study analyses long-term trends in precipitation, temperature, and discharge from more than 50 springs across Europe. Results show that while historical discharge trends align with those of rivers, recent changes are driven by rising temperatures rather than precipitation. These findings highlight climate-driven shifts in groundwater recharge and storage processes.

This study explores the potential of the electrical self-potential (SP) method, a passive geophysical technique, to provide additional insights into tree transpiration rates. We measured SP and sap velocity in three tree species over a year in a Mediterranean climate. Results indicate SP may characterize transpiration rates, especially during dry seasons. Additionally, the electrokinetic coupling coefficients of these trees align with values typically found in porous geological media.

The article presents a temporal and spatial analysis of ice cover occurrence on rivers in the Carpathian Mountains (central Europe) from a regional perspective. The study reveals a decrease in the frequency of ice cover formation and a shift in its structure, characterized by an increased frequency of border ice and a reduced occurrence of total ice cover. These changes in the ice regime of rivers are primarily driven by climatic changes, amplified by the operation of reservoirs.

Suspended sediment is a natural component of rivers, but extreme suspended sediment concentrations (SSCs) can have negative impacts on water use and aquatic ecosystems. We identify the main factors influencing the spatial and temporal variability of annual SSC regimes and extreme SSC events. Our analysis shows that different processes are more important for annual SSC regimes than for extreme events and that compound events driven by glacial melt and high-intensity rainfall led to the highest SSCs.

This review highlights how geological structures like faults and fractures influence groundwater potential in volcanic rocks of Borena Sayint, northwestern Ethiopian Plateau. These structures enhance secondary porosity, affecting aquifer behaviour. The study stresses integrating geological, geophysical, and hydrological data for sustainable groundwater management in tectonically complex volcanic regions.

Thirsty Earth is an educational game where students are farmers making water decisions amid climate uncertainty, common-pool costs, and resource constraints. The game is web-based and adapted for remote learning either as a light Google Sheets version or as a fully interactive graphical interface. It bridges technical water management and governance concepts from the social sciences, providing experiential interdisciplinary learning that traditional science and engineering curricula often overlook.

Groundwater is a crucial resource at risk due to droughts. To understand drought effects on groundwater levels in Germany, we grouped 6626 wells into six regional and two national patterns. Weather explained half of the level variations with varied response times. Shallow groundwater responds fast and is more vulnerable to short droughts (a few months). Dampened deep heads buffer short droughts but suffer from long droughts and recoveries. Two nationwide trend patterns were linked to human water use.

The Desaguadero–Salado–Chadiluevú–Curacó (DSCC) River is a semiarid river which is heavily dammed at its tributaries which collect the snowmelt runoff. This runoff feeds mostly gravitational irrigation systems of very low efficiency. As a result, the DSCC River does not have natural runoff. The proposed hydrological regime index (HRI) is able to discriminate and quantify regime alterations under permanent and non-permanent flow conditions and with low- and high-impoundment conditions.

We evaluate the historical performance and future projections from a large-scale hydrologic model, the Community Water Model, against a watershed hydrologic model, Variable Infiltration Capacity, for the Liard River basin in Canada. Results from the two models are generally consistent at annual and monthly timescales, suggesting that a calibrated global hydrologic model can provide robust projections. We explain the differences in projections in terms of model uncertainties.

We present a novel method for soil water extraction – circulating-air soil water extraction – together with a newly developed apparatus specifically designed for its purposes. The method has been tested across a range of soil types, demonstrating an accuracy level that matches or surpasses that of conventional extraction techniques. The apparatus is optimized for small-scale, high-precision studies in which an unambiguous determination of the water origin is required.

We present directional statistics to characterize seasonality, capturing the timing of streamflow (center of mass timing) and the strength of its seasonal cycle (concentration). Directional statistics are more robust than several widely used metrics to quantify streamflow seasonality. The introduced metrics can improve our understanding of streamflow seasonality and associated changes and can also be used to study the seasonality of other environmental fluxes within and beyond hydrology.

This study explores two methods for predicting soil properties using the frequency domain electromagnetic induction technique in Belgium. We compared deterministic models, which often require extensive data adjustments, to empirical models. Our findings suggest that empirical models are generally more effective for soil analysis, although each method has its limitations. This research helps improve soil property prediction, crucial for agriculture and environmental management.

This study compares long short-term memory (LSTM) neural networks with traditional hydrological models to predict future streamflow under climate change. Using data from 148 catchments, it finds that LSTM models, which learn from extensive data sequences, perform differently and often better than traditional hydrological models. The continental LSTM model, which includes data from diverse climate zones, is particularly effective for understanding climate impacts on water resources.

We measured nitrate-nitrogen concentrations using a UV nitrate sensor over 4 years in a shallow gravel aquifer. We observed pulses of nitrate nitrogen in response to forestry harvesting during subsequent rainfall recharge. Our novel hysteresis analysis helped elucidate event-scale nitrate-nitrogen dynamics, while integrating continuous data improved nitrate-nitrogen load estimates. This research provides new insights into the variability of nitrate nitrogen in permeable aquifers.

In this paper, we use models to demonstrate that even small flood reservoirs – which capture water to avoid floods downstream – can be repurposed to release water in drier conditions without affecting their ability to protect against floods. By capturing water and releasing it once levels are low, we show that reservoirs can greatly increase the water available in drought. Having more water available to the reservoir, however, is not necessarily better for drought protection.

Persistent droughts change how rivers respond to rainfall. Our study of over 5000 catchments worldwide found that hydrological and soil moisture droughts decrease river-flow response to rain, especially in arid regions, while vegetation decline slightly increases it. Snow-covered areas are more resilient due to stored water buffering changes. Droughts can also cause long-lasting changes, with short and intense droughts reducing river response to rainfall and prolonged droughts increasing it.

In semi-arid regions, the timing and duration of the rainy season are crucial for agriculture. This study introduces a new framework for improving estimations of the onset and end of the rainy season by testing how well they fit local vegetation data. We improve the performance of existing methods and present a new one with higher performance. Our findings can help us to make informed decisions about water usage, and the framework can be applied to other regions as well.

Coordinate transformations are often used in the finite element method. Here, three existing approaches are discussed that can be applied to the same mapping problem. From a mathematical viewpoint, the approaches are quite different. It is shown that all three approaches provide the same result. Interestingly, the more mathematically challenging approaches provide an expression that is the most practical to implement in a finite element code.

There is a lack of understanding of how urban aquatic nature-based solutions (aquaNBSs) affect ecohydrology and how they in turn are affected by urbanization and climate change. We use a multi-tracer approach of stable water isotopes, hydrochemistry, and microbial and macrophyte diversity to disentangle the effects of hydroclimate and urbanization. The results show potential limitations of aquaNBSs regarding water quality and biodiversity in response to hydroclimate and urban water sources.

This study introduces a multiagent socio-hydrologic framework for city-, inter-city-, and watershed-scale integrated green infrastructures (GIs) and water resource management. Applied to the Upper Mississippi River basin, it explores GI-driven water-sharing dynamics in a watershed. It identifies four city-scale water use patterns and characterizes cost and equity on broader scales, thereby enhancing comprehension of the role of GIs in water resource management and aiding decision-making.

We assessed the value of high-resolution data and parameter transferability across temporal scales based on seven catchments in northern China. We found that higher-resolution data do not always improve model performance, questioning the need for such data. Model parameters are transferable across different data resolutions but not across computational time steps. It is recommended to utilize a smaller computational time step when building hydrological models even without high-resolution data.

In the last decade, the development of on-site field laboratories to measure water chemistry at sub-hourly measurement intervals has drastically advanced, while there is no literature that provides detailed technical, organisational and operational guidelines in running such equipment. Based on our experiences of running three French field laboratories over 7 years, we share the main stages in the deployment of this tool in the field, the difficulties encountered and the proposed solutions. 

There is an increasing interest in rewetting drained peatlands to regain their important ecosystem functions. However, as peatland rewetting is a relatively new strategy, the scientific foundation for this approach is not solid. Therefore, we investigated the impact of rewetting on flood mitigation using high-resolution hydrological field observations. Our results showed that peatland rewetting significantly reduced peak flow and runoff coefficient and mitigated flashy hydrograph responses.

Global soil information introduces uncertainty into models that simulate soil hydrothermal changes. Using the Noah with Multiparameterization (Noah-MP) model with two different global soil datasets, we find under-represented soil properties in wet loam, causing a dry bias in soil moisture. This bias is more pronounced and drought categories are more severe in the SoilGrids dataset. We conclude that models should incorporate detailed, region-specific soil information to minimize model uncertainties.

The data-driven approach enhances soil water content measurements by improving the precision of cosmic ray neutron sensors (CRNSs). This study demonstrates a new method to account for dynamics in air pressure, atmospheric humidity, and incoming neutron intensity. Soil water content measured by CRNSs showed reduced errors compared to reference measurements. The findings highlight the need for precise adjustments to better measure soil moisture for agricultural, water, and climate monitoring.