In this contribution, the East Anatolian Fault Zone groundwater observation data over the past 13 years are compared with the groundwater chemical composition after the double earthquakes in 2023 to tracing the origin of geothermal fluid, restore the water-rock interaction process, and evaluate the influence of seismic activity on the geothermal fluid circulation process.

Earth scientists assume that evaporation depends on the gradient in water vapour concentration (Fick’s law), but this is only true in an isothermal system. Temperature gradients can impact evaporation via the Soret effect. Here we evaluate the relative magnitude of the Soret effect and find that it is at least 2 orders of magnitude smaller than classical concentration-dependent mass (“Fickian”) diffusion. This result justifies the standard practice of assuming evaporation follows Fick’s law.

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.

The availability of high-resolution rainfall data is limited. This study presents a method to generate hourly and 1 km gridded rainfall data for detailed hydrodynamic flood modelling purposes, using point measurements and thin-plate spline interpolation. The analysis shows that the proposed dataset outperforms other gridded datasets in representing spatial distributions and daily and hourly variations of rainfall. The data is suitable for any study where high-resolution rainfall data is needed.

We used a new method to measure how streamflow responds to precipitation across a network of watersheds in Iran. Our analysis shows that streamflow is more sensitive to precipitation when groundwater levels are shallower, climates are more humid, topography is steeper, and drainage basins are smaller. These results are a step toward more sustainable water resource management and more effective flood risk mitigation.

We present a neural network model that estimates river discharge based on gridded elevation, precipitation, and solar radiation. Some instances of our model produce more accurate forecasts than the European Flood Awareness System (EFAS) when simulating discharge with lead times of 50 days on the Neckar river network in Germany. It consists of multiple components that are designed to model distinct sub-processes. We show that this makes the model behave in a more physically realistic way.

Our study compares neural network models for predicting discharge in ungauged basins. We evaluated convolutional neural networks (CNNs), long short-term memory (LSTM) and gated recurrent units (GRUs) using 28 years of weather data. CNNs showed the best accuracy, while GRUs were faster and nearly as accurate. Adding static features improved all models. The research enhances flood forecasting and water management in regions lacking direct measurements, offering efficient and accurate predictive tools.

In water-scarce regions, precipitation is a highly variable and essential resource for crop production. Developing countries like India have an uneven distribution of rain gauges, so reliance on satellite- and reanalysis-based precipitation products is critical for their prudent management. Hence, this study statistically evaluated different precipitation products against station data for water-scarce regions in Tamil Nadu and found that the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 Land (ERA5-Land) performed the best, followed by the Multi-Source Weighted-Ensemble Precipitation (MSWEP).

Understanding groundwater flow is crucial for both environmental and economic reasons. In our study, the dynamic interaction of the forces driving groundwater flow is presented, partly in synthetic models and partly in a real deep geothermal reservoir. We point out that ignoring certain driving forces can lead to oversimplification of groundwater flow and even misinterpretation of the phenomena, causing environmental problems or economic losses.

A systematic laboratory experiment elucidates two-phase heat transport due to water flow in saturated porous media to understand thermal propagation in aquifers. Results reveal delayed thermal arrival in the solid phase, depending on grain size and flow velocity. Analytical modeling using standard local thermal equilibrium (LTE) and advanced local thermal non-equilibrium (LTNE) theory fails to describe temperature breakthrough curves, highlighting the need for more advanced numerical approaches.

Data-driven techniques have shown the potential to outperform process-based models in rainfall–runoff simulations. Hybrid models, combining both approaches, aim to enhance accuracy and maintain interpretability. Expanding the set of test cases to evaluate hybrid models under different conditions, we test their generalization capabilities for extreme hydrological events.

Here we propose a new mathematically robust and cost-effective model to improve glacial lake water storage estimation. We have also provided a dataset of measured water storage in glacial lakes through field depth measurements. Our model incorporates an automated calculation process and outperforms previous ones, achieving an average relative error of only 14 %. This research offers a valuable tool for researchers seeking to improve the risk assessment of glacial lake outburst floods.

Soil loss results from the hydrological and hydromechanical responses. The soil loss intensity theoretically relates to the soil water storage and the suction stress. This work contributes to exhibiting the close relationship between erosion intensity in the bed and on the slope and the soil water storage and suction stress. The results of this work highlight that antecedent precipitation should be considered in predicting soil loss in the permanent gully rather than solely from rainfall amount.

This study provides the first assessment of an European meteorological dataset (E-OBS) for hydrological applications. We compared the dataset to meteorological datasets developed at a country level, and tested how the different data influenced the simulation of streamflow with hydrological model. Our findings show that, despite some limitations, the European dataset offers a reasonable basis for hydrological modelling in most river catchments across Europe.

This work presents a new approach to simulate sewer drainage effects from incomplete information in the context of urban flooding, given missing information like flow directions and nodal depths. Tested in Yinchuan, China, our approach exhibits high accuracy in reproducing flood depths and reliably outperforms existing methods in various rainfall scenarios. Our method offers a reliable tool for cities with limited sewer data to improve flood simulation performance.

To study floods and droughts that are likely to change in the future, we use climate projections from climate models. However, we first need to adjust the systematic biases of these projections at the catchment scale before using them in hydrological models. Our study compares statistical methods that can adjust these biases but specifically for climate projections that enable a quantification of internal climate variability. We provide recommendations on the most appropriate methods.

Statkraft requires projections of future streamflow to plan hydropower investments. Setting up a hydrological model for new regions can be too time-consuming to meet the often short delivery deadlines. We have developed an interpretable machine learning method that links streamflow to precipitation and temperature, and can serve as a first screening approach. This method is then applied to climate model simulations of precipitation and temperature to obtain streamflow projections for Brazil.

Existing correction methods may introduce large errors, and more seriously cause unrealistic negative values in P, ET and Q in up to 10 % of cases. A novel IWE-Res method is proposed to improve the accuracy and consistency of corrected satellite-based water budget component data. In most river basins (except cold regions), the best correction is achieved by adjusting 40 % to 90 % of the total water imbalance error.

How drought impacts communities is complex and not yet fully understood. We examined a disaster dataset and compared various drought measures to pinpoint affected regions. Our new combined drought indicator (CDI) was found to be the most effective in identifying drought events compared to other traditional drought indices. This underscores the CDI's importance in evaluating drought risks and directing attention to the most impacted areas.

Mining activities threaten aquatic ecosystems, soil ecosystems, and human health worldwide. This study establishes a high-quality database and a national 0.5° gridded dataset to reveal the status and spatial pattern of mining-affected water pollution, human health risks, and potential multifaceted challenges in China. It provides in-depth insights to guide policymakers in designing differentiated management strategies for the sustainable development of mines.

This paper reformulates the model equations of the distributed Xinanjiang hydrological model in fully differential form and incorporates two-dimensional slope runoff routing methods, which are solved using appropriate numerical techniques. The main contribution is to provide an approach for distributed hydrological models that have evolved from lumped counterparts to reduce inherited numerical errors and to improve terrain representation, thereby enhancing their physical realism and simulation accuracy.

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.

Water budget non-closure is a widespread phenomenon among multisource datasets which undermines the robustness of hydrological inferences. This study proposes a Multisource Dataset Correction Framework grounded in Physical Hydrological Process Modelling to enhance water budget closure, termed PHPM-MDCF. We examined the efficiency and robustness of the framework using the CAMELS dataset and achieved an average reduction of 49 % in total water budget residuals across 475 CONUS basins.

This study finds three cycles in yearly rainfall worldwide of approximately 13, 20 and 28 years. The cycles rise and fall together across continents and also appear in the El Niño–Southern Oscillation (ENSO), a major climate driver of rain. However the signal in ENSO is too small to explain the strong local influence, the results point to another, still-unknown force that may shape both the climate modes and global rainfall.

This study constructed a backpropagation neural network surrogate–artificial hummingbird algorithm inversion framework to accurately and synergistically identify the pollution source information and hydrogeological parameters, which provided a reliable basis for groundwater contamination remediation and management.

This study presents a multi-method framework assessing managed aquifer recharge (MAR) and extraction effects on groundwater dynamics in a typical temperate semi-arid fissured karst system, Jinan's Baotu Spring area. Results show MAR and extraction significantly influence karst groundwater levels and quality. The complementary techniques enhance the quantitative research accuracy and provide practical references for MAR in karst regions with similar hydrogeological conditions.

The 2021 drought in Quebec showcased the province’s potential vulnerability. This study uses a storyline approach to explore the impacts of future extreme droughts under +2 °C and +3 °C global warming scenarios. Results show that future droughts similar to that of 2021 could worsen significantly, with water levels dropping for longer periods and impacting ecosystems and human activities.

Understanding the changes in water movement in earth is crucial for everyone. To quantify this water movement there are several techniques. We examined how different methods of estimating evaporation impact predictions of various types of water movement across Europe. We found that, while these methods generally agree on whether changes are increasing or decreasing, they differ in magnitude. This means selecting the right evaporation method is crucial for accurate predictions of water movement.

Groundwater can be replenished by rainfall that percolates from the surface to the water table. The amount of rainfall that is needed to generate this groundwater recharge is hard to measure. We determined this rainfall amount by identifying recharge events as water percolates from the surface, through a cave. During our monitoring, an intense fire occurred above the cave, and we were able to quantify any change in the amount of rainfall necessary to generate recharge before and after the fire.

This study combines the global hydrological model PCRaster Global Water Balance with the World Food Studies crop model to analyze feedbacks between hydrology and crop growth. It quantifies one-way and two-way interactions, revealing patterns in crop yield and irrigation water use. Dynamic interactions enhance understanding of climate variability impacts on food production, highlighting the importance of two-way model coupling for accurate assessments.

The fate of liquid water from melting snow in winter and spring is difficult to understand in the mountains. This work uses a multi-instrumental network to accurately monitor the dynamics of snowmelt and infiltration at different depths in the ground and at different altitudes. The results show that melting snow quickly infiltrates into the upper layers of the soil but is also quickly transferred through the soil along the slopes towards the river.

The Aral Sea is both an example of large-scale environmental degradation caused by human activity and a message of hope through its partial restoration. Our study shows that the restored part of the Aral Sea is now healthy in terms of vertical mixing and oxygenation. However, small perturbations of water level or transparency could significantly alter the entire ecosystem. The results contribute to understanding the consequences of large-scale lake management worldwide.