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.

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.

Stream water temperature (SWT) is a key indicator of water quality with implications for public use and health, ecosystem function, and aquatic life survival. Advances in modeling have helped improve our understanding of SWT dynamics, but challenges remain. Recently, machine learning (ML) has been used in SWT modeling, but questions remain about how the use of ML improves insight into SWT causes and effects. This work reviews ML-SWT modeling studies (and metrics) and considers future directions.

The simultaneous or sequential occurrence of different flood processes, including extreme storm surges and heavy precipitation, tends to trigger compound floods, which are often destructive. We develop a land–river–ocean coupled model for the prediction of compound floods in a big river delta region. The coupled model is shown to perform well in simulating the combined effect of heavy precipitation, river flood routing, storm surges, and inundation for events induced by tropical cyclones.

During geological carbon sequestration, the injected supercritical CO₂, being less dense, floats above the brine. The dissolution of CO₂ into brine helps mitigate the risk of CO₂ leakage. As CO₂ dissolves into the brine, it increases the density of brine in the upper layer, initiating gravity-driven convection (GDC), which significantly enhances the rate of CO₂ dissolution. We derived two empirical formulas to predict the asymptotic dissolution rate driven by GDC in heterogeneous fields.