This extended review asks whether hydrological (river flow) droughts have become more severe over time in the UK based on literature review and original analyses. The UK is a good international exemplar, given the richness of available data. We find that there is little compelling evidence for a trend towards worsening river flow droughts, at odds with future climate change projections. We outline reasons for this discrepancy and make recommendations to guide researchers and policymakers.

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.

We studied how rivers and groundwater interact in an arid region of Northwest China, where water is scarce and vital for farming. By analyzing the chemistry and natural markers in water samples, we found that the connection between river water and groundwater changes from mountains to plains. These findings help explain how water moves through the region and offer guidance for managing water resources more sustainably.

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.

This study explores how to improve models predicting water flow in southern Vietnam's Saigon and Dongnai rivers, where data are scarce. By testing three different methods to adjust the river model using river water level and river discharge measurements, we found ways of better predicting river behavior. These findings can help manage water resources more effectively and aid in decision-making for flood protection and environmental conservation.

Simulations of peatland rewetting in Sweden under various conditions of climate, local hydrology, and rewetting practices showed insubstantial changes in landscape flow extremes due to mixing with runoff from various landcovers. The impacts on local hydrological extremes are governed by groundwater levels prior to rewetting and reduced tree cover; hence wetland allocation and management practices are crucial if the purpose is to reduce flow extremes in peatland streams.

Balancing exploration and exploitation in conventional inversion algorithms is challenging. We evaluated the effectiveness of tandem neural network architecture (TNNA) in addressing this challenge. Inversion problems with different heterogeneous scenarios were designed to compare TNNA with four metaheuristic algorithms. Results show that TNNA significantly improves computational efficiency and accuracy, offering a promising framework for developing robust inversion algorithms.

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.

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.

Rain falling on snow (RoS) can accelerate snowmelt and affect runoff, potentially causing severe flooding. We assessed the regional and seasonal variations in the occurrence of RoS in mountainous catchments in central Europe under different perturbations of future climate. Our results showed that RoS changes driven by climate change vary greatly among regions, across elevations, and within the cold season. However, most projections suggested a decrease in RoS events and RoS-driven runoff.

Our study focuses on the rising concern of pesticides damaging aquatic ecosystems, which puts drinking water, the environment, and human health at risk. We provided more accurate estimates of how pesticides break down and spread in small water systems, helping to improve pesticide management practices. Using unique chemical markers in our analysis, we enhanced the accuracy of our predictions, offering important insights for better protection of water sources and natural ecosystems.

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.

Turbidity is a key factor for water quality monitoring. Here, an image-based procedure is tested in a full-scale river monitoring experiment using digital cameras. This approach can enhance our understanding of the real-time status of waterbodies, overcoming the spatial and temporal resolution limitations of existing methods. It also facilitates early-warning systems, advances water research through increased data availability and reduces operating costs.

Continuous and high-quality meteorological datasets are crucial to study extreme hydro-climatic events. We here conduct a comprehensive spatio-temporal evaluation of precipitation and temperature for four climate reanalysis datasets, focusing on mean and extreme metrics, variability, trends, and the representation of droughts and floods over Switzerland. Our analysis shows that all datasets have some merit when limitations are considered, and that one dataset performs better than the others.

To address challenges related to unreliable hydrological simulations, we present an enhanced hydrological simulation with a refined climate model and a more comprehensive hydrological model. The model with the two parts outperforms that without, especially in migrating bias in peak flow and dry-season flow. Our findings highlight the enhanced hydrological simulation capability, with the refined climate and lake module contributing 24 % and 76 % improvement, respectively.

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.

Modeling reservoir outflow and storage is challenging due to limited publicly available data and human decision-making. For 100 reservoirs in the US, we examined how calibrating reservoir algorithms against outflow and storage-related variables affects performance. We found that calibration notably improves storage simulations, while outflow simulations are more influenced by the quality of inflow data. We recommend using remotely sensed storage anomalies to calibrate reservoir algorithms.

In the boreal Krycklan Catchment, 40 years of warming has notably altered hydrological patterns, with increases in winter runoff and decreases in summer flows. Winter climate indices effectively predicted minimum winter flows, while summer runoff variability was influenced by temperature extremes across seasons. Isotope data revealed a growing contribution of precipitation to winter runoff, indicating potential challenges for catchment water storage under continued warming.

This study compares two prominent hydrometeorological datasets across 47 shared watersheds in the United States to assess their compatibility. While daily temperature and precipitation data showed notable discrepancies, agreement improved at monthly, seasonal, and annual scales. The variability between the datasets is comparable to the inherent variability within each individual dataset, and our findings validate the results of previous research employing either dataset. 

Understanding water balance in lakes is complex. We studied Lake Groβ Glienicke in Germany, using an innovative method that combines isotope measurements and a hydrological model to improve estimates of water inflow and evaporation. Our findings show a high correlation between the two approaches, leading to better predictions of lake water dynamics. This research offers a reliable way of evaluating the model outputs.

We developed a new method using sap-flow and water-potential data to estimate key plant water-use properties without destructive sampling. Testing on drooping sheoak showed that our approach captures seasonal changes in plant water transport and that these hydraulic properties have seasonal variation. This method could improve models predicting how vegetation responds to drought and climate change.

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.

A method for estimating extreme rainfall from radar observations is provided. Extreme value statistics are applied on merged radar rainfall product covering different area sizes from a single point up to about 1000 km². The rainfall extremes are supposed to decrease as the area increases. This behavior could not be confirmed by the radar observations. The reason is the limited single-point sampling approach for extreme value analysis. New multiple-point sampling strategies are proposed to mitigate this problem.

Using advances in transit time estimation and tracer data, we tested if fast-flow transit times are controlled solely by soil moisture or if they are also controlled by precipitation intensity. We used soil-moisture-dependent and precipitation-intensity-conditional transfer functions. We showed that a significant portion of event water bypasses the soil matrix through fast flow paths (overland flow, tile drains, preferential-flow paths) in dry soil conditions for both low- and high-intensity precipitation.

Instruments that use absorption of laser light to measure isotopic concentrations in water are advancing our understanding of the water cycle, but for some sample types these instruments suffer from major biases caused by organic compounds. A new dataset of water from >1800 plant and soil samples shows that these effects are common and severe for many plant species but can be mathematically corrected to obtain high-quality research data.

This study explored the occurrence of flow on and just below the soil surface for 14 small vegetated plots across a pre-Alpine catchment. Overland flow and lateral flow through the topsoil occurred frequently. The spatial variation in the occurrence and amount of flow depended on site characteristics, particularly the topographic wetness index. The amount of flow also depended on the antecedent-wetness conditions and total precipitation.

Soil moisture is crucial for the water cycle since it is at the front line of drought. Satellite, model and in situ data help identify soil moisture stress but are challenged by data uncertainties. This study evaluates trends and data coherence of common active/passive microwave sensors and model-based soil moisture data against in situ stations across Europe from 2007 to 2022. Data reliability is increasing, but combining data types improves soil moisture monitoring capabilities.

We produced a new interpolated map of stable isotopes in groundwater in southern Germany and compared it to local precipitation. Interestingly, discrepancies were found between two components of the hydrological cycle, highlighting different recharge patterns and evaporation processes in the northern and southern part of the study area. This research provides insights into understanding different groundwater recharge patterns on a large scale and eventually for groundwater management.

Flow depth and velocity determine various river functions, but their high-resolution simulations are expensive. Here, we developed a downscaling approach that can provide fast and accurate estimation of high-resolution river hydrodynamics. The 84-fold acceleration achieved by the method makes reliable flood risk analysis that needs hundreds or thousands of model runs feasible. More importantly, it provides an opportunity to couple large-scale hydrodynamics with local processes in river models.