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This study evaluates the effectiveness of Wallonia's sustainable nitrogen management program against groundwater nitrate contamination, 20 years post-implementation. We analysed nitrate concentration time series from 36 locations, finding a modest overall improvement and variability across sites. Results reveal interrelated controlling factors, with land use and aquifer characteristics being key. Lack of improvement may be due to a time lag before the impact of regulatory measures is observable.

This study examined how water availability, climate dryness, and plant productivity interact at the catchment scale. Using various indices and statistical methods, it found a 0–2-month lag in these interactions. Strong correlations during peak productivity months were observed, with a notable hysteresis effect in vegetation response to changes in water availability and climate dryness. The findings help better understand catchment responses to climate variability.

When water infiltrates into a snowpack, it alters the thermal state of the system. This work presents a first of its kind multi-phase heat transfer model for local thermal non-equilibrium scenarios of water infiltration into an existing snowpack, such as during Rain-on-Snow events. The model can be used to calculate the formation of ice layers as well as partial melting of the snow. Hence, it can support hazard assessment for flash floods and snow avalanches.

Uncrewed Aerial Systems (UAS) lidar and structure-from-motion (SfM) photogrammetry are effective methods for mapping high-resolution snow depths. However, there are limited studies comparing their performance across different surface features and tracking spatial patterns of snowpack changes over time. Our study found that UAS lidar outperformed SfM photogrammetry. With limited wind effects, the snow spatial structure captured by UAS lidar remained temporally stable throughout the snow season.

Future extreme rainfall events are influenced by changes in both absolute and relative humidity. The impact of increasing absolute humidity is reasonably well understood, but the role of relative humidity decreases over land remains largely unknown. Using hourly observations from France and The Netherlands, we find that lower relative humidity generally leads to more intense rainfall extremes. This relation is only captured well in recently developed convection-permitting climate models.

We introduce a machine-learning framework to study flood spatial characteristics and drivers in eastern China, using 37 years of flood peak data from a vast gauging network. Our analyses provide better understanding of contrasting flood behaviors by explicitly characterizing their spatial extents. This knowledge can help improve flood risk management.

In this article, we show that by taking the optimal parameters calibrated with a semi-lumped model for the discharge at a catchment's outlet, we can accurately simulate runoff at various points within the study area, including three nested and three neighboring catchments. In addition, we demonstrate that employing more intricate melt models, which better represent physical processes, enhances the transfer of parameters in the simulation, until an overparametrization limit is reached.

In mountainous areas, the relationship between slope and annual hydrological processes is pronounced. However, at lower elevations, this relationship is weak in steeper watersheds. In addition, urbanization leads to an increase in annual surface runoff in all watersheds, especially in steep-slope watersheds. Flatter watersheds exhibit a buffering capacity against urbanization. However, this buffering capacity is diminishing as annual rainfall intensity increases.

This study assesses how on-farm reservoirs (OFRs) can impact surface hydrology in Eastern Arkansas, US, 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 reservoirs'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.

Our study develops a new model to study groundwater flow under uncertainty, using mathematical techniques to improve accuracy and efficiency. This approach allows for better management of water resources, particularly in drought-prone areas, by providing more reliable groundwater availability and movement estimations. This research combines traditional techniques with innovative methods to address water scarcity and support sustainable water use.

A statistical approach is developed for the first time to generate hourly rainfall from daily records at multiple avalanche sites of mountainous terrain. The approach reproduces complex temporal patterns of past rainfall information in the future by using auxiliary information from nearby sites. The high temporal resolution data produced is reliable and also produces extreme rainfall patterns well. The hourly precipitation data can be used for better prediction of avalanches and landslides.

Improving the accuracy of flood forecasts is paramount to minimising flood damage. Machine-learning models are increasingly being applied for flood forecasting. Such models are typically trained to large historic hydrometeorological datasets. In this work, we evaluate methods for selecting training datasets, that maximise the spatiotemproal diversity of the represented hydrological processes. Empirical results showcase the importance of hydrological diversity in training ML models.

We worked to understand how climate variability and flow management affected water quality in a key drinking water source. Our focus was on dissolved organic carbon, or DOC, and our work demonstrates that DOC can change rapidly, reaching high concentrations in wet periods, when flow sources are dominated by the local catchment. Results indicate that the impacts of high local flow, and low inflows from managed sources are compounding water quality challenges, creating issues for water treatment.

Floods, droughts, and heatwaves are increasing globally. This is often attributed to CO₂-driven climate change. However, at the global scale, CO₂-driven climate change neither reduces precipitation nor adequately explains droughts. Land-use change, particularly soil sealing, compaction, and drainage, are likely more significant for water losses by runoff leading to flooding and water scarcity and are therefore an important part the solution to mitigate floods, droughts, and heatwaves.

The Unsolved Problems in Hydrology (UPH) initiative has emphasized the need to establish networks of multi-decadal hydrological observatories to tackle catchment-scale challenges on a global scale. This opinion paper provocatively discusses two end members of possible future hydrological observatory (HO) networks for a given hypothesized community budget: a comprehensive set of moderately instrumented observatories or, alternatively, a small number of highly instrumented super-sites.

Rating curves are used to estimate flow from recorded stage at hydrometric stations. Uncertainties on these flows are generally not estimated nor reported. This paper proposed an approach to develop rating curves and assess the corresponding uncertainties on estimated flow that can be readily applied to large-scale hydrometric networks. Mean standard deviation on estimated flows are of the order of 6.5 %. Uncertainties increase significantly for low stages and can reach values larger than 20 %.

We investigate the skill of CESM1 in simulating hydrologic processes over the Mississippi River basin. Model simulated discharge is seasonally delayed relative to observations: model biases are diagnosed using hydroclimate variables from reanalysis data and attributed primarily to precipitation and runoff related processes. We also show that the seasonality of simulated runoff in several CMIP6 models is improved relative to CESM1.

Lake Taihu is the largest eutrophic lake in China that is shallow with a dense river network. Eutrophication is frequently observed in the lake due to excess pollutant loadings. Understanding water transport is essential for solving the problem. We developed an age-tracking rainfall mixing model to calculate residence time of rain and river water using isotope data. The variation of mixing ratio of rainwater is also estimated. The isotope data indicates the control factors of mixing in the lake.

Our study examines how combining climate models impacts future streamflow predictions, crucial for understanding climate change. Comparing six methods across 3,107 North American catchments, we found unequal weighting significantly improves rainfall and temperature projections. However, for streamflow, both equal and unequal weighting perform similarly with bias correction. Our findings underscore the need to carefully select weighting methods and correct biases for accurate climate projections.

This paper reports research on the assessment of a natural solid and a synthesized liquid soil amendments for soil water retention capacity improvement. Soil water retention curves (SWRCs) have been selected to comparing their effects. Two approaches were adopted to obtain a full range of the SWRCs from fully saturated to nearly dry. A new mathematical model was proposed to represent the effect the amendments on the SWRCs and employed to characterize the WR concentration effect.

The paper presents a method for deriving the chance of heavy downpour, the maximum amount expected at various intervals, and explain how the rainfall changes. It suggests that increases are more due to increased amounts on wet days rather than more wet days, and the rainfall intensity is found to be sensitive to future greenhouse gas emissions while the number of wet days appears to be less affected.

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 has significantly reduced peak flow, runoff coefficient, and mitigated flashy hydrograph responses.

This work evaluated how runoff, flood and drought risks might change in China due to climate change. We found annual runoff is expected to increase notably under high emission scenario. Across most months, runoff is expected to increase, particularly during summer. Wetter summers and drier winters are expected in south China, while the opposite is expected in the north. Flood risks are expected to increase in the south, while drought risks are expected to rise in the south and centre.

Machine learning is playing an increasingly important role in hydrological modeling. In this paper, we introduce an adaptation of existing machine learning models forecasting streamflow in river basins, redesigning them with the goal of integrating them into climate models. We demonstrate the effectiveness of our adapted model by showing that it outperforms a physics-based river model. These results motivate further studies of the use of machine learning based river models inside climate models.

Urban flooding can be driven by rain and storm surge or the combination of the two, which is called compound flooding. In this study we analyzed hourly historical rain and surge data for New York City to provide a more detailed statistical analysis than prior studies of this topic. The analyses reveal that tropical cyclones (e.g. hurricanes) have potential for causing more extreme compound floods than other storms, while extratropical cyclones cause more frequent, lesser compound events.

Various methods were proposed to estimate irrigation water requirements (IWR). However, the simulated IWR exhibits large differences. This study evaluates six potential evapotranspiration (PET) methods and proposes a practical approach to estimate IWR. The radiation-based methods show promise in approximating daily PET accurately, and the STEMMUS-SCOPE model can reliably estimate IWR. This research enhances our understanding of different PET methods and their implications for water management.

Most studies assume no difference between transit times of water and nitrate, because nitrate is transported by water. With an 8-year high-frequency dataset of isotopic signatures of both, water and nitrate, and a transit time model, we show the temporal varying difference of nitrate and water transit times. This finding is highly relevant for applied future research related to nutrient dynamics in landscapes under anthropogenic forcing and for managing impacts of nitrate on aquatic ecosystems.

We present a robust method for identification and classification of global land drought events (GLDEs) based on soil moisture. Two models were used to calculate soil moisture and delimit soil drought over global land from 1980–2022, which was clustered into 775/630 GLDEs. Using four spatiotemporal and three motion-related characteristics, we categorized GLDEs into seven severity and seven dynamic categories. The frequency of GLDEs has generally increased in recent decades.

This study proposes a revised Particle Evolution Metropolis Sequential Monte Carlo algorithm and evaluates its effectiveness in multi-dimensional and multi-objective parameter optimization for land surface models. Our comparative analysis of the impacts of single- and multi-objective optimization on parameter estimation and model performance demonstrates that integrating diverse observational data into multi-objective optimization is preferable for parameter calibration in complex dynamic model.

Our research delves into how vegetation affects water flow over land during heavy rain and how accurately we can predict it. We tested various ways of estimating surface roughness in hydrological models, using data from simulated rainfall on hillslopes in Germany. Using advanced modeling techniques, we found that certain approaches, those considering vegetation, perform better in simulating overland flow dynamics.

This paper focuses on the effect of the water balance constraint on the robustness of the long short-term memory (LSTM) network in learning rainfall-runoff relationships. Through large-sample tests, it is found that incorporating this constraint into the LSTM improves the robustness, while the improvement tends to decrease as the amount of training data increases. The results point to the compensation effects between training data and process knowledge on the LSTM’s performance.

This study presents a new algorithm to better model convective storms. We used advanced tracking methods to analyse 165 storm events in Birmingham (UK) and to reconstruct storm cell lifecycles. We found that cell properties like intensity and size are interrelated and vary over time. The new algorithm, based on vine copulas, accurately simulates these properties and their evolution. It also integrates an exponential model for realistic rainfall patterns, enhancing its hydrological applicability.

Extreme floods are more common than expected. Knowing where these floods are likely to occur is key for risk management. Traditional methods struggle with limited data, causing uncertainty. We use common streamflow dynamics to indicate extreme flood propensity. Analyzing data from Atlantic Europe, Northern Europe, and the U.S., we validate this novel approach and unravel intrinsic linkages between regional geographic patterns and extreme flood drivers.

To address the lack of county-level flood dataset in China, we used machine learning techniques to accurately identify flood events and locations from news reports. This dataset offers crucial insights into the spatiotemporal distribution of urban flooding from 2000 to 2022, highlighting increases in flood occurrences and identifying key vulnerable areas. These findings are vital for enhancing urban planning in China, aiming to mitigate the impact of future floods.

The Standardized Precipitation Index (SPI) and related drought indices are used globally to measure drought severity. The index uses a predictable structure, which we leverage to determine the theoretical likelihood of a year with an extreme worse than a given threshold. We show these likelihoods differ by the length (number of months) and resolution (daily vs monthly) of the index. This is important for drought managers when setting decision thresholds or when communicating risk to the public.