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This study developed a convenient and new method to identify the occurrence of droughts, heatwaves, and co-occurring droughts and heatwaves across four seasons. From that, we could know the drought (or heatwave) starts on which day and ends on which day. We found an increase in the frequency of heatwaves and the co-occurring droughts and heatwaves in Belgium caused by climate change. We also found that different months have different chances of co-occurring droughts and heatwaves.

People induce and modify droughts. Yet, we don’t know exactly how relevant human and natural processes interact and how to evaluate co-evolution of people and water. Prospect theory can help us to explain the emergence of drought impacts leading to failed welfare expectations (“prospects”) due to water shortage. Our approach helps to explain sociohydrological phenomena such as reservoir effects and can contribute to integrated drought management taking into account the local context.

This study provides a comparison of methodologies to quantify irrigation to enhance regional irrigation estimates. To evaluate the methodologies, we compared various approaches to quantify irrigation using either soil moisture, evapotranspiration, or both within a novel baseline framework, together with irrigation estimates from other studies. We show, that the synergy from using two equally important components in joint approach within a baseline framework, yield better irrigation estimates.

We study the seasonal and spatial patterns of soil moisture in 0–500 cm soil using 89 monitoring sites in a loess catchment with a monsoonal climate. Seasonal changes in soil moisture were mainly concentrated in the shallow soils, with clear separation between wet and dry conditions. Shallow soil moisture varied with aspect, reflecting spatial variations in evapotranspiration under both wet and dry conditions. Most of the precipitation was seasonally cycled in the soil between 0–300 cm.

(1) Evaporation under ice–free and sublimation under ice–covered conditions and its influencing factors were first quantified based on six years of EC observations. (2) Night evaporation of Qinghai Lake accounts for more than 40 % of the daily evaporation. (3) Lake ice sublimation reaches 175.22±45.98 mm, accounting for 23 % of the annual evaporation. (4) Wind speed weakening may have resulted in an 11.14 % decrease in lake evaporation during the ice–covered period from 2003 to 2017.

Floodplains can be constructed along agricultural streams with the purpose to increase water residence time and thereby reduce instream erosion and intercept nutrient export. In this paper we show how this remediation measure can reduce phosphorus concentrations with up to 30 % through optimized floodplain designs and placement. These reductions were primarily facilitated by protection against erosion rather than promotion of deposition on floodplains.

Hydrological model performance involves comparing simulated states and fluxes with observed counterparts. Often, it is overlooked that there is inherent uncertainty surrounding the observations. This can significantly impact the results. In this publication, we emphasize the significance of accounting for observation uncertainty in model comparison. We propose a practical method that is applicable for any observational time series with available uncertainty estimations.

A framework is proposed that utilizes a decision-making model incorporating the results of two more models (physical and numerical) to determine appropriate countermeasure components. A physical model is developed to investigate the behavior of saltwater intrusion in unconfined coastal aquifers. MODFLOW is utilized to develop a numerical model. Evaluation and classification ratios are computed to evaluate and classify the cases included in this study.

We conducted a systematic and quantitative study of the effects of plant mixtures on the water cycle. Our results confirmed that plant mixtures help facilitate a positive water cycle through mitigating inefficient water consumption. The positive conclusions we reached, as well as the analyses of the influencing factors, mechanisms, and limitations, are helpful for promoting further in-depth research and encouraging the establishment of more plant mixture systems.

A new metric, snow water storage (SwS), is used to characterize the natural reservoir function of snowpacks. In recent decades, there has been more spatially widespread decreases in annual SwS than increases and the greatest losses in monthly SwS occur early in the snow season. The mountainous regions that play an inordinate role in annual SwS have declined by 22 %. Flexible snow metrics such as SwS may become more valuable as we move into a future of increased climate variability.

Are machine learning models able to produce reliable rainfall-runoff predictions and add enough benefits that justify the effort to implement these methods? This study covers a large set of Australian catchments (almost 500) comparing deep learning and traditional model results. The deep learning model matched or exceeded conceptual model performance for more than two-thirds of the study catchments, indicating the general viability of these models in a variety of catchments conditions.

Lumped conceptual groundwater transit time models are widely used for their computational simplicity. But their parameters being empirical, these models are often criticized for their calibration reliance. This study mathematically links lumped conceptual parameters to measurable hydrodynamic properties of a groundwater catchment. This kind of direct parameterization gives lumped models a forward modelling potential, and also improves the choice of parameter constraints in calibration exercises.

High-resolution precipitation data is quite often a 'must' as an input hydrological and hydraulic models (i.e. urban drainage modelling). However, the station or climate projection data usually do not provide required resolution (e.g. sub-hourly). In the study we present two new statistical models of different types to disaggregate precipitation from daily to 10 min scale. Both models were validated using radar data and thereafter applied on climate models for 10 stations in Germany and S. Korea.

We found that in the 0–30 cm soil layer, soil water retention and soil water content in normal Kobresia meadow (NM) were higher than those in biocrust meadow (BM), whereas the 30–40 cm layer’s soil water retention and soil water content in NM were lower than those in BM. The topsoil infiltration rate in BM was lower than that in NM. Our findings revealed that the establishment of biocrust did not improve soil water retention and infiltration.

The McMurdo Dry Valleys (MDV), Antarctica are an extreme environment. They are home to a unique microbial ecosystem that survives on freshwater from glacial melt. We implemented a snow and hydrology model on an MDV glacier to test whether this model can simulate melt and runoff in this environment. We found it necessary to modify the schemes for the meltwater draining vertically in the glacier and how albedo evolves over a summer to accurately model the processes generating melt.

Dynamical systems are used by many agencies worldwide to produce seasonal streamflow forecasts, which are critical for decision-making. Such systems rely on hydrology models, which contain parameters that are typically estimated using a target performance metric (i.e., objective function). This study explores the effects of this decision across mountainous basins in Chile, illustrating tradeoffs between seasonal forecast quality and the models' capability to simulate streamflow characteristics.

In this study, we demonstrate an approach to evaluate the interpretation uncertainty within a manually interpreted geological model in a groundwater model. Using qualitative estimates of uncertainties, several geological realizations are developed and implemented in groundwater models. We confirm existing evidence that if the conceptual model is well-defined, interpretation uncertainties within the conceptual model have limited impact on groundwater model predictions.

This paper intends to demonstrate the adequacy of a hybrid solution to overcome the difficulties related to the incorporation of human behaviour when modelling hydrological processes. Two models were implemented, one to estimate the outflow of a reservoir and the other to simulate the hydrological processes of the watershed. With both models feeding each other, results show that the proposed approach significantly improved the streamflow estimation downstream reservoir.

Most studies that aim to identify the impacts of climate change employ General Circulation Models, however, due to their low spatial resolution, it is necessary to apply scale reduction techniques. In the absence of studies, this work evaluated the performance of three techniques to develop frequency analyses and to estimate both the number of rainy days and the total precipitation per water year. The result showed that the performance of the techniques varies according to the analyzed variable.

Citizen scientists often submit high quality data, but a robust method for assessing data quality is needed. This study develops a semi-automated program that characterizes the mistakes made by citizen scientists by grouping them into communities of citizen scientists with similar mistake tendencies and flags potentially erroneous data for further review. This work may help citizen science programs assess the quality of their data and can inform training practices.

We calculated past, present and future rainfall erosivity in Central Europe from high-resolution precipitation data (~3 km, 1 h) generated by the convection-permitting climate model COSMO-CLM. Future rainfall erosivity can be up to 84 % higher than it was in the past. Such increases are much higher than estimated previously from regional climate model output. Convection-permitting simulations have an enormous and to date unexploited potential for the calculation of future rainfall erosivity.

For the pilot region Mississippi River basin, we explored three model calibration approaches for using both streamflow and total water storage anomaly (TWSA) observations to adjust parameter sets of a global hydrological model. We developed a method for considering the observation uncertainties to determine not only optimal but also behavioral parameter sets and model output uncertainty. Model performance was improved but trade-offs between optimal fits to streamflow and TWSA may be large.

Analytical models estimate subsurface properties from subsurface-tidal load interactions. However, they have limited accuracy in representing subsurface physics and parameter estimation. We derived a new analytical solution which models flow to wells due to atmospheric tides, we applied it to field data and compared our findings with subsurface knowledge. Our results enhance understanding of subsurface systems, providing valuable information on their behavior.

The work presents the first classification of weather types associated to compound events of extreme rainfall and coastal storms (waves). The upper level low resulted in the main general configuration conducive to extreme compound events in NW Mediterranean conditions. We used objective classification methods coupled with a Bayesian Network to assess the non-linear relationships between the obtained weather types and other variables characterizing the events and their local magnitudes.

Mathematical models are key to the study of environmental processes in rivers. Such models often require information on river morphology from geographic information system (GIS) software, which hinders the use of replicable workflows. Here I present rivnet, an R package for simple, robust, GIS-free extraction and analysis of river networks. The package is designed so as to require minimal user input and is oriented towards ecohydrological, ecological and biogeochemical modelling.

Predicting flood magnitude and location helps decision-makers to better prepare for flood events. To increase the speed and availability of data during flooding, this study presents a vision-based framework for measuring water levels and detecting floods. The Deep Learning models use time-lapse images captured by surveillance cameras to detect water extent using semantic segmentation and transform them into water level values with the help of LiDAR data.