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In this study, we set up a water quality model using a 5-year paired high-frequency water quality dataset from a large agricultural stream. The simulations were compared with the 15-min interval measurements and showed very good fits. Based on these, we quantified the N uptake pathway rates and efficiencies at daily, seasonal, and yearly scales. This study offers an overarching understanding of N processing in large agricultural streams across different temporal scales.

Climate Data Records of surface soil moisture, vegetation optical depth and land surface temperature are derived from Passive Microwave Observations. The ability of these datasets to properly detect anomalies and extremes is very valuable in climate research, and can especially help to improve our insight in complex regions where the current climate reanalysis datasets reach their limitations. Here we present a case study over the Okavango Delta, where we focus on inter-annual variability.

In this text, we show how the Eyes on Earth Study politicizing the Chinese mainstream reservoirs affected the transboundary water governance and how to ensure the accountable research dialogue in water science. Our data show that since the publication of the Eyes on Earth Study in April 2020, there are growing disputes which contribute to the political distrusts, degradation of the water science and the rise of anti-science movements undermining the accountable research dialogue.

Watertable fluctuations and seawater intrusion are characteristic features of coastal unconfined aquifers. A modified expression is first proposed for the dynamic effective porosity due to watertable fluctuations. Then, the new expression is implemented in existing Boussinesq equations and a numerical model, allowing for examination of the effects of the dynamic effective porosity on watertable fluctuations and seawater intrusion in coastal unconfined aquifers, respectively.

The Belgian city of Spa is known worldwide for its ferruginous and naturally sparkling groundwater springs that gave their name to the bathing tradition commonly called ‘spa’. However, the origin of the dissolved CO₂ they contain was still a matter of debate. Thanks to new analysis on groundwater samples, particularly carbon and helium isotopes together with dissolved gases, this study has demonstrated the volcanic origin of the CO₂, presumably from the Eifel Volcanic Fields degassing plume.

This study proposes an approach for generating future multisite daily streamflow conditioned on a relationship between annual maximum temperature and annual streamflow. The approach can avoid uncertainties during realization for climate model-based rainfall information. Results show that the generated sequences preserve many of the historical characteristics. For future streamflow simulations, significant decreases are projected, likely giving nontrivial impacts on regional water security.

How to combine multi-source precipitation data effectively is one of the hot topics in hydrometeorological research. This study presents a two-step merging strategy based on machine learning to merge multi-source precipitation over China. The results demonstrate that the proposed method effectively distinguishes the occurrence of precipitation events and reduces the error of precipitation amounts. XGBoost is more efficient and has referential significance for other areas with large data amounts.

A WEAP based hydrological model study exploring the causes of a lake desiccation, was transformed into a multiple question driven process to help answer the diversity of questions instigating conflict in a parallel collaborative process. The identification and evaluation of strategies allowed to better adjust the model, address questions that were causing suspicions and conflicts, while identifying a combination of strategies that were of moderate impact, but of higher local acceptability.

The Digital Earth (DE) metaphor is very useful for both end users and for hydrological modellers. We analyse different categories of models, with the view of making them part of a Digital eARth Twin Hydrology system (called DARTH). We also stress the idea that DARTHs are not models in and of themselves, rather they need to be built on an appropriate information technology infrastructure. The urgency for DARTHs to be in light of the Open Science movement and its ideas.

It is found that the precipitation variability dominantly controls global ET in the dry climates, the net radiation has substantial control over ET in the tropical regions, and VPD is impacting ET trends in boreal mid-latitude climate. The critical role of VPD in controlling ET trends is particularly emphasized due to its influence in controlling the land-atmosphere interactions.

Heat in the subsurface can be used to characterize aquifer flow behaviour. The temperature data obtained can be useful to understand the groundwater flow which is of particular importance in waste disposal studies. Satellite images of surface temperature and a temperature-time curve were used implemented in a heat-transport model. Results indicate conduction playing a major role in the aquifer, and support the usefulness of temperature measurements.

A sound understanding of how floods come about allows the development of more reliable flood management tools that assist in mitigating their negative impacts. This article reviews river flood generation processes and flow paths across space scales, starting from water movement in the soil pores, and moving up to hillslopes, catchments, regions and entire continents. To assist model development, there is a need to learn from observed patterns of flood generation processes at all spatial scales.

Although the “dry gets drier and wet gets wetter” (DDWW) paradigm is widely used to describe the trends in wetting and drying globally, we show that 27.1 % of global land agrees with the paradigm, while 22.4 % shows the opposite pattern during the period 1985–2014 from the perspective of terrestrial water storage change. Similar percentages are discovered under different scenarios during the future period. Our findings will benefit the understanding of hydrological responses under climate change.

Increasing amounts of water are being used for agriculture. Therefore, we looked into how the irrigation requirement will evolve under a changing climate over Europe. Our results show that, for the end of the century and under high emissions, the irrigation water will increase by 30 % compared to the year 2000. Moreover, the irrigation requirement is likely to vary more from one year to another. However, if emissions are mitigated, these effects are reduced.

Managing for environmental streamflows involve the practice of prioritizing healthy stream ecosystems while distributing water resources. Classifying similar streams is a useful step in developing environmental streamflows. Environmental streamflows are often needed on streams that must be modeled because they do not contain flow data. This paper has developed a new method of classification that prioritizes model accuracy. The new method advances environmental streamflow management.

Patchy snow cover is a common phenomenon in mountain landscapes. It leads to a longer period of snowmelt contribution to streams. Our study shows that surface variability of snow cover leads to differences in streamflow, evapotranspiration and snowmelt responses. We have used up-to-date hydrological tools strongly constrained with data collected from field and satellite images. The study finding will be helpful for the mountain hydrology community in the proper estimation of water resources.

Aquifers often act as low-pass filters, dampening high-frequency (infra-annual) and amplifying low-frequency (LFV, multi-annual to multidecadal) variabilities originating from climate variability. By processing groundwater level signals, we show the key role of LFV in the occurrence of extreme groundwater levels (GWE). Results highlight how changes in LFV may impact future GWE, as well as the importance of correct representation of LFV in General Circulation Model outputs for GWE projection.

A concurrent in-depth investigation of rainfall partitioning at inter- and intra-event scales for two xerophytic shrubs was conducted. Inter-event rainfall partitioning amount and percentage depended more on rainfall amount, and rainfall intensity and duration controlled intra-event rainfall partitioning variables. Shrub has larger branch angle, more small branch and smaller canopy area to produce stemflow more efficiently, and the one has larger biomass to intercept more rainfall.

We proposed a deep learning technique-based data-driven flood prediction approach, employing an integration of the LSTM technique and Bayesian optimization approach. Results showed that the proposed model can accurately produce flood maps for various hyetograph inputs, meanwhile with substantial improvements in computation time. The proposed model can potentially replace and/or complement the conventional hydrodynamic model for urban flood assessment and management.

The geologic structure of mountain watersheds is thought to control how groundwater and stream water exchange. We measured bedrock depth at 191 locations along eight headwater streams, and paired with stream temperature records and observations of channel dewatering. The data indicated a prevalence of shallow bedrock. We hypothesized that variation in bedrock depth might cause stream dewatering, which was found to be true for some streams but not others, based on local groundwater supply.

Seasonal forecasts can help in safely and efficiently managing a fresh water reservoir in the Netherlands. We compare hydrological forecast systems of the river Rhine, the lakes most important source and analyze forecast skill for over 1993–2016 and for specific extreme years. On average, forecast skill is high in spring due to Alpine snow and smaller in summer. Dry summers appear to be more predictable, skill increases with event extremity. In those cases, seasonal forecasts are valuable tools.

We developed a Gaussian Bayesian Network (GBN) for seasonal forecasting of lake water quality, including algal bloom risk, in a shallow nutrient-impacted lake in southern Norway. Discrete Bayesian networks are commonly used in environmental modelling, but this is one of very few applications of a continuous BN. The GBN approach proved to be promising, particularly where training datasets are small.

Hydrological models projecting the impact of changing climate carry a lot of uncertainty. Thus, these models usually have a multitude of simulations using different future climate data. This study used the subjective opinion of experts to assess which climate and hydrological models are the most likely to correctly predict climate impacts, thereby easing the computational burden. The experts could select more likely hydrological models, while the climate models were deemed equally probable.

Given the recent developments in socio-hydrology and its potential contributions to reduce disaster risk, we conducted research focused on evaluating the extent to which socio-hydrology studies in the areas of natural hazards and disaster are integrative. Based on the persisting gaps identified, we highlighted the specific research needs that will play an important role in ensuring socio-hydrology is capable of holistically addressing natural hazards and disasters.

In this study, we proposed a new framework that considered the uncertainties of model simulations to quantify the contribution rate of climate change and human activities to streamflow changes. Then, the Lancang River Basin was selected for the case study. The results of quantitative analysis using the new framework showed that the reason for the decrease in the streamflow at Yunjinghong station was mainly due to human activities.

This work shows advances in high resolution satellite data for hydrology. We performed hydrological simulations for the Po River Basin using various satellite products, including precipitation, evaporation, soil moisture and snow depth. Evaporation and snow depth improved a simulation based on high quality ground observations. Interestingly, a model calibration relying on satellite data skillfully reproduces observed discharges, paving the way to satellite-driven hydrological applications.

Sorptivity is one of the most important parameters for the quantification of water infiltration into soils. A specific formulation is commonly used to derive sorptivity from the soil hydraulic diffusivity, initial and final conditions. In this study, we propose a mixed formulation that avoids numerical issues and allows for the computation of sorptivity for all types of models chosen for describing water retention and hydraulic conductivity functions, and all initial and final conditions.

Comparison of streamflow composition of two small experimental catchments simulated with three well-known rainfall-runoff (RR) models against the End-Member Mixing Analysis (EMMA) results. All used RR models and EMMA outcome demonstrate that two neighboring catchments significantly different in mutual dynamics of the runoff fractions. Three data aggregation intervals (season, month and pentad) were applied to assess proximity of the RR models and EMMA hydrograph decomposition outcome.