This paper developed hybrid semi-distributed hydrological models by employing a process-based model as the backbone and utilizing deep learning to parameterize and replace internal modules. The main contribution is to provide a high-performance tool enriched with explicit hydrological knowledge for hydrological prediction and to improve understanding about the hydrological sensitivities to climate change in large alpine basins.

Weather radar is a crucial tool in rainfall estimation, but radar rainfall estimates are subject to many error sources, with the true rainfall field unknown. A flexible model for simulating errors relating to the radar rainfall estimation process is implemented, inverting standard processing methods. This flexible and efficient model performs well in generating realistic weather radar images visually for a large range of event types.

Common practices in water resource management and geothermal applications involve the injection of hot or cold water into aquifers. The resulting thermal changes may lead to chemical disequilibrium and consequent mineral dissolution/precipitation in the rock void space. A mathematical model is developed to study the effects of such thermal fluid injection on the evolution of water composition, aquifer porosity, and permeability. The model is then applied to two important case studies.

This study explores the impact of climatic variability on root zone water storage capacities and, thus, on hydrological predictions. Analysing data from 286 areas in Europe and the US, we found that, despite some variations in root zone storage capacity due to changing climatic conditions over multiple decades, these changes are generally minor and have a limited effect on water storage and river flow predictions.

Human-induced nitrogen (N) from fertilization and septic effluents is the primary N source in urban watersheds. We developed a model to understand how spatial and temporal patterns of these loads affect hydrologic and biogeochemical processes at the hillslope level. The comparable simulations to observations showed the ability of our model to enhance insights into current water quality conditions, identify high-N-retention locations, and plan future restorations to improve urban water quality. 

Research on groundwater-dependent ecosystems (GDEs) started earlier, but because there is no good identification and classification method, most of the related research is concentrated in Europe and Australia. In this study, the lower Yellow River basin in northern China, with well-developed karsts, was selected as the study area, and a four-diagnostic-criteria framework for identifying the GDEs based on remote sensing, GIS data dredging, and hydrogeological surveys was proposed.

We used hydrological models, field measurements, and satellite-based data to study the soil moisture dynamics in a subarctic catchment. The role of groundwater was studied with different ways to model the groundwater dynamics and via comparisons to the observational data. The choice of groundwater model was shown to have a strong impact, and representation of lateral flow was important to capture wet soil conditions. Our results provide insights for ecohydrological studies in boreal regions.

This study analyzes water-stable isotope composition by analyzing the impact of forest thinning on lodgepole pine depth-to-water uptake and water-use strategies. Lodgepole pine's primary source is spring snowmelt and shifts to rely on deeper soil water to maintain water uptake. There was no effect of decreased stand density on depth-to-water uptake. It will become more critical that we know how much water forests are using and which strategies trees use to sustain their water supply.

Large-scale hydrologic simulators are a needed tool to explore complex watershed processes and how they may evolve with a changing climate. However, calibrating them can be difficult because they are costly to run and have many unknown parameters. We implement a state-of-the-art approach to model calibration using neural networks with a set of experiments based on streamflow in the upper Colorado River basin.

This study presents an overview of open-source quality control (QC) algorithms for rainfall data from personal weather stations (PWSs). The methodology and usability along technical and operational guidelines for using every QC algorithm are presented. All three QC algorithms are available for users to explore in the OpenSense sandbox. They were applied in a case study using PWS data from the Amsterdam region in the Netherlands.  The results highlight the necessity for data quality control.

The satellites GRACE and GRACE-FO observe continental terrestrial water storage (TWS) changes. With over 20 years of data, we can look into long-term variations in the East Africa Rift region. We focus on analysing the interannual TWS variations compared to meteorological data and observations of the water storage compartments. We found strong influences of natural precipitation variability and human actions over Lake Victoria's water level.

In Earth sciences, pH-driven reactions in porous environments impact natural processes like mineral dissolution and groundwater remediation. Traditional models struggle due to pore-scale complexities. This study explores how porous structure and flow rate affect mixing and chemical reactions. Surprisingly, pH-driven reactions occur faster than predicted, emphasizing water’s unique pH behavior in porous media.

We present a cluster-based approach for inferring sediment discharge event types from suspended sediment concentration and streamflow. Applying it to a glacierised catchment, we find event magnitude and shape complexity to be the key characteristics separating event types, while hysteresis is less important. The four event types are attributed to compound rainfall–melt extremes, high snowmelt and glacier melt, freeze–thaw-modulated snow-melt and precipitation, and late-season glacier melt.