Freezing-induced groundwater migration and water table decline are widely observed, but quantitative understanding of these processes is lacking. By considering wintertime atmospheric conditions and occurrence of lateral groundwater inflow, a model coupling soil water and groundwater reproduced field observations of soil temperature, soil water content, and groundwater level well. The model results led to a clear understanding of the balance of the water budget during the freezing–thawing cycle.

Plants affect the global water and carbon cycles by modifying their water use and carbon intake in response to soil moisture. Global climate models represent this response with either simple empirical models or complex physical models. We reveal that the latter improves predictions in plants with large flow resistance; however, adding dependence on atmospheric moisture demand to the former matches performance of the latter, leading to a new tool for improving carbon and water cycle predictions.

We coupled a detailed snow–ice model (Crocus) to represent glaciers in the Weather Research and Forecasting (WRF)-Hydro model and tested it on a well-studied glacier. Several observational systems were used to evaluate the system, i.e., satellites, ground-penetrating radar (used over the glacier for snow depth) and stake observations for glacier mass balance and discharge measurements in rivers from the glacier. Results showed improvements in the streamflow projections when including the model.

Understanding the hydrologic cycle in semi-arid landscapes includes knowing the physical processes that govern where and why rivers flow and dry within a given catchment. To gain this understanding, we put together a conceptual model of what processes we think are important and then tested that model with numerical analysis. The results broadly confirmed our hypothesis that there are three distinct regions in our study catchment that contribute to streamflow generation in quite different ways.

This study proposes a multiple-dependence model for estimating streamflow at partially gaged sites. The evaluations are conducted on a case study of the eastern USA and show that the proposed model is suited for infilling missing values. The performance is further evaluated with six other infilling models. Results demonstrate that the proposed model produces more reliable streamflow estimates than the other approaches. The model can be applicable to other hydro-climatological variables.

We assess an operational merged gauge–radar precipitation product over a period of 12 years, using gridded precipitation fields from a dense gauge network (WegenerNet) in southeastern Austria. We analyze annual data, seasonal data, and extremes using different metrics. We identify individual events using a simple threshold based on the interval between two consecutive events and evaluate the events' characteristics in both datasets.

This study was done to develop an improved soil temperature model for the USA Great Plains by using common weather station variables as inputs. After incorporating knowledge of estimated soil moisture and observed daily snow depth, the improved model showed a near 50 % gain in performance compared to the original model. We conclude that our improved model can better estimate soil temperature at the surface soil layer where most hydrological and biological processes occur.

Existing hydrological knowledge has been integrated with genetic programming based on a machine learning algorithm (MIKA-SHA) to induce readily interpretable distributed rainfall–runoff models. At present, the model building components of two flexible modelling frameworks (FUSE and SUPERFLEX) represent the elements of hydrological knowledge. The proposed toolkit captures spatial variabilities and automatically induces semi-distributed rainfall–runoff models without any explicit user selections.

A comprehensive assessment of compound flooding potential is missing for China. We investigate dependence, drivers, and impacts of storm surge and precipitation for coastal China. Strong dependence exists between driver combinations, with variations of seasons and thresholds. Sea level rise escalates compound flood potential. Meteorology patterns are pronounced for low and high compound flood potential. Joint impacts from surge and precipitation were much higher than from each individually.

Instantaneous evapotranspiration (ET), which is detected by the remote sensing technique, needs to be upscaled to daily values in order to practical applications. The accuracy of seven upscaling methods is evaluated by using global observations. The sine function and the evaporative fraction method using extraterrestrial solar irradiance are recommended. Although every upscaling scheme has high accuracy at most sites, it is less accurate at tropical rainforest and tropical monsoon sites.

The acquisition of river-level data is a critical task for the understanding of flood events but is often complicated by the difficulty to install and maintain gauges able to provide such information. This study proposes applying deep learning techniques on river-camera images in order to automatically extract the corresponding water levels. This approach could allow for a new flexible way to observe flood events, especially at ungauged locations.

Snow water resources represent a key hydrological resource for the Mediterranean regions, where most of the precipitation falls during the winter months. This is the case for Lebanon, where snowpack represents 31 % of the spring flow. We have used models to generate snow information corrected by means of remote sensing snow cover retrievals. Our results highlight the high temporal variability in the snowpack in Lebanon and its sensitivity to further warming caused by its hypsography.

Gully headcut erosion is always a difficult issue in soil erosion, which hinders the revelation of gully erosion mechanisms and the establishment of a gully erosion model. This study clarified the spatiotemporal changes in flow properties, energy consumption, and soil loss, confirming that gully head consumed the most of flow energy (78 %) and can contribute 89 % of total soil loss. Critical energy consumption initiating soil erosion of the upstream area, gully head, and gully bed is confirmed.

We used a two-step approach with limited sampling effort in existing storm water infrastructure to illustrate the risk of biocide emission in a 2 ha urban area 13 years after construction had ended. First samples at a swale confirmed the overall relevance of biocide pollution. Then we identified sources where biocides were used for film protection and pathways where transformation products were formed. Our results suggest that biocide pollution is a also continuous risk in aging urban areas.

In situ stable water isotope measurements are a recently developed method to measure water movement from the soil through the plant to the atmosphere in high resolution and precision. Here, we present important advantages of the new method in comparison to commonly used measurement methods in an experimental setup. Overall, this method can help to answer research questions such as plant responses to climate change with potentially shifting water availability or temperatures.

Reforestation can influence climate, but the sensitivity of summer rainfall to reforestation is rarely investigated. We take two reforestation scenarios to assess the impacts of reforestation on summer rainfall under different reforestation proportions and explore the potential mechanisms. This study concludes that reforestation increases summer rainfall amount and extremes through thermodynamics processes, and the effects are more pronounced in populated areas than over the whole basin.

We found with a scientific paper survey (~ 1000 papers) that 45 % of the papers used rainbow color maps or red–green visualizations. Those rainbow visualizations, although attracting the media's attention, will not be accessible for up to 10 % of people due to color vision deficiency. The rainbow color map distorts and misleads scientific communication. The study gives guidance on how to avoid, improve and trust color and how the flaws of the rainbow color map should be communicated in science.

Accurate daily continental water balance predictions are valuable in monitoring and forecasting water availability and land surface conditions. A simple and robust method was developed for an operational water balance model to constrain model predictions temporally and spatially with satellite soil moisture observations. The improved soil water storage prediction can provide constraints in model forecasts that persist for several weeks.

Soil moisture information was shown to be valuable for landslide prediction. Soil moisture was simulated at 133 sites in Switzerland, and the temporal variability was compared to the regional occurrence of landslides. We found that simulated soil moisture is a good predictor for landslides, and that the forecast goodness is similar to using in situ measurements. This encourages the use of models for complementing existing soil moisture monitoring networks for regional landslide early warning.

The stationary assumption in hydrology has become obsolete because of climate changes. In that context, it is crucial to assess the performance of a hydrologic model over a wide range of climates and their corresponding hydrologic conditions. In this paper, numerous, contrasted, climate sequences identified by a hidden Markov model (HMM) are used in a differential split-sample testing framework to assess the robustness of a hydrologic model. We illustrate the method on the Senegal River.

Latin America and the Caribbean face many water-related stresses which are expected to worsen with climate change. To assess the vulnerability, we reviewed over 20 000 multilingual research articles using machine learning and an understanding of the regional landscape. Results reveal that the region’s inherent vulnerability is compounded by research blind spots in niche topics (reservoirs and risk assessment) and subregions (Caribbean nations), as well as by its reliance on one country (Brazil).