Abstracted/indexed

Key innovation of this study is an approach to simulate reactive solute transport in the vadose zone within a Lagrangian model. The new reactive solute transport routine accounts for sorption and degradation processes during transport of reactive substances through a partially saturated soil domain. Model evaluation using bromide and IPU data from field experiments under different flow conditions shows the feasibility of the approach. Comparisons to HYDRUS 1-D results corroborate this finding.

The main objective of this study is to blend the existing hydrological knowledge with the machine learning algorithm 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 subjectivity in model selection.

Runoff regimes in Switzerland will change significantly under climate change. Projected changes are strongly elevation dependent with earlier and stronger changes in high elevation catchments where snow and glacier melt play an important role. The changes and the climate model agreement on the signal of change increase with increasing global mean temperatures or stronger emission scenarios. This amplification highlights the importance of climate change mitigation.

The R programming language has become an important tool for many applications in hydrology. In this study, we provide an analysis of some of the R tools providing hydrological models. Two aspects are uniformly investigated: the conceptualisation of the models and the practicality of their implementation for end-users. These comparisons aim at easing the choice of R tools for users and at improving their usability for hydrology modelling to support more transferable research.

This article examines future changes in land cover and hydrological cycling across the interior of western Canada under climate conditions projected for the 21^st century. Key insights into the mechanisms and interactions of Earth system and hydrological process responses are presented, and this understanding is used together with model application to provide a synthesis of future change. This has allowed more scientifically-informed projections than have hitherto been available.

We present an improved statistical bias correction method for climate models and put a focus on precipitation. Our method corrects model data so that it matches the observations in the historical period in a climatological sense better than other state-of-the-art methods while at the same time keeping long term trends in the future period unmodified. The corrected data can serve as a more accurate input for climate impact studies e.g. in hydrology or forestry.

Soil salinity is a serious threat in numerous arid and semiarid areas of the world. Given this threat, efficient field assessment methods are needed to monitor the dynamics of soil salinity in salt-affected lands efficiently. We demonstrate rapid and non-invasive geophysical measurements modeled by advanced numerical analysis of the signals and coupled with hydrological modeling can provide valuable information to assess the spatio-temporal variability in soil salinity over large areas.

Real-time river discharge forecasts and reforecasts from the Global Flood Awareness System (GloFAS) have been made publicly available, together with an evaluation of forecast skill at the global scale. Results show that GloFAS is skilful in over 93 % of catchments in the short- (1- to 3-days) and medium-range (5- to 15-days) and skilful in over 80 % of catchments out to the extended-range (16- to 30-days). Skill is summarised in a new layer on the GloFAS Web Map Viewer to aid decision making.

The evolution of karst aquifers is often governed by solutions gaining their aggressivity in depth. Although the principles of Hypogene Speleogenesis are known, modeling studies based on reactive flow in fracture networks are missing. We present a model, where dissolution at depth is triggered by the mixing of waters of different origin and chemistry. We show how the initial position of the mixing zone and flow instabilities therein, determine the position and shape of the final conduits.

We have collected and synthesized catchment attributes from multiple sources into an extensive dataset, the Catchment Attributes for Brazil (CABra). The CABra contains streamflow and climate daily series for 735 catchments in the 1980–2010 period, aside from dozens of attributes of topography, climate, streamflow, groundwater, soil, geology, land-cover, and hydrologic disturbance. The CABra intends to pave the way for a better understanding of catchments' behavior in Brazil and the world.

When solar radiation penetrates the frozen surface of lakes, shallower zones underneath warm faster than deep interior waters. This numerical study shows that the transport of excess heat to the lake interior depends on the lake circulation, affected by Earth rotation, and controls the lake warming rates and the spatial distribution of the heat flux across the ice-water interface. This work contributes to the understanding of the circulation and thermal structure patterns of ice-covered lakes.

Dew and fog inputs are rarely investigated in temperate ecosystems. With climate change, prolonged dry periods are expected, hence dew and fog might become a more important water source even in temperate grasslands to alleviate plant drought stress. Our results using stable water isotopes highlight the importance of dew and fog inputs to temperate grasslands during prolonged dry periods and reveal the complexity of the local water cycle including different pathways of dew and fog inputs.

Accurate continental water storage predictions is valuable for water management practitioners and policy makers in support of water resources assessment and agriculture planning. This study improves the accuracy and spatial representation of water storage predictions of an operational water balance modelling system through data assimilation. The integration of satellite soil moisture products can provide persistent constraints in model predictions for several weeks.  

Some river basins are considered to be very similar because they have the similar background such as transboundary, facing threats of human activities. But we still lack understanding of the differences under their general similarities. Therefore, we proposed a framework based on Bayesian network to group watersheds based on similarity levels and compare the causal and systematic differences within the group. We applied it to the Amu and Syr Darya river basin and discussed its universality.

Stream flow distributions are highly skewed (known as an imbalanced domain), resulting in low prediction accuracy of high flows when using Artificial Neural Networks flow forecasting models. In this research, we investigate the use of three resampling, four ensemble, and 12 hybrid techniques, to address the problem of imbalanced datasets, and to evaluate the improvement of high flow prediction accuracy for two Canadian watersheds.

With the rapid growth of population and economy, the utilization of natural resources (especially water resources) is expanding, leading to the deterioration of ecological and environmental health, which is unsustainable for both the earth and human being. This paper proposed a methodology for sustainable development of water resources considering socio-economy development, food safety, and ecological protection. It can give references to policymakers for multiple departments.

The silicon isotopic composition of groundwaters from Rottnest Island, Western Australia were measured to investigate water-rock interactions in a coastal aquifer. Silicon isotopic ratios varied spatially across the island and were related to groundwater processes, such as vertical mixing and rainfall recharge frequency. We find that silicate dissolution occurs in the freshwater-seawater transition zone, supporting the recent recognition of marine groundwater discharge in oceanic silicon cycle.

We collected water from an Alpine catchment in Switzerland and compared the genetic information of eukaryotic organisms conveyed by eDNA with the hydrologic information conveyed by naturally-occurring, hydrologic tracers. At the intersection of two disciplines, our study provides complementary knowledge gains and identifies the next steps to be addressed for using eDNA to achieve complementary insights into Alpine water sources.

This study uses machine learning methods to model the correlation between rainfall time series and outflow rates of urban catchments with sustainable urban drainage systems. The models have good prediction accuracy, and the contribution of rainfall at each time step to runoffs at different steps is identified. The models offer plausible conceptualizations of physical processes, which are useful for various tasks, such as baseflow separation and catchment response time estimation.

Contaminant transport in soils is known to be affected by soil heterogeneities, such as macropores. The transport properties of heterogeneous porous media can be studied in laboratory columns. However, the results reported in this study (a combination of breakthrough experiments, magnetic resonance imaging and computer simulations of transport) show that these properties can be largely affected by the boundary devices of the columns, thus highlighting the need to take their effect into account.

How can we effectively train PhD students both across different knowledge domains in hydrology and water resource engineering, as well as in computer science? To address this issue, the Water Informatics in Science and Engineering Centre for Doctoral Training (WISE CDT) offers a postgraduate programme that fosters enhanced levels of innovation and collaboration by training a cohort of engineers and scientists at the boundary of water informatics, science and engineering.

This paper provides a comprehensive overview of the differences within streamflow droughts using different identification approaches, namely the Variable Threshold, Fixed Threshold, and the Standardized Streamflow Index. Our results clearly show that streamflow droughts derived from different approaches deviate from each other both in occurrence and timing. The use of aggregating daily streamflow data into monthly time windows for forecasting drought is recommended.

This study investigates the combined use of standard hydrologic and meteorologic indicators for monitoring drought characteristics in the Upper Indus Basin of Pakistan. Our results show that these indicators, and especially meteorologic indicators can be used in early operational drought monitoring within Kabul, Chenab and Jhelum basins. Moreover, the indicator datasets produced in this study are publicly available to facilitate future drought investigations in Upper Indus catchments.

In hydrology, it is often necessary to infer from a daily sum of precipitation a possible distribution over the day – for example how much it rained in each hour. In principle, for a given daily sum, there are endless possibilities. However, some are more likely than others. We show that a method from artificial intelligence called GAN can learn what a typical distribution over the day looks like.

Intensified drying of soil and grassland vegetation is raising the impact of fire severity and extent in Southern California. While browned grassland is a common sight during the dry season, this study has shown that there is a pronounced shift in the timing of browning, due to changing climate conditions favoring milder winter temperatures and increased precipitation variability. Vegetation may be limited in their abilities to adapt to these shifts, increasing the risk of widespread wildfires.

Heavy precipitation events are still feeding with a large uncertainty into hydrological models. Based on the highly dense station network WegenerNet (one station per 2 km²) we analyzed the sensitivity of runoff simulations to different rain network densities and interpolation methods in small catchments. We find, and quantify relevant characteristics, that runoff curves especially from short-duration convective rainfall events are strongly influenced by gauge station density and distribution.

Gully headcut erosion is always a difficult issue in soil erosion field, which hinders the revelation of gully erosion mechanism and establishment of gully erosion model. Our study clarified the spatial-temporal changes in flow properties, energy consumption and soil loss, and confirmed that gully head consumed the most of flow energy (78 %) and can contribute 89 % of total soil loss. The critical energy consumption initiating soil erosion of upstream area, gully head and gully bed are determined.

Flood probability estimation is important for applications such as land use planning, reservoir operation, infrastructure design and safety assessments. However, it is a challenging task, especially in estuarine areas where floods are caused by both intense rainfall and storm surge. This study provides a review of approaches to flood probability estimation in these areas. Based analysis of a real-world river system, guidance on method selection is provided.