The estimation of flow and solute transport in unsaturated soil is essential for quantifying groundwater resources or pollution. Usual column laboratory experiments and a new method are analyzed using a global sensitivity analysis. The data sets are composed of water pressure and water content measured inside the column and water flow rate and solute BTC measured at the outflow. Non-invasive methods (using flow rate and BTC only) provide comparable results than usual invasive methods.

Three methods are used to characterize the uncertainty in soil properties. The effect on simulated states and fluxes is quantified using a distributed hydrological model. Different impacts are identified as function of the perturbation method, of the model outputs and of the spatio-temporal resolution. The study underlines the importance of a proper characterization of the uncertainty in soil properties for a correct assessment of their role and further improvements in the model application.

Deforestation is commonly understood to increase and reforestation to reduce flood risk, but scientific evidence at the relevant landscape scale is scarce and contested. A measure of day-to-day flow persistence is defined here. It is proposed as simple performance indicator for watershed health that can respond to changes in climate and land cover quality, quantity and spatial pattern. Data for four watersheds show decrease or increase in flow persistence in degradation and restoration phases.

Deforestation is commonly understood to increase and reforestation to reduce flood risk, but scientific evidence at the relevant landscape scale is scarce and contested. A measure of day-to-day flow persistence is defined here. It is proposed as simple performance indicator for watershed health that can respond to changes in climate and land cover quality, quantity and spatial pattern. Data for four watersheds show decrease or increase in flow persistence in degradation and restoration phases.

Data from 10 urban or peri-urban catchments located in five EU countries are used to analyze the imperviousness distribution and sewer network geometry. Consistent scale invariant features are retrieved for both (fractal dimensions can be defined), which enables to define a level of urbanization. Imperviousness representation in operational model is also found to exhibit scale-invariant features (even multifractality). The research was carried out as part of the UE INTERREG IV RainGain project.

A new methodology to obtain design storms for Mediterranean convective episodes is presented, which not only reproduces observed rainfall intensities but also other variables such as temporal pattern, total rainfall volume or the storm duration. The formulation, without using IDF curves, introduces a 2-parameter analytical function and presents an original method to assign the storm return period. Three design storms with similar magnitude but different shapes are derived for each return period.

Rainfall frequency analyses from radar and satellite estimates over the eastern Mediterranean are compared examining different climatic conditions. Correlation between radar and satellite results is high for frequent events and decreases with return period. The uncertainty related to record length is larger for drier climates. The agreement between different sensors instills confidence on their use for rainfall frequency analysis in ungauged areas of the Earth.

In this study, we focused on exploring the spatial distribution and temporal variation of desert riparian forests and their influencing factors based on field experiment and remote sensing data. Our result revealed how the environmental factors shape the spatial distribution and temporal variation of desert riparian forest in the downstream Heihe river. The results of this study provide support for the effective restoration of desert riparian forest in the hyperarid zone.

We show that the variability of groundwater levels within an Alpine river valley is more strongly affected by human impacts on rivers than by extreme events in precipitation. The influence of precipitation is found to be more pronounced in the shallow wells of the Alpine foreland. Groundwater levels, river stages and precipitation behave more similar under drought than under flood conditions and generally exhibit a tendency towards more similar behavior in the most recent decade.

It is interesting to understand vegetation distribution and dynamics under water limitation in this transect. We apply Eagleson's ecohydrological optimality method in the NECT based on data from 2000 to 2013 to get M^*, then compare with M from NDVI, furthermore to discuss the sensitivity of M^* to vegetation properties and climate factors. The result indicates that the average M^* fits the actual M well. The sensitivity analyses show how M^* changes with vegetation characteristics and climates.

Parametric distribution functions are commonly used to model precipitation amounts at gauged and ungauged locations. Nonparametric distributions offer a more flexible way to model precipitation amounts. However, the nonparametric models do not exhibit parameters that can be easily regionalized for application at ungauged locations. To overcome this deficiency, we present a new interpolation scheme for nonparametric models and evaluate the usage of daily gauges for sub-daily resolutions.

State-of-the-art land surface models (LSMs) rely on poorly constrained parameters. To enhance LSM configuration, new satellite-based Earth observations are essential. This is because multiple observational datasets allow us to assess and validate the representation of coupled processes in LSMs. The resulting improved LSM configuration is beneficial for coupled weather forecasts, and hence valuable to society.

In this research, we explored how the sampling uncertainty of flood variables (flood peak, volume, etc.) can reflect on a structural variable, which in our case was the maximum water level (MWL) of a reservoir controlled by a dam. Next, we incorporated additional information from different sources for a better estimation of the uncertainty in the probability of exceedance of the MWL. Results showed the importance of providing confidence intervals in the risk assessment of a structure.

Soil moisture is a major variable that affects regional climate, weather and hydrologic processes on the Earth's surface. In this study, real-world data of a network of cosmic-ray sensors were assimilated into a regional land surface model to improve model states and soil hydraulic parameters. The results show the potential of these networks for improving model states and parameters. It is suggested to widen the number of observed variables and to increase the number of estimated parameters.

Urban drainage and wastewater systems are heavily impacted by precipitation. Hence, weather forecasts are valuable in improving their management. However, forecasts are intrinsically uncertain, especially when fine model resolution is required, which is the case for urban hydrology. Handling uncertainty is challenging for decision makers. This study presents an economic framework to support the decision-making process by providing information on when acting on the forecast is beneficial.

Agricultural production in the Ganges River basin is affected by the water shortage in the dry months, while the excess water during the rainy season causes floods in the downstream. Annual total surface runoff generated in the basin is about 298 ± 99 Bm3, and runoff in the monsoon months contributes up to 80 % of this total runoff. Comparison of sub-basin-wise surface runoff with the estimated unmet water demand indicated that capturing only a portion of the wet-season runoff would be sufficient.

This paper presents the probabilistic approach to evaluate design floods in a changing climate, adapted in this case to the northern territories. For the Russian Arctic, the regions are delineated, where it is suggested to correct engineering hydrological calculations to account for climate change. An example of the calculation of a maximal discharge of 1 % exceedance probability for the Nadym River at Nadym is provided.

Hydrological prediction is crucial but in tropical lowland it is difficult, considering data scarcity and river system complexity. This study offers a view of the hydrology of two tropical lowlands in Indonesia. Both lowlands exhibit the important role of upstream wetlands in regulating the flow downstream. We expect that this work facilitates a better prediction of fire-prone conditions in these regions.

Hydrological models have large amounts of uncertainty in streamflow predictions. Using extra data (e.g. isotope tracers) helps evaluate whether the model is getting the right answers for the right reasons. In a Canadian basin, three types of isotope in precipitation input are used to drive a tracer-aided model and assess the resulting model uncertainty. This study shows how a tracer-aided model can be used at the larger scale, and that the model can be of value in such regions.