IMPREX pushes the state of the art in a number of directions. We invest in better seasonal forecasts. We develop new concepts to visualize climate change effects. And we zoom in at many applications used by hydropower companies, ship traffic analysers, water resource managers.
Bart van den Hurk, KNMI

FACT SHEET - Urban Water Supply Systems

New water quality forecast approaches by IMPREX enhance the operational efficiency of water treatment plants and help reduce freshwater resources’ vulnerability to hydro-climatic extremes.

FACT SHEET - Drought Preparedness, Mitigation and Management

Innovative approaches for the agricultural sector:

  • New hydro-meteorological forecast products by IMPREX allow for more proactive drought management in the European agricultural sector.
  • New drought risk management tools help mitigate the agricultural sector’s vulnerability to prolonged droughts.
  • Innovative products contribute to stable economic development and increased European food security.

FACT SHEET - Europe's Global Water Risk

New IMPREX approaches boost the operational efficiency and help reduce the European economy’s vulnerability to hydrometeorological extremes occurring worldwide, in particular drought and flood events.

FACT SHEET - Improved Forecasting and Risk Management for the Water Transport Sector

New hydrometeorological forecast products by IMPREX boost the operational efficiency of the European water transportation sector and help mitigate the vulnerability of waterway transport to hydro-meteorological extremes, in particular low flows.

POLICY BRIEF - Probabilistic Approaches for Improved Flood Risk Assessment and Management under consideration of uncertainties

One of the challenges faced by Member States in implementing the Floods Directive is how to factor in uncertainties in flood hazard and flood risk assessments within flood risk management strategies. Tools are needed to support decision-making on appropriate flood measures under consideration of uncertainties. Here is how IMPREX can help.

POLICY BRIEF - The importance of including compound events in the implementation of the Floods Directive

Compound flooding, floods due to multiple flood drivers such as heavy rain, storm surges, or high runoff rates, has caused some of the most destructive floods in Europe. Still, the compounding nature of multiple drivers is not sufficiently emphasized in current flood hazard and risk scenarios developed, for example, in the context of the Floods Directive. This may lead to a biased assessment of flood risk, both for current and future climate conditions. This IMPREX policy brief presents guidance for policies that take into account the risks of compound flooding.

Multi-variable flood damage modelling with limited data using supervised learning approaches

Abstract. Flood damage assessment is usually done with damage curves only dependent on the water depth. Several recent studies have shown that supervised learning techniques applied to a multi-variable data set can produce significantly better flood damage estimates. However, creating and applying a multi-variable flood damage model requires an extensive data set, which is rarely available, and this is currently holding back the widespread application of these techniques. In this paper we enrich a data set of residential building and contents damage from the Meuse flood of 1993 in the Netherlands, to make it suitable for multi-variable flood damage assessment. Results from 2-D flood simulations are used to add information on flow velocity, flood duration and the return period to the data set, and cadastre data are used to add information on building characteristics. Next, several statistical approaches are used to create multi-variable flood damage models, including regression trees, bagging regression trees, random forest, and a Bayesian network. Validation on data points from a test set shows that the enriched data set in combination with the supervised learning techniques delivers a 20% reduction in the mean absolute error, compared to a simple model only based on the water depth, despite several limitations of the enriched data set. We find that with our data set, the tree-based methods perform better than the Bayesian network.

How to cite: Wagenaar, D., de Jong, J., and Bouwer, L. M.: Multi-variable flood damage modelling with limited data using supervised learning approaches, Nat. Hazards Earth Syst. Sci., 17, 1683-1696,, 2017.

Contribution of Potential Evaporation Forecasts to 10-day streamflow forecast skill for the Rhine river, HESS, 2018.


Medium term hydrologic forecast uncertainty is strongly dependent on the forecast quality of meteorological vari- ables. Of these variables, the influence of precipitation has been studied most widely, while temperature, radiative forcing and their derived product potential evapotranspiration (PET) have received little attention from the perspective of hydrologi- cal forecasting. This study aims to fill this gap by assessing the usability of potential evaporation forecasts for 10-day-ahead streamflow forecasting in the Rhine basin, Europe. In addition, the forecasts of the meteorological variables are compared with observations.

Bart van Osnabrugge1,2, Remko Uijlenhoet2, and Albrecht Weerts1,2

1 Deltares, Operational Water Management Department, Delft, The Netherlands
2 Wageningen University, Hydrology and Quantitative Water Management Group, Wageningen, The Netherlands
Correspondence: Bart van Osnabrugge (

Open Access The 2013/14 Thames Basin Floods: Do Improved Meteorological Forecasts Lead to More Skillful Hydrological Forecasts at Seasonal Time Scales?

Jessica NeumannDepartment of Geography and Environmental Science, University of Reading, Reading, United Kingdom - Louise ArnalDepartment of Geography and Environmental Science, University of Reading, and European Centre for Medium Range Weather Forecasts, Reading, United Kingdom - Linus MagnussonEuropean Centre for Medium Range Weather Forecasts, Reading, United Kingdom - Hannah ClokeDepartment of Geography and Environmental Science, and Department of Meteorology, University of Reading, Reading, United Kingdom

Can seasonal hydrological forecasts inform local decisions and actions? An in-the-moment decision-making activity


Jessica L. Neumann et al.