Without relevant data no well-informed water-related decisions are possible. We focus on testing and integrating various data acquisition technologies (gauges, remote sensing, citizens observatories). Data forms the input to models. Three major modelling paradigms are explored including physically based (simulation) modelling, data driven modelling and agent based modelling.
The objectives are to develop new algorithms and to refine existing algorithms to be able to make better process-based predictions with less uncertainty. New techniques are also being developed for integrating different modelling paradigms in what is termed hybrid modelling. Considerable research efforts relate to the data-driven models (DDM) and the use of computational intelligence methods, and to the new approaches in environmental modelling.
Modelling efforts are aimed at uncertainty-aware modelling various processes related to aquatic environment: water flows, environmental processes and erosion/sediment transport. Special attention is given to modelling and forecasting related to hydrological extremes – floods and droughts. We also aim at developing advanced tools supporting both the model- and scenario-based analysis of environmental impacts on society and human impacts on environment, in perspective linking these developments to the paradigm of the Anthropocene.
Whereas the focus of the research is on the development of modelling systems based on the three paradigms, their effectiveness and efficiency are evaluated through application to specific areas:
- Flood and drought forecasting, flood/drought risk mapping and management
- Process-based modelling of hydrological systems and water resources
- Decision support systems
- Eco-hydraulics and environmental modelling
- Modelling of lakes and resevoirs
- Urban flood modelling and management
- Urban asset modelling and management
- Coastal morphology and deltas
- Ocean surge forecasting
- Anticipatory and adaptive water management
- Methods for uncertainty analysis of models