FloodPrep – Supporting emergency services in heavy rain management during pluvial flooding
Funding
Contents
Pluvial flooding caused by heavy rainfall poses a growing challenge for cities and municipalities. Dealing with these sudden floods is particularly challenging for emergency services, local decision-makers and infrastructure operators, as predictions have so far been limited. Comprehensive heavy rainfall management is therefore essential. The central problem lies in the early detection of endangered areas, rapid response during the event and systematic analysis to optimise protective measures.
This is where FLOODPREP comes in, with the aim of providing the best possible support to decision-makers and emergency services before, during and after a heavy rainfall event. At the same time, it creates a stronger link between science and practice in order to tailor simulations to the requirements of real crisis situations.
Core objectives of the project
- Experimental real-time simulation of heavy rainfall events to support emergency response
In FLOODPREP, two hydrodynamic 1D/2D simulation models are being experimentally tested for the case studies in Graz and Innsbruck. The aim is to evaluate the calculation times for real-time application during heavy rainfall events. In addition, the linking of surface runoff and drainage system models is being further developed in order to realistically map flood hotspots. - Optimisation of operational coordination and decision support
The models are to be tested in order to support emergency services and crisis teams through user-friendly visualisation of simulation results. Real-time situation analyses are being tested to facilitate decision-making under time pressure. Simulation-based assessments will be used to identify particularly vulnerable urban areas in order to prioritise preventive measures. - AI-supported model improvement through operational documentation
The simulations will be validated using operational data, photos and video recordings from fire brigades and disaster control authorities. AI and machine learning are used to investigate the extent to which models can be continuously optimised based on past event data. In addition, long-term analyses and climate scenarios are evaluated in order to identify future flood risks at an early stage. - Sustainable integration into urban planning and flood management
The experimental results are incorporated into the evaluation of existing flood protection measures in order to identify potential for improvement. The models are used to support long-term drainage planning and urban flood strategies.
Project partners
- University of Innsbruck (lead)
- Graz University of Technology
- safeREACH GmbH
- KAWUMMS Naturgefahrenmanagement GmbH
- ÖSTAP Engineering & Consulting GmbH
- Innsbrucker Kommunalbetriebe AG
- City of Graz
- City of Innsbruck
Contact
Univ.-Prof. Dipl.-Ing. Dr Manfred Kleidorfer
Dipl.-Ing. Martina Hauser
University of Innsbruck
umwelttechnik@uibk.ac.at