FloodCitiSense aims at developing an urban pluvial flood early warning service for, but also by citizens and city authorities, building upon the state-of-the-art knowledge, methodologies and smart technologies provided by research units and private companies. FloodCitiSense targets the co-creation of this innovative public service in an urban living lab context with all local actors. This service will reduce the vulnerability of urban areas and citizens to pluvial floods, which occur when heavy rainfall exceeds the capacity of the urban drainage system. Due to their fast onset and localized nature, they cause significant damage to the urban environment and are challenging to manage. Monitoring and management of peak events in cities is typically in the hands of local governmental agencies. Citizens most often just play a passive role as people negatively affected by the flooding, despite the fact that they are often the ‘first responders’ and should therefore be actively involved. The FloodCitiSense project aims at integrating crowdsourced hydrological data, collaboratively monitored by local stakeholders, including citizens, making use of low-cost sensors and web-based technologies, into a flood early warning system. This will enable ‘citizens and cities’ to be better prepared for and better respond to urban pluvial floods. Three European pilot cities are targeted: Brussels – Belgium, Rotterdam – The Netherlands and Birmingham – UK.

Original languageEnglish
Title of host publicationNew Trends in Urban Drainage Modelling - UDM 2018
EditorsGiorgio Mannina
Number of pages5
ISBN (Print)9783319998664
Publication statusPublished - 2019
Event11th International Conference on Urban Drainage Modelling, UDM 2018 - Palermo, Italy
Duration: 23 Sep 201826 Sep 2018

Publication series

NameGreen Energy and Technology
ISSN (Print)1865-3529
ISSN (Electronic)1865-3537


Conference11th International Conference on Urban Drainage Modelling, UDM 2018

    Research areas

  • Citizen science, Flood early warning system, Urban pluvial flooding

ID: 66602385