EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Community Mapping Supports Comprehensive Urban Flood Modeling for Flood Risk Management in a Data-Scarce Environment

Louise Petersson1, Marie-Claire ten Veldhuis2, Govert Verhoeven3, Zoran Kapelan2, Innocent Maholi4, and Hessel Winsemius2,3
Louise Petersson et al.
  • 1World Bank, Dar es Salaam, Tanzania (
  • 2Department of Water Management, Delft University of Technology, Delft, Netherlands
  • 3Inland Water Systems Unit, Deltares Research Institute, Delft, Netherlands
  • 4Humanitarian OpenStreetMap Team, Dar es Salaam, Tanzania

We demonstrate a framework for urban flood modeling with community mapped data, particularly suited for flood risk management in data-scarce environments. The framework comprises three principal stages: data acquisition with survey design and quality assurance, model development and model implementation for flood prediction. We demonstrate that data acquisition based on community mapping can be affordable, comprehensible, quality assured and open source, making it applicable in resource-strained contexts. The framework was demonstrated and validated on a case study in Dar es Salaam, Tanzania. The results obtained show that the community mapped data supports flood modeling on a level of detail that is currently inaccessible in many parts of the world. The results obtained also show that the community mapping approach is appropriate for datasets that do not require extensive training, such as flood extent surveys where it is possible to cross-validate the quality of reports given a suitable number and density of data points. More technically advanced features such as dimensions of urban drainage system elements still require trained mappers to create data of sufficient quality. This type of mapping can, however, now be performed in new contexts thanks to the development of smartphones. Future research is suggested to explore how community mapping can become an institutionalized practice to fill in important gaps in data-scarce environments.

How to cite: Petersson, L., ten Veldhuis, M.-C., Verhoeven, G., Kapelan, Z., Maholi, I., and Winsemius, H.: Community Mapping Supports Comprehensive Urban Flood Modeling for Flood Risk Management in a Data-Scarce Environment, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4221,, 2021.


Display file