Flood simulations are important for flood (fatality) risk assessment. This article provides insight into the sensitivity of flood fatality risks to the model resolution of flood simulations and to several uncertain parameters in the loss of life model used. A case study is conducted for river flooding in a polder in the Netherlands (the Bommelerwaard) where the Dutch approach for loss of life estimation is applied. Flood models with resolutions of 100, 25, and 5 m are considered. Results show locally increased mortality rates in higher resolution simulations nearby structures including road embankments, dikes, and culverts. This causes a larger maximum individual risk value (annual probability of death for a person due to flooding) which has consequences for safety standards based on the individual risk criterion. Mortality rate in the breach zone is also affected by representations of buildings as solid objects versus as roughness elements. Furthermore, changes in the loss of life estimation approach via alternative ways of including people's behaviour, building characteristics, and age of the population, have a significant impact on flood fatality risk. Results from this study can be used to support future risk assessments and decision making with respect to safety standards.

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In this paper 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 data-scarce environments. 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.@en