Over the past two decades, Haiti was struck by 30 storm events and 40 floods, affecting over 3.5 million people. Being the poorest country in the Northern hemisphere, it is unable to allocate funds to risk assessment and management. Therefore, this research developed a low‐cost methodology to analyse flood risk in data‐sparse regions. The floodplain of the river Moustiques was chosen as study area. First, a methodology was developed and input data were gathered from existing data, literature, field data, and open source data. Then, a flood risk assessment was performed for the area. The resulting economic risk map and social risk map indicate that the region is at risk for nearly 2 million USD and has potentially 60 casualties per year. Although the assessment was performed as a quantitative analysis, the resulting maps should be interpreted qualitatively, as the values could not be validated. Nonetheless, the results clearly indicate the high‐risk areas where measures should be taken. Furthermore, this research shows the potential of citizen science, in the form of a questionnaire survey conducted in the floodplain. This low‐cost and fast acquisition method provided many different input data for flood risk assessment, from population data to damage factors and validation information on historic flooding.

The Small Island Developing States are characterized by an unstable economy and low-lying, densely populated cities, resulting in a high vulnerability to natural hazards. Flooding affects more people than any other hazard. To limit the consequences of these hazards, adequate risk assessments are indispensable. Satisfactory input data for these assessments are hard to acquire, especially in developing countries. Therefore, in this study, a methodology was developed and evaluated to test the sensitivity of a flood model towards its input data in order to determine a minimum set of indispensable data. In a first step, a flood damage assessment model was created for the case study of Annotto Bay, Jamaica. This model generates a damage map for the region based on the flood extent map of the 2001 inundations caused by Tropical Storm Michelle. Three damages were taken into account: building, road and crop damage. Twelve scenarios were generated, each with a different combination of input data, testing one of the three damage calculations for its sensitivity. One main conclusion was that population density, in combination with an average number of people per household, is a good parameter in determining the building damage when exact building locations are unknown. Furthermore, the importance of roads for an accurate visual result was demonstrated.

Flood risk assessments predict the potential consequences of flooding, leading to more effective risk management and strengthening resilience. However, adequate assessments rely on large quantities of high-quality input data. Developing regions lack reliable data or funds to acquire them. Therefore, this research has developed a flexible, low-cost methodology for mapping flood hazard, vulnerability and risk. A generic methodology was developed and customized for freely available data with global coverage, enabling risk assessment worldwide. The default workflow can be enriched with region-specific information when available. The practical application is assured by a modular toolbox developed on GDAL and PCRASTER. This toolbox was tested for the catchment of the river Moustiques, Haiti, for which several flood hazard maps were developed. Then, the toolbox was used to create social, economic and physical vulnerability maps. These were combined with the hazard maps to create the three corresponding flood risk maps. After creating these with the default data, more detailed information, gathered during field work, was added to verify the results of the basic workflow. These first tests of the developed toolbox show promising results. The toolbox allows policy makers in developing countries to perform reliable flood risk assessments and generate the necessary maps.