Scholarly record

FLOOD RISK ASSESSMENT IN DATA SPARSE REGIONS: THE USE OF QUESTIONNAIRES TO COLLECT HISTORIC FLOOD DATA - A CASE STUDY FOR THE RIVER MOUSTIQUES IN HAITI

Hanne Glas

Abstract

Flooding caused by storm surges, tropical storms and hurricanes causes severe damages in Haiti every year. When these inundations occur, the affected areas become inaccessible, preventing field work to register the flood water height and extent. Highresolution satellite imagery and aerial photography, on the other hand, are too expensive for a developing country like Haiti. The lack of this data results in a lack of adequate flood maps and models, making it impossible to predict the cost of the damages and take the right measures to minimise them. The information necessary to generate flood maps can, however, also be found in the knowledge of the inhabitants of the affected areas. These people remember if their house was flooded and if their properties were damaged due to flooding. Therefore, a questionnaire was drawn up and 294 households in the study area, the plain of the river Moustiques in the Northwest of Haiti, were questioned about the most recent and the most severe flood in their memory. In total, 19 different historic flood events were described in 347 answered questionnaires. This research aims to create flood maps by combining the coordinates of the house of each questioned person with the flood height they remembered. Furthermore, flood damage factors were generated from the questionnaires, in which the inhabitants indicated how much damage was caused by each inundation to their houses, crops, livestock and vehicles. The average damage percentages for houses show an increase when the flood height increases. For crops, livestock and vehicles, however, this increase is not visible. This can be due to the fact that these elements are not located in the same area as the houses of which the coordinates and flood height are used. The collected data can thus not be taken too literally, as the memory of people is not always correct. However, by combining this information with other geographic data of the study area, it is possible to create flood maps and damage factors in order to establish a flood risk assessment of a data sparse region against a low cost.

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DOI resolver: doi.org/10.5593/sgem2018/2.3/s11.048

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