A GIS-based tool for flood damage assessment and delineation of a methodology for future risk assessment: case study for Annotto Bay, Jamaica

Flood risk assessments and damage estimations form integral parts of the disaster risk management in Jamaica, owing its vulnerability to hydrometeorological hazards. Although island wide damage and risk assessments have been carried out for major flood events in Jamaica, few studies have been conducted for the creation of damage and risk maps for vulnerable areas. In this study, a risk-based tool was developed by transferring a proven methodology for flood risk assessment in Flanders, called LATIS, to areas with limited data resources. The town of Annotto Bay was chosen as case study due to its vulnerability to coastal and riverine flooding. The model uses input parameters such as flood data, land use, and socioeconomic data and rainfall values to estimate the damage. The flooding of 2001, caused by tropical storm Michelle, as well as a storm surge with a 100-year return period, was input for the model in order to estimate damage from fresh and saltwater for Annotto Bay. The produced maps show the spatial variation of the damage costs, which correlates with the flood depths. The total calculated damage cost from the freshwater flood of 2001 in the study area was estimated just over USD 7 million. Saltwater damages were calculated at USD 30 million. Although validation of the exact damage costs was not possible, the damage spread and number of affected elements were accurate. The model output also shows the potential number of people who would be killed as a result of the event, which was calculated at only 2 casualties for freshwater. Since in reality no one died, this low estimate can be considered accurate. The casualties caused by the saltwater flooding with a return period of 100 years were estimated at 150 people killed. The results of this approach can be extended to other vulnerable areas of the island having topographical and geographical similarities and being affected by similar hydrometeorological events. Hence, the method allows damage assessment for data-sparse regions, aiding in planning and mitigation measures for flood-prone communities.