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EVALUATING THE CLIMATE RESILIENCE IN DATA-SPARSE REGIONS - AN AUTOMATED GIS-BASED TOOLBAR

Hanne Glas

Abstract

Every year, storm surges, flooding, droughts, and other meteorological natural hazards cause major damages worldwide. Due to climate change, the frequency and intensity of these disasters will increase drastically over the next few decades. Since governments and decision makers are struggling to cope with the effects of these weather disasters as it is, implementing adequate mitigation and adaptation measures that will protect the area against current hazard events as well as future ones, is essential, especially in developing countries where funds and resources are scarce. In this research, a toolbar for ArcGIS that guides the user through a clear workflow to identify the high-risk areas, now and in the future, and to assess planned measures and their impact on the environment, was developed. This toolbar offers decision makers in data-sparse regions a quick, easy-to-use analysis methodology, based on open source input data. The results of the assessment are visual as well as numeric. Output climate risk maps, on which the user can quickly identify the risks and problems in the study area, are created. Tables with numeric data, such as percentages of land use at risk, offer more in-depth information on the climate resilience of the area. Furthermore, planned measures and projects can be added to analyze their impact on the environment and the climate resilience of the area. Two study areas were tested using the toolbar: the urban area of Annotto Bay in Jamaica and the rural plain of the river Moustiques in Haiti. The goal of the research was to create a user-friendly, quick and adequate climate test for data-sparse regions. While this assessment simplifies the complex nature of climate change and its effects, it does provide useful information for governments and decision makers in the form of a first adequate analysis of the climate resilience of the chosen study area and the impact of future projects.

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DOI resolver: doi.org/10.5593/sgem2019/2.2/s11.100

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