A spatiotemporal approach for determining disaster-risk potential based on damage consequences of multiple hazard events

Abstract

Natural hazards have become increasingly frequent in the Philippines, making the determination of risks associated with large-scale natural hazards and disasters in this area increasingly important. This study developed a method for estimating the risk of disasters from multiple hazards in this country at the province level. The locational probability and consequences of five natural hazards were analyzed over a 30-year period (1982–2011), and the disaster-risk potential of provinces was estimated based on the combined damage cost per capita from five hazards over that period. Information from the Center for Research on the Epidemiology of Disasters Emergency Disasters Database (CRED-EMDAT) was used to analyze the areas and populations affected, damage costs, and frequency and duration of five hazards (i.e. meteorological, climatological, hydrological, geophysical, and biological). The estimated values were then presented spatially using a geographic information system. The results suggested that meteorological hazards were the most common hazards affecting most provinces in the Philippines, whereas hydrological hazards produced the most damage. Small island provinces and coastal zones were among the areas with the highest disaster-risk potential. This assessment can aid in decision making with regard to financing disaster prevention schemes and in planning for the increasing occurrences of natural hazard-related disasters.

Keywords:

• multi-risk assessment
• spatiotemporal approach
• geographic information system
• Philippines

Acknowledgements

This research was supported by a Japanese Government (Monbukagakusho) Scholarship and the Program for Risk Information on Climate Change of Ministry of Education, Culture, Sports, Science and Technology of Japan, and by the Hokkaido University Sustainable Low Carbon Society Project. The authors also thank the Center for Research on the Epidemiology of Disasters at the University of Louvain (Belgium) for providing the information for building the datasets used in the analysis.