Deriving information on disasters caused by natural hazards from limited data: a Guatemalan case study

This work proposes a method to overcome data limitations encountered when analyzing disasters at a local scale in disaster-prone areas. Research is required to understand the processes underlying the disasters in disaster-prone areas. However, many such areas lack sufficient data for the statistically significant studies that would strongly support disaster risk-reduction measures. Disasters are studied extensively at the national scale, but local-scale disaster research is greatly needed, specifically because the hazard exposures and vulnerabilities of populations are mainly site specific. The proposed method compiles data using two databases: the System of Information Management in case of Emergency or Disaster (SISMICEDE) and the Disaster Inventory System (DesInventar). SISMICEDE has a short time span and high spatial resolution, while DesInventar has a longer time span but low spatial resolution. SISMICEDE’s spatial distribution was used to sort DesInventar disaster data, analyzing them spatially and temporally at a local scale. The Samala River basin in Guatemala was selected to exemplify a disaster-prone area for which there are insufficient disaster data. The results indicate that it was useful to combine the two databases to optimally describe disasters over time and space in the studied area. The refinement of the disaster data highlighted the discrepancies between administrative boundaries and local particularities. The results indicate that the municipal scale is too sparse for spatial analyses and that specific location details are needed. According to the limited data available, disasters, during the rainy season, are increasing over time in the study area. This work demonstrates a way to perform local-scale disaster studies of areas for which data are not readily available. These local-scale studies would enable research and actions intended to improve disaster risk-reduction management and measures. This study could also help promote an improved information system in Guatemala that includes complete information useful for emergency response and post-disaster analyses.