Interpreting Disasters From Limited Data Availability: A Guatemalan Study Case

Guatemala is located in a geographical area exposed to multiple natural hazards. Although Guatemalan populations live in hazardous conditions, limited scientific research is being focused in this particular geographical area. Thorough studies are needed to understand the disasters occurring in the country and consequently enable decision makers and professionals to plan future actions, yet available data is limited. Data comprised in the available data sources is limited by their timespan or the size of the events included and therefore is insufficient to provide the whole picture of the disasters in the country. This study proposes a methodology to use the available data within one of the most important catchments in the country, the Samala River basin, to look for answers to what kind of disasters occurs? Where such events happen? And, why do they happen? Three datasets from different source agencies -one global, one regional, and one local- have been analyzed numerically and spatially using spreadsheets, numerical computing software, and geographic information systems. Analyses results have been coupled in order to search for possible answers to the established questions. It has been found a relation between the compositions of data of two of the three datasets analyzed. The third has shown a very different composition probably because the inclusion criteria of the dataset exclude smaller but more frequent disasters in its records. In all the datasets the most frequent type of disasters are those caused by hydrometeorological hazards i.e. floods and landslides. It has been found a relation between the occurrences of disasters and the records of precipitation in the area, but this relation is not strong enough to affirm that the disasters are the direct result of rain in the area and further studies must be carried out to explore other potential causes. Analyzing the existing data contributes to identify what kind of data is needed and this would be useful to feedback systems towards a collection of higher quality data, an increase in the capacity of studying disasters and improved risk management systems.