Modeling the association between socioeconomic features and risk of flood damage: A local-scale case study in Sri Lanka.

Floods cause severe damage to people as well as to properties. The same flood can cause different levels of damage to different households, but investigations into floods tend to be conducted on regional and national scales, thereby missing these local variations. It is therefore necessary to understand individual experiences of flood damage to implement effective flood management strategies on a local scale. The main objectives of this study were to develop a model that represents the relationship between socioeconomic conditions and flood damage at a local scale, and to understand the socioeconomic factors most closely tied to flood damage. The analysis is novel in that it considers not only the impact of flood characteristics, but also the impact of social, economic, and geographic factors on flood damage. This analysis derives from a quantitative modeling approach based on community responses, with the responses obtained through questionnaire surveys that consider four consecutive floods of differing severity. Path analysis was used to develop a model to represent the relationships between these factors. A randomly selected sample of 150 data points was used for model development, and nine random samples of 150 data points were used to validate the model. Results suggest that poor households, located in vulnerable, low-lying areas near rivers, suffer the most from being exposed to frequent, severe floods. Further, the results show that the socioeconomic factors with the most significant bearing on flood damage are per capita income and geographic location of the household. The results can be represented as a cycle, showing that social, economic, geographic, and flood characteristics are interrelated in ways that influence flood damage. This empirical analysis highlights a need for local-scale flood damage assessments, as offered in this article but seldom seen in other relevant literature. Our assessment was achieved by analyzing the impact of socioeconomic and geographic conditions and considering the relationship between flood characteristics and flood damage.