Articles | Volume 16, issue 5
https://doi.org/10.5194/nhess-16-1063-2016
https://doi.org/10.5194/nhess-16-1063-2016
Research article
 | 
10 May 2016
Research article |  | 10 May 2016

Statistical model for economic damage from pluvial floods in Japan using rainfall data and socioeconomic parameters

Rajan Bhattarai, Kei Yoshimura, Shinta Seto, Shinichiro Nakamura, and Taikan Oki

Abstract. The assessment of flood risk is important for policymakers to evaluate damage and for disaster preparation. Large population densities and high property concentration make cities more vulnerable to floods and having higher absolute damage per year. A number of major cities in the world suffer from flood inundation damage every year. In Japan, approximately USD 1 billion in damage occurs annually due to pluvial floods only. The amount of damage was typically large in large cities, but regions with lower population density tended to have more damage per capita. Our statistical approach gives the probability of damage following every daily rainfall event and thereby the annual damage as a function of rainfall, population density, topographical slope and gross domestic product. Our results for Japan show reasonable agreement with area-averaged annual damage for the period 1993–2009. We report a damage occurrence probability function and a damage cost function for pluvial flood damage, which makes this method flexible for use in future scenarios and also capable of being expanded to different regions.

Download
Short summary
The assessment of flood risk is important for policymakers to evaluate flood damage and for disaster preparation. Large population densities and high property concentration make cities more vulnerable to floods and have higher damage per year. In Japan, about one billion USD in damage occurs annually due to floods related to rainfall only. In this paper, we report a damage occurrence probability function and a damage cost function for pluvial flood damage to estimate annual flood damage.
Altmetrics
Final-revised paper
Preprint