Rajan Bhattarai1,2, Kei Yoshimura1, Shinta Seto3, Shinichiro Nakamura4, and Taikan Oki5
1Atmosphere and Ocean Research Institute, The University of Tokyo, 5-1-5, Kashiwano-ha, Kashiwashi, Chiba, 277-8564, Japan
2Department of Irrigation, Government of Nepal, Jawalakhel, Lalitpur, Nepal
3Graduate School of Engineering, Nagasaki University, 1-14 Bukyomachi, Nagasaki, Japan
4Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan
5Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, Japan
Received: 15 Sep 2015 – Discussion started: 08 Oct 2015
Revised: 20 Mar 2016 – Accepted: 08 Apr 2016 – Published: 10 May 2016
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.
Citation:
Bhattarai, R., Yoshimura, K., Seto, S., Nakamura, S., and Oki, T.: Statistical model for economic damage from pluvial floods in Japan using rainfall data and socioeconomic parameters, Nat. Hazards Earth Syst. Sci., 16, 1063-1077, https://doi.org/10.5194/nhess-16-1063-2016, 2016.