Historical precipitation and flood damage in Japan: functional data analysis and evaluation of models - Atsushi Wakai; Yasuaki Hijioka; Masayuki Yokozawa; Manabu Watanabe; Gen Sakurai (PLoS ONE, 2025)
The future increase of large-scale weather disasters resulting from the increased frequency of extreme weather events caused by climate change is a matter of concern. Predicting future flood damage through statistical analysis requires accurate modeling of the relationship between historical precipitation and flood damage. An analysis that considers precipitation as a time series may be appropriate for this purpose. Functional data analysis was applied to model the relationship between historical daily precipitation and daily flood damage for river basins in the Kanto and Koshin regions of Japan. Flood damage statistics from the national government and 1-km grid past precipitation data from the National Agriculture and Food Research Organization were used. The models obtained through the functional data analysis were more accurate than those derived from the simple linear regression without considering the time series of precipitation. The new models were also about four times more accurate in estimating the annual sum of flood damage, compared to the flood damage of each flood event. The accuracy of prediction was higher in recent years than in earlier years of the study period (1993–2020). The results showed that the influence of precipitation on flood damage was more apparent in recent years. This findings may imply that the progress of the river development project and the resulting improvement of the structures along the river have indirectly affected levels of flood damage associated with levels of precipitation.