Daily Precipitation Threshold for Rainstorm and Flood Disaster in the Mainland of China: An Economic Loss Perspective

Abstract

Exploring precipitation threshold from an economic loss perspective is critical for rainstorm and flood disaster risk assessment under climate change. Based on the daily gridded precipitation dataset and direct economic losses (DELs) of rainstorm and flood disasters in the mainland of China, this paper first filtered a relatively reasonable disaster-triggering daily precipitation threshold (DDPT) combination according to the relationship between extreme precipitation days and direct economic loss (DEL) rates at province level and then comprehensively analyzed the spatial landscape of DDPT across China. The results show that (1) the daily precipitation determined by the combination of a 10 mm fixed threshold and 99.3th percentile is recognized as the optimal DDPT of rainstorm and flood disasters, and the correlation coefficient between annual extreme precipitation days and DEL rates reached 0.45 (p < 0.01).(2) The optimal DDPT decreases from southeast (up to 87 mm) to northwest (10 mm) across China, and the DDPTs of 7 out of 31 provinces are lower than 25 mm, while 5 provinces are higher than 50 mm on average. These results suggest that DDPTs exist with large spatial heterogeneity across China, and adopting regional differentiated DDPT is helpful for conducting effective disaster risk analysis.Sustainability 2020, 12, 407 2 of 14 (DDPT) [15]. Currently, there is no uniform threshold standard for the determination of extreme precipitation events [16]. There are various methods for determining the extreme precipitation globally, including fixed threshold method, parametric method, non-parametric method, and detrended fluctuation analysis method [14][15][16][17][18]. The fixed threshold method uses an absolute daily precipitation intensity as the critical value of the region [14]. For example, precipitation exceeding 50 mm over 24 h is usually defined as extreme precipitation in China [16,19]. This method is suitable for small-scale areas and has strong subjectivity and experience [20][21][22].The most commonly used nonparametric method is the percentile method, which ranks the amount of precipitation over a certain period of time and selects a certain percentile daily precipitation value as the extreme precipitation criterion for the region [23]. The percentiles selected varied from study to study. Zhai et al. [24] considered the 95th percentile of all rain days during 1961-1990 as the criterion for judging extreme precipitation events. Beniston et al. [25] and Pielke et al.[2] regarded 90th percentile of the observed precipitation data as a standard of extreme precipitation events. This method is suitable for the definition and comparison of extreme precipitation events in different regions [26,27]. Parametric methods include probabilistic analysis [28], the peak over threshold (POT) method [16], the generalized extreme value distribution (GEV) method [29]. These parametric methods depend on the size of the data series and the probability distribution, which could greatly affect the results [22].As a key criterion...

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