Abstract
Frequency estimates of extreme precipitation are revised using a regional L-moments method based on the annual maximum series and Chow’s equation at lower return periods for the Jiangsu area in China. First, the study area is divided into five homogeneous regions, and the optimum distribution for each region is determined by an integrative assessment. Second, underestimation of quantiles and the applicability of Chow’s equation are verified. The results show that quantiles are underestimated based on the annual maximum series, and that Chow’s formula is applicable for the study area. Next, two methods are used to correct the underestimation of frequency estimation. A set of rational and reliable frequency estimations is obtained using the regional L-moments method and the two revised methods, which can indirectly provide a robust basis for flood control and water resource management. This study extends previous works by verifying underestimation of the quantiles and the provision of two improved methods for obtaining reliable quantile estimations of extreme precipitation at lower recurrence intervals, especially in solving reliable estimates for a 1-year return period from the integral lower limit of the frequency distribution.
Highlights
IntroductionNatural flood disasters occur frequently in China
The study area was divided into five homogeneous regions according to the aboveregions, the optimummentioned distribution is determined based(Figure on the2).regional
Some of the main findings obtained from the research are as follows: The study area is categorized into five homogeneous regions using cluster analysis
Summary
Natural flood disasters occur frequently in China. Flood control is an important topic relevant to the preservation of human life, property, and society [1,2]. Scientific and robust flood control standards are critical to engineering and urban flood control design, for which an important theoretical basis of estimation is hydrological frequency calculation [3]. Rapid economic development and enhanced environmental consciousness have led to increased attention on extreme hydrometeorological events and growing concern for events occurring at lower return periods, fueled by the increasing seriousness of urban waterlogging disasters [1,4]. The sampling method and the choice of probability distribution can influence frequency estimations at low recurrence intervals [5]. Knowledge regarding sampling and the optimum distribution is a key element of frequency analysis
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