Developing the Actual Precipitation Probability Distribution Based on the Complete Daily Series

Abstract

The defense against urban pluvial flooding relies on the prediction of rainfall frequency, intensity, and long-term trends. The influence of the choice of the complete time series or the wet-day series on the rain analyses remains unclear, which affects the adaptive strategies for the old industrial cities such as Changchun in Northeastern China, with the outdated combined sewer systems. Based on the data from the two separate weather stations, four types of distributions were compared for analyzing the complete daily precipitation series, and their fitting accuracy was found in decreasing order of Pearson III, Pareto–Burr–Feller distribution (PBF), generalized extreme value (GEV), and Weibull. The Pearson III and the PBF probability distribution functions established based on the complete time series were found to be at least 458% and 227%, respectively, more accurate in fitting with the consecutive observations than those built from the wet-day-only series, which did not take account of the probability of the dry periods between the rain events. The rain depths of the return periods determined from the wet-day-only series might be over-predicted by at least 76% if the complete daily series were regarded as being more closely representative of the real condition. A clear threshold of 137 days was found in this study to divide the persistent or autocorrelated time series from the antipersistent or independent time series based on the climacogram analysis, which provided a practical way for independence determination. Due to the significant difference in the rain analyses established from the two time series, this work argued that the complete daily series better represented the real condition and, therefore, should be used for the frequency analysis for flood planning and infrastructure designs.