Abstract
Several natural disasters occur because of torrential rainfalls. The change in global climate most likely increases the occurrences of such downpours. Hence, it is necessary to investigate the characteristics of the torrential rainfall events in order to introduce effective measures for mitigating disasters such as urban floods and landslides. However, one of the major problems is evaluating the number of torrential rainfall events from a statistical viewpoint. If the number of torrential rainfall occurrences during a month is considered as count data, their frequency distribution could be identified using a probability distribution. Generally, the number of torrential rainfall occurrences has been analyzed using the Poisson distribution (POI) or the Generalized Poisson Distribution (GPD). However, it was reported that POI and GPD often overestimated or underestimated the observed count data when additional or fewer zeros were included. Hence, in this study, a zero-inflated model concept was applied to solve this problem existing in the conventional models. Zero-Inflated Poisson (ZIP) model, Zero-Inflated Generalized Poisson (ZIGP) model, and the Bayesian ZIGP model have often been applied to fit the count data having additional or fewer zeros. However, the applications of these models in water resource management have been very limited despite their efficiency and accuracy. The five models, namely, POI, GPD, ZIP, ZIGP, and Bayesian ZIGP, were applied to the torrential rainfall data having additional zeros obtained from two rain gauges in South Korea, and their applicability was examined in this study. In particular, the informative prior distributions evaluated via the empirical Bayes method using ten rain gauges were developed in the Bayesian ZIGP model. Finally, it was suggested to avoid using the POI and GPD models to fit the frequency of torrential rainfall data. In addition, it was concluded that the Bayesian ZIGP model used in this study provided the most accurate results for the count data having additional zeros. Moreover, it was recommended that the ZIP model could be an alternative from a practical viewpoint, as the Bayesian approach used in this study was considerably complex.
Highlights
The number of extreme rainfall events has increased drastically during the last two decades [1].In addition, the extreme daily rainfall averaged over both dry and wet regimes shows robust increases in both observations and climate models over the six decades [2]
As this study focuses on the number of torrential rainfall occurrences, the data, namely, count data, can be modeled using the discrete probability distributions
The identification of a statistical property to evaluate the occurrences of torrential rainfalls is very important for water resource management because the natural disasters due to torrential rainfall threaten the economic stability and cost human lives
Summary
The number of extreme rainfall events has increased drastically during the last two decades [1]. The extreme daily rainfall averaged over both dry and wet regimes shows robust increases in both observations and climate models over the six decades [2]. The global flood cost due to extreme rainfall events has reached a total of USD 470 billion since 1980 [3]. 8835 disasters, 1.94 million deaths and USD 2.4 trillion of economic losses were reported globally as a results of droughts, floods, windstorms, etc. Climate change would likely exacerbate this trend in the near future. The rainfall observations showed a general increase in the
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