| Summary: | 碩士 === 國立臺灣大學 === 生物環境系統工程學研究所 === 102 === Extreme rainfall events occur increasingly and cause enormous loss; consequently, regional frequency analysis has become more important and widely used to estimate the return period of floods. However, the dependence among stations was not considered in the past. Studies have shown that the variation of rainfall patterns was affected by monsoon, especially during winter, in I-Lan area, i.e. the northeast corner of Taiwan. The most important factor that affects the precipitation in I-Lan is the local circulation caused by the triangle-shaped terrain. Therefore, without the influence of strong weather systems, e.g. typhoon, extreme rainfall events still occur.
The purpose of this study is applying techniques of copula to analyze the multisite stochastic hourly rainfall patterns and regional frequency considering the dependence among surrounding rainfall stations during the period of 1960-2011. This model is following three steps to analyze the rainfall patterns and return periods. First, we use copulas to model the dependence among rainfall stations without the influence of marginal distributions, and then pair-copula structures are applied to separate the multivariate copula into several of bivariate copulas. Second, conditional probability density function is used to realize the major space-time pattern of local precipitation with different scenarios. Finally, copula-based regional frequency analysis is compared with the index flood methods with L-moments, and the return period simulation
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considering dependence among stations is also exhibited in this part.
After constructing the dependent structure, the joint density function can be used to simulate the complicate regional rainfall patterns and regional return periods. In a specific scenario, the rainfall in downstream is more than in upstream when the extreme rainfall event occurs in midstream. Furthermore, copula-based return periods considering dependence are more accord with the real events than the index flood methods with L-moments. Considering dependent structure by copula can not only simulate the complex rainfall patterns but also reduce the possibility of underestimated or overestimated situation.
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