A Frequency-Factor Based Approach for Bivariate Gamma Simulation and Its Application for Assessment of Climate Change Impact on Annual Typhoon Rainfall
Date Issued
2010
Date
2010
Author(s)
Hou, Ju-Chen
Abstract
Many studies related to climate change focused on global, continental or regional scale effect in space and annual or seasonal scale effect in time. However, for practical planning and engineering design, it is necessary to deal with local (spatial) and event (temporal) scales. However, the mathematical expressions of many previous stochastic simulation models are complex with a large number of parameters to be calibrated from the observed rainfall data and have computational limitations. In this dissertation, a continuous stochastic storm-rainfall simulation model (SRSM) is presented to accommodate the aforementioned scales and provide quantitative assessment of the impact on annual typhoon rainfall under given scenarios of climate change. The SRSM is a parametric stochastic simulation model which considers random processes of four major storm types: frontal rainfall, Mei-Yu, convective storms and typhoons occurring annually in Taiwan. Random process of a storm rainfall event is characterized by (1) inter-arrival time of storm events and (2) joint probability distribution of storm duration and total rainfall depth. Occurrences of storm events of a certain storm type can be modeled as a Poisson process and the inter-arrival time is modeled as a random variable with exponential distribution. A frequency-factor based bivariate gamma distribution model is proposed for generating random sample pairs (duration and total depth), which have not only the desired marginal densities of component random variables but also their correlation coefficient. Under certain scenarios of climate change, i.e. when the average number of typhoon events for the study site increases or decreases, we can assess the impact of climate change on annual typhoon rainfall from a stochastic point of view.
Subjects
SRSM
Inter-arrival time
Poisson process
Gauss-Markov random process
Climate change
SDGs
Type
thesis
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