globalchange  > 气候变化与战略
DOI: 10.5194/hess-24-5077-2020
论文题名:
Uncertainty in nonstationary frequency analysis of South Korea's daily rainfall peak over threshold excesses associated with covariates
作者: Lee O.; Choi J.; Won J.; Kim S.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2020
卷: 24, 期:11
起始页码: 5077
结束页码: 5093
语种: 英语
Scopus关键词: Bayesian networks ; Global warming ; Markov chains ; Pareto principle ; Rain ; Time series analysis ; Titration ; Dewpoint temperature ; Generalized Pareto Distributions ; Markov Chain Monte-Carlo ; Non-stationary model ; Peak over threshold ; Peak-over-threshold series ; Surface air temperatures ; Uncertainty reduction ; Uncertainty analysis ; air temperature ; confidence interval ; dew point ; frequency analysis ; Markov chain ; parameter estimation ; precipitation assessment ; probability ; rainfall ; time series analysis ; uncertainty analysis ; South Korea
英文摘要: Several methods have been proposed to analyze the frequency of nonstationary anomalies. The applicability of the nonstationary frequency analysis has been mainly evaluated based on the agreement between the time series data and the applied probability distribution. However, since the uncertainty in the parameter estimate of the probability distribution is the main source of uncertainty in frequency analysis, the uncertainty in the correspondence between samples and probability distribution is inevitably large. In this study, an extreme rainfall frequency analysis is performed that fits the peak over threshold series to the covariate-based nonstationary generalized Pareto distribution. By quantitatively evaluating the uncertainty of daily rainfall quantile estimates at 13 sites of the Korea Meteorological Administration using the Bayesian approach, we tried to evaluate the applicability of the nonstationary frequency analysis with a focus on uncertainty. The results indicated that the inclusion of dew point temperature (DPT) or surface air temperature (SAT) generally improved the goodness of fit of the model for the observed samples. The uncertainty of the estimated rainfall quantiles was evaluated by the confidence interval of the ensemble generated by the Markov chain Monte Carlo. The results showed that the width of the confidence interval of quantiles could be greatly amplified due to extreme values of the covariate. In order to compensate for the weakness of the nonstationary model exposed by the uncertainty, a method of specifying a reference value of a covariate corresponding to a nonexceedance probability has been proposed. The results of the study revealed that the reference covariate plays an important role in the reliability of the nonstationary model. In addition, when the reference covariate was given, it was confirmed that the uncertainty reduction in quantile estimates for the increase in the sample size was more pronounced in the nonstationary model. Finally, it was discussed how information on a global temperature rise could be integrated with a DPT or SAT-based nonstationary frequency analysis. Thus, a method to quantify the uncertainty of the rate of change in future quantiles due to global warming, using rainfall quantile ensembles obtained in the uncertainty analysis process, has been formulated. © 2020 BMJ Publishing Group. All rights reserved.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/162564
Appears in Collections:气候变化与战略

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作者单位: Lee, O., Department of Environmental Engineering, Pukyong National University, Busan, 48513, South Korea; Choi, J., Division of Earth Environmental System Science (Major of Environmental Engineering), Pukyong National University, Busan, 48513, South Korea; Won, J., Division of Earth Environmental System Science (Major of Environmental Engineering), Pukyong National University, Busan, 48513, South Korea; Kim, S., Department of Environmental Engineering, Pukyong National University, Busan, 48513, South Korea

Recommended Citation:
Lee O.,Choi J.,Won J.,et al. Uncertainty in nonstationary frequency analysis of South Korea's daily rainfall peak over threshold excesses associated with covariates[J]. Hydrology and Earth System Sciences,2020-01-01,24(11)
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