气候变化领域集成服务门户(CCPortal): A generalized framework for process-informed nonstationary extreme value analysis

CCPortal

DOI	10.1016/j.advwatres.2019.06.007
	A generalized framework for process-informed nonstationary extreme value analysis
	Ragno E.; AghaKouchak A.; Cheng L.; Sadegh M.
发表日期	2019
ISSN	3091708
起始页码	270
结束页码	282
卷号	130
英文摘要	Evolving climate conditions and anthropogenic factors, such as CO2 emissions, urbanization and population growth, can cause changes in weather and climate extremes. Most current risk assessment models rely on the assumption of stationarity (i.e., no temporal change in statistics of extremes). Most nonstationary modeling studies focus primarily on changes in extremes over time. Here, we present Process-informed Nonstationary Extreme Value Analysis (ProNEVA) as a generalized tool for incorporating different types of physical drivers (i.e., underlying processes), stationary and nonstationary concepts, and extreme value analysis methods (i.e., annual maxima, peak-over-threshold). ProNEVA builds upon a newly-developed hybrid evolution Markov Chain Monte Carlo (MCMC) approach for numerical parameters estimation and uncertainty assessment. This offers more robust uncertainty estimates of return periods of climatic extremes under both stationary and nonstationary assumptions. ProNEVA is designed as a generalized tool allowing using different types of data and nonstationarity concepts physically-based or purely statistical) into account. In this paper, we show a wide range of applications describing changes in: annual maxima river discharge in response to urbanization, annual maxima sea levels over time, annual maxima temperatures in response to CO2 emissions in the atmosphere, and precipitation with a peak-over-threshold approach. ProNEVA is freely available to the public and includes a user-friendly Graphical User Interface (GUI) to enhance its implementation. Elsevier Ltd
英文关键词	Methods for nonstationary analysis; Physical-based covariates/drivers; Process-informed nonstationary extreme value analysis
scopus关键词	Carbon dioxide; Graphical user interfaces; Markov processes; Population statistics; Risk assessment; Risk perception; Sea level; Value engineering; Covariates; Extreme value analysis; Graphical user interfaces (GUI); Markov chain Monte Carlo; Non-stationary analysis; Nonstationary; Statistics of extremes; Uncertainty assessment; Uncertainty analysis; carbon emission; climate conditions; extreme event; human activity; Markov chain; Monte Carlo analysis; risk assessment; river discharge; sea level; software
来源期刊	Advances in Water Resources
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/176378
作者单位	Department of Civil and Environmental Engineering, University of California, Irvine, United States; Department of Geosciences, University of Arkansas, Fayetteville, AR 72701, United States; Department of Civil Engineering, Boise State UniversityID, United States
推荐引用方式 GB/T 7714	Ragno E.,AghaKouchak A.,Cheng L.,et al. A generalized framework for process-informed nonstationary extreme value analysis[J],2019,130.
APA	Ragno E.,AghaKouchak A.,Cheng L.,&Sadegh M..(2019).A generalized framework for process-informed nonstationary extreme value analysis.Advances in Water Resources,130.
MLA	Ragno E.,et al."A generalized framework for process-informed nonstationary extreme value analysis".Advances in Water Resources 130(2019).