A generalized framework for process-informed nonstationary extreme value analysis
A generalized framework for process-informed nonstationary extreme value analysis
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DOI：
10.1016/j.advwatres.2019.06.007
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发表时间：
2019-08-01
影响因子：
4.7
通讯作者：
Sadegh, Mojtaba
中科院分区：
文献类型：
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作者：
Ragno, Elisa;AghaKouchak, Amir;Sadegh, Mojtaba
不断变化的气候条件和人为因素，如二氧化碳排放、城市化和人口增长，可能会导致天气和气候极端情况的变化。大多数当前的风险评估模型依赖于平稳性的假设(即，极值的统计不随时间变化)。大多数非平稳建模研究主要关注极值随时间的变化。在这里，我们提出了过程知晓的非平稳极值分析(ProNEVA)作为一种通用工具，用于结合不同类型的物理驱动因素(即潜在过程)、平稳和非平稳概念以及极值分析方法(即年度最大值、峰值过阈值)。ProNEVA建立在一种新发展的混合进化马尔科夫链蒙特卡罗(MCMC)方法的基础上，用于数值参数估计和不确定性评估。这为平稳和非平稳假设下的气候极端重现期提供了更稳健的不确定性估计。ProNEVA被设计为一个通用工具，允许使用不同类型的数据和非平稳性概念(基于物理的或纯统计的)。在这篇文章中，我们展示了一系列描述变化的应用：城市化对年最大河流流量的响应、年最大海平面随时间的变化、年最高温度对大气中二氧化碳排放的响应以及采用峰值阈值方法的降水量。ProNEVA向公众免费提供，并包括一个用户友好的图形用户界面(GUI)，以增强其实施。
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 peakover-threshold approach. ProNEVA is freely available to the public and includes a user-friendly Graphical User Interface (GUI) to enhance its implementation.