A generalized framework for process-informed nonstationary extreme value analysis. (August 2019)

• Record Type: Journal Article
• Authors: Ragno, Elisa; AghaKouchak, Amir; Cheng, Linyin; Sadegh, Mojtaba
• Is Part Of: Advances in water resources. Volume 130(2019)
• Page Start: 270
• Page End: 282
• Publication Date: 2019-08
• Subjects: Process-informed nonstationary extreme value analysis -- Physical-based covariates/drivers -- Methods for nonstationary analysis; Hydrology -- Periodicals; Hydrodynamics -- Periodicals; Hydraulic engineering -- Periodicals; 551.48
• DOI: 10.1016/j.advwatres.2019.06.007
• Languages: English
• ISSNs: 0309-1708

Abstract

Highlights: We introduce a process-informed extreme value analysis framework, namely ProNEVA. ProNEVA allows incorporating physical drivers into statistical analysis of extremes. ProNEVA allows both stationary and nonstationary modeling of climatic extremes. Process-informed nonstationary models better capture the changing extreme events. ProNEVA was applied to urbanization-flooding, CO2-temperature, sea level rise data. Abstract: 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.