北印度洋显著波高的非稳态建模

IF 3.9 3区 环境科学与生态学 Q1 ENGINEERING, CIVIL Stochastic Environmental Research and Risk Assessment Pub Date : 2024-08-27 DOI:10.1007/s00477-024-02775-3
P. Dhanyamol, V. Agilan, Anand KV
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引用次数: 0

摘要

极端气象条件的统计描述对于海洋环境中结构的安全可靠设计和运行至关重要。显波高度(\({H}_{S}\))是沿岸和近海结构设计中最基本的波参数之一。最近的研究报告指出,\({H}_{S}\) 中存在一个全球性的时变分量。因此,\({H}_{S}}\ 的年最大序列的非稳态行为对各种海洋工程应用非常重要。本研究旨在利用非平稳广义极值(GEV)分布对\({H}_{S}\)序列的非平稳性进行建模,从而分析北印度洋上空\({H}_{S}\)的频率。研究使用了欧洲中期天气预报中心(ECMWF)全球大气再分析数据集收集的每小时最大值 ({H}_{S}\)数据(空间分辨率为 0.5° 经度 × 0.5° 纬度)。为了使用非稳态 GEV 分布来模拟 \({H}_{S}\) 的年最大序列,在 GEV 分布的位置和尺度参数中引入了两个物理协变量(厄尔尼诺/南方涛动(ENSO)和印度洋偶极子(IOD))和时间协变量。估算了非稳态条件下 \({H}_{S}\)不同频率的回归水平。从结果来看,研究区域内 25 年、50 年、100 年和 200 年重现期的平均增幅分别为 13.46%、13.66%、13.85% 和 14.02%。各重现期的重现水平最大降幅为 33.3%,最大增幅为 167%。非平稳回归水平随时间的变化凸显了对\({H}_{S}\)中的非平稳性建模的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Modeling non-stationarity in significant wave height over the Northern Indian Ocean

Statistical descriptions of extreme met-ocean conditions are essential for the safe and reliable design and operation of structures in marine environments. The significant wave height (\({H}_{S}\)) is one of the most essential wave parameters for coastal and offshore structural design. Recent studies have reported that a time-varying component exists globally in the \({H}_{S}\). Therefore, the non-stationary behavior of an annual maximum series of \({H}_{S}\) is important for various ocean engineering applications. This study aims to analyze the frequency of \({H}_{S}\) over the northern Indian Ocean by modeling the non-stationarity in the \({H}_{S}\) series using a non-stationary Generalized Extreme Value (GEV) distribution. The hourly maximum \({H}_{S}\) data (with a spatial resolution of 0.5° longitude × 0.5° latitude) collected from the global atmospheric reanalysis dataset of the European Centre for Medium-Range Weather Forecasts (ECMWF) is used for the study. To model the annual maximum series of \({H}_{S}\) using a non-stationary GEV distribution, two physical covariates (El-Ni \(\widetilde{n}\) o Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD)) and time covariates are introduced into the location and scale parameters of the GEV distribution. The return levels of various frequencies of \({H}_{S}\) are estimated under non-stationary conditions. From the results, average increases of 13.46%, 13.66%, 13.85%, and 14.02% are observed over the study area for the 25-year, 50-year, 100-year, and 200-year return periods, respectively. A maximum percentage decrease of 33.3% and a percentage increase of 167% are observed in the return levels of various return periods. The changes in the non-stationary return levels over time highlight the importance of modeling the non-stationarity in \({H}_{S}\).

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来源期刊
CiteScore
7.10
自引率
9.50%
发文量
189
审稿时长
3.8 months
期刊介绍: Stochastic Environmental Research and Risk Assessment (SERRA) will publish research papers, reviews and technical notes on stochastic and probabilistic approaches to environmental sciences and engineering, including interactions of earth and atmospheric environments with people and ecosystems. The basic idea is to bring together research papers on stochastic modelling in various fields of environmental sciences and to provide an interdisciplinary forum for the exchange of ideas, for communicating on issues that cut across disciplinary barriers, and for the dissemination of stochastic techniques used in different fields to the community of interested researchers. Original contributions will be considered dealing with modelling (theoretical and computational), measurements and instrumentation in one or more of the following topical areas: - Spatiotemporal analysis and mapping of natural processes. - Enviroinformatics. - Environmental risk assessment, reliability analysis and decision making. - Surface and subsurface hydrology and hydraulics. - Multiphase porous media domains and contaminant transport modelling. - Hazardous waste site characterization. - Stochastic turbulence and random hydrodynamic fields. - Chaotic and fractal systems. - Random waves and seafloor morphology. - Stochastic atmospheric and climate processes. - Air pollution and quality assessment research. - Modern geostatistics. - Mechanisms of pollutant formation, emission, exposure and absorption. - Physical, chemical and biological analysis of human exposure from single and multiple media and routes; control and protection. - Bioinformatics. - Probabilistic methods in ecology and population biology. - Epidemiological investigations. - Models using stochastic differential equations stochastic or partial differential equations. - Hazardous waste site characterization.
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