Estimation of regional and at-site quantiles of extreme winds under flood index procedure

被引:2
作者
Ahmad, Ishfaq [1 ]
Ahmad, Touqeer [2 ]
Shahzad, Usman [1 ]
Ameer, Muhammad Athar [1 ]
Emam, Walid [3 ]
Tashkandy, Yusra [3 ]
Badar, Zanib [4 ]
机构
[1] Int Islamic Univ, Fac Basic & Appl Sci, Dept Math & Stat, Islamabad 44000, Pakistan
[2] CREST ENSAI, 51 Rue Blaise Pascal, F-35170 Bruz, France
[3] King Saud Univ, Fac Sci, Dept Stat & Operat Res, POB 2455, Riyadh 11451, Saudi Arabia
[4] Govt Associate Coll Girls, Chak Beli, Rawalpindi, Pakistan
关键词
Linear moments; Monte Carlo simulation; Quantile estimates; Wind speed; Regional frequency analysis; FREQUENCY-ANALYSIS; POWER; RAINFALL;
D O I
10.1016/j.heliyon.2023.e23388
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Extreme winds are becoming more common among environmental events with the most catastrophic societal consequences. A regional frequency analysis of Daily Annual Maximum Wind Speed (DAMWS) is necessary not only for a comprehensive understanding of wind hazards but also for infrastructure design and safety, wind energy potential, disaster risk reduction, insurance and risk assessment in a particular region of study. This study investigated regional frequency analysis of DAMWS of Baluchistan and Sindh provinces of Pakistan. L-moments regionalization techniques along with flood index procedure were applied to DAMWS records of 21 stations from 1990 to 2019 across the study area. We intended to find the regional frequency distribution for maximum winds and predict the returns for extreme winds events in the future. Only one station namely Lasbella was found to be discordant. With the help of cluster analysis, the remaining 20 stations were further divided into two homogeneous. Heterogeneity measures validate that both regions are homogenous with allotted stations. Regional quantiles for both regions are estimated through best-fit probability distribution among Generalized Normal (GNO), Generalized Logistic (GLO), Pearson Type 3 (P3), Generalized Pareto (GPA), and Generalized Extreme Value (GEV). Robustness of GLO distribution compared to GEV distribution is assessed through Monte Carlo simulations of relative bias and relative root mean square error. Findings clearly show that GLO distribution is the best for regional modeling. Furthermore, with the help of index flood procedure we determined at-site quantiles of all stations for various return periods. These estimated quantiles are of valuable information for various sectors, including infrastructure, energy, disaster management, and climate resilience, leading to improved planning, development, and risk reduction in the face of wind-related hazards in Sindh and Balochistan provinces of Pakistan.
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页数:15
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