Analysis of the spatio-temporal variations of significant wave height in the northern South China Sea and the return period estimation methods of its extreme based on WW3
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摘要: 本文基于第3代海浪模式WAVEWATCH III (WW3)模拟的1996–2015年海浪后报数据,分析了南海北部有效波高及其极值的时空变化特征,并采用Pearson-III和Gumbel两种极值分布方法对该区极值波高重现期进行了估算。结果表明,南海北部有效波高的季节变化和空间分布与季风风场基本一致,呈现秋冬高春夏低,并自吕宋海峡西侧向西南降低的特征,与ERA5再分析数据结果高度相似。有效波高极值(简称极值波高)的时空分布特征受时间分辨率强烈影响,采用极值数据的分辨率越高(如逐小时),所展现的台风型波浪特征越显著。扣除季节变化信号后的有效波高和年极值波高均体现出较强的线性增高趋势,近20年升高的比例分别为7.7%和31.6%,值得警惕和关注。该区多年一遇极值波高存在若干个大值区,且与台风的路径、强度有直接联系,表明台风是引发该区域极端大浪的最主要机制。对比Pearson-III和Gumbel极值分布估算结果发现:若极值波高较低,频率随极值波高升高缓慢降低,此时两种极值分布的估算都比较准确,差异极小,可忽略不计;但当研究时间范围内,某年极值波高远超其他年份时,Pearson-III极值分布估算结果明显高于Gumbel极值分布估算结果,且更接近实际极值波高,即Pearson-III极值分布在此情况下表现更好。本研究表明对于特定海区,在出现超强台风引发极值波高远超出其他年份时,不同计算方法对极值波高的估算差异较大,会显著影响重现期的评估。此外,南海北部年极值波高的强烈增高趋势,也可能给计算未来极值波高重现期和海上工程防护带来不可忽视的影响。
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关键词:
- 南海北部 /
- 有效波高 /
- 极值波高 /
- 重现期 /
- Pearson-III极值分布 /
- Gumbel极值分布
Abstract: The spatio-temporal variations of significant wave height and its extremum in the northern South China Sea from 1996 to 2015 are analyzed based on the wave hindcast simulation by the third-generation wave model WaveWatch III (WW3). Two extreme value distribution methods, Pearson-III and Gumbel methods, have been used to estimate the return period of the extreme significant wave height in the northern South China Sea. The results show that the seasonal variation and spatial distribution of significant wave height in the northern South China Sea are consistent with the monsoon field, similar to the results of reanalysis data. It is high in autumn and winter and low in spring and summer, and decreases from the west part of the Luzon Strait to the southwest. But the extremum of significant wave height is strongly affected by the temporal resolution. The higher resolution (such as hourly), the more typhoon wave characteristics are displayed. After deducting the seasonal cycle signal, both the significant wave height and its annual extremum show an intense linear trend, increasing by 7.7% and 31.6% in the last 20 years respectively. There are several large value regions of the return period wave height in this area. They are related to the typhoon's track and intensity directly, indicating that typhoons are the primary mechanism causing extreme waves in this region. Comparing the Pearson-III and Gumbel distribution, it is found that if the extreme significant wave height was relatively low and the frequency decreased slowly with the growth of the extremum, the two methods are both accurate, with little difference which could be ignored. However, when the extreme significant wave height was much higher in one year than that in other years, the estimated result of the Pearson-III would be much higher than that of the Gumbel method, and also closer to the actual value. In other words, the Pearson-III extreme value distribution behaves better in this situation. This study shows that when the extreme significant wave height caused by a super typhoon is much higher than that in other years, the estimation from different methods differs greatly, which will significantly affect the assessment of the return period. Besides, the strong increasing trend of the extreme significant wave height in the northern South China Sea will also bring a non-negligible impact on the calculation of return period wave height and marine engineering protection in the future. -
图 4 1996–2015年WW3模拟结果南海北部有效波高不同极值算法比较
a. 月平均结果的年极值平均;b. 月平均结果的极大值;c. 逐小时结果的年极值平均;d. 逐小时结果的极大值
Fig. 4 The comparison of the significant wave height extreme values in different definitions over the northern South China Sea from 1996 to 2015 based on WW3 simulation
a. Average by annual extreme of the monthly data; b. maximum value of the monthly data; c. average by annual extreme of the hourly data; d. maximum value of the hourly data
图 5 WW3中南海北部有效波高(蓝线)和极值波高(红线)的时间序列
a. 日平均和对应日极值;b. 年平均和对应年极值,虚线为各曲线对应的线性趋势
Fig. 5 The long-time variations of the WW3 significant wave height (blue lines) and its extreme values (red lines) over the northern South China Sea
a. Daily average and its extremum; b. annual average and its extremum, the dashed line is linear trend of the corresponding curve
图 7 采用Gumbel(a,c)和Pearson-III(b,d)两种方法计算的南海北部不同重现期极值波高对比
白色字母C,D代表两个极值波高最大的海区,黑色字母A,B代表两个极值波高较低的海区。为了方便对比,a和b的色图取值范围与图3d一致,且c和d只在较a和b取值增大的范围内增加了新的紫色调,在极值波高低于16 m时,4个分图的取色是完全一致的
Fig. 7 The extreme wave height in different return periods from Gumbel (a, c) and Pearson-III (b, d) methods over the northern South China Sea
The white letters C and D represent the two regions with the largest extreme wave height, while the black letters A and B represent the two regions with low extreme wave height. For the convenience of comparison, the color map of a and b is consistent with that of Figure 3d, and c and d only adds a new purple tone in the range of increasing value compared with a and b. When the extreme wave height is lower than 16 m, the color map of the four sub-graphs is completely consistent
表 1 南海北部年最强台风路径与对应极值波高的分类
Tab. 1 The classification of the yearly strongest typhoon path and corresponding extreme wave height over the northern South China Sea
分类 特征概述 典型年份 (1)南北型 如图6a所示,最强台风路径自南向北运动,对应极值波高大值区也是南北走向* 2006, 2010 (2)北部型 如图6b所示,最强台风出现于吕宋海峡北部至珠江口沿线,对应极值波高主要影响广东中东部沿岸,对北部湾影响较小 1996, 2008 (3)多区域混合型 如图6c所示,一般于南海北部的北、中、南区域均出现超强台风,对应了3个极值波高的大值区域。 2001, 2005, 2012, 2013 (4)中部型 如图6d所示,最强台风出现于吕宋海峡南部至雷州半岛沿线,对应极值波高主要影响雷州半岛海南省东侧,其次影响广东中东部沿岸,可能影响北部湾 2003, 2011, 2014 (5)低极值型 全年台风均较弱,极值波高基本均低于10 m 1997–2000, 2002, 2004, 2007, 2009 注:第3列中字体加粗的年份为图6中选用的代表年;*2006年最强台风“象神”是东西走向,但对极值波高影响较小,所以仍计入第一类,详见正文。 表 2 南海北部4个关键区代表点不同重现期的极值波高统计(单位:m)
Tab. 2 The extreme wave height in different return periods of the representative points in the four key areas of the northern South China Sea (unit: m)
代表点 计算方法 重现期 20年 50年 100年 200年 300年 A Gumbel 8.89 9.66 10.23 10.8 11.13 Pearson-III 9.24 10.08 10.68 11.27 11.61 B Gumbel 6.70 7.71 8.46 9.21 9.65 Pearson-III 7.11 8.58 9.71 10.85 11.52 C Gumbel 11.03 12.73 14.00 15.27 16.02 Pearson-III 11.5 14.85 17.55 20.34 22.01 D Gumbel 11.82 13.80 15.28 16.76 17.63 Pearson-III 12.51 15.93 18.63 21.39 23.03 -
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