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南海两个代表性海区藻华事件特征的比较研究

高心雨 王天浩 苏华 吴文挺 卢文芳

高心雨,王天浩,苏华,等. 南海两个代表性海区藻华事件特征的比较研究[J]. 海洋学报,2023,45(5):90–106 doi: 10.12284/hyxb2023058
引用本文: 高心雨,王天浩,苏华,等. 南海两个代表性海区藻华事件特征的比较研究[J]. 海洋学报,2023,45(5):90–106 doi: 10.12284/hyxb2023058
Gao Xinyu,Wang Tianhao,Su Hua, et al. Comparative study on the characteristics of marine bloom events in two representative areas of the South China Sea[J]. Haiyang Xuebao,2023, 45(5):90–106 doi: 10.12284/hyxb2023058
Citation: Gao Xinyu,Wang Tianhao,Su Hua, et al. Comparative study on the characteristics of marine bloom events in two representative areas of the South China Sea[J]. Haiyang Xuebao,2023, 45(5):90–106 doi: 10.12284/hyxb2023058

南海两个代表性海区藻华事件特征的比较研究

doi: 10.12284/hyxb2023058
基金项目: 国家自然科学基金项目(41906019)。
详细信息
    作者简介:

    高心雨(1996-),女,安徽省亳州市人,主要从事海洋水色遥感研究。E-mail:gaoxy_09@qq.com

    通讯作者:

    卢文芳,男,副教授,主要从事海洋生态–动力耦合研究。E-mail:luwf6@mail.sysu.edu.cn

  • 中图分类号: P735

Comparative study on the characteristics of marine bloom events in two representative areas of the South China Sea

  • 摘要: 定量海洋浮游植物藻华事件的特征有利于认识海洋生态环境及动力过程,但由于遥感数据易受云层遮蔽的影响,以往对于南海藻华事件的研究多采用不连续的观测数据和遥感数据进行,因此未能系统性地认识南海中的藻华事件发生特征和控制规律。本文借鉴海洋热浪事件的统计分析框架,基于前期研究中构建的2005−2019年逐日、无缺失的遥感叶绿素a浓度资料,提取冬季吕宋西北部海域和夏季越南东南部海域两个代表性海区的藻华事件,分析两个代表性海区中藻华事件的持续时间、强度和长期趋势等特征。结果显示,吕宋西北部海域藻华频率降低,强度增大,且大部分海区趋势显著;越南东南部海域大部分海域趋势不显著。进一步分析两个海区藻华事件的影响因子,发现海面风场(正相关)和海表温度(负相关)对藻华事件影响最大;两个代表性海区藻华事件主要都由上升流控制,风是最重要的影响因子。分析藻华事件的先兆条件,发现温度锋面代表的亚中尺度活动也是一个重要的影响因子。本研究以南海为典型案例,为探讨海洋生态环境提供了新的研究视角。
  • 图  1  研究区域水深图

    灰色为等深线(单位:m);白色框为后文分析中的两个关注区域

    Fig.  1  Bathymetric map of study area

    Gray lines represent the isobath (unit: m); the white boxes are the two areas of concern in the following analysis

    图  2  藻华事件定义示意图

    Fig.  2  Schematic diagram of marine bloom event definition

    3  叶绿素浓度以及各影响因子的季节平均态

    白框为后文分析藻华事件叶绿素浓度(区域A和区域B)以及各影响因子进行平均的关键区域

    3  Chlorophyll concentration and the seasonal mean state of each influencing factor

    The white box shows the key regions for averaging the chlorophyll concentration (Region A and Region B) and influencing factors of marine bloom events in the following analysis

    图  4  区域A(a)和区域B(b)平均叶绿素浓度、90%阈值线和气候态的时间序列

    紫色阴影分别表示冬季(11月至2月)和夏季(6月至9月);红色箭头表示藻华事件

    Fig.  4  Time series of mean chlorophyll concentrations, 90% threshold lines, and climatic states in regions A (a) and B (b)

    Purple shades indicate winter (November to February) and summer (June to September); red arrows indicate marine bloom events

    图  5  提取的区域A(a1−a4)和区域B(b1−b4)藻华事件指标

    橘色柱形图代表15年间强度最大的事件

    Fig.  5  Marine bloom event indicators extracted from Region A (a1−a4) and Region B (b1−b4)

    Orange bars indicate the most intense events during 2005 and 2020

    图  6  2005−2019年吕宋西北部海域(a1−a4)和越南东南部海域(b1−b4)藻华事件指标趋势(p<0.1时用黑色点表示)

    Fig.  6  Trend of marine bloom event indicators in the northwest Luzon (a1−a4) and southeast Vietnam (b1−b4) from 2005 to 2019 (black dots for p<0.1)

    图  7  区域A(a)和区域B(b)2005−2010年和2014−2019年叶绿素浓度异常(减气候态)的频率直方图

    红色和蓝色实线为正态分布拟合;虚线为均值μ(单位:mg/m3);标准差为σ(单位:mg/m3);黑色实线为2005−2019年90%阈值(单位:mg/m3);a1代表区域A,2005–2010年;a2代表区域A,2014–2019年;b1代表区域B,2005–2010年;b2代表区域B,2014–2019年

    Fig.  7  Frequency histograms of chlorophyll a concentration anomalies (minus climatic states) for regions A (a) and B (b) from 2005 to 2010 and 2014 to 2019

    The solid red and blue lines are fitted with normal distribution; the dashed line is the mean value μ (unit: mg/m3); the standard deviation is σ (unit: mg/m3); the solid black line is the 90% threshold from 2005 to 2019 (unit: mg/m3). a1 represents Region A, 2005–2010; a2 represents Region A, 2014–2019; b1 represents Region B, 2005–2010; b2 represents Region B, 2014–2019

    图  8  区域A(a1−a6)和区域B(b1−b6)叶绿素浓度和各影响因子关于延迟天数和相关系数茎状图及各影响因子相关性最大延迟天数的茎状图(a7,b7)

    实心圆表示通过90%显著性检验,空心圆表示未通过

    Fig.  8  Stem-like plots of chlorophyll a concentrations and influencing factors in Region A (a1−a6) and Region B (b1−b6) on days of delay and correlation coefficients, and stem-like graph of maximum delay days of each influencing factor (a7, b7)

    Solid circles indicate passing the 90% significance test, hollow circles indicate failing

    图  9  区域A(a1−a6)和区域B(b1−b6)归一化藻华事件及对应的影响因子及延迟相关

    x轴表示藻华发生的天数(例如x=0代表藻华事件发生的前一天);各影响因素和叶绿素浓度为12个(区域A)和14个(区域B)藻华事件的平均;图中均列出相关性最大延迟回归天数以及对应的相关系数(99%显著);风场和温度锋面数据经过5 d的滑动窗口滤波

    Fig.  9  Normalized bloom event and corresponding influencing factors in regions A (a1−a6) and B (b1−b6) with lagged correlation

    The x-axis represents the number of days of marine bloom (for example, x=0 represents the day before the marine bloom event); the influencing factors and chlorophyll a concentration are the average of 12 (Region A) and 14 (Region B) marine bloom events. The maximum lagged regression days and the corresponding correlation coefficient (99% significant) are listed above each panel. The wind field and temperature front data are filtered by a 5-day sliding window

    图  10  区域A(a1−a5)和区域B(b1−b5)藻华事件发生前5天的平均叶绿素浓度以及各影响因子的空间分布及区域A(a6−a10)和区域B(b6−b10)藻华事件峰值为中心的5 d平均叶绿素浓度以及各影响因子的空间分布

    从上到下分别为叶绿素浓度(a1、a6、b1、b6,单位:mg/m3);海表温度(a2、a7、b2、b7,单位:℃);海面高度(a3、a8、b3、b8,单位:cm);海面风场(a4、a9、b4、b9,单位:m/s);温度锋面(a5、a10、b5、b10,单位:℃/km)

    Fig.  10  Spatial plots of averaged chlorophyll a concentration and corresponding influencing factors in regions A (a1−a5) and B (b1−b5) 5-day before the marine bloom event and spatial plots of average chlorophyll a concentration and corresponding influencing factors in regions A (a6−a10 ) and B (b6−b10) of a 5-day window centered at the peak of marine bloom events

    From top to bottom: chlorophyll a concentration (a1, a6, b1, b6, unit: mg/m3); sea surface temperature (a2, a7, b2, b7, unit: ℃); sea surface height (a3, a8, b3, b8, unit: cm); sea surface wind vectors and speed (a4, a9, b4, b9, unit: m/s); and temperature front (a5, a10, b5, b10, unit: ℃/km)

    表  1  藻华事件统计指标

    Tab.  1  Statistical indicators of marine bloom events

    统计指标定义单位
    持续时间开始时间−结束时间d
    最大强度藻华事件期间叶绿素浓度异常的最大值mg/m3
    平均强度藻华事件期间叶绿素浓度异常的平均值mg/m3
    累积强度藻华事件期间叶绿素浓度异常的逐天累加和mg/(m3∙d)
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-08-08
  • 修回日期:  2022-11-17
  • 网络出版日期:  2022-12-08
  • 刊出日期:  2023-05-01

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