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引用本文:杭鑫,李心怡,谢小萍,李亚春.基于通径分析法的太湖蓝藻水华定量气象评估模型.湖泊科学,2019,31(2):345-354. DOI:10.18307/2019.0204
HANG Xin,LI Xinyi,XIE Xiaoping,LI Yachun.The quantitative meteorological evaluation model of cyanobacterial bloom in Lake Taihu based on path analysis. J. Lake Sci.2019,31(2):345-354. DOI:10.18307/2019.0204
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基于通径分析法的太湖蓝藻水华定量气象评估模型
杭鑫1, 李心怡2, 谢小萍1, 李亚春1
1.江苏省气象服务中心, 南京 210008;2.南京信息工程大学应用气象学院, 南京 210044
摘要:
利用2005-2017年太湖周边区域气象观测资料和基于遥感解译的蓝藻水华信息,基于信息量权数法构建太湖蓝藻水华影响程度指数(简称为蓝藻指数),应用通径分析法,分析年平均气温(Ty)、1-3月平均气温(T1-3)、年降水量(Ry)、6-7月降水量(R6-7)和年高温日数(DTmax)5个气象因子对蓝藻水华影响的直接效应和间接效应,在此基础上构建太湖蓝藻水华气象评估模型.结果表明,2007年蓝藻指数值最大,为0.759,2017年其次,为0.709,2009年最小,仅为0.113,蓝藻指数与实际情况基本相符;直接通径系数中TyT1-3为正值,其余为负值,表明TyT1-3对蓝藻水华的发生发展具有正效应,而RyR6-7DTmax具有负效应,总通径系数绝对值排序为:Ty > T1-3 > Ry > R6-7 > DTmax,由此可以反映各气象因子对蓝藻水华影响程度的权重.根据模型计算的综合气象指数与蓝藻指数之间的相关系数达0.826,通过0.01显著性检验,根据百分位法将蓝藻指数和气象指数进行等级划分,分类总精度为84.6%,其中中度以上达90.9%,表明模型能够较好地反映综合气象因子与蓝藻水华发生发展程度的关系,在水体富营养化程度没有明显改善的情况下,可用于太湖蓝藻水华定量气象评估.上述研究结果有助于更好地理解环境因子、尤其是气象因子在蓝藻生长和水华形成机制中所起的作用,从而为太湖蓝藻水华的监测、预测预警和精细化防控提供理论依据.
关键词:  蓝藻水华  卫星遥感  通径分析  气象评估  太湖
DOI:10.18307/2019.0204
分类号:
基金项目:江苏省基础研究计划太湖专项(BK2007745)、江苏省科技支撑计划项目(BE2011840)和江苏省气象局重点项目(KZ201403)联合资助.
The quantitative meteorological evaluation model of cyanobacterial bloom in Lake Taihu based on path analysis
HANG Xin1, LI Xinyi2, XIE Xiaoping1, LI Yachun1
1.Jiangsu Meteorological Service Center, Nanjing 210008, P. R. China;2.Nanjing University of Science and Technology, School of Applied Meteorology, Nanjing 210044, P. R. China
Abstract:
Based on the meteorological data and satellite imageries of Lake Taihu and surrounding areas from 2005 to 2017, the paper built a cyanobacterial bloom index according to the information weight method, and analyzed the direct and indirect effects of cyanobacterial bloom from 5 meteorological factors (the annual average temperature (Ty), the average temperature from January to March(T1-3), the annual precipitation(Ry), the precipitation from June to July(R6-7), the annual high temperature days(DTmax) based on path analysis. The meteorological evaluation model of cyanobacterial bloom was built on this basis. The results show that the cyanobacterial bloom index of 2007 is the biggest (0.759), 2017 is the second (0.709), 2009 is the lowest (0.113). The cyanobacterial bloom index is basically consistent with the actual situation. Ty and T1-3 from direct path coefficient is positive, the rest is negative, it showed that Ty and T1-3 have positive effect on the occurrence and development of cyanobacteria bloom, however, the rest have the negative effect. The ordering of the absolute value of the total path coefficient is:Ty > T1-3 > Ry > R6-7 > DTmax, this can reflect the weight of meteorological factors affecting cyanobacteria bloom. According to this model, the correlation coefficient between the cyanobacterial bloom index and the comprehensive meteorological index passed 0.01 significance test. Then we ranked the cyanobacterial bloom index and meteorological factors according to percentile method. The total classification accuracy was 84.6%, and the moderate above it come up to 90.9%. It showed that the model can reflect the relationship between the comprehensive meteorological factors and the occurrence and development of cyanobacteria bloom better, so it can be used in the quantitative meteorological evaluation of cyanobacteria bloom in Lake Taihu under the circumstances of eutrophication degree without significant improvement. The research above help to better understand the role of environmental factors, especially meteorological factors, in the formation mechanism of cyanobacterial bloom, and provide the basis for the prediction, early warning and fine prevention & control of cyanobacteria bloom in Lake Taihu.
Key words:  Cyanobacterial bloom  remote sensing  path analysis  meteorological evaluation  Lake Taihu
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