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引用本文:罗晓春,杭鑫,曹云,杭蓉蓉,李亚春.太湖富营养化条件下影响蓝藻水华的主导气象因子.湖泊科学,2019,31(5):1248-1258. DOI:
LUO Xiaochun,HANG Xin,CAO Yun,HANG Rongrong,LI Yachun.Dominant meteorological factors affecting cyanobacterial blooms under eutrophication in Lake Taihu. J. Lake Sci.2019,31(5):1248-1258. DOI:
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太湖富营养化条件下影响蓝藻水华的主导气象因子
罗晓春1, 杭鑫1, 曹云2, 杭蓉蓉3, 李亚春1
1.江苏省气象服务中心, 南京 210008;2.南京市水利规划设计院股份有限公司, 南京 210001;3.南京航天宏图信息技术有限公司, 南京 210006
摘要:
利用2004-2018年卫星遥感解译的太湖蓝藻水华信息构建蓝藻综合指数,采用随机森林机器学习算法分析同期气象因子与蓝藻水华综合指数的关系,定量评估影响蓝藻水华的主要气象因子特征变量的重要性度量和贡献率.结果表明,在光、温、水、风等主要气象要素中,气温对蓝藻水华综合指数起着主导的作用,其次是风速和降水,日照时间的影响或可忽略.其中气温条件中重要性度量最大的是年平均气温,其次是冬、春季节的平均气温;风速因子中影响较大的是7月份的平均风速;水分条件中主导因子是9月累计降水量.优选的随机森林模型模拟值与实际蓝藻水华综合指数的变化趋势基本一致,拟合优度为0.91,通过0.01显著性检验,随机森林模型模拟效果较好.用随机森林模型模拟值对太湖蓝藻水华分等级评估,模型模拟精度达到了86.7%,其中5个重度等级年份模型模拟结果完全一致,中度等级的6个年份模型模拟值有5年与之相符,中度以上等级的模拟精度达90.9%,模型能够反映气象因子对蓝藻水华综合指数的综合影响,对中、重度蓝藻水华的模拟效果更好.随机森林模型有助于理解富营养化状态下影响蓝藻水华的主导气象因子,利用气象因子的可预测性可以促进蓝藻水华预测预警能力的提升.
关键词:  蓝藻水华  主导气象因子  随机森林  太湖
DOI:
分类号:
基金项目:江苏省科技支撑计划项目(BE2011840)、江苏省气象局重点项目(KZ201403)和江苏省气象局青年基金项目(KQ201819)联合资助.
Dominant meteorological factors affecting cyanobacterial blooms under eutrophication in Lake Taihu
LUO Xiaochun1, HANG Xin1, CAO Yun2, HANG Rongrong3, LI Yachun1
1.Jiangsu Meteorological Service Center, Nanjing 210008, P. R. China;2.Nanjing Water Planning and Designing Institute Co., Ltd, Nanjing 210001, P. R. China;3.Nanjing Piesat Information Technology Co., Ltd., Nanjing 210006, P. R. China
Abstract:
Based on the cyanobacteria comprehensive index(Ic) constructed by the satellite imageries of Lake Taihu in 2004-2018, the random forest machine learning algorithm was used to analyze the relationship between meteorological factors and Ic, and quantitatively evaluate the importance measures and contribution rate of the main meteorological features. The results show that among the main meteorological elements such as light, temperature, water and wind, temperature plays a leading role in cyanobacterial comprehensive index, followed by wind speed and precipitation, and the influence of sunshine hours may be neglected. Among them, the most important measure of importance in temperature conditions is the annual average temperature, followed by the average temperature in winter and spring. The most important in the wind factors is the average wind speed in July. The dominant factor in the water condition is the cumulative precipitation in September. The optimal random forest model simulation value is basically consistent with the actual cyanobacteria comprehensive index, and the determination coefficient is 0.91. The random forest model simulation effect is better by the 0.01 significance test. Using the random forest model simulation value to evaluate the cyanobacteria blooms in Lake Taihu, the model simulation accuracy reached 86.7%. The simulation results of the five severe grades of the year model are completely consistent. The simulation values of the six grade models of the medium grade are consistent with the five years, and the simulation accuracy of the medium and above grades is 90.9%. The model can reflect the comprehensive effects of meteorological factors on the cyanobacteria comprehensive index, and the simulation effect on medium and severe cyanobacteria blooms is better. The random forest model is helpful to understand the dominant meteorological factors affecting cyanobacterial blooms under eutrophication conditions. The predictability of meteorological factors can promote the improvement of cyanobacterial bloom prediction and early warning ability.
Key words:  Cyanobacteria bloom  dominant meteorological factors  random forest  Lake Taihu
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