太湖富营养化条件下影响蓝藻水华的主导气象因子
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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.
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.
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.
引文
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[5] Zhang M,Yang Z,Shi XL. Expansion and drivers of cyanobacterial blooms in Lake Taihu. J Lake Sci,2019,31(2):336-344. DOI:10.18307/2019.0203．[张民，阳振，史小丽．太湖蓝藻水华的扩张与驱动因素．湖泊科学，2019,31(2):336-344.]
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[10] Hans WP,Timothy GO. Harmful cyanobacterial blooms:Causes,consequences,and controls. Microbial Ecology,2013,65(4):995-1010.
[11] Rigosi A,Carey CC,Ibelings BW et al. The interaction between climate warming and eutrophication to promote cyanobacteria is dependent on trophic state and varies among taxa. Limnology and Oceanography,2014,59(1):99-114.
[12] Kong FX,Gao G. Hypothesis on cyanobacteria bloom-forming mechanism in large shallow eutrophic lakes. Acta Ecologica Sinica,2005,25(3):589-595．[孔繁翔，高光．大型浅水富营养化湖泊中蓝藻水华形成机理的思考．生态学报，2005,25(3):589-595.]
[13] Zhao QH,Sun GD,Wang JJ et al. Coupling effect of water temperature and light energy on the algal growth in Lake Taihu.J Lake Sci,2018,30(2):385-393. DOI:10.18307/2018.0210．[赵巧华，孙国栋，王健健等．水温、光能对春季太湖藻类生长的耦合影响．湖泊科学，2018,30(2):385-393.]
[14] Jessica R,Claire M,Stephen CM et al. Effects of multiple stressors on cyanobacteria abundance vary with lake type. Glob Change Biol,2018,24:5044-5055. DOI:10.1111/gcb.14396.
[15] Thomas MK,Litchman E. Effects of temperature and nitrogen availability on the growth of invasive and native cyanobacteria. Hydrobiologia,2016,763(1):357-369. DOI:10.1007/s10750-015-2390-2.
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