太湖水华程度及其生态环境因子的时空分布特征

CORC > 生态环境研究中心 > 中国科学院生态环境研究中心 > 城市与区域生态国家重点实验室

	太湖水华程度及其生态环境因子的时空分布特征
	张艳会; 李伟峰; 陈求稳
刊名	生态学报
	2016
卷号	36 期号:14 页码:4337-4345
关键词	太湖水华程度自组织特征映射神经网络环境因子藻种结构
其他题名	Spatial-temporal variance of the intensity of algal bloom and related environmental and ecological factors in Lake Taihu
中文摘要	湖泊水华是全世界面临的严重生态环境问题之一,对人类和生态系统健康都有重大影响。由于湖泊水华受流域面源、点源污染、气候、水文因子以及湖泊生态系统自身特征等众多因素影响,水华是否爆发、其严重程度及时空分布特征呈现明显的复杂性。以我国太湖为研究区域,基于近年的水华及水环境的监测数据,用自组织特征映射神经网络对太湖不同监测点的水华程度进行了自动聚类分析。结果表明,太湖水华程度呈现为明显的无水华、轻度、中度和重度水华4类。不同程度水华的叶绿素a、水温、COD_(Mn)、营养盐、浮游植物生物量以及藻种(蓝藻、绿藻、硅藻)结构的时空差异显著,不同变量间的关系复杂,有助于深入认识太湖近年水华发生的时空变异特性。
英文摘要	Algal bloom,which results in significant adverse effects on aquatic ecosystem health,drinking water safety,and human beings, is one of the most serious environmental problems in lakes. Since many factors, such as non-point and point source pollution, meteorological and hydrodynamic conditions, and morphological features and characteristics of the lake ecosystem itself, can influence the outbreak of algal bloom, its mechanism is very complex and highly uncertain. In particular, large water bodies such as Lake Taihu have eco-environmental conditions with significant spatial and temporal variations. In the study, Lake Taihu was selectedand continuous monthly (2008-2010) on-site (33 sites) monitoring data were used. The self-organizing map (SOM) neural network approach was applied to automatically evaluate the algal bloom status according to long-term on-site monitoring data of the entire Lake. Then, for different intensities of algal bloom, the spatial and temporal distribution and variation of environmental and ecological factors (Chlorophyll-a,water temperature, COD_(Mn),TN,TP, main algae composition) were analyzed. The relation between the intensity of water bloom and the environmental and ecological factors were assessed. The intensity of algal blooms in Taihu Lake was classified into four degrees, no, light, moderate, and severe water blooms. The spatial -temporal occurrence of algal bloom in Lake Taihu with different intensity was clearly different. Spatially, the algal bloom intensity of Lake Taihu decreased from the northwest to the southeast. The most severe bloom occurred in the north and northwest areas, which is the main entrance of major rivers flowing into Lake Taihu. Moderate bloom occurred in the north, west, and southwest areas but seldom occurred in the east and central areas. Light bloom appeared across the entire lake, except for the southeast. Temporally, the most severe bloom outbreaked occurred during July to October. Moderate, light, and no blooms appeared from April to November. For different degrees of algal blooms, the corresponding environmental-ecological variables of chlorophyll-a, water temperature, COD_(Mn), TN,TP, and main algae composition (Cyanobacteria, Chlorophyta, Bacillariophyta) were clearly varied. The relations between these environmental-ecological variables were very complex. Generally,water temperature and the concentration of chlorophyll-a, COD_(Mn), TN, and TP increased from no algal bloom to severe algal bloom. For all the algal blooms, distinct variations were observed among the concentrations of chlorophyll-a and TP,while there were no marked differences among the water temperature,COD_(Mn),and TN. In relation to phytoplankton communities,cyanobacteria was dominant in all the algal blooms with different intensities. These findings are not only important for comprehensively understanding the spatial-temporal variations of algal bloom in Lake Taihu,but also support further identification of the mechanisms of algal bloom. In addition, this study might help the government and related decision-makers in establishing policies and practices on algal bloom monitoring and prevention.
内容类型	期刊论文
源URL	[http://ir.rcees.ac.cn/handle/311016/36556]
专题	生态环境研究中心_城市与区域生态国家重点实验室
推荐引用方式 GB/T 7714	张艳会,李伟峰,陈求稳. 太湖水华程度及其生态环境因子的时空分布特征[J]. 生态学报,2016,36(14):4337-4345.
APA	张艳会,李伟峰,&陈求稳.(2016).太湖水华程度及其生态环境因子的时空分布特征.生态学报,36(14),4337-4345.
MLA	张艳会,et al."太湖水华程度及其生态环境因子的时空分布特征".生态学报 36.14(2016):4337-4345.