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富营养化状态下太湖蓝藻水华气象条件影响的评估
引用本文:杭鑫,徐敏,谢小萍,李亚春.富营养化状态下太湖蓝藻水华气象条件影响的评估[J].科学技术与工程,2019,19(7).
作者姓名:杭鑫  徐敏  谢小萍  李亚春
作者单位:南京信息工程大学大气物理学院,南京210044;江苏省气象服务中心,南京210008;江苏省气象服务中心,南京,210008
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);江苏省气象局重点项目(KZ201403)
摘    要:根据2005~2017年太湖流域气象观测数据与同步卫星观测数据分析结果,表明:太湖蓝藻水华在日平均气温为0~35℃的区间内都会出现,高发区间为15.0~35℃,大面积蓝藻水华也多集中在20.0~35℃区间内,33℃以上的高温会产生抑制作用;蓝藻水华在卫星过境前6h平均风速为0.5~3.4 m/s区间内集中出现,占94.7%,89%的大面积蓝藻水华出现在0~2.0 m/s,风向主要影响蓝藻水华的移动和空间分布;降水的影响总体上呈负效应,但大面积蓝藻水华在前24小时有降水的情况下,其中小雨量级(<10mm)的占比达88%,说明降水的适度扰动可能有利于大面积蓝藻水华形成;日照充足可能有利于蓝藻生长,但并非蓝藻水华形成的必要条件。在上述分析基础上,构建了气象评估多元回归模型,实际值和模拟值拟合的相关系数达0.83,通过了0.001显著性检验,可用于蓝藻水华的定量气象评估,从而为政府防控蓝藻水华提供决策依据。

关 键 词:蓝藻水华  卫星遥感  气象因子特征  评估模型  太湖
收稿时间:2018/9/6 0:00:00
修稿时间:2018/11/5 0:00:00

Assessment of the influence of meteorological conditions on cyanobacteria bloom in Taihu Lake under eutrophication
Hang Xin,and.Assessment of the influence of meteorological conditions on cyanobacteria bloom in Taihu Lake under eutrophication[J].Science Technology and Engineering,2019,19(7).
Authors:Hang Xin  and
Abstract:According to the analysis results of meteorological observation data and synchronous satellite observation data from 2005 to 2017, it is shown that the Taihu Lake cyanobacteria blooms in the daily average temperature range of 0-35 °C, and the high-incidence interval is 15.0-35 °C. The bloom in the interval of 20.0 - 35 °C, the high temperature above 33 °C will produce inhibition. The cyanobacterial bloom mainly occur in the first 6 hours. For the interval of 0.5-3.4 m/s, the proportion is 94.7%. The large-scale cyanobacterial blooms mainly occur in the first 6h, and the average wind speed is 0-2.0 m/s, accounting for 89%. The wind direction mainly affects the movement and spatial distribution of cyanobacterial bloom. Precipitation generally has a negative impact on the occurrence and development of cyanobacterial bloom. Large-area cyanobacteria blooms have precipitation in the first 24 hours, of which 88% are in the form of small rainfall (<10mm), indicating light rain. The moderate disturbances may have a certain promoting effect on large-scale cyanobacteria bloom. Sufficient sunshine may be one of the conditions for cyanobacterial bloom, but it is not a necessary condition. Based on the above analysis, a multivariate regression model of meteorological evaluation was constructed. The correlation coefficient between the actual value and the simulated value was 0.83. It passed the 0.001 significance test and can be used for quantitative meteorological evaluation of cyanobacterial blooms, Therefore, it provides decision-making basis for the government to prevent and control cyanobacteria bloom.
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