Research on meteorological factors and Logistic prediction model of cyanobacterial blooms in Erhai Lake

Using remote sensing observations of cyanobacterial blooms in Lake Erhai during summer and autumn 2013, we examined the key meteorological factors driving bloom formation. Statistical analyses show that blooms frequently occurred after transitions from cloudy or rainy weather to clear conditions, with prolonged strong solar radiation and large diurnal temperature ranges as primary triggers. Low wind speed and low atmospheric pressure further facilitated buoyancy and aggregation of cyanobacteria, enhancing bloom development. A logistic regression model was established with bloom occurrence as the dependent variable and meteorological factors as predictors. The model achieved 87.5% prediction accuracy, confirming both the critical influence of meteorological conditions on bloom formation and the feasibility of using meteorological data to support monitoring, forecasting, and early warning.

Chen L., Zhang J., Chen X., Cai M., Li H. (2016). Research on meteorological factors and Logistic prediction model of cyanobacterial blooms in Erhai Lake. Journal of China Central Normal University. 50(4):606-611. (In Chinese)
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