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Forecasting short-term cyanobacterial blooms in Lake Taihu, China, using a coupled hydrodynamic–algal biomass model

Authors

  • Wei Li,

    1. State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Science, Nanjing, China
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  • Boqiang Qin,

    Corresponding author
    1. State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Science, Nanjing, China
    • Correspondence to: Boqiang Qin, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Science, 73 East Beijing Road, Nanjing 210008, China.

      E-mail: qinbq@niglas.ac.cn

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  • Guangwei Zhu

    1. State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Science, Nanjing, China
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ABSTRACT

Lake Taihu, the third largest freshwater lake of China, provides drinking water supply for five million people. Over the last 30 years, the lake has suffered from serious cyanobacterial blooms that deteriorate drinking water quality and in some cases have led to serious water supply crises. For local government to respond quickly to the onset of a cyanobacterial bloom, it is crucial to forecast the probability, areas, and intensity of the bloom. In this paper, an attempt to forecast the cyanobacterial bloom in Lake Taihu is documented. The forecast is based on a short-term cyanobacterial bloom forecasting numerical model containing a three-dimensional, coupled hydrodynamic–algal biomass model and a probability of bloom occurrence forecasting model. The former model was based on solving the governing equations of the cyanobacterial bloom dynamics in shallow lakes. Unstructured mesh division was used to fit the irregular coastal boundaries where harmful blooms often happened. The finite volume method discretized the governing equations, and the conservation laws were preserved. To drive the model, the initial algae chlorophyll a concentrations were obtained from 18 automatic monitoring buoys and boat survey measurements. By combining calculation and prediction of the hydrological and meteorologic scenarios over the ensuing 3 days, the dynamic distributions of the algae concentration scenarios in Lake Taihu were simulated. Blooming probabilities were then predicted by a forecast model that included the weight of algal biomass, wind velocity, and weather condition. The model was applied to predict the occurrences of the algae blooms of the next 3 days in Lake Taihu during April to September in 2009 and 2010. Independent evaluations from remote sensing images and boat survey data showed that the accuracy of these bloom forecasts was more than 80%. Copyright © 2013 John Wiley & Sons, Ltd.

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