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引用本文:李未,秦伯强,张运林,朱广伟.富营养化浅水湖泊藻源性湖泛的短期数值预报方法——以太湖为例.湖泊科学,2016,28(4):701-709. DOI:10.18307/2016.0402
LI Wei,QIN Boqiang,ZHANG Yunlin,ZHU Guangwei.Numerical forecasting of short-term algae-induced black bloom in eutrophic shallow lake:A case study of Lake Taihu. J. Lake Sci.2016,28(4):701-709. DOI:10.18307/2016.0402
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富营养化浅水湖泊藻源性湖泛的短期数值预报方法——以太湖为例
李未, 秦伯强, 张运林, 朱广伟
中国科学院南京地理与湖泊研究所湖泊与环境国家重点实验室, 南京 210008
摘要:
本文建立了一种富营养化浅水湖泊藻源性湖泛的短期数值预报方法. 选取表征藻源性湖泛的代表性指标叶绿素a和溶解氧浓度作为预测变量,以天气预报中的风场为驱动力,求解浅水湖泊三维水动力水质耦合数值模型,计算未来3 d浅水湖泊叶绿素a和溶解氧浓度的时空分布,然后结合未来3 d的气象因子信息建立经验公式,计算湖泛易发水域发生湖泛的概率,并进一步确定湖泛发生位置和面积. 以太湖为例,采用构建的方法于2013,2014年夏、秋季对太湖7段湖泛易发水域的湖泛发生概率及发生面积进行未来3 d的预测预报,预报正确率在80%以上.
关键词:  藻源性湖泛  数值模型  短期预报  浅水湖泊  太湖
DOI:10.18307/2016.0402
分类号:
基金项目:国家自然科学基金项目(41471401)、科技部国际科技合作与交流专项(2015DFG91980)、国家水体污染控制与治理科技重大专项(2012ZX07101-010)和国家自然科学基金重点项目(41230744)联合资助.
Numerical forecasting of short-term algae-induced black bloom in eutrophic shallow lake:A case study of Lake Taihu
LI Wei, QIN Boqiang, ZHANG Yunlin, ZHU Guangwei
State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, P.R.China
Abstract:
In this paper, an attempt to forecast the algae-induced black bloom in eutrophic shallow lake was documented. Taken chlorophyll-a concentration and dissolved oxygen concentration as the representative variables, a three-dimensional, coupled hydrodynamic-water quality numerical model was built. By combining calculation and prediction of the hydrological and meteorological scenarios over the ensuing 3 days, the dynamic distributions of algae concentration and dissolved oxygen concentration scenarios in Lake Taihu were simulated. Black Bloom probabilities were then predicted by a forecast empirical model that included the weight of algal biomass, dissolved oxygen concentration, wind velocity, and weather condition. If the probabilities were larger than 50%, the area of black bloom should be calculated. The model was applied to predict the occurrences of the black bloom of the next 3 days in Lake Taihu from April to September in 2013 and 2014. Independent evaluations from boat survey data showed that the accuracy of these bloom forecasts was more than 80%.
Key words:  Algae-induced black bloom  numerical model  short-term forecast  shallow lake  Lake Taihu
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