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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
作者单位
李未 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 
秦伯强 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 
张运林 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 
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
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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