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  

Numerical forecasting of short-term algae-induced black bloom in eutrophic shallow lake:A case study of Lake Taihu

作　　者：李未[1] 秦伯强[1] 张运林[1] 朱广伟[1] 

机构地区：[1]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

出　　处：《湖泊科学》2016年第4期701-709,共9页Journal of Lake Sciences

基　　金：国家自然科学基金项目(41471401);国家自然科学基金重点项目(41230744)联合资助;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)

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.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 hydro- dynamic-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 dissolve^t 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%.

关 键 词：藻源性湖泛 数值模型 短期预报 浅水湖泊 太湖 

分 类 号：X524[环境科学与工程—环境工程]