%0 Journal Article %T 太湖蓝藻水华的年度情势预测方法探讨 %T Seasonal forecast method of cyanobacterial bloom intensity in eutrophic Lake Taihu, China %A 朱广伟,施坤,李未,李娜,邹伟,国超旋,朱梦圆,许海,张运林,秦伯强 %A ZHU,Guangwei %A SHI,Kun %A LI,Wei %A LI,Na %A ZOU,Wei %A GUO,Chaoxuan %A ZHU,Mengyuan %A XU,Hai %A ZHANG,Yunlin %A QIN,Boqiang %J 湖泊科学 %J Journal of Lake Sciences %@ 1003-5427 %V 32 %N 5 %D 2020 %P 1421-1431 %K 蓝藻水华;季度预测;水温;降雨量;营养盐;太湖 %K Cyanobacterial blooms;seasonal forecast;water temperature;rainfall;nutrient;Lake Taihu %X 在太湖、巢湖、滇池、洱海、三峡水库等我国重要湖泊和水库,蓝藻水华时常发生但年际之间藻情往往有较大差异,给蓝藻水华的防控物资及人员投入、湖库水源地水质安全保障带来较大的挑战,亟待探索周年尺度的蓝藻水华强度预测方法.本文收集了太湖连续15年的蓝藻水华情势观测数据和同步的气象、水文数据用于构建蓝藻水华预测模型,提出了利用遥感反演的蓝藻水华面积(ABL)及人工观测的水体浮游植物叶绿素a浓度([Chl.a]LB)共同表征的蓝藻水华强度指标(BI).分析了太湖年尺度的BI值与环境条件的关系,提出了基于年初能够掌握的气象、水文、营养盐等综合环境指标进行年度BI预测的统计模型.结果表明,太湖年度BI值与冬季及初春(12—3月)日均水温(WT12-3)、冬春季有效积温(AT12-3)、前一年降雨总量(RFYB)等环境因子呈显著正相关,与冬季及初春的水体总氮(TN12-3)、溶解性总氮(DTN12-3)、总磷(TP12-3)及溶解性总磷(DTP12-3)不存在统计上的显著相关关系.此外,本研究开展了基于上述因子(BI为因变量,其余环境因子为自变量)的多元(或一元)回归分析,并遴选出最优模型.总体而言,最优模型的模拟计算结果与实测浓度具有较高的一致性,因此本研究得出的模型对太湖蓝藻水华年际强度预测具有较高精度.本研究对太湖等富营养化湖库蓝藻水华的中长期预测具有指导意义. %X Many important lakes and reservoirs of China, including Lake Taihu, Lake Chaohu, Lake Dianchi, Lake Erhai and Three Gorges Reservoir, were plagued with cyanobacterial blooms. However, the intensity of the blooms in these freshwaters varied significantly in different years, which exhibited significant challenges to the blooms collection organizations and drinking water plants, leading to the urgent need to cyanobacteria blooms prediction model based on annual dataset. Therefore, the long-term (15 years) observation data and meteorological and hydrological datasets of Lake Taihu were collected for the prediction of algal blooms. In current study, cyanobacterial bloom intensity index (BI) were proposed with the consideration of yearly average blooms area interpret by high frequency remote sensing images and whole lake average chlorophyll-a concentration. Furthermore, environmental factors, such as water temperature, rainfall, water level, nitrogen and phosphorus concentrations were used as the crucial factors to predict BI. Our results showed that average water temperature in winter and early spring, as well as the rainfall of the former year were significant positive factors of the yearly BI value in Lake Taihu. While the nutrient-related factors in early spring had no significant relationships with BI. In addition, a multiple (or univariate) regression analysis based on the above factors (BI was the dependent variable and the remaining environmental factors were the independent variables) were performed in this study, and the optimal model was selected. In general, the predicted results of the selected optimal model had a high consistency with the measured concentrations, thus the model obtained in this study had relatively high accuracy for predicting the interannual intensity of cyanobacteria blooms in Taihu Lake. This study may serve reliably for the medium- and long-term prediction of cyanobacteria blooms in Lake Taihu, and other eutrophic lakes. %R 10.18307/2020.0504 %U http://www.jlakes.org/ch/reader/view_abstract.aspx %1 JIS Version 3.0.0