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引用本文: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.太湖蓝藻水华的年度情势预测方法探讨.湖泊科学,2020,32(5):1421-1431. DOI:10.18307/2020.0504
ZHU Guangwei,SHI Kun,LI Wei,LI Na,ZOU Wei,GUO Chaoxuan,ZHU Mengyuan,XU Hai,ZHANG Yunlin,QIN Boqiang.Seasonal forecast method of cyanobacterial bloom intensity in eutrophic Lake Taihu, China. J. Lake Sci.2020,32(5):1421-1431. DOI:10.18307/2020.0504
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太湖蓝藻水华的年度情势预测方法探讨
朱广伟1, 施坤1, 李未1, 李娜2, 邹伟1, 国超旋1, 朱梦圆1, 许海1, 张运林1, 秦伯强1
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, 太湖湖泊生态系统研究站, 南京 210008;2.南京师范大学虚拟地理环境教育部重点实验室, 南京 210023
摘要:
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.
关键词:  蓝藻水华  季度预测  水温  降雨量  营养盐  太湖
DOI:10.18307/2020.0504
分类号:
基金项目:国家自然科学基金项目(41671494,41830757)、国家水体污染控制与治理科技重大专项(2017ZX07203001)、中国科学院前沿科学重点研究项目(QYZDJ-SSW-DQC008)和山东省重大科技创新工程项目(2018YFJH0902)联合资助.
Seasonal forecast method of cyanobacterial bloom intensity in eutrophic Lake Taihu, China
ZHU Guangwei1, SHI Kun1, LI Wei1, LI Na2, ZOU Wei1, GUO Chaoxuan1, ZHU Mengyuan1, XU Hai1, ZHANG Yunlin1, QIN Boqiang1
1.Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, P. R. China;2.Key Laboratory of Virtual Geographic Environment of Education Ministry, Nanjing Normal University, Nanjing 210023, P. R. China
Abstract:
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.
Key words:  Cyanobacterial blooms  seasonal forecast  water temperature  rainfall  nutrient  Lake Taihu
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