Predicting algal blooms in different zones of Lake Chaohu based on satellite-derived algal biomass and meteorological factors

湖泊能为人类提供不可或缺的资源,而全球普遍存在的湖泊富营养化导致的藻华频繁暴发正不断损害湖泊生态环境服务功能.为合理保护湖泊环境和防治藻华危害,需预测藻华暴发.以我国富营养巢湖为研究区,本文构建了一种基于遥感藻总量和气象因子的不同湖区藻华暴发概率预测方法.基于MODIS/Aqua数据,研究首先反演了2003—2019年日尺度的藻华分布和考虑垂向结构的水柱藻总量.然后,统计了西、中和东巢湖的藻华面积,判别了藻华/非藻华日,并匹配日平均藻总量和气象因子.最后,筛选出藻华形成的关键影响因子——藻总量、气温和水汽压,并构建了不同湖区日藻华暴发概率的Logistic预测模型.不同湖区月平均藻总量基本一致,但藻华暴发日占比呈“西高东低”特征.对西、中和东巢湖的藻华/非藻华检验样本,模型精度分别为90%、85%和89.5%,模型也适用于2020年夏秋季和冬春季藻华预测.湖泊藻华暴发是藻类大量增殖并在一定气象条件下的产物,故基于遥感藻总量和气象因子的藻华暴发概率预测科学合理,可推广应用于太湖等其他富营养湖泊.;Lakes provide indispensable resources for humans, but the frequent outbreaks of algal blooms caused by eutrophication in lakes worldwide are continuously damaging ecological services lakes. In order to reasonably protect lake and prevent harm blooms, it is necessary predict outbreak blooms. Taking typical eutrophic Lake Chaohu as study area, this constructed a method probability based on satellite-derived water column-integrated biomass meteorological factors. Based MODIS/Aqua data, firstly retrieved bloom distributions different dates during 2003-2019. Then, we calculated area West Chaohu, Middle East identified or non-algal matched corresponding factors data. Finally, screened out key affecting formation including biomass, air temperature vapor pressure, Logistic models predicting daily zones Chaohu. We found monthly average were almost same proportion days with was high west low east. The accuracy model 90%, 85% 89.5% respectively, also applicable prediction winter-spring summer-autumn seasons 2020. Algal results proliferation along certain conditions, so remote sensing data scientific reasonable, can be applied other such Taihu.