State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing (LIESMARS), Wuhan University, Wuhan, 430079, China.
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing (LIESMARS), Wuhan University, Wuhan, 430079, China; National Engineering Research Center for Geographic Information System, School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan, 430074, China.
J Environ Manage. 2022 May 15;310:114782. doi: 10.1016/j.jenvman.2022.114782. Epub 2022 Mar 2.
Occurrence of cyanobacterial blooms in most lakes has dramatic changes in time and space. However, most current studies only focused on daily or seasonal scales to obtain a relatively coarse resolution result. To explore the possibility of fine changes occurring within a day in Lake Taihu (China), the area coverage of surface cyanobacterial blooms was quantified from the hourly Geostationary Ocean Color Imager (GOCI) data using a GOCI-derived cyanobacterial index. Based on that, diurnal change characteristics were explored at two scales, and the environmental impacts were investigated. For that, an classification method was first designed to identify the types of diurnal change patterns of cyanobacterial blooms automatically. This method classified the patterns into four types, including the decreasing (Type1), decreasing first and then increasing (Type2), increasing (Type3), increasing first and then decreasing (Type4). Based on that, the types of diurnal change patterns of blooms in Lake Taihu (from April 1, 2011 to October 31, 2020) were identified at pixel (500 m) and synoptic scales. Results indicated that Type1 and Type3 were two hot diurnal change patterns of blooms, and lakeshore was the hotspot occurring severe diurnal changes, and autumn was the hot season occurring frequent diurnal changes. Specifically, hotspot of Type1 was lakeshore, while hotspot of Type3 was Central Regions. Environmental impacts were analyzed at two scales. At pixel scale (500 m), diurnal variation of temperature affected the regional occurence of each type ofdiurnal changes patterns of blooms, and the afternoon temperature played the most critical role (p < 0.001, N = 8316). The occurrence frequency of Type1 was positively (R = 0.41) related with the afternoon temperature, and the occurrence frequency of Type3 was negatively (R = -0.37) related with it. Diurnal variation of wind speed was another key factor impacting the occurrence of obvious diurnal blooms changes, and the wind impacts should be distinguished when the wind speed was over or below 3.5 m/s. At synoptic scale, the interaction of multi environmental factors influenced the diurnal change degree of blooms area, and the environmental contributions were 71%.Comparing with the existing manual classifying workat synoptic scale, the designed classification method can identify the types of diurnal change patterns of blooms at a higher spatial resolution (500 m). These explorations on diurnal dynamics of cyanobacterial blooms in Lake Taihu provide a new insight for advanced cyanobacteria dynamics studies and regional water management.
大多数湖泊中蓝藻水华的发生在时间和空间上都有显著变化。然而，目前大多数研究仅关注日或季节尺度，以获得相对粗糙的分辨率结果。为了探索太湖（中国）内一天内可能发生的细微变化，利用从小时静止气象卫星海洋成像仪（GOCI）数据得出的 GOCI 衍生蓝藻指数，对水面蓝藻水华的面积覆盖范围进行了量化。在此基础上，在两个尺度上探讨了昼夜变化特征，并研究了环境影响。为此，首先设计了一种分类方法来自动识别蓝藻水华昼夜变化模式的类型。该方法将模式分为四种类型，包括减少（Type1）、先减少后增加（Type2）、增加（Type3）和先增加后减少（Type4）。在此基础上，在像素（500m）和天气尺度上识别了太湖（2011 年 4 月 1 日至 2020 年 10 月 31 日）水华的昼夜变化模式类型。结果表明，Type1 和 Type3 是两种蓝藻水华的热点昼夜变化模式，湖滨是发生严重昼夜变化的热点，秋季是发生频繁昼夜变化的热点季节。具体来说，Type1 的热点是湖滨，而 Type3 的热点是中心区域。在两个尺度上分析了环境影响。在像素尺度（500m）上，温度的昼夜变化影响了每种类型的水华区域发生，下午的温度起着最关键的作用（p<0.001，N=8316）。Type1 的发生频率与下午的温度呈正相关（R=0.41），而 Type3 的发生频率与下午的温度呈负相关（R=-0.37）。风速的昼夜变化也是影响明显蓝藻水华昼夜变化发生的关键因素，当风速超过或低于 3.5m/s 时，应区分风的影响。在天气尺度上，多环境因素的相互作用影响了水华面积的昼夜变化程度，环境贡献为 71%。与现有的天气尺度手动分类工作相比，设计的分类方法可以以更高的空间分辨率（500m）识别水华昼夜变化模式的类型。这些对太湖蓝藻水华昼夜动态的探索为高级蓝藻动力学研究和区域水资源管理提供了新的见解。
