State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, People's Republic of China.
Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 200092, People's Republic of China.
Environ Sci Technol. 2024 Jul 2;58(26):11727-11736. doi: 10.1021/acs.est.4c03391. Epub 2024 Jun 5.
Satellite evidence indicates a global increase in lacustrine algal blooms. These blooms can drift with winds, resulting in significant changes of the algal biomass spatial distribution, which is crucial in bloom formation. However, the lack of long-term, large-scale observational data has limited our understanding of bloom drift. Here, we have developed a novel method to track the drift using multi-source remote sensing satellites and presented a comprehensive bloom drift data set for four typical lakes: Lake Taihu (China, 2011-2021), Lake Chaohu (China, 2011-2020), Lake Dianchi (China, 2003-2021), and Lake Erie (North America, 2003-2021). We found that blooms closer to the water surface tend to drift faster. Higher temperatures and lower wind speeds bring blooms closer to the water surface, therefore accelerating drift and increasing biomass transportation. Under ongoing climate change, algal blooms are increasingly likely to spread over larger areas and accumulate in downwind waters, thereby posing a heightened risk to water resources. Our research greatly improves the understanding of algal bloom dynamics and provides new insights into the driving factors behind the global expansion of algal blooms. Our bloom-drift-tracking methodology also paves the way for the development of high-precision algal bloom prediction models.
卫星证据表明,湖泊藻类水华在全球范围内呈增加趋势。这些水华可以随风漂移,导致藻类生物量空间分布发生显著变化,这对水华形成至关重要。然而,长期、大规模观测数据的缺乏限制了我们对水华漂移的理解。在这里,我们开发了一种利用多源遥感卫星跟踪漂移的新方法,并为四个典型湖泊(中国太湖[2011-2021]、中国巢湖[2011-2020]、中国滇池[2003-2021]和北美伊利湖[2003-2021])提供了全面的水华漂移数据集。我们发现,靠近水面的水华往往漂移得更快。较高的温度和较低的风速使水华更接近水面,从而加速了漂移并增加了生物量的输送。在当前的气候变化下,藻类水华越来越有可能在更大的区域内扩散,并在顺风水域积聚,从而对水资源构成更高的风险。我们的研究大大提高了对藻类水华动态的认识,并为藻类水华全球扩张的驱动因素提供了新的见解。我们的水华漂移跟踪方法也为高精度藻类水华预测模型的开发铺平了道路。