Deng Jianming, Shan Kun, Shi Kun, Qian Song S, Zhang Yunlin, Qin Boqiang, Zhu Guangwei
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, China.
Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China.
Water Res. 2023 Jun 1;236:119946. doi: 10.1016/j.watres.2023.119946. Epub 2023 Apr 7.
Although nutrient reduction has been used for lake eutrophication mitigation worldwide, the use of this practice alone has been shown to be less effective in combatting cyanobacterial blooms, primarily because of climate change. In addition, quantifying the climate change contribution to cyanobacterial blooms is difficult, further complicating efforts to set nutrient reduction goals for mitigating blooms in freshwater lakes. This study employed a continuous variable Bayesian modeling framework to develop a model to predict spring cyanobacterial bloom areas and frequencies (the responses) using nutrient levels and climatic factors as predictors. Our results suggested that both spring climatic factors (e.g., increasing temperature and decreasing wind speed) and nutrients (e.g., total phosphorus) played vital roles in spring blooms in Lake Taihu, with climatic factors being the primary drivers for both bloom areas and frequencies. Climate change in spring had a 90% probability of increasing the bloom area from 35 km to 180 km during our study period, while nutrient reduction limited the bloom area to 170 km, which helped mitigate expansion of cyanobacterial blooms. For lake management, to ensure a 90% probability of the mean spring bloom areas remaining under 154 km (the 75th percentile of the bloom areas in spring), the total phosphorus should be maintained below 0.073 mg·L under current climatic conditions, which is a 46.3% reduction from the current level. Our modeling approach is an effective method for deriving dynamic nutrient thresholds for lake management under different climatic scenarios and management goals.
尽管全球都在采用减少营养物质的方法来缓解湖泊富营养化，但事实证明，仅采用这种方法在应对蓝藻水华方面效果较差，主要原因是气候变化。此外，量化气候变化对蓝藻水华的影响很困难，这使得为减轻淡水湖泊水华而设定营养物质减少目标的工作更加复杂。本研究采用连续变量贝叶斯建模框架，开发了一个模型，以营养水平和气候因素作为预测变量，来预测春季蓝藻水华面积和发生频率（响应变量）。我们的结果表明，春季气候因素（如气温升高和风速降低）和营养物质（如总磷）在太湖春季水华中都起着至关重要的作用，其中气候因素是水华面积和发生频率的主要驱动因素。在我们的研究期间，春季气候变化有90%的可能性使水华面积从35平方公里增加到180平方公里，而减少营养物质则将水华面积限制在170平方公里，这有助于减轻蓝藻水华的扩张。对于湖泊管理而言，为确保春季平均水华面积有90%的概率保持在154平方公里以下（春季水华面积的第75百分位数），在当前气候条件下，总磷应维持在0.073毫克·升以下，这比当前水平降低了46.3%。我们的建模方法是一种有效的方法，可用于在不同气候情景和管理目标下推导湖泊管理的动态营养阈值。
