Abstract

Development of effective monitoring programs and early alert systems of cyanobacterial harmful blooms (CyanoHABs) is a challenge due to their rapid temporal dynamics and high spatial heterogeneity. We provide a new approach for monitoring CyanoHABs in large ecosystems using a strategy combining modeling with in situ data and remote sensing methods. Between 2014 and 2021, we sampled phytoplankton and measured temperature and conductivity (continuously) at a coastal site at the Río de la Plata estuary (South America). We used a Bayesian model to predict favorable conditions for bloom occurrences, using temperature and conductivity (a proxy for salinity). We defined a polygon area of 40 km² and obtained 121 cloud-free satellite images (Sentinel-2) in which 10 “small” (< 1% of polygon), 4 “medium” (> 1% and < 5%), and 2 “large” (> 5%) blooms were detected. A 7-day period of favorable environmental conditions was the best time frame to predict large blooms and medium size blooms. Integrating the bloom extent with modeling outputs generates valuable new information for management. The continuous model predictions allow for evaluation of the persistence/growth of blooms and they fill in an important gap for management when images are not available (i.e., cloud cover). We propose a monitoring strategy that combines information about the size of the remotely detected blooms with in situ conditions to evaluate the actions that stakeholders should take. Our approach is a rapid and cost-effective strategy with high potential for developing early warning systems for monitoring CyanoHABs in large ecosystems.

中文翻译：

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监测蓝藻水华：预测模型和遥感方法相结合的策略

摘要

由于蓝藻有害水华（CyanoHAB）快速的时间动态和高度的空间异质性，制定有效的监测计划和早期预警系统是一项挑战。我们提供了一种使用建模与现场数据和遥感方法相结合的策略来监测大型生态系统中 CyanoHAB 的新方法。2014 年至 2021 年间，我们在拉普拉塔河口（南美洲）的沿海地点对浮游植物进行了采样，并（连续）测量了温度和电导率。我们使用贝叶斯模型，利用温度和电导率（盐度的代表）来预测水华发生的有利条件。^{我们定义了 40 km 2}的多边形区域，并获得了 121 个无云卫星图像 (Sentinel-2)，其中 10 个“小”（< 多边形的 1%）、4 个“中”（> 1% 且 < 5%），并检测到 2 次“大”（> 5%）开花。7 天的有利环境条件是预测大花和中型花的最佳时间范围。将水华程度与建模输出相结合，可以为管理生成有价值的新信息。连续模型预测允许评估水华的持久性/生长，并且当图像不可用（即云层覆盖）时，它们填补了管理的重要空白。我们提出了一种监测策略，将远程检测到的水华规模信息与现场条件相结合，以评估利益相关者应采取的行动。我们的方法是一种快速且具有成本效益的策略，在开发用于监测大型生态系统中 CyanoHAB 的早期预警系统方面具有很大潜力。