Large scale seasonal forecasting of peak season algae metrics in the Midwest and Northeast U.S.
Large scale seasonal forecasting of peak season algae metrics in the Midwest and Northeast U.S.
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DOI：
10.1016/j.watres.2022.119402
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发表时间：
2022-11
期刊：
影响因子：
12.8
通讯作者：
M. Beal;G. Wilkinson;P. Block
中科院分区：
文献类型：
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作者：
M. Beal;G. Wilkinson;P. Block
近几十年来，许多内陆湖泊出现了潜在有害藻类的增加。在许多内陆湖泊，藻类丰富的高峰季节（北半球的夏季和初秋）与娱乐使用的高峰季节相吻合。目前，在内陆湖泊生产力高峰期之前，关于预期藻类状况的信息很少。高峰期藻类状况受到一系列季前（春季和初夏）当地和全球尺度变量的影响；确定这些变量以进行预测，可能有助于管理有害藻类对公众健康构成的潜在威胁。利用LAGOS-NE数据集，从现成的网格数据集中确定了美国东北部和中西部178个湖泊的季节前本地和全球高峰季节藻类指标（以叶绿素-a表示）的驱动因素。根据相关季前预测因子，为每个湖泊建立预报模型。对季节性叶绿素浓度的大小、严重程度和持续时间进行预报。季前海水表面温度和季前叶绿素-a区域对旺季藻类指标的预测能力最强，所得到的模型显示出显著的技能。根据分类预报指标，超过70%的震级模式和90%的持续时间模式优于气候学。在大型中营养型和寡营养型湖泊中，高和严重藻类规模的预报效果最好，而高藻类持续时间的预报对湖泊特征的依赖性较小。这些模型提供的藻类生物量升高的提前通知可以使湖泊管理者更好地为内陆湖泊高利用季节藻类带来的挑战做好准备。
In recent decades, many inland lakes have seen an increase in the prevalence of potentially harmful algae. In many inland lakes, the peak season for algae abundance (summer and early fall in the northern hemisphere) coincides with the peak season for recreational use. Currently, little information regarding expected algae conditions is available prior to the peak season for productivity in inland lakes. Peak season algae conditions are influenced by an array of pre-season (spring and early summer) local and global scale variables; identifying these variables for forecast development may be useful in managing potential public health threats posed by harmful algae. Using the LAGOS-NE dataset, pre-season local and global drivers of peak-season algae metrics (represented by chlorophyll-a) are identified for 178 lakes across the Northeast and Midwest U.S. from readily available gridded datasets. Forecasting models are built for each lake conditioned on relevant pre-season predictors. Forecasts are assessed for themagnitude, severity, anddurationof seasonal chlorophyll concentrations. Regions of pre-season sea surface temperature, and pre-season chlorophyll-a demonstrate the most predictive power for peak season algae metrics, and resulting models show significant skill. Based on categorical forecast metrics, more than 70% of magnitude models and 90% of duration models outperform climatology. Forecasts of high and severe algae magnitude perform best in large mesotrophic and oligotrophic lakes, however, high algae duration performance appears less dependent on lake characteristics. The advance notice of elevated algae biomass provided by these models may allow lake managers to better prepare for challenges posed by algae during the high use season for inland lakes.