Development of a sub-seasonal cyanobacteria prediction model by leveraging local and global scale predictors
Development of a sub-seasonal cyanobacteria prediction model by leveraging local and global scale predictors
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DOI：
10.1016/j.hal.2021.102100
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发表时间：
2021-09-09
期刊：
影响因子：
6.6
通讯作者：
Block, Paul
中科院分区：
文献类型：
--
作者：
Beal, Maxwell R. W.;O'Reilly, Bryan;Block, Paul
近几十年来，世界各地沿海沃茨和内陆湖泊的文化富营养化导致了潜在有毒蓝藻的迅速扩张，威胁着水生和人类系统。对于许多地方，一系列复杂的物理，化学和生物变量导致蓝藻生物量显着的年际变化，调制的本地和大规模的气候现象。然而，目前，关于预期夏季蓝藻生物量条件的信息很少，在季节之前，限制了湖泊和海滩安全的积极管理和准备战略。为了解决这个问题，亚季节（两个月）蓝藻生物量预测模型的开发，借鉴季节前的预测，包括流排放，磷负荷，浮游藻类指数，和大规模的海表温度区域，应用到威斯康星州的门多塔湖。采用两阶段统计建模方法，以反映预测因子（年际变化的驱动因素）和蓝藻生物量水平之间的非对称关系。该模型说明了有前途的整体性能，特别是在预测以上正常蓝藻生物量的条件下，湖泊和海滩管理人员的首要任务技能。
In recent decades, cultural eutrophication of coastal waters and inland lakes around the world has contributed to a rapid expansion of potentially toxic cyanobacteria, threatening aquatic and human systems. For many locations, a complex array of physical, chemical, and biological variables leads to significant inter-annual variability of cyanobacteria biomass, modulated by local and large-scale climate phenomena. Currently, however, minimal information regarding expected summertime cyanobacteria biomass conditions is available prior to the season, limiting proactive management and preparedness strategies for lake and beach safety. To address this, subseasonal (two-month) cyanobacteria biomass prediction models are developed, drawing on pre-season predictors including stream discharge, phosphorus loads, a floating algae index, and large-scale sea-surface temperature regions, with an application to Lake Mendota in Wisconsin. A two-phase statistical modeling approach is adopted to reflect identified asymmetric relationships between predictors (drivers of inter-annual variability) and cyanobacteria biomass levels. The model illustrates promising performance overall, with particular skill in predicting above normal cyanobacteria biomass conditions which are of primary importance to lake and beach managers.