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, sub-seasonal (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.

近几十年来，世界各地沿海水域和内陆湖泊的文化富营养化导致具有潜在毒性的蓝藻迅速扩张，威胁着水生和人类系统。在许多地方，一系列复杂的物理、化学和生物变量导致蓝藻生物量的显着年际变化，受局部和大规模气候现象的调节。然而，目前，在季节之前可获得关于预期夏季蓝藻生物量条件的信息很少，限制了湖泊和海滩安全的主动管理和准备策略。为了解决这个问题，开发了亚季节（两个月）蓝藻生物量预测模型，利用季节前预测因子，包括河流流量、磷负荷、漂浮藻类指数、和大规模海面温度区域，适用于威斯康星州的门多塔湖。采用两阶段统计建模方法来反映预测因子（年际变化的驱动因素）和蓝藻生物量水平之间已确定的不对称关系。该模型展示了总体上有希望的性能，特别是在预测高于正常蓝藻生物量条件方面的技能，这对湖泊和海滩管理者来说是最重要的。