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Development of a sub-seasonal cyanobacteria prediction model by leveraging local and global scale predictors

机译:利用当地和全球规模预测因子开发次季节性蓝细菌预测模型

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摘要

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.
机译:近几十年来,世界各地的沿海水域和内陆湖泊的文化富营养化有助于迅速扩张潜在有毒的蓝藻,威胁水生和人类系统。对于许多地方,一种复杂的物理,化学品和生物变量阵列导致肌腱生物量的显着年度年度变异性,由局部和大规模的气候现象调节。然而,目前,关于预期的夏季蓝细菌生物量条件的最小信息在本赛季之前可用,限制了湖泊和海滩安全的主动管理和准备策略。为了解决这一问题,开发了季期期(为期两月)蓝色细菌生物量预测模型,绘制在赛季前预测器,包括流放电,磷荷载,浮藻指数和大型海面温度区域,将应用到湖泊Mendota在威斯康星州。采用两相统计建模方法反映预测因子(年间变异性的驱动因素)和青霉菌生物量水平之间的识别不对称关系。该模型的总体表现出了良好的性能,特别是预测高于正常的蓝藻生物量条件,这对湖泊和海滩经理具有重要意义。

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