STOTEN: Modeling the sensitivity of cyanobacteria blooms to plausible changes in precipitation and air temperature variability

Many recent studies have attributed the observed variability of cyanobacteria blooms to meteorological drivers and have projected blooms with worsening societal and ecological impacts under future climate scenarios. Nonetheless, few studies have jointly examined their sensitivity to projected changes in both precipitation and temperature variability. Using an Integrated Assessment Model (IAM) of Lake Champlain's eutrophic Missisquoi Bay, we demonstrate a factorial design approach for evaluating the sensitivity of concentrations of chlorophyll a (chl-a), a cyanobacteria surrogate, to global climate model-informed changes in the central tendency and variability of daily precipitation and air temperature. An Analysis of Variance (ANOVA) and multivariate contour plots highlight synergistic effects of these climatic changes on exceedances of the World Health Organization's moderate 50 μg/L concentration threshold for recreational contact. Although increased precipitation produces greater riverine total phosphorus loads, warmer and drier scenarios produce the most severe blooms due to the greater mobilization and cyanobacteria uptake of legacy phosphorus under these conditions. Increases in daily precipitation variability aggravate blooms most under warmer and wetter scenarios. Greater temperature variability raises exceedances under current air temperatures but reduces them under more severe warming when water temperatures exceed optimal values for cyanobacteria growth more often. Our experiments, controlled for wind-induced changes to lake water quality, signal the importance of larger summer runoff events for curtailing bloom growth through reductions of water temperature, sunlight penetration and stratification. Finally, the importance of sequences of wet and dry periods in generating cyanobacteria blooms motivates future research on bloom responses to changes in interannual climate persistence.近年来诸多研究将观测到的蓝藻水华变异性归因于气象驱动因素，并预测在未来气候情景下，蓝藻水华所引发的社会与生态影响将进一步加剧。然而，鲜有研究同时探讨蓝藻水华对降水与温度变异性未来变化的敏感性。本研究依托尚普兰湖（Lake Champlain）富营养化的密西斯阔伊湾（Missisquoi Bay）综合评估模型（Integrated Assessment Model, IAM），提出一种析因设计方法，用于评估作为蓝藻替代指标的叶绿素a（chlorophyll a, chl-a）浓度对全球气候模型（global climate model）所刻画的日降水与气温的集中趋势及变异性变化的敏感性。 方差分析（Analysis of Variance, ANOVA）与多元等高线图结果表明，上述气候变化对世界卫生组织（World Health Organization）制定的供娱乐性水体接触的50 μg/L中等浓度阈值的超标频次存在协同影响。尽管降水增加会提升河流总磷负荷，但暖干情景下的蓝藻水华最为严重——这是因为该情景下沉积物中遗留磷的活化量与蓝藻摄取量均有所提升。在暖湿情景下，日降水变异性的提升会最大程度加剧蓝藻水华。在当前气温水平下，气温变异性增大会提升浓度超标频次；但在极端升温情景下，当水温频繁超出蓝藻生长的最优区间时，气温变异性增大则会降低超标频次。本研究的实验控制了风致湖泊水质变化的干扰，结果显示夏季大型径流事件可通过降低水温、削弱光照穿透率与水体层化，从而抑制蓝藻水华的发展。最后，干湿周期序列在蓝藻水华形成过程中的重要性，推动了未来针对蓝藻水华对年际气候持续性变化响应的相关研究。