Water Center, Graham Sustainability Institute, University of Michigan, 625 E. Liberty St., Suite 300, Ann Arbor, MI 48104, USA.
Water Center, Graham Sustainability Institute, University of Michigan, 625 E. Liberty St., Suite 300, Ann Arbor, MI 48104, USA.
Sci Total Environ. 2017 Jan 1;575:294-308. doi: 10.1016/j.scitotenv.2016.10.023. Epub 2016 Oct 13.
Cyanobacteria blooms are a major environmental issue worldwide. Our understanding of the biophysical processes driving cyanobacterial proliferation and the ability to develop predictive models that inform resource managers and policy makers rely upon the accurate characterization of bloom dynamics. Models quantifying relationships between bloom severity and environmental drivers are often calibrated to an individual set of bloom observations, and few studies have assessed whether differences among observing platforms could lead to contrasting results in terms of relevant bloom predictors and their estimated influence on bloom severity. The aim of this study was to assess the degree of coherence of different monitoring methods in (1) capturing short- and long-term cyanobacteria bloom dynamics and (2) identifying environmental drivers associated with bloom variability. Using western Lake Erie as a case study, we applied boosted regression tree (BRT) models to long-term time series of cyanobacteria bloom estimates from multiple in-situ and remote sensing approaches to quantify the relative influence of physico-chemical and meteorological drivers on bloom variability. Results of BRT models showed remarkable consistency with known ecological requirements of cyanobacteria (e.g., nutrient loading, water temperature, and tributary discharge). However, discrepancies in inter-annual and intra-seasonal bloom dynamics across monitoring approaches led to some inconsistencies in the relative importance, shape, and sign of the modeled relationships between select environmental drivers and bloom severity. This was especially true for variables characterized by high short-term variability, such as wind forcing. These discrepancies might have implications for our understanding of the role of different environmental drivers in regulating bloom dynamics, and subsequently for the development of models capable of informing management and decision making. Our results highlight the need to develop methods to integrate multiple data sources to better characterize bloom spatio-temporal variability and improve our ability to understand and predict cyanobacteria blooms.
蓝藻水华是全球范围内的一个主要环境问题。我们对驱动蓝藻增殖的生物物理过程的理解，以及开发能够为资源管理者和政策制定者提供信息的预测模型的能力，都依赖于对水华动态的准确描述。量化水华严重程度与环境驱动因素之间关系的模型通常是根据一组单独的水华观测数据进行校准的，很少有研究评估不同观测平台之间的差异是否会导致在相关水华预测因子及其对水华严重程度的估计影响方面产生截然不同的结果。本研究的目的是评估不同监测方法在（1）捕捉短期和长期蓝藻水华动态和（2）识别与水华变化相关的环境驱动因素方面的一致性程度。本研究以西密歇根湖为案例研究，应用了 boosted regression tree (BRT) 模型来分析多个原位和遥感方法的长期蓝藻水华估算时间序列，以量化理化和气象驱动因素对水华变化的相对影响。BRT 模型的结果与蓝藻的已知生态需求（例如营养负荷、水温以及支流流量）具有显著的一致性。然而，不同监测方法在年际和季节内水华动态方面的差异导致了选择环境驱动因素与水华严重程度之间的模型关系的相对重要性、形状和符号存在一些不一致。对于具有高短期变异性的变量（例如风强迫）尤其如此。这些差异可能会影响我们对不同环境驱动因素在调节水华动态中的作用的理解，从而影响到能够为管理和决策提供信息的模型的开发。我们的研究结果强调了需要开发方法来整合多个数据源，以更好地描述水华时空变异性，并提高我们理解和预测蓝藻水华的能力。
