Advancing the predictive modelling framework to improving understanding of the Cyanobacterial Harmful Algal Blooms (CHAB) in the future context of global warming and climate change

了解调节湖泊和水库中蓝藻增殖及其释放的毒素的因素对于水资源和公共卫生的管理是必不可少的。我过去几年对大西洋省New Brunswick （NB）和Nova Scotia （NS）的不同水体进行的研究表明，蓝藻中有四种主要的有毒物种，包括Dolichospermum flos-aqua， d.p plantonicum， Microcystis aeruginosa和Aphanizomenon sp.本研究计划旨在为CHAB及其增殖，毒素产生和多种可能降低水质的压力因素之间的相关性提供明确的解释。主要目标是从概念（机制）和经验（定量）两个角度预测水华发生和理解CHAB的主要驱动因素，以优化水资源管理。它将通过两种建模方法进行处理，包括基于过程（PB）和数据驱动（DD）模型，以频繁预测未来情景（PB）和短期预测（DD）。我的长期愿景有三个明确的具体目标：(1)通过使用耦合生物-物理模型，在环境因素背景下推进藻类生长动力学的知识；(2)构建CHAB时空预测框架；(3)整合互补技术，提出与遥感（RS）和GIS相结合的实比例尺交互模型。我的项目的主要研究方法包括实地数据和样本收集、实验室分析、数学模拟和卫星数据的空间地理制图。为受CHAB影响的不同领域（加拿大大西洋地区的两个饮用水水库和十个休闲湖泊）制定了一个监测良好的定期数据收集方案。在建模方案上，首先采用概率方法确定开花模式的通用阈值。其次，将确定性模型引入该框架。该创新依靠图灵不稳定性下的流体动力学运动和藻类细胞守恒方程来阐明真实生态系统尺度下CHAB的耦合生物物理过程及其生态响应。RS技术提供了可负担的CHAB光学特性的时空分辨率，这将是全面验证预测模型以进行可靠估计的另一种创新方法。成果将有助于加拿大的淡水质量管理和政策、环境保护以及公共卫生监管机构对所有受CHAB影响的加拿大省份的用水安全和水生营养食品的重大关切，特别是土著地区的淡水质量。这也将为水生生态学和生物修复的研究开辟一个新的视野，并为研究人员和当局更好地预测和调节CHAB模式提供服务。

Understanding factors involved in regulating of cyanobacterial proliferation in lakes and reservoirs as well as their released toxins becomes indispensable for the management of water resources and public health. My research since last several years on different waterbodies in two Atlantic provinces New Brunswick (NB) and Nova Scotia (NS) have shown there are four dominant toxic species of cyanobacteria including Dolichospermum flos-aqua, D. planctonicum, Microcystis aeruginosa and Aphanizomenon sp. This research program aims to provide clear explanations for the correlations between CHAB, their proliferation, toxin production, and multiple stressors which may diminish the water quality. The main goal is to anticipating bloom occurrence and understanding the main drivers of CHAB to optimize water resources management, from both conceptual (mechanistic) and empirical (quantitative) perspectives. It will be processing via two modelling approaches including process-based (PB) and data-driven (DD) models, to frequently predict future scenarios (PB) and to short-term forecasts (DD). My long-term vision has three clear specific objectives: (1) Advancing the knowledge of algal growth dynamics in the context of environmental factors by using coupled biological-physical modelling; (2) Building a framework for CHAB spatio-temporal prediction; and (3) Bringing together complementary techniques to propose the real scale interactive models coupled with remote sensing (RS) and GIS. Key-research approaches in my program include field data and sample collection, laboratory analyses, mathematical simulations, and spatial geo-mapping with satellite data. A well-monitored program of regular data collection for different fields affected by CHAB (two drinking water reservoirs and ten recreational lakes in Atlantic Canada) is planned. Regarding the modelling plan, the probabilistic approach firstly will be used to determining the universal thresholds of bloom patterns. Secondly, deterministic models will be introduced into this framework. The innovation relies on the fluid dynamic motion and algal cell conservation equations under the Turing's instability to elucidate coupled bio-physical processes of CHAB in the real ecosystem scale, and their ecological responses. RS technologies provide affordable spatio-temporal resolution of optical properties of CHAB that will be another innovative approach for the comprehensive validation of predictive models for reliable estimates. Outcomes will assist the freshwater quality management and policies in Canada, environmental protection, and the substantial concern for public health regulators in relation to water use safety and aquatic nutri-foods for all Canadian provinces affected by CHAB, especially for the freshwater quality in Indigenous regions. That will also open a horizon for research in aquatic ecology and bioremediation as well as serve researchers and authorities to better predict and regulate CHAB patterns.