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

推进预测模型框架，以提高对未来全球变暖和气候变化背景下蓝藻有害藻华（CHAB）的了解

• 批准号: RGPIN-2022-03906
• 负责人: NguyenQuang, Tri
• 金额: $ 2.04万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752735
• 关键词: Advancing; predictive; modelling; framework; improving

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.

了解湖泊和水库中蓝藻增殖的调控因素及其释放的毒素对于水资源管理和公共卫生是必不可少的。我在过去几年中对两个大西洋省份新玩法（NB）和新斯科舍省（NS）的不同水体的研究表明，有四种主要的有毒蓝藻，包括Dolichospermum flos-aqua，D.本研究旨在为CHAB，其增殖，毒素产生和可能降低水质的多种应激因素之间的相关性提供明确的解释。其主要目标是预测水华的发生，并从概念（机制）和经验（定量）的角度了解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，包括基于过程的（PB）和数据驱动（DD）模型，以经常预测未来情景（PB）和短期预测（DD）。我的长期愿景有三个明确的具体目标：（1）通过使用耦合的生物物理模型，在环境因素的背景下提高藻类生长动力学的知识;（2）建立CHAB时空预测框架;（3）汇集互补技术，提出与遥感（RS）和地理信息系统相结合的真实的比例尺交互式模型。 在我的程序中的关键研究方法包括现场数据和样品收集，实验室分析，数学模拟，并与卫星数据的空间地理测绘。计划为受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）定期收集数据。关于建模计划，首先将使用概率方法来确定开花模式的通用阈值。其次，确定性模型将被引入到这个框架中。该创新基于图灵不稳定性下的流体动力学运动和藻类细胞守恒方程，阐明了CHAB在真实的生态系统尺度上的耦合生物物理过程及其生态响应。遥感技术为CHAB的光学特性提供了负担得起的时空分辨率，这将是全面验证可靠估计的预测模型的另一种创新方法。研究结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受CHAB影响的所有加拿大省份的用水安全和水生营养食品的重大关注，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。