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

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.