Algal blooms outbreak mechanisms and proactive early warning approaches

Algal blooms, driven by complex environmental and climatic factors, present significant ecological and economic challenges in aquatic systems worldwide. This study investigates the outbreak characteristics, driving mechanisms, and early warning approaches for harmful algal blooms (HABs).In analyzing bloom characteristics, we evaluate the total bloom duration, individual event peaks, and the rate of concentration change, identifying distinct patterns across different lake systems. We further classify these events into clusters based on duration and peak concentration, offering insight into the heterogeneity of bloom dynamics across varied water bodies. The driving mechanisms of algal blooms are elucidated through causal and network pathway analysis, revealing the direct and indirect effects of environmental variables on chlorophyll-a concentrations. Across lakes, water temperature and nutrient concentrations (TN, TP) emerge as primary drivers, while factors like nitrogen-phosphorus ratios act as limiting constraints in some systems, particularly in Dongtinghu and Dianchi. The interactions between these drivers highlight the complexity of algal bloom outbreaks, with both positive and negative feedback loops playing critical roles in bloom formation. To improve the predictive capacity for HABs, we propose a Proactive Early Warning Model of Algal Blooms (PEWAB), integrating causal discovery using the Peter and Clark Momentary Conditional Independence (PCMCI) algorithm with a Graph Convolutional Network (GCN). This hybrid approach captures the spatial and temporal dynamics of environmental factors, allowing for the identification of significant causal pathways and time-lagged dependencies. The GCN-based model processes these causal relationships to forecast future chlorophyll-a concentrations, providing a robust proactive early warning system for managing and mitigating the impacts of algal blooms. Our findings contribute to an understanding of the environmental drivers behind algal blooms and offer a proactive, data-driven framework for early detection and prevention, crucial for preserving aquatic ecosystems and public health.