A Climate-Sensitive, Decision-Linked Multilayer Framework for Early Warning of Harmful Cyanobacterial Blooms: Sequential Cross-Layer Validation for Decision Support

Harmful cyanobacterial blooms (CyanoHABs) are intensifying worldwide due to nutrient enrichment, hydrological alteration, and climate-driven extremes, yet freshwater monitoring and early-warning systems remain fragmented, limiting timely and consistent decision-making. Here, we present a climate-sensitive, decision-linked multilayer framework that integrates real-time photopigment sensing, high-frequency phenotypic imaging, molecular and toxin confirmation, and basin-scale satellite–model fusion. The framework operationalizes multimodal integration through climate-adaptive sampling, uncertainty-aware data fusion, and escalation-based decision logic within a unified system architecture. A key innovation is the incorporation of phycoerythrin-sensitive, depth-resolved indicators, enabling early detection of metalimnetic taxa such as Planktothrix rubescens , which are often overlooked by conventional approaches. Comparative analyses indicate that sequential cross-layer validation (L1–L4) reduces time-to-decision and improves advisory confidence relative to single-modality monitoring. Collectively, this framework establishes CyanoHAB monitoring as an operational decision-support system, providing a scalable pathway toward proactive, climate-resilient water governance. A directional, convergence-driven early-warning workflow integrating multi-layer observations (L1–L4) into escalation-relevant decisions. Climate accelerators (e.g., heatwaves, stratification, nutrient pulses) trigger adaptive monitoring, while high-frequency sensor anomalies (L1), phenotypic transitions (L2), molecular/toxin confirmation (L3), and basin-scale satellite observations (L4) are sequentially integrated through a data fusion and predictive modeling hub. Cross-layer signal convergence reduces uncertainty and translates heterogeneous observations into tiered decision outcomes (green → yellow → red).