A climate-sensitive, decision-linked multilayer framework for early warning of harmful cyanobacterial blooms: Sequential cross-layer validation for decision support

Abstract

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.