Documents episodic severe cyanobacterial blooms in nutrient-poor lakes, suggesting a new predictive framework.
Abnormal phytoplankton proliferation is increasingly observed in nutrient-poor lakes, yet studies focusing on severe blooms defined by absolute cell-density thresholds remain limited. Through 4 years of high-frequency monitoring in the deep and overall nutrient-poor Lake Qiandaohu, China, we documented recurrent blooms with cyanobacterial densities exceeding 2 × 10 7 cells/L (peaking >4 × 10 7 cells/L) in its river-lake transition zone. We propose a Liebig–Blackman sequential constraint-alleviation framework to mechanistically explain these events. Rainstorms first alleviate Liebig-type final yield limitation by supplying nitrogen and phosphorus, thereby increasing the attainable cyanobacterial density ceiling for ∼30 days. Subsequent heatwaves, coupled with poststorm declines in turbidity and flow, alleviate Blackman-type growth rate limitation, thereby favoring rapid cyanobacterial accumulation. The temporal overlap of these two switches forms a critical “bloom window” that enables cyanobacterial densities to rapidly reach severe levels. A mechanism-informed forecasting model, integrating lagged rainfall, current hydrology and water quality, and future short-term meteorological data, achieved good predictive accuracy ( R 2 = 0.60–0.95). This transferable mechanism is particularly important in the context of the rapid global expansion of deep drinking-water reservoirs and the increasing occurrence of storm–heatwave coupling under climate change, offering a general framework for understanding bloom risk in river–lake transition zones in oligotrophic lakes and reservoirs.
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Zou et al. (2026) studied this question.
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