Coupling satellite imagery and numerical models to advance understanding and modelling of diel cyanobacterial blooms in shallow eutrophic lakes.

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Citation abstract

Satellite observations have revealed substantial diurnal changes in cyanobacterial blooms in shallow eutrophic lakes. However, the underlying mechanisms remain inadequately understood, owing to the complex interplay between physical mixing and physiological processes. In this study, satellite observations, numerical simulations, and in situ measurements were integrated to identify plausible mechanisms driving diurnal bloom changes, and novel insights into critical processes governing bloom dynamics were incorporated to refine deterministic models. Observations from Geostationary Ocean Color Imager (GOCI) and GOCI-II indicated that, from April to December, diurnal bloom intensity in Lake Taihu typically followed a unimodal pattern under low wind conditions, with peak timing shifting progressively later in the day as water temperature and light intensity decreased. Based on the insights inferred from satellite observations and in situ measurements, buoyancy regulation models for Microcystis colonies were refined to incorporate the physiological process of oxygen bubble dynamics within colony mucilage. Simulation analysis of the diel vertical migrations of Microcystis colonies across seasons revealed that oxygen bubble growth, rather than cell density changes, triggered the accelerated ascent of cyanobacteria after dawn; as water temperature and light intensity declined, oxygen bubble growth slowed, resulting in a delayed peak in the diurnal bloom. Coupled with hydrodynamic models, the improved agent-based model (ABM) successfully reproduced diurnal bloom dynamics across seasons and under varying wind conditions in Lake Taihu. These results demonstrate that diurnal bloom variability, at coarse spatial resolutions (e.g., 2.5 km), is primarily dictated by the interplay between turbulent mixing and colony buoyancy. Overall, this study refined buoyancy regulation models through the explicit incorporation of oxygen bubble dynamics within colony mucilage, thereby laying the groundwork for assessing associated diel risks under future climate change scenarios.