Investigating the sub-daily dynamics of cyanobacterial blooms by coupling high-frequency time-series remote sensing with hydro-ecological modelling

Abstract

Cyanobacterial Harmful Algal Blooms (CyanoHABs) are a health-threatening and increasingly prevalent environmental issue at both regional and global levels. An improved understanding of the short-term dynamics of CyanoHABs is required to better capture their spatial pattern and temporal evolution. However, the heterogeneous and dynamic nature of CyanoHABs, and the interacting factors and processes that drive them, make interpreting and predicting the blooms a very challenging task. In this paper, we used an integrative approach that combines high-frequency time-series remote sensing with hydro-ecological modelling, to reproduce and investigate the sub-daily dynamics of CyanoHABs in Taihu Lake. Results show that the distribution of CyanoHABs is highly patchy and dynamic without intensive wind-induced circulation and turbulence, which suggests that the dynamic pattern may be largely caused by the migratory behavior of cyanobacteria. The hydro-ecological model well reproduced the observed pattern and trend, and the average of Root Mean Square Error (RMSE) and coefficient of determination (R2) were 9.82 μg/L and 0.52, respectively. Results from sensitivity analysis suggest that photosynthesis rate and respiration rate are two most influential model parameters. Conclusively, there is a lack of adequate representation of physiological processes in currently used modelling framework, thereby suggesting the need for microscale modelling for future modelling exercises of CyanoHABs.