Environmentally Driven Risk Assessment for Algal Bloom Occurrence in Shallow Lakes

Algal blooms due to the rapid growth of microscopic phytoplankton are often observed in eutrophic shallow lakes, which may give rise to a variety of adverse effects. An algal bloom is a complex hydro-biological phenomenon driven by multi-attribute environmental processes, and thus is still difficult to predict. In this paper, a comprehensive modelling framework for forecasting algal bloom risks in shallow lakes is presented, in which key meteorological, hydrodynamic and biochemical factors, as well as their combined influence are properly accounted for. In the case of Taihu Lake, we statistically identify the major environmental factors causing algal blooms, based on long-term measured data at 14 monitoring stations; it is noted that, for the closed shallow lake, the influence of hydrodynamics can be indirectly reflected by the variation of wind speed. Despite the complexity of the in-situ data observed across a broad range of changing environmental conditions, the suitable ranges of the major factors is first investigated, additionally the individual influence of various driving factors is quantified quantitatively, using an integrated statistical approach of orthogonal design and regression analysis. By analyzing the possible combined effects of the major driving factors and the relationship between algal bloom risk and major bloom-driving factors, through a parameter optimization and prediction comparison routine, a cost-effective environmentally driven risk assessment model is developed to forecast the likelihood of algal bloom occurrence. The risk model has been calibrated and validated against long-term field observations of algal blooms in Taihu Lake that achieving the prediction accuracy of both non-bloom and bloom occurrence of higher than 70%, which only requires readily available meteorological and water quality data. This study not only provides a practicable framework for the development of algal bloom early warning schemes for shallow lakes, but also helps to understand the combined function of complex bloom-driving factors.