A multivariate Chain-Bernoulli-based prediction model for cyanobacteria algal blooms at multiple stations in South Korea.

Predicting the occurrence of algal blooms is of great importance in managing water quality. Moreover, the demand for predictive models, which are essential tools for understanding the drivers of algal blooms, is increasing with global warming. However, modeling cyanobacteria dynamics is a challenging task. We developed a multivariate Chain-Bernoulli-based prediction model to effectively forecast the monthly sequences of algal blooms considering hydro-environmental predictors (water temperature, total phosphorus, total nitrogen, and water velocity) at a network of stations. The proposed model effectively predicts the risk of harmful algal blooms, according to performance measures based on categorical metrics of a contingency table. More specifically, the model performance assessed by the LOO cross-validation and the skill score for the POD and CSI during the calibration period was over 0.8; FAR and MR were less than 0.15. We also explore the relationship between hydro-environmental predictors and algal blooms (based on cyanobacteria cell count) to understand the dynamics of algal blooms and the relative contribution of each potential predictor. A support vector machine is applied to delineate a plane separating the presence and absence of algal bloom occurrences determined by stochastic simulations using different combinations of predictors. The multivariate Chain-Bernoulli-based prediction model proposed here offers effective, scenario-based, and strategic options and remedies (e.g., controlling the governing environmental predictors) to relieve or reduce increases in cyanobacteria concentration and enable the development of water quality management and planning in river systems.

Stochastic modeling of chlorophyll-a for probabilistic assessment and monitoring of algae blooms in the Lower Nakdong River, South Korea.

Eutrophication is one of the critical water quality issues in the world nowadays. Various studies have been conducted to explore the contributing factors related to eutrophication symptoms. However, in the field of eutrophication modeling, the stochastic nature associated with the eutrophication process has not been sufficiently explored, especially in a multivariate stochastic modeling framework. In this study, a multivariate hidden Markov model (MHMM) that can consider the spatio-temporal dependence in chlorophyll-a concentration over the Nakdong River of South Korea was proposed. The MHMM can effectively cluster the intra-seasonal and inter-annual variability of chlorophyll-a, thereby enabling us to understand the spatio-temporal evolutions of algal blooms. The relationships between hydro-climatic conditions (e.g., temperature and river flow) and chlorophyll-a concentrations were evident, whereas a relatively weak relationship with water quality parameters was observed. The MHMM enables us to effectively infer the conditional probability of the eutrophication state for the following month. The self-transition likelihood of staying in the current state is substantially higher than the likelihood of moving to other states. Moreover, the proposed modeling approach can effectively offer a probabilistic decision-support framework for constructing an alert classification of the eutrophication. The potential use of the proposed modeling framework was also provided.