Spatiotemporal dynamics of summer chlorophyll-a concentrations under varying drought conditions in a hierarchical Bayesian model

Harmful algal blooms’ increasing frequency and severity are associated with weather and climate, yet their specific interrelation with climate extremes remains underexplored. While it is known that extreme climate events such as drought and heatwave contribute to algal bloom proliferation, their potential for use as predictors in water quality modeling remains unknown. This research develops a hierarchical Bayesian model to integrate drought association into the prediction of summer algal biomass through Chlorophyll-a (Chl-a) concentration in the Nakdong River basin. By employing multiple short-term meteorological and hydrological drought indices (e.g., SPI, SPEI, SSI, EDDI, and EDDISPI) on a 1- to 3-month timescale, along with key physicochemical properties of the river basin, the study explores spatiotemporal factors influencing summer bloom potential. Drought indices, as well as anomalies in water temperature and streamflow, were found to be highly correlated with Chl-a concentration. Using Bayesian inference, the response of predictors to Chl-a levels was examined through sensitivity and uncertainty assessments of posterior distributions, emphasizing the role of trophic states in bloom dynamics. Drought indices demonstrated stronger predictive power for summer Chl-a under eutrophic and hypertrophic conditions than nutrient concentrations (TN and TP). The river basin’s physical properties, particularly streamflow and water temperature anomalies, emerged as the most consistent predictors of summer blooms. As climate extremes increasingly influence these physical conditions, this study presents the role of drought indices in water quality prediction models and offers valuable insights for adaptive water resource management in a changing climate.