Cyanobacterial harmful algal bloom modeling in eutrophic water bodies

Abstract

Harmful algal bloom (or HAB) is a global phenomenon in the rising trend of environmental concerns that impacts public health and the economy through declining water quality and toxicity. A rapid increase in cyanobacteria concentrations in water bodies is a primary cause of HABs. Enhanced eutrophication and warmer climate are considered vital driving factors for the proliferation of HAB events in the United States and worldwide. Dynamic modeling of cyanobacteria concentrations can help manage and reduce the impact of toxic blooms by better understanding the conditions for cyanobacteria growth and providing recommendations for early advisory warnings to the public for eutrophic water bodies in the agriculture dominated watersheds of the Midwest. In this study, sub-daily time series of cyanobacteria concentration and other environmental, physical-chemical variables were collected at the USGS sites in southcentral Kansas at Cheney Reservoir near the City of Wichita and in northeast Kansas at Kansas River near Wamego. Statistical analysis of the data revealed positive correlations between cyanobacteria concentration and water temperature, irradiation, phosphorus concentration, and storage volume. Correlation of dissolved oxygen depletion with cyanobacteria growth indicated an adverse impact of HABs on aquatic systems. A process-based mathematical framework for the kinetics of cyanobacteria growth was implemented at two sites considering bacteria natural growth, non-predatory loss, outflow washout, and accounting for the changes in water temperature (T), solar irradiance (I), and available nutrients (phosphorus [P] and nitrogen [N]). Four models were developed to facilitate examination of potential data limitation in sampling and continuous observations: (i) T-based, (ii) T, I-based, (iii) T, I, P- based, and (iv) complete four-factor model (T, I, P, N-based). The models were calibrated using continuous observations in 2013 - 2014 with time intervals from 2 days to 15 days (NSE = 0.41 to 0.71), and validated for 2018 (NSE = 0.56). Simulations revealed model efficiency in short-term (one day to bi-weekly) forecasting of cyanobacteria concentration for both nutrient-rich sites. The performance of TIP-based and TIPN-based models was found acceptable for long-term forecasting in the Cheney Reservoir. Data sampling at a 15-day interval was found adequate for the forecasting of cyanobacteria growth. A stochastic modeling approach was applied to the TIPN model that converted a kinetic growth model to a modified Fokker-Planck equation for the probability density function of the cyanobacteria concentration to account for variability in influent nutrient concentrations and their impact on HABs. Several single storm event scenarios were simulated to evaluate the impact of high nutrient runoff into the lake on cyanobacteria. Stochastic model simulations showed that mechanistic modeling forecasting uncertainty increased along time propagation and higher uncertainty in initial concentrations of the cyanobacteria. The process-based mechanistic model was found to be useful for simulating future HAB events in the data-scarce eutrophic conditions, and preliminary insights into the stochastic modeling approach showed potential for future modeling direction under variable nutrient lake condition.