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Despite the implementation of intensive phosphorus reduction measures, periodic outbreaks of cyanobacterial blooms in large rivers remain a problem in Korea, raising the need for more effective solutions to reduce their occurrence. This study sought to evaluate whether phosphorus or nitrogen limitation is an effective approach to control cyanobacterial (Microcystis) blooms in river conditions that favor this non-nitrogen-fixing genus. These conditions include nutrient enrichment, high water temperature, and thermal stratification during summer. Mesocosm bioassays were conducted to investigate the limiting factors for cyanobacterial blooms in a river reach where severe Microcystis blooms occur annually. We evaluated the effect of five different nitrogen (3, 6, 9, 12, and 15 mg/L) and phosphorus (0.01, 0.02, 0.05, 0.1, and 0.2 mg/L) concentrations on algae growth. The results indicate that nitrogen treatments stimulated cyanobacteria (mostly Microcystis aeruginosa) more than phosphorus. Interestingly, phosphorus additions did not stimulate cyanobacteria, although it did stimulate Chlorophyceae and Bacillariophyceae. We conclude that phosphorus reduction might have suppressed the growth of Chlorophyceae and Bacillariophyceae more than that of cyanobacteria; therefore, nitrogen or at least both nitrogen and phosphorus control appears more effective than phosphorus reductions alone for reducing cyanobacteria in river conditions that are favorable for non-nitrogen-fixing genera.

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Hyperspectral imagery (HSI) provides substantial information on optical features of water bodies that is usually applicable to water quality monitoring. However, it generates considerable uncertainties in assessments of spatial and temporal variation in water quality. Thus, this study explored the influence of different optical methods on the spatial distribution and concentration of phycocyanin (PC), chlorophyll-a (Chl-a), and total suspended solids (TSSs) and evaluated the dependence of algal distribution on flow velocity. Four ground-based and airborne monitoring campaigns were conducted to measure water surface reflectance. The actual concentrations of PC, Chl-a, and TSSs were also determined, while four bio-optical algorithms were calibrated to estimate the PC and Chl-a concentrations. Artificial neural network atmospheric correction achieved Nash-Sutcliffe Efficiency (NSE) values of 0.80 and 0.76 for the training and validation steps, respectively. Moderate resolution atmospheric transmission 6 (MODTRAN 6) showed an NSE value >0.8; whereas, atmospheric and topographic correction 4 (ATCOR 4) yielded a negative NSE value. The MODTRAN 6 correction led to the highest R2 values and lowest root mean square error values for all algorithms in terms of PC and Chl-a. The PC:Chl-a distribution generated using HSI proved to be negatively dependent on flow velocity (p-value = 0.003) and successfully indicated cyanobacteria risk regions in the study area.

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