Assessing Countermeasure Effectiveness in Controlling Cyanobacterial Exceedance in Riverine Systems Using Probabilistic Forecasting Alternatives

Abstract

Cyanobacterial blooms are expected to be more frequent over time as more favorable environmental conditions are created in a warming climate. This study proposes strategies to effectively mitigate/reduce the concentration of cyanobacterial blooms based on probabilistic modeling. The model adopted in this study is a probabilistic forecasting model of cyanobacteria cell counts that uses two dominant variables contributing to cyanobacterial bloom in a river: water temperature (T) and water velocity (V). Both T and V can be manipulated by controlling the upstream flow in the river through altering reservoir operations, thereby changing water release downstream. We test and evaluate a variety of discharge scenarios in two river systems in South Korea. Our findings suggest that releasing cold dam water is quite effective in suppressing cyanobacterial bloom growth. We extend the analysis by also including the cost of each operation and conducting a cost–benefit analysis. Following this, we propose response scenarios for each river system by which optimal proactive actions can be taken according to river conditions.