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 favourable environmental conditions are created in a warming climate. This study proposes strategies to effectively mitigate/reduce the concentration of cyanobacterial blooms based on a probabilistic modelling. The model adopted in this study is a probabilistic forecasting model of cyanobacteria cell counts that uses two dominant variables contributing to cyanobacterial bloom namely, water temperature (T) and water velocity (V) in a river. Both T and V can be manipulated by controlling the upstream flow in the river by altering reservoir operation 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 to suppress cyanobacterial blooms growth. As a next step we extend the analysis, by also including the cost of each operation and conduct a cost benefit analysis. Following this, we propose response scenarios for each river system by which optimal proactive actions can be taken according to the river conditions given.