Flow control flushing water from reservoirs has been imposed in South Korea for mitigating harmful cyanobacterial blooms (CyanoHABs) in rivers. This measure, however, can cause water shortage in reservoirs, as the measure adopting this flow control may require an additional amount of water which exceeds the water demand allocated to the reservoirs. In terms of sustainability, a trade-off between improving water quality and alleviating water shortage needs to be considered. This study aimed at establishing a practical framework for a decision support system for optimal joint operation of the upstream reservoirs (Andong and Imha) to reduce the frequency of CyanoHABs in the Nakdong River, South Korea. Methodologically, three models were introduced: (1) a machine learning model (accuracy 88%) based on the k-NN (k-Nearest Neighbor) algorithm to predict the occurrence of CyanoHABs at a selected downstream location (the Chilgok Weir located approximately 140 km downstream from the Andong Dam), (2) a multiobjective optimization model employing NSGA-II (Nondominated Sorting Genetic Algorithm II) to determine both the quantity and quality of water released from the reservoirs, and (3) a river water quality model (R2 0.79) using HEC-RAS to simulate the water quality parameter at Chilgok Weir according to given upstream boundary conditions. The applicability of the framework was demonstrated by simulation results using observational data from 2015 to 2019. The simulation results based on the framework confirmed that the frequency of CyanoHABs would be decreased compared with the number of days when CyanoHABs were observed at Chilgok Weir. This framework, with a combination of several models, is a novelty in terms of efficiency, and it can be a part of a solution to the problem of CyanoHABs without using an additional amount of water from a reservoir.@en

The World Health Organization (WHO) and the U.S. Environmental Protection Agency (EPA) provide guidelines on the maximum levels of nitrate nitrogen (NO3-N) contained in drinking water since excess nitrate ingestion may harm human health. Thus, monitoring and controlling the NO3-N concentration is of paramount importance, especially in sources of drinking water such as the Nakdong River in South Korea. This study addresses NO3-N pollution in the Nakdong River in South Korea, where such pollution mostly comes from diffuse sources in the catchment due to the agricultural use of fertilizers. The objective of this study is to suggest guidelines for designing strategies to control NO3-N in this river using a process-based model developed with HEC-RAS. The model was built based on water quality parameters (water temperature, dissolved oxygen, ammonia nitrogen, etc.) related to NO3-N dynamics incorporating hydraulic and meteorological data. This model simulated NO3-N dynamics downstream under 55 scenarios while focusing on a section near locations of drinking water intakes. The scenarios were constructed based on variations in water quantity and quality upstream. The simulation results showed that the peak concentration of NO3-N downstream could be directly controlled by limiting the NO3-N concentration upstream. Additionally, control of the flow rate upstream could also lead to a reduction in the overall average concentration of NO3-N downstream, but this predominantly occurred when the NO3-N concentration was decreasing. In conclusion, the design and implementation of strategies for the control of NO3-N downstream should be carried out after performing a quantitative analysis of the impact of different control measures for different downstream conditions using a water quality model. @en