EGU23-10669
https://doi.org/10.5194/egusphere-egu23-10669
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Prediction and analysis of algal bloom trend in Yeongsan River using EFDC

Hye Yeon Oh1, Hye Won Lee2, and Jung Hyun Choi3
Hye Yeon Oh et al.
  • 1Ewha womans university, Department of Environmental Science and Engineering, Seoul, Republic of Korea (hye0529@ewhain.net)
  • 2Ewha womans university, Department of Environmental Science and Engineering, Seoul, Republic of Korea (hwon@ewha.ac.kr)
  • 3Ewha womans university, Department of Environmental Science and Engineering, Seoul, Republic of Korea (jchoi@ewha.ac.kr)

 Green algae, which is called water bloom, refers to a phenomenon in which cyanobacteria proliferate in large quantities and change the color of water to green. Algal bloom is one of the major water quality problems in freshwater ecosystems because it causes oxygen depletion in deep layer, oxygen supersaturation and toxicity in the surface layer, odor generation, fish death, and scum formation. Green algae are caused by hydraulic factors such as increased residence time due to the installation of hydraulic structures such as weirs, as well as physicochemical factors such as excessive influx of nutrients and rise in water temperature. One of Korea's four major rivers, the Yeongsan River, which originates in Damyang-gun, Jeollanam-do and flows into the West Sea, is experiencing water pollution problems, including algae, as the water quality and hydraulic environment change due to the construction and opening of weirs. Accordingly, Gwangju Metropolitan City, a large city where more than 80% of the population of the Yeongsan River basin resides, and Seungchon Weir, one of the two artificial weirs located in the Yeongsan River, were selected as the study area. In this study, the Environmental Fluid Dynamics Code (EFDC), a three-dimensional hydraulic and water quality dynamics model that can simulate various water quality indicators such as Chl-a, DO, T-N, and T-P, which was used to predict the trend of algal bloom in the study area.

How to cite: Oh, H. Y., Lee, H. W., and Choi, J. H.: Prediction and analysis of algal bloom trend in Yeongsan River using EFDC, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-10669, https://doi.org/10.5194/egusphere-egu23-10669, 2023.