2024 Volume 80 Issue 16 Article ID: 23-16135
Understanding the spatio-temporal variation of water temperature is important for water quality management in headwater reservoirs. Although field observations provide relatively reliable data, a numerical fluid dynamics model is indispensable to predict spatio-temporal changes. In this study, we tried to improve the prediction accuracy by implementing ensemble Kalman filter in a three-dimensional environmental fluid model, Fantom-Refined. As a result, sequential assimilation continued to reduce the model error, and we can confirm the robustness of the data assimilation system. 3-week average MAE and RMSE were reduced to 48% and 74%, respectively. Also, in a stratified reservoir during the summer season, the accuracy of spatio-temporal water temperature prediction was improved even with limited observation data.
