Improved dynamic simulation technique for hydrodynamics and water quality of river-connected lakes

• Published in: Water science & technology. Water supply Vol. 20; no. 8; pp. 3752 - 3767
• Main Authors: Liu, Bojun, Xia, Jun, Yang, Libin, Cui, Changyong, Wang, Linwei, Li, Tiansheng
• Format: Journal Article
• Language: English
• Published: London IWA Publishing 01.12.2020
• Subjects: Accuracy; Climate change; Computational fluid dynamics; Computer applications; Creeks & streams; Drag coefficient; Drag coefficients; Flow velocity; Fluid flow; Fluid mechanics; Hydrodynamics; Hypercubes; Kalman filters; Lakes; Latin hypercube sampling; Mathematical models; Noise; Parameter identification; Parameter sensitivity; Parameters; Pollutants; Rain; Random variables; Regression analysis; Rivers; Roughness; Roughness coefficient; Runoff; Seasons; Simulation; Surface water; Two dimensional models; Water levels; Water quality; Wind; Wind resistance; China; Huaihe River
• ISSN: 1606-9749; 1607-0798
• DOI: 10.2166/ws.2020.125

Abstract In this study, a two-dimensional hydrodynamic water-quality model is proposed for river-connected lakes in an effort to improve calibration accuracy and reduce computational burden. To achieve this, the sensitivity of parameters involved in the hydrodynamic model is analyzed using stepwise rank regression and Latin hypercube sampling (LHS), and the roughness coefficient, wind drag coefficient and wind resistance coefficient are identified as the most important parameters affecting the hydrodynamics of the Hongze Lake. Then, the ensemble Kalman filter (EnKF) is used to assimilate observations to the proposed hydrodynamic and water quality model. It is found that assimilation of both state variables and model parameters results in a significant improvement of the simulation of the water level, flow velocity and pollutant concentration in the Hongze Lake.