@article{LI2025, 
author = {Youlin LI and Feng YANG and Guosong FEI and Jinning WU and Feiyan SHAO and Wenjie ZHU and Chuanhai WANG and Gang CHEN},
title = {Study on river pollution confluence based on Muskingum method},
year = {2025},
journal = {Journal of Hohai University (Natural Sciences)},
volume = {53},
number = {6},
pages = {57-64},
keywords = {water quality prediction, one-dimensional water quality equation, Muskingum method, hilly region},
url = {https://www.sciopen.com/article/10.3876/j.issn.1000-1980.2025.06.007},
doi = {10.3876/j.issn.1000-1980.2025.06.007},
abstract = {To address the issues of the large amount of data required and the high difficulty in obtaining data during the modeling process of hydrodynamic water quality models, a method based on hydrological approaches for solving the basic equations of one-dimensional water quality models was proposed. This method substituted river topography and flow velocity data with Muskingum method parameters K and X for river confluence in hydrology. Moreover, coupled with the basic equations of the one-dimensional water quality model, a river pollution confluence model based on the Muskingum method was developed. Since taking a constant value for K is unreasonable for calculating pollutant propagation time in the developed model, the functions of K and X relative to flow rate Q were derived to solve this problem, and the nonlinear Muskingum model was coupled with the one-dimensional water quality model to build a river pollution confluence model based on the nonlinear Muskingum method. The verification results of the Dapuling-Changtaiguan section of the upper reaches of the Huai River show that the river pollution confluence model based on the nonlinear Muskingum method has ensured accurate prediction of peak time compared with the river pollution confluence model based on the Muskingum method. At the same time, the deterministic coefficient of the pollutant mass concentration prediction results has been increased by 0.04, the root mean square error has been reduced by 0.07 mg/L, and the average absolute error has been decreased by 0.022 mg/L.}
}