Water quality modelling based on 4-dimensional variational assimilation method in the Nanfei River, Lake Chaohu Basin<sup>*</sup>

Abstract

针对河流模拟中未知不确定性源对模拟精度的影响，以巢湖流域南淝河为研究对象，建立了基于四维变分同化方法的南淝河干流水质模型，研究了含未知污染源的南淝河水质过程模拟.模型以未知污染负荷的动态变化过程为控制变量，通过同化沿河不同断面的逐日水质监测数据，识别不同河段的逐日入河污染负荷过程来实现水质过程的模拟，改变了常规模型模拟需提前预知并输入污染负荷的应用前提.模拟结果表明，采用四维变分同化方法的水质模拟结果有明显改进，重点河段水质模拟的纳什效率系数从小于0提高到0.5以上.识别的入河污染过程与降雨过程波动总体一致，证实南淝河的入河污染与降雨过程密切；同时，模型也可识别异常的入河负荷，提高模型对水环境问题的诊断分析能力.该方法可推广应用于复杂河流系统，为巢湖等流域污染来源定量解析、水质预测预警及污染管控提供支持.;To reduce model accuracy caused by unknown uncertain pollutant sources, a water quality model based on a 4-dimensional variational assimilation method was established. Water quality modelling was conducted in the mainstream of the Nanfei River, Lake Chaohu Basin, in which water quality is highly unstable influenced by unmeasured pollutant sources. The proposed model takes the unsteady process of non-point source loads as the control variable. By assimilating the daily water quality monitoring data in different gauges along the river, the water quality process was successfully reproduced by identifying the daily pollution load process of each reach. It changes the premise that the traditional model simulation needs the right pollution load in advance. The numerical experiment shows that the results of water quality simulation using variational assimilation method are significantly improved, and the Nash efficiency coefficient of water quality simulation in key river reaches is increased from less than 0 to more than 0.5. The identified pollution process consistent with the observed rainfall process proves that the pollution into the river is closely related to the rainfall. The model can also identify some abnormal pollutant loads into the river and improve the diagnosis and analysis ability of the model for water environment problems.