School of Water Resources and Environment, China University of Geosciences (Beijing), Beijing, 100083, People's Republic of China.
Key Laboratory of Groundwater Circulation and Evolution, China University of Geosciences (Beijing), Ministry of Education, Beijing, 100083, People's Republic of China.
Environ Sci Pollut Res Int. 2021 Jan;28(4):4404-4416. doi: 10.1007/s11356-020-08879-x. Epub 2020 Sep 17.
Water pollution from surface runoff is an important non-point pollution source, which has been a great threat to our environment. The model proposed by Gao et al. (2004) is of great significance to solve the non-point source pollution problem, which is a numerical advection-diffusion equation (ADE) model for chemical transport from soil to surface runoff. The ensemble Kalman filter (EnKF), the data assimilation (DA) method, is easy to be implemented and widely used in hydrology field. In this study, we use the EnKF method to update model state variables such as chemical concentrations in surface runoff and calibrate model parameters such as water transfer rate in Gao et al. (2004) under different study cases, while other model parameters are assumed to be known. The observations are generated from the simulation results based on synthetic real parameters. The objective of this study was to extend the application of the EnKF to the ADE-based prediction model of chemical transport from soil to surface runoff. The results of the predicted chemical concentration in the surface runoff with EnKF are greatly improved than those without EnKF in comparison with the observations, and the updated parameters are close to the real parameters. We explored feasibility of the EnKF method from six factors, including the initial parameter estimate, the ensemble size, the influence of multi-parameters, the assimilation time interval, the infiltration boundary conditions, and the relationship between the standard deviations of the observation error and initial parameter. Different study strategies are proposed for different factors. For assimilation time interval, the key observation can reduce the assimilation frequency. With the situation of much larger observation error covariance than the prediction covariance, we analyzed influences of the standard deviation of the observation error and initial parameter on the feasibility of the EnKF method. According to the study results, it is concluded that the EnKF is efficient to update the parameter for the ADE-based prediction model of chemical transport from soil to surface runoff.
地表径流造成的水污染是一种重要的非点污染源，对我们的环境构成了巨大威胁。Gao 等人（2004）提出的模型对于解决非点源污染问题具有重要意义，这是一个用于从土壤到地表径流的化学输运的数值平流-扩散方程（ADE）模型。集合卡尔曼滤波（EnKF）是一种数据同化（DA）方法，易于实现，在水文学领域得到了广泛应用。在本研究中，我们使用 EnKF 方法更新模型状态变量，例如地表径流中的化学浓度，并在不同的研究案例中校准 Gao 等人（2004）模型中的水迁移率等模型参数，同时假设其他模型参数是已知的。观测值是根据基于综合真实参数的模拟结果生成的。本研究的目的是将 EnKF 应用于基于 ADE 的土壤到地表径流化学输运预测模型。与没有 EnKF 的情况相比，EnKF 对地表径流中预测化学浓度的结果大大提高，与观测值的一致性更好，并且更新后的参数更接近真实参数。我们从六个因素探讨了 EnKF 方法的可行性，包括初始参数估计、集合大小、多参数影响、同化时间间隔、入渗边界条件以及观测误差和初始参数的标准差之间的关系。针对不同的因素提出了不同的研究策略。对于同化时间间隔，关键观测值可以减少同化频率。在观测误差协方差远大于预测协方差的情况下，我们分析了观测误差和初始参数的标准差对 EnKF 方法可行性的影响。根据研究结果得出结论，EnKF 有效地更新了基于 ADE 的土壤到地表径流化学输运预测模型的参数。
