Wu Lei, Liu Xia, Chen Junlai, Ma Xiaoyi
Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Yangling, 712100, Shaanxi, China.
Blackland Research and Extension Center, Texas A&M AgriLife Research, Texas A&M University, Temple, TX, 76502, USA.
Environ Sci Pollut Res Int. 2023 May;30(24):65470-65481. doi: 10.1007/s11356-023-27075-1. Epub 2023 Apr 21.
Calibration methodologies must extract as much information as possible from available data, but it is not well understood in investigating the multi-objective synchronous calibration strategy by using multiple sources of information and by exploiting the data in better ways. The non-dominated sorting genetic algorithm II (NSGA-II) is introduced to study the calibration performance of runoff and sediment parameters under nine targeted scenarios, which considers the best choice to obtain high-cost performance results for decision makers through multi-objective optimization and the calculation of Pareto-optimal front with a high precision. (i) SWAT model has good adaptability in the runoff simulation of the Yanhe River watershed. Both the calibration results of NSGA-II and sequential uncertainty fitting approach-version 2 (SUFI-2) can meet the accuracy requirements of runoff simulation. Particularly, the NSGA-II based on multiple objective functions not only has strong applicability but also can better constrain the parameter process, making the calibrated model more in line with the physical conditions of the watershed. (ii) The two-site synchronous calibration of runoff or sediment can make full use of data information of different sites, reduce the impact of spatial heterogeneity on model parameters, and improve the calibration efficiency of the model. The single-site synchronous calibration of runoff and sediment based on NSGA-II not only has high calibration efficiency but also can avoid the tedious steps of calibrating runoff and sediment separately. (iii) The two-site synchronous calibration of runoff and sediment based on NSGA-II combines the advantages of the above synchronous calibration strategies, which can get Pareto-optimal front and represent the best trade-offs among different objectives, and its applicability is stronger than the traditional single-site or single-element calibration strategy. This study provides new and competing ways to evaluate hydrological models and their performance, and the multiple criteria approach for watershed modeling is one of the focuses in future research extensions.
校准方法必须从可用数据中提取尽可能多的信息,但在利用多种信息源并以更好的方式利用数据来研究多目标同步校准策略方面,人们对此还没有很好的理解。引入非支配排序遗传算法II(NSGA-II)来研究九种目标情景下径流和泥沙参数的校准性能,该算法通过多目标优化以及高精度计算帕累托最优前沿,为决策者考虑获得高性价比结果的最佳选择。(i)SWAT模型在延河流域径流模拟中具有良好的适应性。NSGA-II和序列不确定性拟合方法版本2(SUFI-2)的校准结果均能满足径流模拟的精度要求。特别是基于多目标函数的NSGA-II不仅适用性强,而且能更好地约束参数过程,使校准后的模型更符合流域的物理条件。(ii)径流或泥沙的两点同步校准可以充分利用不同站点的数据信息,减少空间异质性对模型参数的影响,提高模型的校准效率。基于NSGA-II的径流和泥沙单点同步校准不仅校准效率高,而且可以避免分别校准径流和泥沙的繁琐步骤。(iii)基于NSGA-II的径流和泥沙两点同步校准结合了上述同步校准策略的优点,能够得到帕累托最优前沿并代表不同目标之间的最佳权衡,其适用性强于传统的单点或单要素校准策略。本研究为评估水文模型及其性能提供了新的且具有竞争力的方法,流域建模的多准则方法是未来研究扩展的重点之一。