水文模型是环境管理的重要工具。水文参数模型鲁棒性的弱点将不确定性传递给模型输出。对于水流,优化后的参数是不确定性的主要来源。减少模型模拟中预测不确定性的可靠校准方法对于增强模型的鲁棒性和可靠性至关重要。参数范围的优化是参数标定的关键,然而,缺乏有关水文模型参数范围优化的文献。在本文中,我们介绍了一种应用聚类技术的参数校准策略,特别是自组织映射(SM),为了在白山盆地每月流动模拟的土壤和水评估工具(SWAT)模型的校准过程中智能地导航参数空间,吉林省,中国。我们选择了具有代表性的算法,顺序不确定性拟合版本2(SUFI-2),从常用的SWAT校准和不确定度程序进行比较。我们开发了三种方案:SUFI-2,SUFI-2-缩小(SUFI-2-ND),和SM。使用多个诊断错误指标来比较模拟准确性和预测不确定性。在所有计划中,SM在描述分水岭水流方面优于其他人,在模拟春季融雪径流(基流期)方面尤其出色。此外,预测不确定性得到有效控制,展示了SM在区间优化过程中的适应性和可靠性。这为管理者提供了更可信的预测结果,强调其作为水文建模中有价值的校准工具的潜力。
Hydrological models are vital tools in environmental management. Weaknesses in model robustness for hydrological parameters transfer uncertainties to the model outputs. For streamflow, the optimized parameters are the primary source of uncertainty. A reliable
calibration approach that reduces prediction uncertainty in model simulations is crucial for enhancing model robustness and reliability. The optimization of parameter ranges is a key aspect of parameter
calibration, yet there is a lack of literature addressing the optimization of parameter ranges in hydrological models. In this paper, we introduce a parameter
calibration strategy that applies a clustering technique, specifically the Self-Organizing Map (SM), to intelligently navigate the parameter space during the
calibration of the Soil and Water Assessment Tool (SWAT) model for monthly streamflow simulation in the Baishan Basin, Jilin Province,
China. We selected the representative algorithm, the Sequential Uncertainty Fitting version 2 (SUFI-2), from the commonly used SWAT Calibration and Uncertainty Programs for comparison. We developed three schemes: SUFI-2, SUFI-2-Narrowing Down (SUFI-2-ND), and SM. Multiple diagnostic error metrics were used to compare simulation accuracy and prediction uncertainty. Among all schemes, SM outperformed the others in describing watershed streamflow, particularly excelling in the simulation of spring snowmelt runoff (baseflow period). Additionally, the prediction uncertainty was effectively controlled, demonstrating the SM\'s adaptability and reliability in the interval optimization process. This provides managers with more credible prediction results, highlighting its potential as a valuable calibration tool in hydrological modeling.