Automatic calibration of a large-scale sediment model using suspended sediment concentration, water quality, and remote sensing data

ABSTRACT Calibration and validation are two important steps in the application of sediment models requiring observed data. This study aims to investigate the potential use of suspended sediment concentration (SSC), water quality and remote sensing data to calibrate and validate a large-scale sediment model. Observed data from across 108 stations located in the Doce River basin was used for the period between 1997-2010. Ten calibration and validation experiments using the MOCOM-UA optimization algorithm coupled with the MGB-SED model were carried out, which, over the same period of time, resulted in 37 calibration and 111 validation tests. The experiments were performed by modifying metrics, spatial discretization, observed data and parameters of the MOCOM-UA algorithm. Results generally demonstrated that the values of correlation presented slight variations and were superior in the calibration step. Additionally, increasing spatial discretization or establishing a background concentration for the model allowed for improved results. In a station with high quantity of SSC data, calibration improved the ENS coefficient from -0.44 to 0.44. The experiments showed that the spectral surface reflectance, total suspended solids and turbidity data have the potential to enhance the performance of sediment models.

摘要：率定与验证是泥沙模型应用中依托实测数据开展的两个核心步骤。本研究旨在探究利用悬移质泥沙浓度（SSC）、水质数据及遥感数据对大尺度泥沙模型进行率定与验证的可行性。研究选取多西河流域内108个监测站点1997—2010年的实测数据作为支撑。本研究结合MGB-SED模型与MOCOM-UA优化算法，开展了10组率定与验证试验，在对应时段内共计完成37组率定试验与111组验证试验。试验通过调整评价指标、空间离散化方案、实测数据集及MOCOM-UA算法参数进行设置。结果整体表明，相关系数数值呈现小幅波动，且在率定阶段表现更优。此外，提升空间离散化精度或为模型设定背景浓度可有效改善模拟效果。在某悬移质泥沙浓度数据充足的站点中，率定使纳什效率系数（ENS）从-0.44提升至0.44。试验结果证实，地表光谱反射率、总悬浮颗粒物及浊度数据具备提升泥沙模型模拟性能的潜力。