Relating BTOPMC model parameters to physical features of MOPEX basins
Relating BTOPMC model parameters to physical features of MOPEX basins
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DOI：
10.1016/j.jhydrol.2005.07.006
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发表时间：
2006-03
影响因子：
6.4
通讯作者：
T. Ao;H. Ishidaira;K. Takeuchi;A. Kiem;Junich Yoshitari;K. Fukami;J. Magome
中科院分区：
文献类型：
--
作者：
T. Ao;H. Ishidaira;K. Takeuchi;A. Kiem;Junich Yoshitari;K. Fukami;J. Magome
本文着重于参数区域化的块明智的使用的TOPMODEL与马斯京根-Cunge路由方法（BTOPMC）。预先确定的定量关系与物理盆地的特点是用来减少参数估计的不确定性，使预测在无资料的盆地。BTOPMC参数值，这导致了以前在有效的模型性能，应用于10模型参数估计实验（MOPEX）盆地没有任何参数调整，以测试模型的性能，使用“盲”先验参数估计。它定量地表明，参数不能令人满意地估计先验参数调整，无论是通过校准或传递函数连接参数的物理盆地功能，被称为“改进的”先验参数估计。为了比较使用不同参数估计技术的模型性能，通过自动优化重新估计BTOPMC参数。正如预期的那样，当使用这些优化参数时，模型性能显著提高。当使用优化参数时，在19年验证和20年校准结果之间也观察到模型性能的良好相关性。这表明BTOPMC的预测精度强烈依赖于校准精度，这意味着良好优化的参数值可以确保模型预测的准确性。它似乎是合理的，使用优化的参数值，从成功的建模应用程序中选择，使传递函数，“改善”先验参数估计。“改进”的先验参数估计是通过多元线性回归分析BTOPMC参数的土壤和植被类型内的盆地建模。结果表明，初步参数传递函数令人鼓舞，并且“改进的”先验参数估计技术有可能减少参数不确定性并实现对无资料盆地的预测。为了提高模型的参数估计和预测精度，需要进一步研究以确定更准确的值，以及它们与物理盆地特征的关系。
This paper focuses on parameter regionalization for the block-wise use of the TOPMODEL with the Muskingum–Cunge routing method (BTOPMC). Pre-determined quantitative relationships with physical basin characteristics are used to reduce uncertainty in parameter estimation and enable prediction in ungauged basins. BTOPMC parameter values, which have previously resulted in efficient model performance, are applied to ten Model Parameter Estimation Experiment (MOPEX) basins without any parameter adjustment to test model performance using ‘blind’ a priori parameter estimates. It is quantitatively shown that parameters can not be satisfactorily estimated a priori without parameter adjustment, either through calibration or transfer functions linking parameters to physical basin features, referred to as ‘improved’ a priori parameter estimation. To compare model performance using different parameter estimation techniques, BTOPMC parameters are re-estimated by automatic optimization. As anticipated, model performance improves significantly when these optimized parameters are used. Good correlation of model performance is also observed between the 19-year validation and 20-year calibration results when optimized parameters are used. This indicates that BTOPMC's predictive accuracy depends strongly on calibration accuracy, implying that well-optimized parameter values can ensure the accuracy of model predictions. It appears reasonable to use optimized parameter values, selected from successful modelling applications, to make transfer functions which ‘improve’ a priori parameter estimates. The ‘improved’ a priori parameter estimation is achieved through multiple linear regression analysis relating BTOPMC parameters to soil and vegetation types within the basin being modelled. The results suggest that the preliminary parameter transfer functions are encouraging and that ‘improved’ a priori parameter estimation techniques have the potential to reduce parameter uncertainty and enable prediction in ungauged basins. To improve parameter estimation and predictive accuracy of the model, further research to determine more accurate values, and their relationship with physical basin characteristics, is needed.