Sensitivity analysis of hydrological model is the key for model uncertainty quantification. However, how to effectively validate model and identify the dominant parameters for distributed hydrological models is a bott...Sensitivity analysis of hydrological model is the key for model uncertainty quantification. However, how to effectively validate model and identify the dominant parameters for distributed hydrological models is a bottle-neck to achieve parameters optimization. For this reason, a new approach was proposed in this paper, in which the support vector machine was used to construct the response surface at first. Then it integrates the SVM-based response surface with the Sobol＇ method, i.e. the RSMSoboI＇ method, to quantify the parameter sensi- tivities. In this work, the distributed time-variant gain model （DTVGM） was applied to the Huaihe River Basin, which was used as a case to verify its validity and feasibility. We selected three objective functions （i.e. water balance coefficient WB, Nash-Sutcliffe efficiency coefficient NS, and correlation coefficient RC） to assess the model performance as the output responses for sensitivity analysis. The results show that the parameters gl and g2 are most important for all the objective functions, and they are almost the same to that of the classical approach. Furthermore, the RSMSobol method can not only achieve the quantification of the sensitivity, and also reduce the computational cost, with good accuracy compared to the classical approach. And this approach will be effective and reliable in the global sensitivity analysis for a complex modelling system.展开更多
基金National Key Basic Research Program of China,No.2010CB428403National Grand Science and Technology Special Project of Water Pollution Control and Improvement,No.2009ZX07210-006
文摘Sensitivity analysis of hydrological model is the key for model uncertainty quantification. However, how to effectively validate model and identify the dominant parameters for distributed hydrological models is a bottle-neck to achieve parameters optimization. For this reason, a new approach was proposed in this paper, in which the support vector machine was used to construct the response surface at first. Then it integrates the SVM-based response surface with the Sobol＇ method, i.e. the RSMSoboI＇ method, to quantify the parameter sensi- tivities. In this work, the distributed time-variant gain model （DTVGM） was applied to the Huaihe River Basin, which was used as a case to verify its validity and feasibility. We selected three objective functions （i.e. water balance coefficient WB, Nash-Sutcliffe efficiency coefficient NS, and correlation coefficient RC） to assess the model performance as the output responses for sensitivity analysis. The results show that the parameters gl and g2 are most important for all the objective functions, and they are almost the same to that of the classical approach. Furthermore, the RSMSobol method can not only achieve the quantification of the sensitivity, and also reduce the computational cost, with good accuracy compared to the classical approach. And this approach will be effective and reliable in the global sensitivity analysis for a complex modelling system.
