引用方式: Li, Z. , Shao, Q. , Xu, Z. , & Cai, X. . (2010). Analysis of parameter uncertainty in semi-distributed hydrological models using bootstrap method: a case study of swat model applied to yingluoxia watershed in northwest china. Journal of Hydrology (Amsterdam), 385(1-4), 76-83.
| 文献信息 | |
| 标题 | Analysis of parameter uncertainty in semi-distributed hydrological models using bootstrap method: A case study of SWAT model applied to Yingluoxia watershed in northwest China |
| 年份 | 2010 |
| 出版社 | Journal of Hydrology |
| 链接 | |
| 语言 | en |
| DOI | 10.1016/j.jhydrol.2010.01.025 |
| 摘要 | Summary Much attention has been paid to uncertainty issues in hydrological modelling due to their great effects on prediction and further on decision-making. The uncertainty of model parameters is one of the major uncertainty sources in hydrological modelling. The aim of this study is to quantify the parameter uncertainty in Soil and Water Assessment Tool (SWAT) model using bootstrap method with application to Yingluoxia watershed located in the upper reaches of Heihe River basin. Bootstrap method is a nonparametric technique for simulating the parameter distribution. Nine sensitive aggregate parameters are investigated. The results from bootstrap method show that six of the nine marginal distributions are not normally distributed and each parameter has its own uncertainty range. Further investigation about the effects of parameter uncertainty on simulation results shows that although the parameter uncertainty is one of the important sources of uncertainties, its contribution to simulation uncertainty is relatively small. Only 12–13% of the observed runoff data fall inside the 95% simulation confidence intervals in the calibration and validation periods. For a better understanding of the applicability of bootstrap method, the commonly used Bayesian approach is also investigated for comparison. Results show that the approximate results are obtained from both methods, not only in the percentage of observations falling inside the 95% confidence interval of simulations, but also in the uncertainty range of parameters, although the range obtained from Bayesian method is slightly narrower than that from bootstrap method, possibly due to the correlation structure amongst parameters in the MCMC (Markov Chain Monte Carlo) simulation employed in Bayesian method. The computational efficiencies of both methods presented are comparable as well. |
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