Parameter conditioning and prediction uncertainties of the LISFLOOD-WB distributed hydrological model | |
Lin Zhonghui; Liu Suxia; Mo Xingguo | |
2006 | |
关键词 | Discharge (fluid mechanics) Mathematical models Monte Carlo methods Parameter estimation Sensitivity analysis Time series analysis |
英文摘要 | Distribined hydrological models are considered to be a promising tool for predicting the impacts of global change on the hydrological processes at the basin scale. However, distributed models typically require values of many parameters to be specified or calibrated, which exacerbates model prediction uncertainty. This study uses the generalized likelibood uncertainty estimation (GLUE) technique to analyse the parameter sensitivities of a distributed hydrological model, LISFLOOD-WB. Discharge time series and event volume data of the Luo River at upstream and downstream sites, Lingkou and Lushi, are used to analyse parameter uncertainty. Eight key parameters in the model are selected for conditioning and sampled using the Monte Carlo method on er assumed prior distributions. The results show that maximum efficiency of model Performance is lower and the number of behavioural parameter sets giving acceptable performance is fewer in the Lingkou sub-basin than in the Lushi sub-basin with the same criteria of acceptability. For both sub-basins the distribution shape parameter B in the fast runoff generation scheme is the most sensitive in predicting both discharge time series and event volume at the oudet. It is also shown that the value of parameter B at which the highest efficiency is derived is shifted from a value for Lushi to a low value for Lingkou consistent with past of model calibration that the larger the basin the larger the B value is. The channel Manning coefficient N |
出处 | Hydrological Sciences Journal
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卷 | 51期:1页:45-65 |
收录类别 | EI |
语种 | 英语 |
内容类型 | EI期刊论文 |
源URL | [http://ir.igsnrr.ac.cn/handle/311030/24679] ![]() |
专题 | 地理科学与资源研究所_历年回溯文献 |
推荐引用方式 GB/T 7714 | Lin Zhonghui,Liu Suxia,Mo Xingguo. Parameter conditioning and prediction uncertainties of the LISFLOOD-WB distributed hydrological model. 2006. |
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