Home > Research > Publications & Outputs > Parameter conditioning and prediction uncertain...
View graph of relations

Parameter conditioning and prediction uncertainties of the LISFLOOD-WB distributed hydrological model

Research output: Contribution to journalJournal article

<mark>Journal publication date</mark>02/2006
<mark>Journal</mark>Hydrological Sciences Journal
Issue number1
Number of pages21
Pages (from-to)45-65
<mark>Original language</mark>English


Distributed 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 likelihood 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 under 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 outlet. It is also shown that the value of parameter B at which the highest efficiency is derived is shifted from a high value for Lushi to a low value for Lingkou, consistent with past experience of model calibration that the larger the basin, the larger the B value is. The channel Manning coefficient Nc shows some sensitivity in the prediction of discharge time series, but less in the prediction of event volumes. The other key parameters show little sensitivity and good simulations are found across the full range of parameter values sampled. The uncertainty bounds of predicted discharges at the Lushi sub-basin are broad in the peak and narrow in the recession. The normalized difference between the upper and lower uncertainty bounds for both discharge and evapotranspiration are broad in summer and narrow in winter and that of recharge is the opposite.