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Hydrological models are useful tools for better understanding the hydrological processes and performing the hydrological prediction. However, the reliability of the prediction depends largely on its uncertainty range. This study mainly focuses on estimating model parameter uncertainty and quantifying the simulation uncertainties caused by sole model parameters and the co-effects of model parameters and model structure in a lumped conceptual water balance model called WASMOD (Water And Snow balance MODeling system). The validity of statistical hypotheses on residuals made in the model formation is tested as well, as it is the base of parameter estimation and simulation uncertainty evaluation. The bootstrap method is employed to examine the parameter uncertainty in the selected model. The Yingluoxia watershed at the upper reaches of the Heihe River basin in north-west of China is selected as the study area. Results show that all parameters in the model can be regarded as normally distributed based on their marginal distributions and the KolmogorovSmirnov test, although they appear slightly skewed for two parameters. Their uncertainty ranges are different from each other. The model residuals are tested to be independent, homoscedastic and normally distributed. Based on such valid hypotheses of model residuals, simulation uncertainties caused by co-effects of model parameters and model structure can be evaluated effectively. It is found that the 95% and 99% confidence intervals (CIs) of simulated discharge cover 42.7% and 52.4% of the observations when only parameter uncertainty is considered, indicating that parameter uncertainty has a great effect on simulation uncertainty but still cannot be used to explain all the simulation uncertainty in this study. The 95% and 99% CIs become wider, and the percentages of observations falling inside such CIs become larger when co-effects of parameters and model structure are considered, indicating that simultaneous consideration of both parameters and model structure uncertainties accounts sufficient contribution for model simulation uncertainty. Copyright (C) 2012 John Wiley & Sons, Ltd.