Various uncertainty analysis methods have been used in various studies to analyze the uncertainty of rainfall-runoff models; however, these methods are difficult to apply immediately as they require a long learning time. In this study, we propose a simple uncertainty-screening method that allows modelers to investigate relatively easily the uncertainty of rainfall-runoff models. The 100 best parameter values of three rainfall-runoff models were extracted using the efficient sampler DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm, and the distribution of the parameter values was investigated. Additionally, the ranges of the values of a model performance evaluation statistic and indicators of hydrologic alteration corresponding to the 100 parameter values for the calibration and validation periods was analyzed. The results showed that the Sacramento model, which has the largest number of parameters, had uncertainties in parameters, and the uncertainty of one parameter influenced all other parameters. Furthermore, the uncertainty in the prediction results of the Sacramento model was larger than those of other models. The IHACRES model had uncertainty in one parameter related to the slow flow simulation. On the other hand, the GR4J model had the lowest uncertainty compared to the other two models. The uncertainty-screening method presented in this study can be easily used when the modelers select rainfall-runoff models with lower uncertainty.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.