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Quantitatively analyzing models’ uncertainty is essential for agricultural models due to the effect of inputs parameters and processes on increasing models’ uncertainties. The main aim of the current study was to explore the influence of input parameter uncertainty on the output of the well-known surface irrigation software model of WinSRFR. The generalized likelihood uncertainty estimation (GLUE) framework was used to explicitly evaluate the uncertainty model of WinSRFR. The epistemic uncertainties of WinSRFR furrow irrigation simulations, including the advance front curve, flow depth hydrograph, and runoff hydrograph, were assessed in response to change key input parameters related to the Kostiakov–Lewis infiltration function, Manning’s roughness coefficient, and the geometry cross section. Three likelihood measures of Nash–Sutcliffe efficiency (NSE), percentage bias (PBIAS), and the coefficient of determination (R2) were used in GLUE analysis for selecting behavioral estimations of the model outputs. The uncertainty of the WinSRFR model was investigated under two furrow irrigation system conditions, closed end and open end. The results showed the likelihood measures considerably influence the width of uncertainty bounds. WinSRFR outputs have high uncertainty for cross section parameters relative to soil infiltration and roughness parameters. Parameters of soil infiltration and roughness coefficient play an important role in reducing the uncertainty bound width and number of observations, especially by filtering non-behavioral data using likelihood measures. The simulation errors of advance front curve and runoff hydrograph outputs with a PBIAS function were relatively lower and stable compared with other those of the likelihood functions. The 95% prediction uncertainties (95PPU) of the advance front curve were calculated to be 87.5% in both close-ended and open-ended conditions whereas, it was 91.18% for the runoff hydrograph in the open-ended condition. Additionally, the NSE likelihood function more explicitly determined the uncertainty related to flow depth hydrograph estimations. The outputs of the model showed more uncertainty and instability in response to variability in soil infiltration parameters than the roughness coefficient did. Therefore, applying accurate field methods and equipment and proper measurements of soil infiltration is recommended. The results highlight the importance of accurately monitoring and determining model input parameters to access a suitable level of WinSRFR uncertainty. In conclusion, considering and analyzing the uncertainty of input parameters of WinSRFR models is critical and could provide a reference to obtain realistic and stable furrow irrigation simulations.