Accurate assessment of streamflow is crucial for operational water resources management projects. The aim of this study was to estimate the uncertainties in the surface runoff simulated by a monthly water balance model in a mountainous watershed of the Portaikos river, a tributary of the Pinios river, Thessaly, Greece. The University of Thessaly (UTHBAL) monthly water balance model was developed in the R statistical computing environment language, named ‘R-UTHBAL’, to estimate surface water balance in data-scarce watersheds. Two sources of uncertainties in hydrological modelling were considered: the uncertainties in input data estimation and in model parameters. The uncertainties were estimated with the use of the R-package ‘hydroPSO’, a global Particle Swarm Optimisation (PSO) algorithm for the calibration of environmental models. The R-UTHBAL was integrated with the hydroPSO algorithm and advanced sensitivity analyses, and user-friendly evaluation plots were estimated to facilitate the interpretation and assessment of the calibration results. Areal input datasets were estimated using typical engineering methods (i.e. precipitation/temperature gradients, Thiessen polygons) and several objective functions, (i.e. Nash-Sutcliffe Efficiency and variations or adaptations), addressing different parts of the hydrograph have been used to assess both the skill and the robustness of the R-UTHBAL model to perform consistent streamflow predictions. Confidence intervals in the simulated runoff due to input data uncertainty, parameter uncertainty and total uncertainty were calculated. Application of R-UTHBAL with the hydroPSO showed that the uncertainty in streamflow estimation should always be accounted for and evaluated in operational water resources management projects.