The objectives of this study are to analyze the influence of systematic and random error inrainfall data, discharge data, and the spatial representation of rainfall data on the performance of a distributed hydrologicalmodel, BTOPMC. A framework for uncertainty analysis was developed using a Monte Carlo approach, which was applied to the Kalu River basin in Sri Lanka. Findings show that a systematic error exceeding +/-10% in rainfall or discharge data is detrimental to model results. A random error with standarddeviation is equal to10% ofrainfall or discharge isnot substantial. Calibrationof parameters can compensate for some error. The impact of systematic error in rainfall in terms of Nash-Sutcliffe Efficiency (NSE) is higher than that in discharge. The impact of random error in discharge in terms of NSE is higher than that inrainfall. The impact of the random error is the lowest for the gauge network of the highest density, but the impact of systematic error is not the lowest for this case.