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[1] This study aims to assess the impact of a class of radar rainfall errors on prediction uncertainty of a conceptual water balance model. Uncertainty assessment is carried out by means of the Generalized Likelihood Uncertainty Estimation procedure (GLUE). The effects of model input and structural error are separated, and the potential for compensating errors between them is investigated. It is shown that the radar rainfall bias term operates in a multiplicative sense on the model structural uncertainty, by either magnifying or reducing it according to the sign of the bias. The results show also that adjustment of radar rainfall, aimed to remove local biases and to reduce random errors, allows ensuring that a larger percentage of the observed flows are enclosed by the uncertainty bounds, with respect to nonadjusted radar input. However, this is obtained at the price of increasing the wideness of the uncertainty bounds. This effect is emphasized with increasing the radar beam elevation. As a second step, the issue of the impact of radar rainfall estimation error on model parameter distribution and parameter transferability across sites under the radar umbrella is examined. Radar data at different radar beam elevations are used to simulate radar estimation errors at different distances from the radar site and to analyze the impact of these errors on prediction uncertainty. The results show that distortion of parameter distribution due to radar error may be considerable and that adjustment of radar rainfall estimates improves the regionalization potential of radar-based precipitation estimates (at least for ranges less than 70 km).