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Recent studies have demonstrated that hydrological model calibrations are impaired by uncertainties in observations and model structures. For a rainfall-driven model, the error in precipitation observations can lead to biased parameter estimates and predictions. For a conceptual ecohydrological model, an appropriate description of input error is essential because rainfall controls both hydrological processes and vegetation growth in the model. However, to date the impact of precipitation uncertainty on ecohydrologic model parameters and outputs has not been widely explored (Fuentes et al., 2006; Shields and Tague, 2012). The increased dimensionality of these types of models and the uncertainties associated with calibration data can make traditional approaches for characterizing precipitation errors problematic. Our study aims to investigate the impact of precipitation uncertainty for a Bayesian multi-objective calibration approach in an ecohydrological modeling study. A conceptual model that combines a hydrologic model and a modified bucket grassland model is implemented for a forested catchment in Australia. In the study, different input error descriptions are used in both single and multi-objective Bayesian calibration case studies aimed at simulating streamflow and Leaf Area Index (LAI). The emphasis on each objective is represented as different prior distributions defined for error parameters for multi-objective cases. Results show better parameter estimates and predictions for the cases including input error. Comparing the results from the cases in which different input error descriptions are used, a simple bias term works well for both streamflow and LAI estimations. Although a more complex rainfall multiplier approach to represent input error performs best for streamflow predictions, increasing the dimensionality of the input error model is not always justified given the information content of the data. In addition, some of the rainfall multipliers values are not meaningful in the real case, reflecting an overfitting problem.