Discharge simulation from snow-dominated catchments seems to be an easy task. Any spatially explicit precipitation-runoff model coupled to a temperature-index snow model generally yields simulations that mimic well the observed daily discharges. The robustness of such models is, however, questionable: in the presence of strong annual discharge cycles, small model residuals do not guarantee high explanatory power of the underlying model. This paper proposes a methodology for snow hydrological model identification within a limits-of-acceptability framework, where acceptable model simulations are the ones that reproduce a set of signatures within an a priori specified range. The signatures proposed here namely include the relationship between the air temperature regime and the discharge regime, a new snow hydrology signature that can be readily transferred to other Alpine settings. The discriminatory power of all analysed signatures is assessed with a new measure of their discriminatory power in the model prediction domain. The value of the proposed snow hydrology signatures and of the limits-of-acceptability approach is demonstrated for the Dischma river in Switzerland, whose discharge shows a strong temporal variability of hydrologic forcing conditions over the last 30 years. The signature-based model identification for this case study leads to the surprising conclusion that the observed discharge data contains a multi-year period that cannot be reproduced with the model at hand. This model-data mismatch might well result from a yet to be identified problem with the discharge observations, which would have been difficult to detect in a classical residual-based model identification approach. Overall, the detailed results for this case study underline the robustness of the limits-of-acceptability approach in the presence of error-prone observations if it is applied in combination with relatively robust signatures. Future work will show whether snow hydrology signatures and their limits-of-acceptability can be regionalized to ungauged catchments, which would make this model selection approach particularly powerful for Alpine environments. Copyright (C) 2016 John Wiley & Sons, Ltd.