In a previous paper, Vrugt et al. (2005) presented a combined parameter and state estimation method, entitled SODA, to improve the treatment of input, output, parameter and model structural error during model calibration. The argument for using SODA is that explicit treatment of all sources of uncertainty should result in parameter estimates that closer represent system properties, instead of parameter values that are compensating for input, output and model structural errors. In this study we provide further support for this claim by applying the SODA method to the calibration of a simple 2-parameter snow model, using data from the Lake Eldora SNOwpack TELemetry (SNOTEL) site in Colorado, USA.