Although precipitation/snowfall are a first order control on the spatial distribution of snow at the onset of melt, standard precipitation interpolation methods usually result in a relatively homogeneous snow cover. A recent approach to address this consists of assimilating distributed lidar snow depths into physically-based models (e.g., iSnobal/AWSM and Alpine3D), which has markedly improved model skill to accurately estimate storage volumes and snowmelt, although the snowpack characterization prior to the first available lidar flight remains unresolved. Here, we extend the original approach of Vögeli et al. (2016) for snowfall interpolation to utilizing time series of distributed snow water equivalent (SWE) (Hedrick et al, 2018) in the Tuolumne River Basin in California for the period 2013-2017. Our focus is two-fold: first, we demonstrate the spatial and temporal features of peak SWE across the basin and their consistency despite interannual climatic differences; and second, we demonstrate how distributed peak SWE can be used to inform snowfall interpolation. During the study period, peak snowpack storage in the 1180 km^2 basin ranged between 150 and 1500 million cubic meters with peak storage at elevations between 2750-3250 m. In the extreme conditions of 2017, peak SWE was reached in early March at elevations around 1600 m, while as late as May and June above 3500 m. Peak SWE patterns, however, remained strongly consistent despite marked interannual variability in climatic conditions (Pierson’s r: 0.61-0.86). We tested the approach utilizing peak SWE in 2017 to interpolate hourly snowfall in 2016, resulting in an increase in standard deviation and range throughout the season, although preserving precipitation mass and reproducing observed patterns of snow distribution. This approach will allow us to improve modeled snowpack storage and snow distribution using the iSnobal/AWSM modeling system in watersheds with lidar snow depths and/or modeled SWE information, which is of paramount importance to water managers in mountainous regions.