The spatial distribution of frozen soil and snow cover at the start of the spring melt season plays an important role in the generation of spring runoff and in the exchange of energy between the land surface and the atmosphere. Field observations were made at the University of Minnesota's Rosemount Agricultural Experiment Station to identify statistical distributions that can be used to describe the spatial variability of frozen soil and snow in macroscale hydrology models. These probability distributions are used to develop algorithms that simulate the subgrid spatial variability of snow and soil ice content for application within the framework of the variable infiltration capacity macroscale hydrologic model. Point simulations show that the new snow algorithm increases the melt rate for thin snowpacks, and the new soil frost algorithm allows more drainage through the soil during the winter. Simulations of the Minnesota River show that the new snow algorithm makes little difference to regional streamflow but does play an important role in the regional energy balance, especially during the spring snowmelt season. The new soil frost algorithm has a larger impact on spring streamflow and plays a minor role in the surface energy balance during the spring soil thaw season.