As the population and industrial infrastructure of the United States continue to grow, the demand for water and the need to forecast water resources accurately are intensifying. Hence, the National Weather Service maintains a set of conceptual, continuous, hydrologic simulation models used to generate extended streamflow predictions, water supply outlooks and flood forecasts that are the basis for major water management and disaster emergency services decisions for the United States. A vital component of the hydrologic simulation models is a snow accumulation and ablation model that uses observed temperature and precipitation data to simulate snow cover conditions. The simulated model states are updated throughout the snow season using snow water equivalent estimates obtained from airborne and ground‐based snow water equivalent data. The National Weather Service has developed a spatial model to obtain integrated snow water equivalent estimates for updating the snow model; however, it is designed to incorporate only ground‐based data. In this research, we describe the spatial model and show how to modify it to include data of two different supports (the airborne and ground‐based data) so that more precise integrated snow water equivalent estimates can be obtained. The results are illustrated on snow data collected in the Upper Colorado River basin.