Increasing the precision of snow water equivalent estimates obtained from spatial modeling of airborne and ground-based snow data

With the increased demand for water in the United States, Particularly in the West, it is essential that water resources be accurately monitored. Consequently, 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. 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 geostatistical model to estimate snow water equivalent for updating the snow model. In this research, we describe how to increase the precision of the snow water equivalent estimates by incorporating knowledge of the measurement errors that exist in the airborne and ground-based data in to the spatial model.