Estimating the uncertainty in spatial estimates of areal snow water equivalent

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 (NWS) maintains a set of conceptual, continuous, ic 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 (estimates of the water content of snowpack) obtained from airborne and ground-based snow water equivalent data. The National Weather Service has developed a spatial geostatistical model to estimate the areal snow water equivalent in a river basin. The estimates, which are obtained for river basins throughout the West, are used to update the snow model. To facilitate accurate updating of the simulated snow water equivalent estimates generated by the snow model, it is necessary to incorporate measures of uncertainty of the areal snow water equivalent estimates. In this research, we derive the expression for the mean-squared prediction error of the areal snow water equivalent estimate and illustrate the methodology with an example from the Upper Colorado River basin.