Spatial modeling of snow water equivalent using covariances estimated from spatial and geomorphic attributes

As the demand for water in the USA rapidly approaches the total available water supply, it is essential that water resources be accurately monitored. Consequently, the National Weather Service (NWS) maintains a set of conceptual, continuous, hydrologic simulation models used to generate extended streamflow predictions, water supply outlooks, and flood forecasts. To obtain accurate predictions and forecasts, it is necessary, periodically throughout the snow season, to estimate the snow water equivalent in river basins throughout the USA. The estimates are obtained using a geostatistical model and snow course, SNOTEL, and airborne snow data. in this research, we develop a positive-definite spatial covariance function that allows researchers to incorporate geomorphic site attributes when snow water equivalent estimates are obtained. We illustrate our approach using snow course and SNOTEL data collected in the North Fork Clearwater River basin. Our results indicate that by incorporating elevation into the covariance model used for the North Fork Clearwater River basin we are able to improve substantially the accuracy of the snow water equivalent estimates.