Abstract. We present a simple method that allows snow depth measurements to
be converted to snow water equivalent (SWE) estimates. These estimates are
useful to individuals interested in water resources, ecological function,
and avalanche forecasting. They can also be assimilated into models to help
improve predictions of total water volumes over large regions. The
conversion of depth to SWE is particularly valuable since snow depth
measurements are far more numerous than costlier and more complex SWE
measurements. Our model regresses SWE against snow depth (h), day of water
year (DOY) and climatological (30-year normal) values for winter (December,
January, February) precipitation (PPTWT), and the difference (TD) between mean
temperature of the warmest month and mean temperature of the coldest month,
producing a power-law relationship. Relying on climatological normals rather
than weather data for a given year allows our model to be applied at
measurement sites lacking a weather station. Separate equations are obtained
for the accumulation and the ablation phases of the snowpack. The model is
validated against a large database of snow pillow measurements and yields a
bias in SWE of less than 2 mm and a root-mean-squared error (RMSE) in SWE of
less than 60 mm. The model is additionally validated against two completely
independent sets of data: one from western North America and one from the
northeastern United States. Finally, the results are compared with three other
models for bulk density that have varying degrees of complexity and that
were built in multiple geographic regions. The results show that the model
described in this paper has the best performance for the validation data
sets.
