Converting snow depth to snow water equivalent using climatological variables

Abstract

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.

Continuous and autonomous snow water equivalent measurements by a cosmic ray sensor on an alpine glacier

“…These influences are avoided by placing the CRS on an ice surface such as a polar ice sheet or a mountain glacier. In the recent study by Howat et al (2018), the CRS was deployed below the snowpack on the Greenland Ice Sheet. With almost 24 months of measurements, they find an instrument precision of approximately 0.7 % and a good agreement with manual measurements.…”

“…The empirical parameters min , max , a 1 , a 2 and a 3 (Table 4) were provided by the manufacturer for use on glaciers and were also used by Howat et al (2018). Note that the parameters min and max are respectively the asymptotic values of the effective attenuation lengths for low and high SWE values and that the parameters a 1 , a 2 and a 3 define the curvature of a sigmoidal function.…”

“…More importantly, the correction of the raw neutron count rate with solar activity and air pressure may influence the results. The applied correction functions have been previously used for SWE (e.g., Howat et al, 2018) or soil moisture studies (e.g., Zreda et al, 2012;Andreasen et al, 2017). In contrast to previous studies of aboveground CRS, changes in atmospheric moisture are not taken into consideration.…”

“…In contrast to previous studies of aboveground CRS, changes in atmospheric moisture are not taken into consideration. We assume that for the sub-snow CRS, fast neutrons are produced within the snowpack rather than in the atmosphere, an assumption also made in Howat et al (2018) and implicitly made by preceding authors in their studies (e.g., Kodama et al, 1979;Paquet and Laval, 2005;Gottardi et al, 2013). Another source of uncertainty is the semiempirical fit that has been used in this study.…”

Retrieval of Spatial and Temporal Variability in Snowpack Depth over Glaciers in Svalbard Using GPR and Spaceborne POLSAR Measurements

“…Seasonal snow accumulation on glaciers plays an important role as the input for mass and energy balance, as well as in controlling the thermal state of glaciers. The information on snowpack parameters is also decisive for forecasting of snowmelt runoff [1] and avalanche activity [2] in snow-covered mountainous areas. Furthermore, local meteorological [3] and topographical [4] conditions control distribution of snow accumulation and spatial variability in snowpack parameters, for example and hence considers the local topography variation as required in the mountainous regions [13].…”