Modeling bulk density and snow water equivalent using daily snow depth observations

Abstract

Abstract. Bulk density is a fundamental property of snow relating its depth and mass. Previously, two simple models of bulk density (depending on snow depth, date, and location) have been developed to convert snow depth observations to snow water equivalent (SWE) estimates. However, these models were not intended for application at the daily time step. We develop a new model of bulk density for the daily time step and demonstrate its improved skill over the existing models.Snow depth and density are negatively correlated at short (10 days) timescales while positively correlated at longer (90 days) timescales. We separate these scales of variability by modeling smoothed, daily snow depth (long timescales) and the observed positive and negative anomalies from the smoothed time series (short timescales) as separate terms. A climatology of fit is also included as a predictor variable.Over half a million daily observations of depth and SWE at 345 snowpack telemetry (SNOTEL) sites are used to fit models and evaluate their performance. For each location, we train the three models to the neighboring stations within 70 km, transfer the parameters to the location to be modeled, and evaluate modeled time series against the observations at that site. Our model exhibits improved statistics and qualitatively more-realistic behavior at the daily time step when sufficient local training data are available. We reduce density root mean square error (RMSE) by 9.9 and 4.5 % compared to previous models while increasing R 2 from 0.46 to 0.52 to 0.56 across models. Focusing on the 21-day window around peak SWE in each water year, our model reduces density RMSE by 24 and 17.4 % relative to the previous models, with R 2 increasing from 0.55 to 0.58 to 0.71 across models. Removing the challenge of parameter transfer over the full observational record increases R 2 scores for both the existing and new models, but the gain is greatest for the new model (R 2 = 0.75). Our model shows general improvement over existing models when data are more frequent than once every 5 days and at least 3 stations are available for training.

LiDAR measurement of seasonal snow accumulation along an elevation gradient in the southern Sierra Nevada, California

“…Elder et al (1998), Anderton et al (2004) and Anderson et al (2014) found the variability of spring snow density to be insignificantly correlated with elevation in their studies, while Zhong et al (2014) found negative correlations with elevation in a meta-study of densities in the former USSR. A range of results has also been reported for the snow-density correlation with depth showing both positive and negative correlations depending on the age of the snow and season (Arons and Colbeck, 1995;McCreight and Small, 2014).…”

Characterizing permafrost active layer dynamics and sensitivity to landscape spatial heterogeneity in Alaska

“…Compared with snow density, snow depth shows much larger temporal variability (Sturm et al, 2010), which makes it difficult to assign temporally varying uncertainty levels for the snow depth estimates. However, the above scheme partially accounts for uncertainties in the snow depth data due to a positive correlation between snow depth and density at longer timescales (McCreight and Small, 2014). For both model baseline simulations and sensitivity runs, the model was spunup for 50 years to bring the top 10 m soil temperature profile into dynamic equilibrium with model inputs for the year 2000, followed by a transit run from 2001 to 2015.…”

A New Snow Density Parameterization for Land Data Initialization

“…There are many such methods available to predict snow density in this way, from empirical models that estimate density range in complexity from linear regressions (e.g., Elder et al 1991;Marchand and Killingtveit 2004;Lundberg et al 2006;Jonas et al 2009) to the inclusion of multiple predictor variables (e.g., SD, day of year, elevation) and lookup tables (e.g., Jonas et al 2009;Sturm et al 2010;McCreight and Small 2014). Avanzi et al (2015a) compared 18 empirical density models to 10 SNOTEL observations and found a general overestimation of density and an increase in error with increasing elevation.…”