Modelling snowpack bulk density using snow depth, cumulative degree‐days, and climatological predictor variables

• Published in: Hydrological processes Vol. 37; no. 1
• Main Authors: Szeitz, Andras J., Moore, R. Dan
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.01.2023; Wiley Subscription Services, Inc
• Subjects: Bulk density; Catchment area; Catchment scale; Catchments; Densification; Density; Depth; empirical model; Equivalence; Errors; Lidar; Meteorological conditions; Modelling; North American Regional Reanalysis; Random errors; Regression models; Remote sensing; Resource management; Snow; Snow accumulation; Snow courses; snow density; Snow depth; Snowpack; Surveys; Variables; Water depth; Water management; Water resources; Water resources management
• ISSN: 0885-6087; 1099-1085
• DOI: 10.1002/hyp.14800

Snowpack water equivalent (SWE) is a key variable for water resource management in snow‐dominated catchments. While it is not feasible to quantify SWE at the catchment scale using either field surveys or remotely sensed data, technologies such as airborne LiDAR (light detection and ranging) support the mapping of snow depth at scales relevant to operational water management. To convert snow depth to water equivalent, models have been developed to predict SWE or snowpack density based on snow depth and additional predictor variables. This study builds upon previous models that relate snowpack density to snow depth by including additional predictor variables to account for (1) long‐term climatologies that describe the prevailing conditions influencing regional snowpack properties, and (2) the effect of intra‐ and inter‐year variability in meteorological conditions on densification through a cumulative degree‐day index derived from North American Regional Reanalysis products. A non‐linear model was fit to 114 506 snow survey measurements spanning 41 years from 1166 snow courses across western North America. Under spatial cross‐validation, the predicted densities had a root‐mean‐square error of 47.1 kg m−3, a mean bias of −0.039 kg m−3, and a Nash‐Sutcliffe Efficiency of 0.70. The model developed in this study had similar overall performance compared to a similar regression‐based model reported in the literature, but had reduced seasonal biases. When applied to predict SWE from simulated depths with random errors consistent with those obtained from LiDAR or Structure‐from‐Motion, 50% of the SWE estimates for April and May fell within −45 to 49 mm of the observed SWE, representing prediction errors of −15% to 20%. A non‐linear regression was fit to 114 506 snow course survey measurements from across western North America to develop an empirical snow density model. Model predictors included climatological and annual weather variables to account for the within‐ and inter‐year influences on snowpacks. Under spatial cross‐validation, the models predicted snowpack bulk density with a root‐mean‐square error of 47.1 kg m−3, with a mean bias of −0.039 kg m−3, and a Nash‐Sutcliffe Efficiency of 0.70.