Preprints
https://doi.org/10.5194/tc-2023-141
https://doi.org/10.5194/tc-2023-141
20 Sep 2023
 | 20 Sep 2023
Status: a revised version of this preprint is currently under review for the journal TC.

Spatially distributed snow depth, bulk density, and snow water equivalent from ground-based and airborne sensor integration at Grand Mesa, Colorado, USA

Tate G. Meehan, Ahmad Hojatimalekshah, Hans-Peter Marshall, Elias J. Deeb, Shad O'Neel, Daniel McGrath, Ryan W. Webb, Randall Bonnell, Mark S. Raleigh, Christopher Hiemstra, and Kelly Elder

Abstract. Spaceborne remote sensing of snow currently enables landscape-scale snow covered area, but estimating snow mass in the mountains remains a major challenge from space. Airborne LiDAR can retrieve snow depth, and some promising results have recently been shown from spaceborne platforms, yet density estimates are required to convert snow depth to snow water equivalent (SWE). However, the retrieval of snow bulk density remains unsolved, and limited data is available to evaluate model estimates of density in mountainous terrain. Knowledge of the spatial patterns and predictors of density is critical for accurate assessment of SWE and essential snow physics, such as energy balance and mechanics related to hazards and over-snow mobility. Toward the goal of landscape-scale retrievals of snow density, we estimated bulk density and length-scale variability by combining ground-penetrating radar (GPR) two-way travel-time observations and airborne LiDAR snow depths collected during the mid-winter NASA SnowEx 2020 campaign at Grand Mesa, Colorado, USA. Key advancements of our approach include an automated layer picking method that leverages co- and cross-polarization coherence and distributed LiDAR–GPR inferred bulk density with machine learning. The root-mean-square error between the distributed estimates is 12 cm for depth, 27 kg/m3 for density, and 42 mm for SWE, and the median relative uncertainty in distributed SWE is 7 %. Wind, terrain, and vegetation interactions display corroborated controls on bulk density that show model and observation agreement. The spatially continuous snow density and SWE estimated over approximately 16 km2 represents the next step towards broad-scale SWE retrieval.

Tate G. Meehan, Ahmad Hojatimalekshah, Hans-Peter Marshall, Elias J. Deeb, Shad O'Neel, Daniel McGrath, Ryan W. Webb, Randall Bonnell, Mark S. Raleigh, Christopher Hiemstra, and Kelly Elder

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2023-141', Anonymous Referee #1, 03 Nov 2023
    • AC1: 'Reply on EC1', Tate Meehan, 07 Dec 2023
  • RC2: 'Comment on tc-2023-141', César Deschamps-Berger, 07 Nov 2023
    • AC1: 'Reply on EC1', Tate Meehan, 07 Dec 2023
  • RC3: 'Comment on tc-2023-141', Kat J. Bormann, 08 Nov 2023
    • AC1: 'Reply on EC1', Tate Meehan, 07 Dec 2023
  • EC1: 'Editor's recommendation', Florent Dominé, 09 Nov 2023
    • AC1: 'Reply on EC1', Tate Meehan, 07 Dec 2023
Tate G. Meehan, Ahmad Hojatimalekshah, Hans-Peter Marshall, Elias J. Deeb, Shad O'Neel, Daniel McGrath, Ryan W. Webb, Randall Bonnell, Mark S. Raleigh, Christopher Hiemstra, and Kelly Elder
Tate G. Meehan, Ahmad Hojatimalekshah, Hans-Peter Marshall, Elias J. Deeb, Shad O'Neel, Daniel McGrath, Ryan W. Webb, Randall Bonnell, Mark S. Raleigh, Christopher Hiemstra, and Kelly Elder

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Short summary
Snow water equivalent (SWE) is a critical parameter for yearly water supply forecasting and can be calculated by multiplying the snow depth by the snow density. We combined high-spatial resolution snow depth information with ground-based radar measurements to solve for snow density. Extrapolated density estimates over our study area resolved detailed patterns that agree with the known interactions of snow with wind, terrain, and vegetation and were utilized in the calculation of SWE.