The Cryosphere
The Cryosphere
TC
Articles | Volume 18, issue 7
Articles | Volume 18, issue 7
https://doi.org/10.5194/tc-18-3253-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-18-3253-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 7
Research article
 | 
22 Jul 2024
Research article |  | 22 Jul 2024

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

Supplement

https://doi.org/10.5194/tc-18-3253-2024-supplement

Data sets

SnowEx20 Grand Mesa IOP Lidar and GPR-Derived Snow Water Equivalent, Snow Depth, and Snow Density Tate G. Meehan and Ahmad Hojatimalekshah https://doi.org/10.5067/LANQ53RTJ2DR

SnowEx20 Grand Mesa Intensive Observation Period Snow Pit Measurements, Version 1 C. M. Vuyovich et al. https://doi.org/10.5067/DUD2VZEVBJ7S

SnowEx20 Grand Mesa Reference GIS Data Sets, Version 1 C. Hiemstra et al. https://doi.org/10.5067/YDZXY4Q79VIJ

SnowEx20 Grand Mesa IOP BSU 1 GHz Multi-polarization GPR, Version 1 T. G. Meehan https://doi.org/10.5067/Q2LFK0QSVGS2

SnowEx20 Grand Mesa IOP UNM 800 and 1600MHz MALA GPR, Version 1 R. W. Webb https://doi.org/10.5067/WE9GI1GVMQF6

SnowEx20 Grand Mesa IOP QSI Lidar Snow Depth Data, Version 1 Tate G. Meehan and Ahmad Hojatimalekshah https://doi.org/10.5067/M9TPF6NWL53K

ASO L4 Lidar Snow Depth 3m UTM Grid, Version 1 T. Painter https://doi.org/10.5067/KIE9QNVG7HP0

Model code and software

tatemeehan/SnowEx2020_BSU_pE_GPR: Multipolarization Radargram Processing SnowEx 2020 Grand Mesa IOP (v1.0.0) Tate Meehan https://doi.org/10.5281/zenodo.11521496

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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.
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