Combining Ground‐Penetrating Radar With Terrestrial LiDAR Scanning to Estimate the Spatial Distribution of Liquid Water Content in Seasonal Snowpacks

Many communities and ecosystems around the world rely on mountain snowpacks to provide valuable water resources. An important consideration for water resources planning is runoff timing, which can be strongly influenced by the physical process of water storage within and release from seasonal snowpa...

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Bibliographic Details
Published in:Water resources research 2018-12, Vol.54 (12), p.10,339-10,349
Main Authors: Webb, R. W., Jennings, K. S., Fend, M., Molotch, N. P.
Format: Article
Language:English
Subjects:
Detection
Distribution
Dynamics
Ecosystems
ground‐penetrating radar
Ice
LiDAR
liquid water in snow
Measuring instruments
Methods
Moisture content
Mountain snow
Radar
Runoff
Snow
Snowmelt
Snowpack
Spatial distribution
Temperature
Water
Water content
Water resources
Water resources planning
Water storage
Windows (intervals)
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Summary:Many communities and ecosystems around the world rely on mountain snowpacks to provide valuable water resources. An important consideration for water resources planning is runoff timing, which can be strongly influenced by the physical process of water storage within and release from seasonal snowpacks. The aim of this study is to present a novel method that combines light detection and ranging with ground‐penetrating radar to nondestructively estimate the spatial distribution of bulk liquid water content in a seasonal snowpack during spring snowmelt. We develop these methods in a manner to be applicable within a short time window, making it possible to spatially observe rapid changes that occur to this property at subdaily timescales. We applied these methods at two experimental plots in Colorado, showing the high variability of liquid water content in snow. Volumetric liquid water contents ranged from near zero to 19%vol within the scale of meters. We also show rapid changes in bulk liquid water content of up to 5%vol that occur over subdaily timescales. The presented methods have an average uncertainty in bulk liquid water content of 1.5%vol, making them applicable for future studies to estimate the complex spatio‐temporal dynamics of liquid water in snow. Key Points We present a new method to nondestructively observe liquid water in snow at new spatial and temporal scales Results display the high spatio‐temporal variability of liquid water in snow not previously observed The presented method could easily be expanded to collect data at subdaily timescales for transects up to 1,000 m in length or more
ISSN:0043-1397
1944-7973
DOI:10.1029/2018WR022680