Spatiotemporal Variations in Liquid Water Content in a Seasonal Snowpack: Implications for Radar Remote Sensing

Radar instruments have been widely used to measure snow water equivalent (SWE) and Interferometric Synthetic Aperture Radar is a promising approach for doing so from spaceborne platforms. Electromagnetic waves propagate through the snowpack at a velocity determined by its dielectric permittivity. Ve...

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Autores principales:	Randall Bonnell, Daniel McGrath, Keith Williams, Ryan Webb, Steven R. Fassnacht, Hans-Peter Marshall
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	snow water equivalent ground-penetrating radar liquid water content snow melt Science Q
Acceso en línea:	https://doaj.org/article/80b5328a3e884f41b7ce000cfebaba65
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spelling	oai:doaj.org-article:80b5328a3e884f41b7ce000cfebaba652021-11-11T18:50:35ZSpatiotemporal Variations in Liquid Water Content in a Seasonal Snowpack: Implications for Radar Remote Sensing10.3390/rs132142232072-4292https://doaj.org/article/80b5328a3e884f41b7ce000cfebaba652021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4223https://doaj.org/toc/2072-4292Radar instruments have been widely used to measure snow water equivalent (SWE) and Interferometric Synthetic Aperture Radar is a promising approach for doing so from spaceborne platforms. Electromagnetic waves propagate through the snowpack at a velocity determined by its dielectric permittivity. Velocity estimates are a significant source of uncertainty in radar SWE retrievals, especially in wet snow. In dry snow, velocity can be calculated from relations between permittivity and snow density. However, wet snow velocity is a function of both snow density and liquid water content (LWC); the latter exhibits high spatiotemporal variability, there is no standard observation method, and it is not typically measured by automated stations. In this study, we used ground-penetrating radar (GPR), probed snow depths, and measured in situ vertically-averaged density to estimate SWE and bulk LWC for seven survey dates at Cameron Pass, Colorado (~3120 m) from April to June 2019. During this cooler than average season, median LWC for individual survey dates never exceeded 7 vol. %. However, in June, LWC values greater than 10 vol. % were observed in isolated areas where the ground and the base of the snowpack were saturated and therefore inhibited further meltwater output. LWC development was modulated by canopy cover and meltwater drainage was influenced by ground slope. We generated synthetic SWE retrievals that resemble the planned footprint of the NASA-ISRO L-band InSAR satellite (NISAR) from GPR using a dry snow density model. Synthetic SWE retrievals overestimated observed SWE by as much as 40% during the melt season due to the presence of LWC. Our findings emphasize the importance of considering LWC variability in order to fully realize the potential of future spaceborne radar missions for measuring SWE.Randall BonnellDaniel McGrathKeith WilliamsRyan WebbSteven R. FassnachtHans-Peter MarshallMDPI AGarticlesnow water equivalentground-penetrating radarliquid water contentsnow meltScienceQENRemote Sensing, Vol 13, Iss 4223, p 4223 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	snow water equivalent ground-penetrating radar liquid water content snow melt Science Q
spellingShingle	snow water equivalent ground-penetrating radar liquid water content snow melt Science Q Randall Bonnell Daniel McGrath Keith Williams Ryan Webb Steven R. Fassnacht Hans-Peter Marshall Spatiotemporal Variations in Liquid Water Content in a Seasonal Snowpack: Implications for Radar Remote Sensing
description	Radar instruments have been widely used to measure snow water equivalent (SWE) and Interferometric Synthetic Aperture Radar is a promising approach for doing so from spaceborne platforms. Electromagnetic waves propagate through the snowpack at a velocity determined by its dielectric permittivity. Velocity estimates are a significant source of uncertainty in radar SWE retrievals, especially in wet snow. In dry snow, velocity can be calculated from relations between permittivity and snow density. However, wet snow velocity is a function of both snow density and liquid water content (LWC); the latter exhibits high spatiotemporal variability, there is no standard observation method, and it is not typically measured by automated stations. In this study, we used ground-penetrating radar (GPR), probed snow depths, and measured in situ vertically-averaged density to estimate SWE and bulk LWC for seven survey dates at Cameron Pass, Colorado (~3120 m) from April to June 2019. During this cooler than average season, median LWC for individual survey dates never exceeded 7 vol. %. However, in June, LWC values greater than 10 vol. % were observed in isolated areas where the ground and the base of the snowpack were saturated and therefore inhibited further meltwater output. LWC development was modulated by canopy cover and meltwater drainage was influenced by ground slope. We generated synthetic SWE retrievals that resemble the planned footprint of the NASA-ISRO L-band InSAR satellite (NISAR) from GPR using a dry snow density model. Synthetic SWE retrievals overestimated observed SWE by as much as 40% during the melt season due to the presence of LWC. Our findings emphasize the importance of considering LWC variability in order to fully realize the potential of future spaceborne radar missions for measuring SWE.
format	article
author	Randall Bonnell Daniel McGrath Keith Williams Ryan Webb Steven R. Fassnacht Hans-Peter Marshall
author_facet	Randall Bonnell Daniel McGrath Keith Williams Ryan Webb Steven R. Fassnacht Hans-Peter Marshall
author_sort	Randall Bonnell
title	Spatiotemporal Variations in Liquid Water Content in a Seasonal Snowpack: Implications for Radar Remote Sensing
title_short	Spatiotemporal Variations in Liquid Water Content in a Seasonal Snowpack: Implications for Radar Remote Sensing
title_full	Spatiotemporal Variations in Liquid Water Content in a Seasonal Snowpack: Implications for Radar Remote Sensing
title_fullStr	Spatiotemporal Variations in Liquid Water Content in a Seasonal Snowpack: Implications for Radar Remote Sensing
title_full_unstemmed	Spatiotemporal Variations in Liquid Water Content in a Seasonal Snowpack: Implications for Radar Remote Sensing
title_sort	spatiotemporal variations in liquid water content in a seasonal snowpack: implications for radar remote sensing
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/80b5328a3e884f41b7ce000cfebaba65
work_keys_str_mv	AT randallbonnell spatiotemporalvariationsinliquidwatercontentinaseasonalsnowpackimplicationsforradarremotesensing AT danielmcgrath spatiotemporalvariationsinliquidwatercontentinaseasonalsnowpackimplicationsforradarremotesensing AT keithwilliams spatiotemporalvariationsinliquidwatercontentinaseasonalsnowpackimplicationsforradarremotesensing AT ryanwebb spatiotemporalvariationsinliquidwatercontentinaseasonalsnowpackimplicationsforradarremotesensing AT stevenrfassnacht spatiotemporalvariationsinliquidwatercontentinaseasonalsnowpackimplicationsforradarremotesensing AT hanspetermarshall spatiotemporalvariationsinliquidwatercontentinaseasonalsnowpackimplicationsforradarremotesensing
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Spatiotemporal Variations in Liquid Water Content in a Seasonal Snowpack: Implications for Radar Remote Sensing

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