Spatial and temporal variability in seasonal snow density

Handle URI:
http://hdl.handle.net/10754/562678
Title:
Spatial and temporal variability in seasonal snow density
Authors:
Bormann, Kathryn J.; Westra, Seth; Evans, Jason P.; McCabe, Matthew ( 0000-0002-1279-5272 )
Abstract:
Snow density is a fundamental physical property of snowpacks used in many aspects of snow research. As an integral component in the remote sensing of snow water equivalent and parameterisation of snow models, snow density may be used to describe many important features of snowpack behaviour. The present study draws on a significant dataset of snow density and climate observations from the United States, Australia and the former Soviet Union and uses regression-based techniques to identify the dominant climatological drivers for snow densification rates, characterise densification rate variability and estimate spring snow densities from more readily available climate data. Total winter precipitation was shown to be the most prominent driver of snow densification rates, with mean air temperature and melt-refreeze events also found to be locally significant. Densification rate variance is very high at Australian sites, very low throughout the former Soviet Union and between these extremes throughout much of the US. Spring snow densities were estimated using a statistical model with climate variable inputs and best results were achieved when snow types were treated differently. Given the importance of snow density information in many snow-related research disciplines, this work has implications for current methods of converting snow depths to snow water equivalent, the representation of snow dynamics in snow models and remote sensing applications globally. © 2013 Elsevier B.V.
KAUST Department:
Water Desalination and Reuse Research Center (WDRC); Biological and Environmental Sciences and Engineering (BESE) Division; Environmental Science and Engineering Program; Earth System Observation and Modelling
Publisher:
Elsevier
Journal:
Journal of Hydrology
Issue Date:
Mar-2013
DOI:
10.1016/j.jhydrol.2013.01.032
Type:
Article
ISSN:
00221694
Sponsors:
We thank Andrew Nolan and Jason Venables at Snowy Hydro Limited for their cooperation in providing snow, precipitation and temperature observations; Robert Cabion from AGL Pty Ltd. for additional snow observations; and Barbara Brougham from the School of Civil, Environmental and Mining Engineering at Adelaide University and Rachel V. Blakey for additional editing and comments on the manuscript. Funding to support this research was received from the Australian Research Council as part of the Discovery Project DP0772665.
Appears in Collections:
Articles; Environmental Science and Engineering Program; Water Desalination and Reuse Research Center (WDRC); Biological and Environmental Sciences and Engineering (BESE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorBormann, Kathryn J.en
dc.contributor.authorWestra, Sethen
dc.contributor.authorEvans, Jason P.en
dc.contributor.authorMcCabe, Matthewen
dc.date.accessioned2015-08-03T11:01:05Zen
dc.date.available2015-08-03T11:01:05Zen
dc.date.issued2013-03en
dc.identifier.issn00221694en
dc.identifier.doi10.1016/j.jhydrol.2013.01.032en
dc.identifier.urihttp://hdl.handle.net/10754/562678en
dc.description.abstractSnow density is a fundamental physical property of snowpacks used in many aspects of snow research. As an integral component in the remote sensing of snow water equivalent and parameterisation of snow models, snow density may be used to describe many important features of snowpack behaviour. The present study draws on a significant dataset of snow density and climate observations from the United States, Australia and the former Soviet Union and uses regression-based techniques to identify the dominant climatological drivers for snow densification rates, characterise densification rate variability and estimate spring snow densities from more readily available climate data. Total winter precipitation was shown to be the most prominent driver of snow densification rates, with mean air temperature and melt-refreeze events also found to be locally significant. Densification rate variance is very high at Australian sites, very low throughout the former Soviet Union and between these extremes throughout much of the US. Spring snow densities were estimated using a statistical model with climate variable inputs and best results were achieved when snow types were treated differently. Given the importance of snow density information in many snow-related research disciplines, this work has implications for current methods of converting snow depths to snow water equivalent, the representation of snow dynamics in snow models and remote sensing applications globally. © 2013 Elsevier B.V.en
dc.description.sponsorshipWe thank Andrew Nolan and Jason Venables at Snowy Hydro Limited for their cooperation in providing snow, precipitation and temperature observations; Robert Cabion from AGL Pty Ltd. for additional snow observations; and Barbara Brougham from the School of Civil, Environmental and Mining Engineering at Adelaide University and Rachel V. Blakey for additional editing and comments on the manuscript. Funding to support this research was received from the Australian Research Council as part of the Discovery Project DP0772665.en
dc.publisherElsevieren
dc.subjectClimate variabilityen
dc.subjectSnow densificationen
dc.subjectSnow densityen
dc.subjectSnow hydrologyen
dc.subjectSpring snow densityen
dc.titleSpatial and temporal variability in seasonal snow densityen
dc.typeArticleen
dc.contributor.departmentWater Desalination and Reuse Research Center (WDRC)en
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Divisionen
dc.contributor.departmentEnvironmental Science and Engineering Programen
dc.contributor.departmentEarth System Observation and Modellingen
dc.identifier.journalJournal of Hydrologyen
dc.contributor.institutionClimate Change Research Centre, University of New South Wales, Sydney, Australiaen
dc.contributor.institutionSchool of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, Australiaen
dc.contributor.institutionSchool of Civil and Environmental Engineering, University of New South Wales, Sydney, Australiaen
kaust.authorMcCabe, Matthewen
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