Item talk:Q257495
From geokb
{
"USGS Publications Warehouse": {
"@context": "https://schema.org",
"@type": "Article",
"additionalType": "Journal Article",
"name": "Snowpack relative permittivity and density derived from near-coincident lidar and ground-penetrating radar",
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"value": "10.1002/hyp.14996",
"url": "https://doi.org/10.1002/hyp.14996"
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"journal": {
"@type": "Periodical",
"name": "Hydrological Processes",
"volumeNumber": "37",
"issueNumber": "10"
},
"inLanguage": "en",
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"datePublished": "2023",
"dateModified": "2023-10-18",
"abstract": "Depth-based and radar-based remote sensing methods (e.g., lidar, synthetic aperture radar) are promising approaches for remotely measuring snow water equivalent (SWE) at high spatial resolution. These approaches require snow density estimates, obtained from in-situ measurements or density models, to calculate SWE. However, in-situ measurements are operationally limited, and few density models have seen extensive evaluation. Here, we combine near-coincident, lidar-measured snow depths with ground-penetrating radar (GPR) two-way travel times (twt) of snowpack thickness to derive >20\u2009km of relative permittivity estimates from nine dry and two wet snow surveys at Grand Mesa, Cameron Pass, and Ranch Creek, Colorado. We tested three equations for converting dry snow relative permittivity to snow density and found the Kovacs et al. (1995) equation to yield the best comparison with in-situ measurements (RMSE\u2009=\u200954\u2009kg\u2009m\u22123). Variogram analyses revealed a 19\u2009m median correlation length for relative permittivity and snow density in dry snow, which increased to >30\u2009m in wet conditions. We compared derived densities with estimated densities from several empirical models, the Snow Data Assimilation System (SNODAS), and the physically based iSnobal model. Estimated and derived densities were combined with snow depths and\u00a0twt\u00a0to evaluate density model performance within SWE remote sensing methods. The Jonas et al. (2009) empirical model yielded the most accurate SWE from lidar snow depths (RMSE\u2009=\u200951\u2009mm), whereas SNODAS yielded the most accurate SWE from GPR\u00a0twt\u00a0(RMSE\u2009=\u200941\u2009mm). Densities from both models generated SWE estimates within \u00b110% of derived SWE when SWE averaged >400\u2009mm, however, model uncertainty increased to >20% when SWE averaged <300\u2009mm. The development and refinement of density models, particularly in lower SWE conditions, is a high priority to fully realize the potential of SWE remote sensing methods.",
"description": "e14996, 17 p.",
"publisher": {
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"name": "Wiley"
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"author": [
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"name": "Bonnell, Randall",
"givenName": "Randall",
"familyName": "Bonnell",
"affiliation": [
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"@type": "Organization",
"name": "Department of Geosciences, Colorado State University"
}
]
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"name": "Department of Geosciences, Colorado State University, Fort Collins, CO"
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"name": "Department of Geosciences, Colorado State University"
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"name": "Colorado State University"
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"name": "Department of Civil and Architectural Engineering and Construction Management, University of Wyoming"
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"name": "Department of Civil and Environmental Engineering, University of Vermont"
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"name": "Colorado Water Science Center",
"url": "https://www.usgs.gov/centers/colorado-water-science-center"
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"name": "Hedrick, Andrew",
"givenName": "Andrew",
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"affiliation": [
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"name": "Northwest Watershed Research Center, USDA Agricultural Research Service"
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"@type": "Person",
"name": "Trujillo, Ernesto",
"givenName": "Ernesto",
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"affiliation": [
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"name": "Department of Geosciences, Boise State University"
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"@type": "Person",
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"name": "Cold Regions Research and Engineering Laboratory, U.S. Army Corps of Engineers"
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"name": "GAGE Facility, UNAVCO Inc."
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"name": "Department of Geosciences, Boise State University"
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"name": "Colorado Water Science Center",
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"url": "https://www.usgs.gov/centers/colorado-water-science-center"
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