Spatial Patterns of Snow Distribution for Improved Earth System Modelling in the Arctic
The spatial distribution of snow plays a vital role in Arctic climate, hydrology, and ecology due to its fundamental influence on the water balance, thermal regimes, vegetation, and carbon flux. However, for earth system modelling, the spatial distribution of snow is not well understood, and therefo...
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ftcopernicus:oai:publications.copernicus.org:tcd98869 2023-05-15T14:50:13+02:00 Spatial Patterns of Snow Distribution for Improved Earth System Modelling in the Arctic Bennett, Katrina E. Miller, Greta Busey, Robert Chen, Min Lathrop, Emma R. Dann, Julian B. Nutt, Mara Crumley, Ryan Dafflon, Baptiste Kumar, Jitendra Bolton, W. Robert Wilson, Cathy J. 2021-11-18 application/pdf https://doi.org/10.5194/tc-2021-341 https://tc.copernicus.org/preprints/tc-2021-341/ eng eng doi:10.5194/tc-2021-341 https://tc.copernicus.org/preprints/tc-2021-341/ eISSN: 1994-0424 Text 2021 ftcopernicus https://doi.org/10.5194/tc-2021-341 2021-11-22T17:22:29Z The spatial distribution of snow plays a vital role in Arctic climate, hydrology, and ecology due to its fundamental influence on the water balance, thermal regimes, vegetation, and carbon flux. However, for earth system modelling, the spatial distribution of snow is not well understood, and therefore, it is not well modeled, which can lead to substantial uncertainties in snow cover representations. To capture key hydro-ecological controls on snow spatial distribution, we carried out intensive field studies over multiple years for two small (2017–2019, ~2.5 km 2 ) sub-Arctic study sites located on the Seward Peninsula of Alaska. Using an intensive suite of field observations (> 22,000 data points), we developed simple models of spatial distribution of snow water equivalent (SWE) using factors such as topographic characteristics, vegetation characteristics based on greenness (normalized different vegetation index, NDVI), and a simple metric for approximating winds. The most successful model was the random forest using both study sites and all years, which was able to accurately capture the complexity and variability of snow characteristics across the sites. Approximately 86 % of the SWE distribution could be accounted for, on average, by the random forest model at the study sites. Factors that impacted year-to-year snow distribution included NDVI, elevation, and a metric to represent coarse microtopography (topographic position index, or TPI), while slope, wind, and fine microtopography factors were less important. The models were used to predict SWE at the locations through the study area and for all years. The characterization of the SWE spatial distribution patterns and the statistical relationships developed between SWE and its impacting factors will be used for the improvement of snow distribution modelling in the Department of Energy’s earth system model, and to improve understanding of hydrology, topography, and vegetation dynamics in the Arctic and sub-Arctic regions of the globe. Text Arctic Seward Peninsula Alaska Copernicus Publications: E-Journals Arctic |
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Copernicus Publications: E-Journals |
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ftcopernicus |
language |
English |
description |
The spatial distribution of snow plays a vital role in Arctic climate, hydrology, and ecology due to its fundamental influence on the water balance, thermal regimes, vegetation, and carbon flux. However, for earth system modelling, the spatial distribution of snow is not well understood, and therefore, it is not well modeled, which can lead to substantial uncertainties in snow cover representations. To capture key hydro-ecological controls on snow spatial distribution, we carried out intensive field studies over multiple years for two small (2017–2019, ~2.5 km 2 ) sub-Arctic study sites located on the Seward Peninsula of Alaska. Using an intensive suite of field observations (> 22,000 data points), we developed simple models of spatial distribution of snow water equivalent (SWE) using factors such as topographic characteristics, vegetation characteristics based on greenness (normalized different vegetation index, NDVI), and a simple metric for approximating winds. The most successful model was the random forest using both study sites and all years, which was able to accurately capture the complexity and variability of snow characteristics across the sites. Approximately 86 % of the SWE distribution could be accounted for, on average, by the random forest model at the study sites. Factors that impacted year-to-year snow distribution included NDVI, elevation, and a metric to represent coarse microtopography (topographic position index, or TPI), while slope, wind, and fine microtopography factors were less important. The models were used to predict SWE at the locations through the study area and for all years. The characterization of the SWE spatial distribution patterns and the statistical relationships developed between SWE and its impacting factors will be used for the improvement of snow distribution modelling in the Department of Energy’s earth system model, and to improve understanding of hydrology, topography, and vegetation dynamics in the Arctic and sub-Arctic regions of the globe. |
format |
Text |
author |
Bennett, Katrina E. Miller, Greta Busey, Robert Chen, Min Lathrop, Emma R. Dann, Julian B. Nutt, Mara Crumley, Ryan Dafflon, Baptiste Kumar, Jitendra Bolton, W. Robert Wilson, Cathy J. |
spellingShingle |
Bennett, Katrina E. Miller, Greta Busey, Robert Chen, Min Lathrop, Emma R. Dann, Julian B. Nutt, Mara Crumley, Ryan Dafflon, Baptiste Kumar, Jitendra Bolton, W. Robert Wilson, Cathy J. Spatial Patterns of Snow Distribution for Improved Earth System Modelling in the Arctic |
author_facet |
Bennett, Katrina E. Miller, Greta Busey, Robert Chen, Min Lathrop, Emma R. Dann, Julian B. Nutt, Mara Crumley, Ryan Dafflon, Baptiste Kumar, Jitendra Bolton, W. Robert Wilson, Cathy J. |
author_sort |
Bennett, Katrina E. |
title |
Spatial Patterns of Snow Distribution for Improved Earth System Modelling in the Arctic |
title_short |
Spatial Patterns of Snow Distribution for Improved Earth System Modelling in the Arctic |
title_full |
Spatial Patterns of Snow Distribution for Improved Earth System Modelling in the Arctic |
title_fullStr |
Spatial Patterns of Snow Distribution for Improved Earth System Modelling in the Arctic |
title_full_unstemmed |
Spatial Patterns of Snow Distribution for Improved Earth System Modelling in the Arctic |
title_sort |
spatial patterns of snow distribution for improved earth system modelling in the arctic |
publishDate |
2021 |
url |
https://doi.org/10.5194/tc-2021-341 https://tc.copernicus.org/preprints/tc-2021-341/ |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Seward Peninsula Alaska |
genre_facet |
Arctic Seward Peninsula Alaska |
op_source |
eISSN: 1994-0424 |
op_relation |
doi:10.5194/tc-2021-341 https://tc.copernicus.org/preprints/tc-2021-341/ |
op_doi |
https://doi.org/10.5194/tc-2021-341 |
_version_ |
1766321260161138688 |