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Title: Arctic Soil Patterns Analogous to Fluid Instabilities: Supporting Data

Abstract

This dataset characterizes solifluction lobe morphology and spatial patterns using pre-existing LiDAR-derived digital elevation models of 25 sites across Norway with accompanying long term climate data for each site. Data were collected as part of an effort to better understand controls on the formation of solifluction patterns and to test the idea that they are analogous to fluid instabilities. We also provide soil velocity profiles and estimates of effective viscosity from across the world, drawn from literature. They were collected to improve our understanding of the rheology of soliflucting soil. See this article for more information on the theoretical motivation behind this dataset https://doi.org/10.1073/pnas.2101255118. Data files arranged in a hierarchy and include image files *.tif and *.png, GIS shapefiles and geopackages (*.gpkg), *.csv (with same file as *.xlsx), and *.py (Python scripts readable with a text editor). Files also bundled into *.zip files.Note (2021-10-20): unit corrections made on two files: RR.csv and snowfall.csv.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 10-year research effort (2012-2022) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy?s Office of Biological and Environmental Research.The NGEEmore » Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).« less

Authors:
ORCiD logo ; ORCiD logo ; ; ORCiD logo ; ORCiD logo ; ORCiD logo
Publication Date:
Other Number(s):
NGA245
DOE Contract Number:  
DE-AC05-00OR22725
Research Org.:
Next Generation Ecosystems Experiment - Arctic, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (US); NGEE Arctic, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
Collaborations:
ORNL
Subject:
54 Environmental Sciences
Keywords:
lobe width; lobe length; lobe height; topographic slope; riser angle; terrace wavelength; solifluction lobe morphology; rainfall rate; snowfall rate; mean annual air temperature; mean annual temperature amplitude; climate data; Norway
OSTI Identifier:
1768024
DOI:
https://doi.org/10.5440/1768024

Citation Formats

Fratkin, Michael, Glade, Rachel, Nutt, Mara, Pouragha, Mehdi, Seiphoori, Ali, and Rowland, Joel. Arctic Soil Patterns Analogous to Fluid Instabilities: Supporting Data. United States: N. p., 2020. Web. doi:10.5440/1768024.
Fratkin, Michael, Glade, Rachel, Nutt, Mara, Pouragha, Mehdi, Seiphoori, Ali, & Rowland, Joel. Arctic Soil Patterns Analogous to Fluid Instabilities: Supporting Data. United States. doi:https://doi.org/10.5440/1768024
Fratkin, Michael, Glade, Rachel, Nutt, Mara, Pouragha, Mehdi, Seiphoori, Ali, and Rowland, Joel. 2020. "Arctic Soil Patterns Analogous to Fluid Instabilities: Supporting Data". United States. doi:https://doi.org/10.5440/1768024. https://www.osti.gov/servlets/purl/1768024. Pub date:Thu Dec 03 00:00:00 EST 2020
@article{osti_1768024,
title = {Arctic Soil Patterns Analogous to Fluid Instabilities: Supporting Data},
author = {Fratkin, Michael and Glade, Rachel and Nutt, Mara and Pouragha, Mehdi and Seiphoori, Ali and Rowland, Joel},
abstractNote = {This dataset characterizes solifluction lobe morphology and spatial patterns using pre-existing LiDAR-derived digital elevation models of 25 sites across Norway with accompanying long term climate data for each site. Data were collected as part of an effort to better understand controls on the formation of solifluction patterns and to test the idea that they are analogous to fluid instabilities. We also provide soil velocity profiles and estimates of effective viscosity from across the world, drawn from literature. They were collected to improve our understanding of the rheology of soliflucting soil. See this article for more information on the theoretical motivation behind this dataset https://doi.org/10.1073/pnas.2101255118. Data files arranged in a hierarchy and include image files *.tif and *.png, GIS shapefiles and geopackages (*.gpkg), *.csv (with same file as *.xlsx), and *.py (Python scripts readable with a text editor). Files also bundled into *.zip files.Note (2021-10-20): unit corrections made on two files: RR.csv and snowfall.csv.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 10-year research effort (2012-2022) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy?s Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).},
doi = {10.5440/1768024},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {12}
}