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Hillslope flow networks in the upland Arctic include unchannelized water tracks, curvilinear zones of increased soil moisture, and channelized gullies which can exist along the same longitudinally connected flow network. However, controls on patterns of channelization on continuous permafrost landscapes remain poorly constrained in part due to the difficulty of remotely detecting discontinuous channelized segments. Here we introduce a novel method to identify gullies within Arctic hillslope flow networks using high-resolution lidar verified with field observations on Alaska’s North Slope. This method combines slope, tangential curvature, normalized elevation, and a delineated flow network to model and detect gullies on the landscape. Our best-fit model accurately identifies 80% of gullies (n=40/50) and 71% of water tracks (n=115/163) observed in the field. For the ~431 km2 study region, we found that 26% of hillslope flow networks contain gullies. We detected 14,769 water track networks (9,351 km in length, 93% of hillslope network) and 4,261 gully networks (738 km in length, 7% of hillslope flow network). Gully networks were most abundant in coarse, Holocene-aged sediments and preferential patterns in slope, aspect, and profile curvature suggest that localized subsurface processes, including ground ice abundance and soil pipe formation, may be the primary controls on initial gully formation. While the exact location of new gullies on Arctic hillslopes may be difficult to predict, we expect hillslope flow networks to transition from primarily unchannelized to channelized as permafrost thaws, with direct impacts on water, nutrient, and sediment transport.
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This readme file was generated on 19 May 2025 by Brandon A. Yokeley
# GENERAL INFORMATION
Title of Dataset: Arctic gully identification using high-resolution lidar
## Author/Principal Investigator Information
Name: Brandon Yokeley
ORCID: https://orcid.org/0009-0004-4167-1895
Institution: Idaho State University
Address:
Email: brandonyokeley@isu.edu
## Author/Associate or Co-investigator Information
Name: Sarah Godsey
ORCID: https://orcid.org/0000-0001-6529-7886
Institution: Idaho State University
Address:
Email: godsey@isu.edu
## Date of data collection: May 2023 - August 2024
## Geographic location of data collection: Area surrounding Toolik Field Station in Alaska, USA (149.381° W 68.617° N)
## Information about funding sources that supported the collection of the data: Funding was provided by the National Science Foundation (EAR-2102338, EAR-2235308, EAR-2102342, and EAR-1653998)
# SHARING/ACCESS INFORMATION
Additional sources for base data:
NEON Lidar - https://data.neonscience.org/data-products/DP3.30024.001
Surfacial Geology Map - https://dggs.alaska.gov/pubs/id/7191
Recommended citation for this dataset:
Yokeley, B., S. Godsey, S. G. Evans, B. Crosby (2025). Arctic gully identification using high-resolution lidar, HydroShare, http://www.hydroshare.org/resource/147ef57d52184692af86a4e11986a4bb
# DATA & FILE OVERVIEW
## File List:
data_folder - contains all the necassary data to run our gully identification model
data_folder/auxShapeFiles - Contains the auxillary shapefiles needed to run the model, including: the generalized surfical map, field observations, figure extents (Fig. 4 and Fig 6), the pipeline shapefile, the Dalton highway shapefile, the buffered human infrastructure, the lidar bounds, and the generalized surfical geology data
data_folder/figureFolder - a blank folder for storing figures if needed
data_folder/processedNetworks - the outputed water track (wt), gully (tg) and hillslope (hs) networks from our selected model
data_folder/stream_network - contains the final stream network generated from the base DTM
data_folder/pythonScripts - contains all the necassary python scripts to repeate this study
# METHODOLOGICAL INFORMATION
## Description of methods used for collection/generation of data: This paper is currently under review at JGR Earth Surfaces, but is titled "Abundance and topographic characteristics of gullies and water tracks in the upland Arctic using a novel automated identification method"
## Methods for processing the data:
*describe how the submitted data were generated from the raw or collected data*
## Instrument- or software-specific information needed to interpret the data:
Python 3.9 - Packages include: Numpy, SciPy, Seaborn, Matplotlib, GDAL, OGR, Pandas, GeoPandas, WhiteBoxTools (https://www.whiteboxgeo.com/), Datetime, RasterIO, Random, bs4 (https://pypi.org/project/beautifulsoup4/), and rasterstats
QGIS 3.40 or ArcGIS Pro 3.X
Data Services
The following web services are available for data contained in this resource. Geospatial Feature and Raster
data are made available via Open Geospatial Consortium Web Services. The provided links can be copied and pasted
into GIS software to access these data. Multidimensional NetCDF data are made available via a THREDDS Data Server
using remote data access protocols such as OPeNDAP. Other data services may be made available in the future to
support additional data types.
CAREER: Hydrogeologic implications of permafrost thaw - Developing a process-based understanding of biophysical controls and educational tools for rural communities
EAR-2235308
Contributors
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creation of the resource's content but are not considered authors.
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