--------------------------------------------- # Data and Code for "Comprehensive Radar Mapping of Malaspina Glacier (Sít' Tlein), Alaska—The World's Largest Piedmont Glacier—Reveals Potential for Instability" Preferred citation (DataCite format): Tober, B. S., Holt, J. W., Christoffersen, M. S., Truffer, M., Larsen, C. F., Brinkerhoff, D. J., & Mooneyham, S. A. (2023). Comprehensive radar mapping of Malaspina Glacier (Sít' Tlein), Alaska—The world's largest piedmont glacier—Reveals potential for instability. Journal of Geophysical Research: Earth Surface, 128, e2022JF006898. https://doi.org/10.1029/2022JF006898 Corresponding Author: Brandon Scott Tober, Department of Geosciences, btober@arizona.edu License: CC BY 4.0 (data) MIT (code) DOI: https://doi.org/10.25422/azu.data.17054063 --------------------------------------------- ## Summary This repository contains data and code accompanying the publication "Comprehensive Radar Mapping of Malaspina Glacier (Sít' Tlein), Alaska – the World's Largest Piedmont Glacier – Reveals Potential for Instability." Malaspina Glacier, located on the coast of southern Alaska, is the world's largest piedmont glacier. A narrow ice-cored foreland zone undergoing rapid thermokarst erosion separates the glacier from the relatively warm waters of the Gulf of Alaska. Glacier-wide thinning rates for Malaspina are greater than 1 m/yr, and previous geophysical investigations indicated that bed elevation exceeds 300 m below sea level in some places. These observations together give rise to the question of glacial stability. To address this question, glacier evolution models are dependent upon detailed observations of Malaspina's subglacial topography. Here, we map 2,000 line-km of the glacier's bed using airborne radar sounding data collected by NASA's Operation IceBridge. When compared to gridded radar measurements, we find that glaciological models overestimate Malaspina's volume by more than 30%. While we report a mean bed elevation 100 m greater than previous models, we find that Malaspina inhabits a broad basin largely grounded below sea level. Several subglacial channels dissect the glacier's bed: the most prominent of these channels extends at least 35 km up-glacier from the terminus toward the throat of Seward Glacier. Provided continued foreland erosion, an ice-ocean connection may promote rapid retreat along these overdeepened subglacial channels, with a global sea-level rise potential of 1.4 mm. --------------------------------------------- ## Files and Folders Note: when extracting code.zip and data.zip, it may be necessary to remove duplicate root folders or update paths referencing those directories in the included code. . ├── code.zip: Code used in the analysis presented in the manuscript │   ├── fig: Subfolder containing code specifically used for making figures presented in the manuscript (several figures were made using QGIS/Adobe Illustrator, and thus no code is presented for those) │   │   ├── fig_2.ipynb: Jupyter notebook containing code used to draft figure 2 of the manuscript │   │   ├── fig_4.ipynb: Jupyter notebook containing code used to draft figure 4 of the manuscript │   │   ├── fig_s2b.ipynb: Jupyter notebook containing code used to draft figure S2b of the manuscript │   │   ├── fig_s3b.ipynb: Jupyter notebook containing code used to draft figure S3b of the manuscript │   │   ├── fig_s4.ipynb: Jupyter notebook containing code used to draft figure S4 of the manuscript │   │   ├── fig_s6.ipynb: Jupyter notebook containing code used to draft figure S6 of the manuscript │   │   └── fig_s7.ipynb: Jupyter notebook containing code used to draft figure S7 of the manuscript │   ├── gp_interp.ipynb: Jupyter Notebook workflow for interpolating Malaspina Glacier's basal elevation and ice thickness using Gaussian process (GP) regression │   ├── hydro.ipynb: Jupyter Notebook workflow for calculating the subglacial hydraulic potential and predicting subglacial water routing │   └── xover_analysis.ipynb: Jupyter Notebook for calculating crossover disagreement in radar-derived bed elevations ├── data.zip: Data presented and analyzed in the manuscript │   ├── raster: Subfolder containing raster data │   │   ├── farinotti_thickness: Subfolder containing ice thickness from Farinotti et al., 2019 │   │   │   └── farinotti_ice_thickness.tif: Ice thickness from Farinotti et al., 2019 │   │   ├── hydro: Subfolder containing rasters produced through analyzing the subglacial hydraulic potential │   │   │   ├── flow_k0.5.tif: Subglacial flow accumulation following hpot_k0.5.tif │   │   │   ├── flow_k0.75.tif: Subglacial flow accumulation following hpot_k0.75.tif │   │   │   ├── flow_k1.0.tif: Subglacial flow accumulation following hpot_k1.0.tif │   │   │   ├── hpot_k0.5.tif: Subglacial hydraulic potential for kappa=0.5 │   │   │   ├── hpot_k0.75.tif: Subglacial hydraulic potential for kappa=0.75 │   │   │   └── hpot_k1.0.tif: Subglacial hydraulic potential for kappa=1.0 │   │   ├── ifsar: Subfolder containing IFSAR DSM and processing information used in the manuscript │   │   │   ├── IFSAR_DSM_Malaspina_h3338_v4326.tif: IFSAR digital surface model (tiles acquired from https://dggs.alaska.gov/) │   │   │   └── README.txt: Processing steps used to produce IFSAR_DSM_Malaspina_h3338_v4326.tif │   │   ├── millan_thickness: Subfolder containing data from Millan et al. (2022) compared with results of this study │   │   │   ├── millan_ice_thickness.tif: Ice thickness from Millan et al. (2022) for Malaspina Glacier │   │   │   └── millan_ice_thickness_error.tif: Ice thickness error from Millan et al. (2022) for Malaspina Glacier │   │   ├── bed_elevation.tif: Malaspina bed elevation (GP model mean) │   │   ├── bed_elevation_2sig.tif: Uncertainty (2σ) in modeled bed elevation geotiff file │   │   ├── floating_ice_thickness.tif: Malaspina ice thickness above flotation │   │   ├── ice_thickness.tif: Malaspina ice thickness ( - ) │   │   └── malaspina_oli_2014267_geo_4326.tif: Landsat 8 Operation Land Imager Scene of Malaspina Glacier from 2014, Julian date 267, used to show flightpath of IRARES1B_20190928-235534.h5 in fig_2.ipynb │   ├── vector: Subfolder containing vector data │   │   ├── gp_interp: Subfolder containing data used to run gp_interp.ipynb │   │   │   ├── agassiz: GLIMS outlines for Agassiz Glacier (https://www.glims.org/maps/info.html?anlys_id=406283) │   │   │   ├── hayden: GLIMS outlines for Marvine/Hayden Glacier (https://www.glims.org/maps/info.html?anlys_id=406650) │   │   │   ├── out: Subfolder containing output datafiles from GP interpolation │   │   │   │   ├── gp_interp_metrics.csv: metrics of ice volume, bed area below sea level, and sea level rise potential for 100000 random bed samples of the GP posterior distribution │   │   │   │   └── gp_interp_out.csv: output datafile from GP interpolation │   │   │   ├── seward: GLIMS shapefile outlines for Seward Glacier (https://www.glims.org/maps/info.html?anlys_id=406540) │   │   │   ├── ice_margin.csv: Malaspina Glacier terminus ice margin delineated by A.C. Thompson & M.G. Loso (National Park Service) │   │   │   ├── ice_margin_mask.csv: Mask covering terminus ice margin │   │   │   ├── ma_boundary.p: Mask of Seward/Agassiz arbitrary GLIMS boundary │   │   │   ├── mh_boundary.p: Mask of Seward/Marvine/Hayden arbitrary GLIMS boundary │   │   │   ├── model_domain.csv: Mask of GP model domain, comma separated values │   │   │   └── model_domain.gpkg: Mask of GP model domain, geopackaage file │   │   ├── IRARES1B_20190928-235534_pk_bst.csv: Radar pick file from IRARES1B_20190928-235534.h5, used as example radargram picks in fig_2.ipynb │   │   ├── agassiz_its_live.csv: ITS_LIVE surface velocities for the Agassiz lobe │   │   ├── elev_prof1.csv: Surface elevation profile 1, used in fig_3b.ipynb │   │   ├── elev_prof2.csv: Surface elevation profile 2, used in fig_3b.ipynb │   │   ├── elev_prof3.csv: Surface elevation profile 3, used in fig_3b.ipynb │   │   ├── malaspina_oib_radar_pick.csv: Combined Malaspina NASA OIB radar picks │   │   ├── malaspina_oib_radar_pick_format.pdf: Format file for malaspina_oib_radar_pick.csv │   │   ├── malaspina_oib_radar_pick_xover.csv: Crossover analysis for Malaspina NASA OIB radar picks │   │   ├── malaspina_oib_radar_pick_xover_format.pdf: Format file for radar crossover analysis │   │   ├── marvine_its_live.csv: ITS_LIVE surface velocities for the Marvine lobe │   │   └── seward_its_live.csv: ITS_LIVE surface velocities for the Seward lobe │   ├── IRARES1B_20190928-235534.h5: ARES level 1B radar data file from 20190928-235534, used as example radargram in fig_2.ipynb ├── citations.html: Manuscript references └── README.txt: This document --------------------------------------------- ## Materials & Methods All included code was run using Python 3 Glacier bed was digitized using the Radar Analysis Graphical Utility (RAGU; https://github.com/btobers/RAGU) --------------------------------------------- ## Additional Notes Links: - NASA Operation IceBridge lidar and radar data analyzed in the manuscript: https://doi.org/10.5067/AATE4JJ91EHC; https://doi.org/10.5067/Q0AVPHN3250H; https://doi.org/10.5067/X2H7MP5DBTYP