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Stochastic Modeling of Subglacial Topography Exposes Uncertainty in Water Routing at Jakobshavn Glacier

<p>Abstract:</p> <p>These data products accompany the paper &quot;Stochastic Modeling of Subglacial Topography Exposes Uncertainty in Water Routing at Jakobshavn Glacier&quot; (MacKie et al., in review). In this study, geostatistical techniques were used to generate an ensemble of topographic realizations that retain the spatial statistics of radar bed elevation measurements. The simulation was conditioned to local radar data and mass conservation bed estimates. This repository contains the radar and mass conservation conditioning data, the ensemble of topographic realizations, and coordinate data.</p> <p>&nbsp;</p> <p>Content and processing steps:</p> <p>The study area is 75.15 x 48.90 km^2. The grid cell resolution is 150 meters. Each digital elevation model (DEM) has 501 x 326 grid cells. The mass conservation DEM was obtained from BedMachine Greenland (Morlighem and others, 2017).&nbsp; The radar data were acquired from the Center for Remote Sensing of Ice Sheets (CReSIS) 2009 flights (Gogineni, 2012; Gogineni and others, 2014). A probabilistic modeling technique called sequential Gaussian co-simulation (Verly, 1993; Almeida and Journel, 1994; Journel, 1999; Remy, 2005) was used to generate the topographic realizations. The datasets are as follows:</p> <p>&nbsp;</p> <p>1) Jakobshavn_mass_conservation.txt - Mass conservation conditioning data</p> <p>2) Jakobshavn_radar_data.txt - Radar conditioning data</p> <p>3) Jakobshavn_simulation.txt - 250 topographic realizations. The shape of this file is 250 x 163326, where each column corresponds to one topographic realization. Each column should be reshaped to 501 x 326 to view the DEM.</p> <p>4) Jakobshavn_x_data.txt - Polar stereographic X coordinates in meters</p> <p>5) Jakobshavn_y_data.txt - Polar stereographic Y coordinates in meters</p> <p>&nbsp;</p> <p>References:</p> <p>Almeida, A. S., &amp; Journel, A. G. (1994). Joint simulation of multiple variables with a Markov-type coregionalization model.&nbsp;<em>Mathematical Geology</em>,&nbsp;<em>26</em>(5), 565-588.</p> <p>Gogineni, P. (2012). CReSIS radar depth sounder data.&nbsp;<em>Center for Remote Sensing of Ice Sheets, Lawrence, KS https://data. cresis.-ku. edu</em>.</p> <p>Gogineni, S., Yan, J. B., Paden, J., Leuschen, C., Li, J., Rodriguez-Morales, F., ... &amp; Gauch, J. (2014). Bed topography of Jakobshavn Isbr&aelig;, Greenland, and Byrd Glacier, Antarctica.&nbsp;<em>Journal of Glaciology</em>,&nbsp;<em>60</em>(223), 813-833.</p> <p>Journel, A. G. (1999). Markov models for cross-covariances.&nbsp;<em>Mathematical Geology</em>,&nbsp;<em>31</em>(8), 955-964.</p> <p>Morlighem, M., Williams, C. N., Rignot, E., An, L., Arndt, J. E., Bamber, J. L., ... &amp; Fenty, I. (2017). BedMachine v3: Complete bed topography and ocean bathymetry mapping of Greenland from multibeam echo sounding combined with mass conservation.&nbsp;<em>Geophysical research letters</em>,&nbsp;<em>44</em>(21), 11-051.</p> <p>Remy, N. (2005). S-GeMS: the stanford geostatistical modeling software: a tool for new algorithms development. In&nbsp;<em>Geostatistics banff 2004</em>&nbsp;(pp. 865-871). Springer, Dordrecht.</p> <p>Verly, G. W. (1993). Sequential Gaussian cosimulation: a simulation method integrating several types of information. In&nbsp;<em>Geostatistics Troia&rsquo;92</em>&nbsp;(pp. 543-554). Springer, Dordrecht.</p>