Articles | Volume 18, issue 11
https://doi.org/10.5194/tc-18-5139-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-18-5139-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Nov 2024
Research article |
| 14 Nov 2024
| 14 Nov 2024
Characterization of non-Gaussianity in the snow distributions of various landscapes
Civil and Architectural Engineering and Construction Management, University of Wyoming, Laramie, WY, USA
Andrew D. Parsekian
Civil and Architectural Engineering and Construction Management, University of Wyoming, Laramie, WY, USA
Geology & Geophysics, University of Wyoming, Laramie, WY, USA
Benjamin M. Jones
Institute of Northern Engineering,University of Alaska Fairbanks, Fairbanks, AK, USA
Rodrigo C. Rangel
Geology & Geophysics, University of Wyoming, Laramie, WY, USA
Department of Earth Sciences, University of Toronto, Toronto, Ontario, Canada
Kenneth M. Hinkel
Geological and Mining Engineering and Sciences, Michigan Technological University, Houghton, MI, USA
Rui A. P. Perdigão
Meteoceanics Institute for Complex System Science, IUC Physics of Complex Coevolutionary Systems & Fluid Dynamical Systems, Washington, DC, USA
Synergistic Manifolds, Lisbon, Portugal
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New variational principle suggests that a semi-ellipsoid talik shape (3D Stefan equation) is optimum for incoming energy. However, the lake bathymetry tends to be less ellipsoidal due to the ice-rich layers near the surface. Wind wave erosion is likely responsible for the elongation of lakes, while thaw subsidence slows the wave effect and stabilizes the thermokarst lakes. The derived 3D Stefan equation was compared to the field-observed talik thickness data using geophysical methods.
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Computer models should be as simple as possible but not simpler. Simplicity refers to the length of the model and the effort it takes the model to generate its output. Here we present a practical technique for measuring the latter by the number of memory visits during model execution by
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In summer 2018, northwestern Alaska was affected by widespread lake drainage which strongly exceeded previous observations. We analyzed the spatial and temporal patterns with remote sensing observations, weather data and lake-ice simulations. The preceding fall and winter season was the second warmest and wettest on record, causing the destabilization of permafrost and elevated water levels which likely led to widespread and rapid lake drainage during or right after ice breakup.
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Short summary
Snow distribution characterization is essential for accurate snow water estimation for water resource prediction from existing in situ observations and remote-sensing data at a finite spatial resolution. Four different observed snow distribution datasets were analyzed for Gaussianity. We found that non-Gaussianity of snow distribution is a signature of the wind redistribution effect. Generally, seasonal snowpack can be approximated well by a Gaussian distribution for a fully snow-covered area.
Snow distribution characterization is essential for accurate snow water estimation for water...
