2025 CSDMS meeting-095: Difference between revisions

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{{CSDMS meeting select clinics3 2025
{{CSDMS meeting select clinics3 2025
|CSDMS_meeting_select_clinics3_2025=2) Accelerating Glacier and Surface Processes Modeling with Machine Learning and New Python Libraries
|CSDMS_meeting_select_clinics3_2025=1) Landlab’s NetworkSedimentTransporter: A Lagrangian Model for River Bed Material Transport Dynamics
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{{CSDMS meeting abstract yes no 2025
{{CSDMS meeting abstract yes no 2025

Latest revision as of 17:31, 27 March 2025



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Quantifying Spatial and Temporal Variation in LEM Outputs


Vivian Grom, Louisiana State University Baton Rouge Louisiana, United States. vgrom1@lsu.edu
Adam Forte, Louisiana State University Baton Rouge Louisiana, United States. aforte8@lsu.edu



The growing complexity of landscape evolution models (LEMs) has broadened their use to answer a variety of questions, but approaches for statistically assessing model outputs remain underexplored. Here, we suggest enhancing the study of LEM outputs by utilizing Shannon Entropy, Moran's I, and Geary's C, which provide insights into dynamics and variations within and between simulations, both quantitatively and visually. Three experiments were used as case studies; a constant uplift (Experiment 1), a periodic alternating uplift (Experiment 2), and a spatially variable uplift (Experiment 3). Incorporating the proposed metrics as a comparison module in LEMs offers a methodical way to analyze variations in information content, gauge spatial consistency, and spot simulation divergence. Although our focus is on LEM outputs, similar techniques may be applied to any matrix-based data or digital elevation models (DEMs), allowing for thorough model evaluations and better decision-making in topographic analysis and landscape modeling investigations. This material was developed during the 2024 CSDMS Visiting Scholar Program, being supported by NSF under Grant Nos. EAR-2104102 and EAR-1917695.