Preprints
https://doi.org/10.22541/essoar.172494370.04413277/v1
https://doi.org/10.22541/essoar.172494370.04413277/v1
22 Jan 2025
 | 22 Jan 2025
Status: this preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).

Using Network Science to Evaluate Vulnerability of Landslides on Big Sur Coast, California, USA

Vrinda D. Desai, Alexander L. Handwerger, and Karen E. Daniels

Abstract. Landslide events, ranging from slips to catastrophic failures, pose significant challenges for prediction. This study employs a physically inspired framework to assess landslide vulnerability at a regional scale (Big Sur Coast, California). Our approach integrates techniques from the study of complex systems with multivariate statistical analysis to identify areas vulnerable to landslide events. We successfully apply a technique originally developed on the 2017 Mud Creek landslide and refine our statistical metrics to characterize landslide vulnerability within a larger geographical area. Our method is compared against factors such as landslide location, slope, displacement, precipitation, and InSAR coherence using multivariate statistical analysis. Our network analyses, which provides a natural way to incorporate spatiotemporal dynamics, perform better as a monitoring technique than traditional methods. This approach has potential for real-time monitoring and evaluating landslide vulnerability across multiple sites.

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Vrinda D. Desai, Alexander L. Handwerger, and Karen E. Daniels

Status: open (until 05 Mar 2025)

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Vrinda D. Desai, Alexander L. Handwerger, and Karen E. Daniels

Data sets

Data from: Using network science to evaluate vulnerability of landslides on Big Sur Coast, California, USA Vrinda D. Desai and Alexander L. Handwerger https://doi.org/10.5061/dryad.1jwstqk42

Model code and software

networkLandslide Vrinda D. Desai https://github.com/vddesai-97/networkLandslide.git

Vrinda D. Desai, Alexander L. Handwerger, and Karen E. Daniels

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Short summary
Landslide events occur when soil, rock, and debris on slopes become unstable and move downhill, often triggered by heavy rain that reduces friction. Our research evaluates landslide vulnerability using a method that analyzes the spatiotemporal dynamics of landslide-prone areas. We've developed a statistical metric to track changing conditions in these regions. This approach can aid in early warning systems, helping communities and authorities take preventive measures and minimize damage.
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