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Overcoming the data limitations in landslide susceptibility modeling.

Jacob B Woodard¹, Benjamin B Mirus¹

¹U.S. Geological Survey, Geologic Hazards Science Center, 1711 Illinois Street, Golden, CO 80401, USA.

Science Advances

|February 21, 2025

Summary

This study introduces a new method for landslide susceptibility mapping using topography, not landslide data. This approach overcomes data limitations, enabling effective landslide risk assessment in data-scarce regions.

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Area of Science:

Geosciences
Geomorphology
Natural Hazards

Background:

Data-driven landslide susceptibility models require extensive landslide inventory data, which is often unavailable or unrepresentative.
Existing models are limited in data-scarce regions where susceptibility mapping is most needed.

Purpose of the Study:

To develop a novel method for assessing shallow landslide susceptibility that overcomes data limitations.
To propose a probabilistic morphometric analysis based on topography rather than landslide characteristics.

Main Methods:

Developed a morphometric model analyzing landscape topography, specifically relief and gradient.
Assessed landslide susceptibility based on the assumption that higher relief and gradient indicate increased landslide proneness.
Validated the model's performance against data-driven models in the northwestern United States.

Main Results:

The proposed morphometric model demonstrates superior performance compared to traditional data-driven models.
The model effectively predicts landslide susceptibility using only readily available elevation data.
Successfully overcomes limitations associated with landslide inventory data.

Conclusions:

A novel, data-independent method for landslide susceptibility assessment has been developed.
This topographic analysis approach facilitates landslide hazard mapping in data-limited environments.
The method offers a more feasible and effective solution for landslide risk management globally.