Overcoming the data limitations in landslide susceptibility modeling
Citation
Woodard, J.B., Mirus, B.B. (2025). Overcoming the data limitations in landslide susceptibility modeling. Science Advances, 11(8): eadt1541. Link to paper
Abstract
Data-driven models widely used for assessing landslide susceptibility are severely limited by the landslide and environmental data needed to create them. They rely on inventories of past landslide locations, which are difficult to collect and often nonrepresentative. Furthermore, susceptibility maps are most needed in regions without the means to assemble an inventory. To overcome these challenges, we develop a method for assessing shallow landslide susceptibility based on a probabilistic morphometric analysis of the landscape's topography, rather than the characteristics of landslides. The model assumes that hillslopes with higher relief and gradient compared to the surrounding landscape are more prone to landslides. We demonstrate the superior performance of this approach over contrasting data-driven models across the northwestern United States. As our morphometric model only requires elevation data, it overcomes the major limitations of data-driven models and facilitates the creation of effective susceptibility models in areas where it was previously unfeasible.
Author's Interpretation
This groundbreaking study addresses fundamental limitations of data-driven landslide susceptibility models by introducing a morphometric analysis approach that requires only elevation data. By analyzing topographic characteristics rather than relying on incomplete landslide inventories, this method enables effective susceptibility mapping in data-scarce regions where traditional approaches are unfeasible. The demonstrated superior performance across the northwestern United States suggests this approach could transform landslide hazard assessment in regions with limited resources or incomplete historical records.