Slope-Relief Threshold Landslide Susceptibility Models for the United States and Puerto Rico

Landslide susceptibility maps are essential tools in infrastructure planning, hazard mitigation, and risk reduction. Susceptibility maps trained in one area have been found to be unreliable when applied to different areas (Woodard et al., 2023). This limitation leads to the need for a national map that is higher resolution and rigorous, but simple enough to be applied to diverse terrains and landslide types. The susceptibility maps presented here cover the conterminous United States (CONUS), Alaska (AK), Hawaii (HI), and Puerto Rico (PR) with a resolution of 90-m. Other United States (U.S.) territories were not considered due to insufficient landslide and digital elevation data. We also provide information on the proportion of susceptible terrain as well as the density (landslides per square kilometer) of documented landslides within susceptible terrain for each U.S. county. To generate the susceptibility maps we used 1/3 arc-second digital elevation models (DEMs) (U.S. Geological Survey, 2019) to calculate slope and 100-m relief, 613,724 unique landslides from our national landslide inventory compilation (Belair et al., 2022) to train the models and compute U.S. county aggregated susceptibility information, and high-performance computing resources to train the models (Falgout and Gordon, 2023). We present two slope-relief threshold models: (1) a linear regression model weighted by landslide density of each ecoregion (Wiken et al., 2011), and (2) a quantile nonlinear regression model fitted to the 10th quantile of the data. We (1) removed extraneous landslide data, (2) averaged 50 model runs, and then (3) down-sampled the maps from 10-m to 90-m resolution to account for uncertainty in the DEM and landslide position. The nonlinear model (n10) performs better under most topographic conditions and optimally balances our priorities of capturing observed landslides (98.9%) while minimizing area covered by susceptible terrain (44.6%). The weighted linear model (lw) captures slightly fewer landslides (98.8%) and has slightly less susceptible terrain (43.1%). The values of both maps represent the number of susceptible 10-m cells within each 90-m cell after down-sampling and can range from 0 to 81. While landslides are possible within any cells containing susceptible terrain, those with the highest concentration (or cell value) capture the majority of landslides, thus representing higher susceptibility areas. The susceptibility maps were then used to determine the total area of landslide susceptible terrain (square kilometers) for each U.S. county. The national landslide inventory compilation was used to determine the number of documented landslides within susceptible terrain for each county. This information was then used to calculate the proportion of susceptible terrain and the density of documented landslides within susceptible terrain for each county in the United States. This information is provided in tabular format, with columns corresponding to the information discussed above, and each row corresponding to a U.S. county. Further information about this analysis can be found in an interpretive publication (Mirus et al., 2024).

This data release includes: (1) weighted linear susceptibility maps (lw_susc.zip), (2) quantile nonlinear susceptibility maps (n10_susc.zip), (3) landslide data used to develop the models (landslides.csv), (4) county aggregated susceptibility information (county_analysis.csv), (5) readme and analysis files, and (6) metadata.

References Cited

Belair, G. M., Jones, E. S., Slaughter, S. L., and Mirus, B. B., 2022, Landslide Inventories across the United States version 2: U.S. Geological Survey data release, https://doi.org/10.5066/P9FZUX6N

Falgout, J. T., and Gordon, J., 2023, USGS Advanced Research Computing, USGS Yeti Supercomputer: U.S. Geological Survey, https://doi.org/10.5066/F7D798MJ

Mirus, B. B., Belair, G. M., Wood, N. J., Jones, J. M., and Martinez, S. M., 2024, Parsimonious high-resolution landslide susceptibility modeling at continental scales, AGU Advances, https://doi.org/10.1029/2024AV001214

U.S. Geological Survey, 2019, 3D Elevation Program (3DEP) USGS 1/3 arc-second DEM [Data set], Retrieved from https://www.usgs.gov/3d-elevation-program/about-3dep-products-services

Wiken, E., Nava, F. J., and Griffith, G., 2011, North American Terrestrial Ecoregions - Level III [Data set], Montreal, Canada: Commission for Environmental Cooperation, Retrieved from https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continenta…

Woodard, J. B., Mirus, B. B., Crawford, M. M., Or, D., Leshchinsky, B. A., Allstadt, K. E., and Wood, N. J., 2023, Mapping Landslide Susceptibility Over Large Regions With Limited Data, Journal of Geophysical Research: Earth Surface, 128(5), e2022JF006810, https://doi.org/10.1029/2022JF006810