DOI: 10.5066/p13axwaa
title: Morphometric Landslide Susceptibility Results of the Northwestern United States and Southwestern Canada Derived from Elevation Data

description: Landslide susceptibility models show the potential of landslide occurrence at a location. These models are pivotal for reducing losses associated with landslides (Godt and others, 2022). In this data release, we include susceptibility results from the associated manuscript by Woodard and Mirus (2025). This manuscript shows how a morphometric model can create consistent and effective susceptibility models over large regions (> 100 km2) by analyzing the terrain’s topography. The model assumes that areas with high relative slope and hillslope area in comparison to the rest of the terrain are more susceptible to landsliding. As the model’s only input is elevation data, it mitigates the data biases common in the data-driven statistical methods (e.g., machine learning) generally used over these scales. We compare the morphometric model outputs to a parsimonious national susceptibility map and logistic regression machine learning models. The national susceptibility map is available in Belair and others, (2024). The two logistic regression models are trained on the landslide data available in the Willamette Valley Hydrologic Unit Code (HUC) 4 watershed (Oregon Department of Geology and Mineral Industries [DOGAMI], 2024). To account for the effects of the sampling ratio of event to non-event data points, we create two logistic regression models. The first uses a 1:1 sampling ratio of landslide to non-landslide points and the second uses all the data within the training data which results in a 1:33 sampling ratio. Environmental datasets requisite for the logistic regression models are all derived from the three-dimensional elevation program (3DEP) (U.S. Geological Survey, 2019a) preprocessed within the National Hydrography Dataset (U.S. Geological Survey, 2019b). The morphometric model was derived using only the 3DEP dataset without any input of where landslides have occurred. All model outputs are shown with slope units.
 
This data release includes the following files:
1) logistic regression results with 1:1 sampling ratio over Willamette Valley HUC4 watershed (1709) (Logistic_1709_1.zip);
2) logistic regression results with 1:33 sampling ratio over Willamette Valley HUC4 watershed (1709) (Logistic_1709_All.zip);
3) morphometric results with uniform weights over the Willamette Valley HUC4 watershed (1709) (Morph_Uniform_1709.zip);
4) morphometric results with area weights over the 1701 HUC 4 watershed (Morph_Area_1701.zip);
5) morphometric results with area weights over the 1702 HUC 4 watershed (Morph_Area_1702.zip);
6) morphometric results with area weights over the 1703 HUC 4 watershed (Morph_Area_1703.zip);
7) morphometric results with area weights over the 1704 HUC 4 watershed (Morph_Area_1704.zip);
8) morphometric results with area weights over the 1705 HUC 4 watershed (Morph_Area_1705.zip);
9) morphometric results with area weights over the 1706 HUC 4 watershed (Morph_Area_1706.zip);
10) morphometric results with area weights over the 1707 HUC 4 watershed (Morph_Area_1707.zip);
11) morphometric results with area weights over the 1708 HUC 4 watershed (Morph_Area_1708.zip);
12) morphometric results with area weights over the 1709 HUC 4 watershed (Morph_Area_1709.zip);
13) morphometric results with area weights over the 1710 HUC 4 watershed (Morph_Area_1710.zip);
14) morphometric results with area weights over the 1711 HUC 4 watershed (Morph_Area_1711.zip);
15) morphometric results with area weights over the 1712 HUC 4 watershed (Morph_Area_1712.zip).
16) shape file field descriptors (Field_Descriptors.txt)
 
Each zip-file contains the vector shapefiles of interest, which can be extracted using most archiver software.
 
References Cited
Belair, G.M., Jones, J.M., Martinez, S.N., Mirus, B.B., and Wood, N.J., 2024, Slope-relief threshold landslide susceptibility models for the United States and Puerto Rico: U.S. Geological Survey data release, accessed January 21, 2024, at https://doi.org/10.5066/P13KAGU3
Godt, J.W., Wood, N.J., Pennaz, A.B., Mirus, B.B., Schaefer, L.N., and Slaughter, S.L., 2022, National Strategy for Landslide Loss Reduction: U.S. Geological Survey Open-File Report 2022–1075, 36 p.
Oregon Department of Geology and Mineral Industries [DOGAMI], 2024, Statewide Landslide Information Database for Oregon [SLIDO]: accessed January 21, 2024, at https://www.oregon.gov/dogami/slido/Pages/data.aspx
U.S. Geological Survey, 2019a, 3D Elevation Program 1/3 arcsecond: U.S. Geological Survey website, accessed January 21, 2024, at https://apps.nationalmap.gov/downloader/
U.S. Geological Survey, 2019b, U.S. Geological Survey National Hydrography Dataset Plus High Resolution: U.S. Geological Survey website, accessed January 21, 2024, at https://apps.nationalmap.gov/downloader/
Woodard, J.B., Mirus, B.B., 2025, Overcoming the data limitations in landslide susceptibility modelling: Science Advances, v. 11, no. 8, doi:10.1126/sciadv.adt1541.