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

Hurricane Maria induced about 70,000 landslides throughout Puerto Rico, USA (Hughes and others, 2019, https://doi.org/10.5066/P9BVMD74). Data in this project pertain to two areas situated in Puerto Rico’s rugged Cordillera Central range. Combined, these areas account for more than half of the hurricane-induced landslides. One of these areas encloses two neighboring municipalities, Lares Municipio, and Utuado Municipio, and the second area encloses Naranjito Municipio. These data include one-meter (1-m) resolution raster grids derived from post-hurricane light detection and ranging (lidar) digital elevation models (DEM) available at https://apps.nationalmap.gov/lidar-explorer/#/. The elevation data as well as slope and flow accumulation grids derived from them were the primary inputs for soil-depth models and slope-stability models. We used outputs from these models to map susceptibility to landslide initiation and evaluate future landslide impacts from storms like Hurricane Maria for these three municipalities. The data accompany an interpretive paper that is currently under review. The area covering Lares and Utuado is divided into four overlapping tiles. A fifth tile covers Naranjito. Digital elevation model (DEM) tiles extend far enough into neighboring tiles and municipalities to allow assessment of flow accumulation as well as landslide runout and debris-flow inundation in watersheds that straddle boundaries. The raster grids are grouped into zip archives according to their associated tiles and each zip archive contains a complete set of nine 1-m resolution input and output grids of the following data types and naming conventions, in which the "*" identifies the tile location as well as selected model options: *pm.tif - 1m- lidar-derived digital elevation model (DEM), with elevation in meters nar1m*floacc.tif or larutu*floacc.tif - D8 flow accumulation raster, used as input for soil depth estimates *_Zn.tif - input-parameter-zone codes (unitless) to define geologic terrane, used as input for soil depth and slope-stability estimates RG_slope_*.tif - slope of the ground surface (degrees) computed using REGOLITH 1.0 (https://doi.org/10.5066/P9U2RDWJ) and used as input for slope-stability analysis RG_NASD_smo_*.tif - soil depth (meters) estimates computed using REGOLITH 1.0 (https://doi.org/10.5066/P9U2RDWJ) and used as input for slope-stability analysis TRfs_min_*.tif- 1d factor of safety (unitless) computed using TRIGRS 2.1 (https://doi.org/10.5066/F7M044QS) TRp_at_fs_min_*.tif- 1d pressure head (meters) computed using TRIGRS 2.1 (https://doi.org/10.5066/F7M044QS) and used as input for 3D slope-stability analysis SL3_fs3d_*.tif - quasi-three-dimensional (3D) factor of safety (unitless) for trial surface centered at each grid cell computed using Slabs3D 1.0 (https://doi.org/10.5066/P9G4I8IU) SL3_fs3dmn_*.tif - minimum 3D factor of safety (unitless) for any trial surface intersecting each grid cell computed using Slabs3D 1.0 (https://doi.org/10.5066/P9G4I8IU) Each tile has six asociated metadata files. Parameter inputs to and outputs from the programs REGOLITH 1.0, TRIGRS 2.1, and Slabs3D 1.0 are grouped into corresponding metadata files for each tile. The metadata files have the following naming convention, in which the "*" is a code that identifies the tile ("larutu1," or "a1," "larutu2," or "a2," "larutu3," or "a3," and "larutu4," or "a4," for the Lares and Utuado tiles and "nar" for the Naranjito tile): *pm.xml - 1m-lidar-derived DEM *floacc.xml - D8 flow accumulation raster *_Zn.xml - input-parameter-zone raster RG_*.xml - REGOLITH 1.0 parameter input file, slope output raster and soil-depth output raster TR_*.xml - TRIGRS 2.1 parameter input file, pressure head output raster and local factor of safety output raster SL3_*.xml - Slabs3D 1.0 parameter input file, 3D factor of safety output rasters Parameter input files accompany this data release, with the following naming convention, in which the "*" is a code that

Hurricane Maria induced about 70,000 landslides throughout Puerto Rico, USA (Hughes and others, 2019, https://doi.org/10.5066/P9BVMD74). Data in this project pertain to two areas situated in Puerto Rico’s rugged Cordillera Central range. Combined, these areas account for more than half of the hurricane-induced landslides. One of these areas encloses two neighboring municipalities, Lares Municipio, and Utuado Municipio, and the second area encloses Naranjito Municipio. These data include one-meter (1-m) resolution raster grids derived from post-hurricane light detection and ranging (lidar) digital elevation models (DEM) available at https://apps.nationalmap.gov/lidar-explorer/#/. The elevation data as well as slope and flow accumulation grids derived from them were the primary inputs for soil-depth models and slope-stability models. We used outputs from these models to map susceptibility to landslide initiation and evaluate future landslide impacts from storms like Hurricane Maria for these three municipalities. The data accompany an interpretive paper that is currently under review. The area covering Lares and Utuado is divided into four overlapping tiles. A fifth tile covers Naranjito. Digital elevation model (DEM) tiles extend far enough into neighboring tiles and municipalities to allow assessment of flow accumulation as well as landslide runout and debris-flow inundation in watersheds that straddle boundaries. The raster grids are grouped into zip archives according to their associated tiles and each zip archive contains a complete set of nine 1-m resolution input and output grids of the following data types and naming conventions, in which the "*" identifies the tile location as well as selected model options: *pm.tif - 1m- lidar-derived digital elevation model (DEM), with elevation in meters nar1m*floacc.tif or larutu*floacc.tif - D8 flow accumulation raster, used as input for soil depth estimates *_Zn.tif - input-parameter-zone codes (unitless) to define geologic terrane, used as input for soil depth and slope-stability estimates RG_slope_*.tif - slope of the ground surface (degrees) computed using REGOLITH 1.0 (https://doi.org/10.5066/P9U2RDWJ) and used as input for slope-stability analysis RG_NASD_smo_*.tif - soil depth (meters) estimates computed using REGOLITH 1.0 (https://doi.org/10.5066/P9U2RDWJ) and used as input for slope-stability analysis TRfs_min_*.tif- 1d factor of safety (unitless) computed using TRIGRS 2.1 (https://doi.org/10.5066/F7M044QS) TRp_at_fs_min_*.tif- 1d pressure head (meters) computed using TRIGRS 2.1 (https://doi.org/10.5066/F7M044QS) and used as input for 3D slope-stability analysis SL3_fs3d_*.tif - quasi-three-dimensional (3D) factor of safety (unitless) for trial surface centered at each grid cell computed using Slabs3D 1.0 (https://doi.org/10.5066/P9G4I8IU) SL3_fs3dmn_*.tif - minimum 3D factor of safety (unitless) for any trial surface intersecting each grid cell computed using Slabs3D 1.0 (https://doi.org/10.5066/P9G4I8IU) Each tile has six asociated metadata files. Parameter inputs to and outputs from the programs REGOLITH 1.0, TRIGRS 2.1, and Slabs3D 1.0 are grouped into corresponding metadata files for each tile. The metadata files have the following naming convention, in which the "*" is a code that identifies the tile ("larutu1," or "a1," "larutu2," or "a2," "larutu3," or "a3," and "larutu4," or "a4," for the Lares and Utuado tiles and "nar" for the Naranjito tile): *pm.xml - 1m-lidar-derived DEM *floacc.xml - D8 flow accumulation raster *_Zn.xml - input-parameter-zone raster RG_*.xml - REGOLITH 1.0 parameter input file, slope output raster and soil-depth output raster TR_*.xml - TRIGRS 2.1 parameter input file, pressure head output raster and local factor of safety output raster SL3_*.xml - Slabs3D 1.0 parameter input file, 3D factor of safety output rasters Parameter input files accompany this data release, with the following naming convention, in which the "*" is a code that

Landslide susceptibility models show the potential of landslide occurrence at a location. These models are pivotal for reducing losses associated with landslides (Godt et al., 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 et al., (2024). The two logistic regression models are trained on the landslide data available in the Willamette Valley Hydrologic Unit Code (HUC) 4 watershed (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 DOGAMI. (2024). SLIDO (Version 4.5) [Data set]. https://pubs.oregon.gov/dogami/SLIDO/4.5/SLIDO_Release_4p5_wMetadata.gdb.zip. Gina M Belair, Jeanne M Jones, Sabrina N Martinez, Benjamin B Mirus, & Nathan J Wood. (2024). Slope-Relief Threshold Landslide Susceptibility Models for the United States and Puerto Rico [Data Release]. U.S. Geological Survey.

Landslide susceptibility models show the potential of landslide occurrence at a location. These models are pivotal for reducing losses associated with landslides (Godt et al., 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 et al., (2024). The two logistic regression models are trained on the landslide data available in the Willamette Valley Hydrologic Unit Code (HUC) 4 watershed (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 DOGAMI. (2024). SLIDO (Version 4.5) [Data set]. https://pubs.oregon.gov/dogami/SLIDO/4.5/SLIDO_Release_4p5_wMetadata.gdb.zip. Gina M Belair, Jeanne M Jones, Sabrina N Martinez, Benjamin B Mirus, & Nathan J Wood. (2024). Slope-Relief Threshold Landslide Susceptibility Models for the United States and Puerto Rico [Data Release]. U.S. Geological Survey.

To better understand factors potentially contributing to the occurrence of rainfall-induced landslides in Puerto Rico, we evaluated the locations of landslides there following Hurricane Maria (Hughes et al., 2019) and potential contributing factors. This data release provides results of evaluations of landslide locations compared to soil classification and land cover, which involved frequency-ratio analyses (for example, Lee and Pradhan, 2006; Lee et al., 2007; He and Beighley, 2008; Lepore et al., 2012; Chalkias et al., 2014). Soil classification data were obtained from the U.S. Department of Agriculture Natural Resources Conservation Service (2018) and land cover data were obtained from the Puerto Rico Gap Analysis Program (Gould et al., 2008). The data presented herewith were produced during a study described in Hughes, K.S., and Schulz, W.H., ####, Map depicting susceptibility to landslides triggered by intense rainfall, Puerto Rico: U.S. Geological Survey Open-file Report #####. Three files are included with this data release. Data files soil_classification_results.csv and land_cover_results.csv provide results of the analyses of landslide locations compared to soil classification and land cover, respectively. A read-me file (readme.txt) provides the information contained in this summary and additional description of data available from the data files. References Chalkias, C., Kalogirou, S., and Ferntinou, M., 2014, Landslide susceptibility, Peloponnese Peninsula in South Greece: Journal of Maps, v. 10, no. 2, p. 211-222. Gould, W.A., Alarcón, C., Fevold, B., Jiménez, M.E., Martinuzzi, S., Potts, G., Quiñones, M., Solórzano, M., and Ventosa, E., 2008, The Puerto Rico Gap Analysis Project. Volume 1: Land cover, vertebrate species distributions, and land stewardship. Gen. Tech. Rep. IITF-GTR-39. Río Piedras, PR: U.S. Department of Agriculture, Forest Service, International Institute of Tropical Forestry. 165 p. https://www.sciencebase.gov/catalog/item/560c3b2de4b058f706e5411e. Last accessed 12 September 2019. He, Y., and Beighley, R.E., 2008, GIS‐based regional landslide susceptibility mapping: a case study in southern California: Earth Surface Processes and Landforms, v. 33, no. 3, p. 380-393. Hughes, K.S., Bayouth García, D., Martínez Milian, G.O., Schulz, W.H., and Baum, R.L., 2019, Map of slope-failure locations in Puerto Rico after Hurricane María: U.S. Geological Survey data release: https://doi.org/10.5066/P9BVMD74. https://www.sciencebase.gov/catalog/item/5d4c8b26e4b01d82ce8dfeb0. Last accessed 12 September 2019. Lee, S., and Pradhan, B., 2006, Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia: Journal of Earth System Science, v. 115, no. 6, p. 661-672. Lee, S., Ryu, J-H., and Kim, I-S., 2007, Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea: Landslides v. 4, p. 327–338. Lepore, C., Kamal, S.A., Shanahan, P., and Bras, R.L., 2012, Rainfall-induced landslide susceptibility zonation of Puerto Rico: Environmental Earth Sciences, v. 66, p. 1667-1681. U.S. Department of Agriculture Natural Resources Conservation Service, 2018, Soil Survey Geographic (SSURGO) database for Puerto Rico, all regions: https://websoilsurvey.sc.egov.usda.gov/App/WebSoilSurvey.aspx. Last accessed 12 September 2019.

To better understand factors potentially contributing to the occurrence of rainfall-induced landslides in Puerto Rico, we evaluated the locations of landslides there following Hurricane Maria (Hughes et al., 2019) and potential contributing factors. This data release provides results of evaluations of landslide locations compared to soil classification and land cover, which involved frequency-ratio analyses (for example, Lee and Pradhan, 2006; Lee et al., 2007; He and Beighley, 2008; Lepore et al., 2012; Chalkias et al., 2014). Soil classification data were obtained from the U.S. Department of Agriculture Natural Resources Conservation Service (2018) and land cover data were obtained from the Puerto Rico Gap Analysis Program (Gould et al., 2008). The data presented herewith were produced during a study described in Hughes, K.S., and Schulz, W.H., ####, Map depicting susceptibility to landslides triggered by intense rainfall, Puerto Rico: U.S. Geological Survey Open-file Report #####. Three files are included with this data release. Data files soil_classification_results.csv and land_cover_results.csv provide results of the analyses of landslide locations compared to soil classification and land cover, respectively. A read-me file (readme.txt) provides the information contained in this summary and additional description of data available from the data files. References Chalkias, C., Kalogirou, S., and Ferntinou, M., 2014, Landslide susceptibility, Peloponnese Peninsula in South Greece: Journal of Maps, v. 10, no. 2, p. 211-222. Gould, W.A., Alarcón, C., Fevold, B., Jiménez, M.E., Martinuzzi, S., Potts, G., Quiñones, M., Solórzano, M., and Ventosa, E., 2008, The Puerto Rico Gap Analysis Project. Volume 1: Land cover, vertebrate species distributions, and land stewardship. Gen. Tech. Rep. IITF-GTR-39. Río Piedras, PR: U.S. Department of Agriculture, Forest Service, International Institute of Tropical Forestry. 165 p. https://www.sciencebase.gov/catalog/item/560c3b2de4b058f706e5411e. Last accessed 12 September 2019. He, Y., and Beighley, R.E., 2008, GIS‐based regional landslide susceptibility mapping: a case study in southern California: Earth Surface Processes and Landforms, v. 33, no. 3, p. 380-393. Hughes, K.S., Bayouth García, D., Martínez Milian, G.O., Schulz, W.H., and Baum, R.L., 2019, Map of slope-failure locations in Puerto Rico after Hurricane María: U.S. Geological Survey data release: https://doi.org/10.5066/P9BVMD74. https://www.sciencebase.gov/catalog/item/5d4c8b26e4b01d82ce8dfeb0. Last accessed 12 September 2019. Lee, S., and Pradhan, B., 2006, Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia: Journal of Earth System Science, v. 115, no. 6, p. 661-672. Lee, S., Ryu, J-H., and Kim, I-S., 2007, Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea: Landslides v. 4, p. 327–338. Lepore, C., Kamal, S.A., Shanahan, P., and Bras, R.L., 2012, Rainfall-induced landslide susceptibility zonation of Puerto Rico: Environmental Earth Sciences, v. 66, p. 1667-1681. U.S. Department of Agriculture Natural Resources Conservation Service, 2018, Soil Survey Geographic (SSURGO) database for Puerto Rico, all regions: https://websoilsurvey.sc.egov.usda.gov/App/WebSoilSurvey.aspx. Last accessed 12 September 2019.

Landslide susceptibility modeling results as raster data offering a broad assessment of landslide hazards across five regions of Minnesota, USA. Data was created as part of an investigation to understand the link between a previously mapped landslide inventory, various environmental variables, and post-glacial landscape development through multivariate logistic regression analyses.

Landslide susceptibility modeling results as vector shapefile data offering a broad assessment of landslide hazards across five regions of Minnesota, USA. Data was created as part of an investigation to understand the link between a previously mapped landslide inventory, various environmental variables, and post-glacial landscape development through multivariate logistic regression analyses.

Landslides are damaging and deadly, and they occur in every U.S. state. However, our current ability to understand landslide hazards at the national scale is limited, in part because spatial data on landslide occurrence across the U.S. varies greatly in quality, accessibility, and extent. Landslide inventories are typically collected and maintained by different agencies and institutions, usually within specific jurisdictional boundaries, and often with varied objectives and information attributes or even in disparate formats. The purpose of this data release is to provide an openly accessible, centralized map of existing information on landslide occurrence across the entire U.S. The data release includes digital inventories created by both USGS and non-USGS authors. It provides an integrated database of all the landslides with a selection of uniform attributes, but also includes links to the original digital inventory files (whenever available). Given the wide range of landslide information sources in this data compilation, we also provide an attribute to assess the relative confidence in the characterization of the location and extent of each landslide. Further details about each landslide and more recent information (when it exists) can be accessed by clicking the “more information” attribute link to the original source information. This database will be updated intermittently and was most recently updated in March 2019. Please contact gs-haz_landslides_inventory@usgs.gov for more information on how to contribute additional inventories to this community effort.