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

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 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 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

In late September 2017, intense precipitation associated with Hurricane Maria caused extensive landsliding across Puerto Rico. Much of the Lares municipality in central-western Puerto Rico was severely impacted by landslides., Landslide density in this region was mapped as greater than 25 landslides/km2 (Bessette-Kirton et al., 2019). In order to better understand the controlling variables of landslide occurrence and runout in this region, three 2.5-km2 study areas were selected and all landslides within were mapped in detail using remote-sensing data. Included in the data release are five separate shapefiles: geographic areas representing the mapping extent of the four distinct areas (map areas, filename: map_areas), initiation location polygons (source areas, filename: SourceArea), polygons of the entire impacted area consisting of source, transport, and deposition (affected areas, filename: AffectArea), points on the furthest upslope extent of the landslide source areas (headscarp point, filename: HSPoint), and lines reflecting the approximate travel paths from the furthest upslope extent to the furthest downslope extent of the landslides (runout lines, filename: RunoutLine). These shapefiles contain a number of attributes, some subjective (including general geomorphic setting and impact of human activity), some geometric (including length, width, and depth), and others on the underlying geology and soil of the landslides. A table detailing each attribute, attribute abbreviations, the possible choices for each attribute, and a short description of each attribute is provided as a table in the file labeled AttributeDescription.docx. The headscarp point shapefile attribute tables contain closest distance between headscarp and paved road (road_d_m; road data from U.S. Census Bureau, 2015). The runout line shapefile attribute table reflects if the landslide was considered independently unmappable past a road or river (term_drain), the horizontal length of the runout (length_m), the fall height from the headscarp to termination (h_m), the ratio of fall height to runout length (hlratio), distance to nearest paved road (road_d_m), and the watershed area upslope from the upper end of the runout line (wtrshd_m2). All quantitative metrics were calculated using tools available in ESRI ArcMap v. 10.6. The source area shapefile attribute table reflects general source area vegetation (vegetat) and land use (land_use), whether the slide significantly disaggregated during movement (flow), the failure mode (failmode), if the slide was a reactivation of a previous one (reactivate), if the landslide directly impacted the occurrence of another slide (ls_complex), the proportion of source material that left the source area (sourc_evac), the state of the remaining material (remaining), the curvature of the source area (sourc_curv), potential human impact on landslide occurrence (human_caus), potential landslide impact on human society (human_effc), if a building exists within 10 meters of the source area (buildng10m), if a road exists within 50 meters of the source area (road50m), the planimetric area of the source area (area_m2), the dimension of the source area perpendicular to the direction of motion (width_m), the dimension of the source area parallel to the direction of motion (length_m), the geologic formation of the source area (FMATN; from Bawiec, W.J., 1998), the soil type of the source area (MUNAME; from Acevido, G., 2020), the root-zone (0-100 cm deep) soil moisture estimated by the NASA SMAP mission for 9:30 am Atlantic Standard Time on 21 September 2017 (the day after Hurricane María) (smap; NASA, 2017), the average precipitation amount in the source area for the duration of the hurricane (pptn_mm; from Ramos-Scharrón, C.E., and Arima, E., 2019), the source area mean slope (mn_slp_d), the source area median slope (mdn_slp_d), the average depth change of material from the source area after the landslide (mn_dpth_m), the median depth

In late September 2017, intense precipitation associated with Hurricane Maria caused extensive landsliding across Puerto Rico. Much of the Utuado municipality was characterized as a severely impacted area, or having landslides at a density of greater than 25 landslides/km2 (Bessette-Kirton et al., 2019). In order to better understand the controlling variables of landslide occurrence and runout in this region, four 3.0-km2 study areas were selected and all landslides within were mapped in detail using remote-sensing data. Four separate shapefiles were produced: initiation location polygons (source areas), polygons of the entire impacted area consisting of source, transport, and deposition (affected areas), points on the furthest upslope extent of the landslide source areas (headscarp point), and lines reflecting the approximate travel paths from the furthest upslope extent to the furthest downslope extent of the landslides (runout lines). These shapefiles contain a number of attributes, some subjective (including general geomorphic setting and impact of human activity), some geometric (including length, width, and depth), and others involving the underlying geology and soil of the landslides. A table detailing each attribute, attribute abbreviations, the possible choices for each attribute, and a short description of each attribute is provided as a table in the file labeled AttributeDescription.docx. The headscarp point shapefile attribute tables contain closest distance between headscarp and paved road (road_d_m; road data from U.S. Census Bureau, 2015). The runout line shapefile attribute table reflects if the landslide was considered independently unmappable past a road or river (term_drain), the horizontal length of the runout (length_m), the fall height from the headscarp to termination (h_m), the ratio of fall height to runout length (hlratio), distance to nearest paved road (road_d_m), and the watershed area upslope from the upper end of the runout line (wtrshd_m2). All quantitative metrics were calculated using tools available in ESRI ArcMap v. 10.6. The source area shapefile attribute table reflects general source area vegetation (vegetat) and land use (land_use), whether the slide significantly disaggregated during movement (flow), the failure mode (failmode), if the slide was a reactivation of a previous one (reactivate), if the landslide directly impacted the occurrence of another slide (ls_complex), the proportion of source material that left the source area (sourc_evac), the state of the remaining material (remaining), the curvature of the source area (sourc_curv), potential human impact on landslide occurrence (human_caus), potential landslide impact on human society (human_effc), if a building exists within 10 meters of the source area (buildng10m), if a road exists within 50 meters of the source area (road50m), the planimetric area of the source area (area_m2), the dimension of the source area perpendicular to the direction of motion (width_m), the dimension of the source area parallel to the direction of motion (length_m), the geologic formation of the source area (FMATN; from Bawiec, W.J., 1998), the soil type of the source area (MUNAME; from Acevido, G., 2020), the root-zone (0-100 cm deep) soil moisture estimated by the NASA SMAP mission for 9:30 am Atlantic Standard Time on 21 September 2017 (the day after Hurricane María) (smap; NASA, 2017), the average precipitation amount in the source area for the duration of the hurricane (pptn_mm; from Ramos-Scharrón, C.E., and Arima, E., 2019), the source area mean slope (mn_slp_d), the source area median slope (mdn_slp_d), the average depth change of material from the source area after the landslide (mn_dpth_m), the median depth change of material from the source area after the landslide (mdn_dpt_m), the sum of the volumetric change of material in the source area after the landslide (ldr_sm_m3), the major geomorphic landform of the source (maj_ldfrm), and the

In late September 2017, intense precipitation associated with Hurricane Maria caused extensive landsliding across Puerto Rico. Much of the Utuado municipality was characterized as a severely impacted area, or having landslides at a density of greater than 25 landslides/km2 (Bessette-Kirton et al., 2019). In order to better understand the controlling variables of landslide occurrence and runout in this region, four 3.0-km2 study areas were selected and all landslides within were mapped in detail using remote-sensing data. Four separate shapefiles were produced: initiation location polygons (source areas), polygons of the entire impacted area consisting of source, transport, and deposition (affected areas), points on the furthest upslope extent of the landslide source areas (headscarp point), and lines reflecting the approximate travel paths from the furthest upslope extent to the furthest downslope extent of the landslides (runout lines). These shapefiles contain a number of attributes, some subjective (including general geomorphic setting and impact of human activity), some geometric (including length, width, and depth), and others involving the underlying geology and soil of the landslides. A table detailing each attribute, attribute abbreviations, the possible choices for each attribute, and a short description of each attribute is provided as a table in the file labeled AttributeDescription.docx. The headscarp point shapefile attribute tables contain closest distance between headscarp and paved road (road_d_m; road data from U.S. Census Bureau, 2015). The runout line shapefile attribute table reflects if the landslide was considered independently unmappable past a road or river (term_drain), the horizontal length of the runout (length_m), the fall height from the headscarp to termination (h_m), the ratio of fall height to runout length (hlratio), distance to nearest paved road (road_d_m), and the watershed area upslope from the upper end of the runout line (wtrshd_m2). All quantitative metrics were calculated using tools available in ESRI ArcMap v. 10.6. The source area shapefile attribute table reflects general source area vegetation (vegetat) and land use (land_use), whether the slide significantly disaggregated during movement (flow), the failure mode (failmode), if the slide was a reactivation of a previous one (reactivate), if the landslide directly impacted the occurrence of another slide (ls_complex), the proportion of source material that left the source area (sourc_evac), the state of the remaining material (remaining), the curvature of the source area (sourc_curv), potential human impact on landslide occurrence (human_caus), potential landslide impact on human society (human_effc), if a building exists within 10 meters of the source area (buildng10m), if a road exists within 50 meters of the source area (road50m), the planimetric area of the source area (area_m2), the dimension of the source area perpendicular to the direction of motion (width_m), the dimension of the source area parallel to the direction of motion (length_m), the geologic formation of the source area (FMATN; from Bawiec, W.J., 1998), the soil type of the source area (MUNAME; from Acevido, G., 2020), the root-zone (0-100 cm deep) soil moisture estimated by the NASA SMAP mission for 9:30 am Atlantic Standard Time on 21 September 2017 (the day after Hurricane María) (smap; NASA, 2017), the average precipitation amount in the source area for the duration of the hurricane (pptn_mm; from Ramos-Scharrón, C.E., and Arima, E., 2019), the source area mean slope (mn_slp_d), the source area median slope (mdn_slp_d), the average depth change of material from the source area after the landslide (mn_dpth_m), the median depth change of material from the source area after the landslide (mdn_dpt_m), the sum of the volumetric change of material in the source area after the landslide (ldr_sm_m3), the major geomorphic landform of the source (maj_ldfrm), and the

Hurricane Maria caused widespread landsliding throughout Puerto Rico in September 2017. While the majority of landslide inventories following the Hurricane focused on mountainous regions underlain by igneous and volcaniclastic bedrock (Bessette-Kirton et al., 2017, 2019), here we fill an important knowledge gap and document the occurrence of landslides along the greater karst region on the northwest side of the island. To examine the extent and characteristics of landslides in this area, we mapped individual landslides in municipalities including Aguadilla, Aguada, Arecibo, Barceloneta, Bayamon, Camuy, Ciales, Corozal, Dorado, Florida, Hatillo, Isabela, Lares, Manati, Moca, Morovis, Quebradillas, Rincon, San Sebastian, Toa Alta, Toa Baja, Utuado, Vega Alta, and Vega Baja. The boundary of our mapping was defined by the calcareous provence 62 (PROV 62) and nearby semi-calcareous sedimentary units (Bawiec, 1998). We used aerial imagery collected between 9-15 October 2017 (Quantum Spatial, Inc., 2017) to map landslide source and runout areas, and 1-m-resolution pre-event and post-event lidar (U.S. Geological Survey, 2018, 2020) as a digital base map for mapping. Difficulties with using these tools arose when aerial imagery was not correctly georeferenced to the lidar, when cloud cover was present in all images of an area, and in interpreting failure modes using only 2-dimensional aerial photos. These difficulties with aerial imagery were partially resolved using the lidar. The map data is comprised of headscarp points, travel distance lines, source area polygons, and affected area polygons that are provided as point, line, and polygon shapefiles that may be viewed using common geographic information systems. Various characteristics of the landslides and their geomorphic settings are included in attribute tables of the mapped features, and this information is described in the "Attribute Summary" document in the accompanying files. Quantitative attributes (e.g. failure travel distance, failure fall height, watershed contributing area, etc.) were determined using tools available with the ESRI ArcMap v. 10.6.1 geographic information system. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. References Bawiec, W.J., 1998, Geologic terranes of Puerto Rico, in Bawiec, W.J., ed., Geology, geochemistry, geophysics, mineral occur¬rences and mineral resource assessment for the Commonwealth of Puerto Rico: U.S. Geological Survey Open-File Report 98–038, p. 59–71, accessed February 28, 2020, at https://pubs.usgs.gov/of/1998/of98-038/. Bessette-Kirton, E.K., Cerovski-Darrian, C., Schulz, W.H., Coe, J.A., Kean, J.W., Godt, J.W., Thomas, M.A. and Hughes, K.S., 2019, Landslides triggered by Hurricane Maria: Assessment of an extreme event in Puerto Rico: GSA Today, v. 29, no. 6. Bessette-Kirton, E.K., Coe, J.A., Godt, J.W., Kean, J.W., Rengers, F.K., Schulz, W.H., Baum, R.L., Jones, E.S., and Staley, D.M., 2017, Map data showing concentration of landslides caused by Hurricane Maria in Puerto Rico: U.S. Geological Survey data release, https://doi.org/10.5066/F7JD4VRF. Quantum Spatial, Inc., 2017 FEMA PR Imagery: https://s3amazonaws.com/fema-cap-imagery/Others/Maria (accessed October 2017). U.S. Geological Survey, 2018, USGS NED Original Product Resolution PR Puerto Rico 2015: http://nationalmap.gov/elevation.html (accessed October 2018). U.S. Geological Survey, 2020, USGS NED Original Product Resolution PR Puerto Rico 2015: http://nationalmap.gov/elevation.html (accessed October 2018)

Hurricane Maria caused widespread landsliding throughout Puerto Rico in September 2017. While the majority of landslide inventories following the Hurricane focused on mountainous regions underlain by igneous and volcaniclastic bedrock (Bessette-Kirton et al., 2017, 2019), here we fill an important knowledge gap and document the occurrence of landslides along the greater karst region on the northwest side of the island. To examine the extent and characteristics of landslides in this area, we mapped individual landslides in municipalities including Aguadilla, Aguada, Arecibo, Barceloneta, Bayamon, Camuy, Ciales, Corozal, Dorado, Florida, Hatillo, Isabela, Lares, Manati, Moca, Morovis, Quebradillas, Rincon, San Sebastian, Toa Alta, Toa Baja, Utuado, Vega Alta, and Vega Baja. The boundary of our mapping was defined by the calcareous provence 62 (PROV 62) and nearby semi-calcareous sedimentary units (Bawiec, 1998). We used aerial imagery collected between 9-15 October 2017 (Quantum Spatial, Inc., 2017) to map landslide source and runout areas, and 1-m-resolution pre-event and post-event lidar (U.S. Geological Survey, 2018, 2020) as a digital base map for mapping. Difficulties with using these tools arose when aerial imagery was not correctly georeferenced to the lidar, when cloud cover was present in all images of an area, and in interpreting failure modes using only 2-dimensional aerial photos. These difficulties with aerial imagery were partially resolved using the lidar. The map data is comprised of headscarp points, travel distance lines, source area polygons, and affected area polygons that are provided as point, line, and polygon shapefiles that may be viewed using common geographic information systems. Various characteristics of the landslides and their geomorphic settings are included in attribute tables of the mapped features, and this information is described in the "Attribute Summary" document in the accompanying files. Quantitative attributes (e.g. failure travel distance, failure fall height, watershed contributing area, etc.) were determined using tools available with the ESRI ArcMap v. 10.6.1 geographic information system. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. References Bawiec, W.J., 1998, Geologic terranes of Puerto Rico, in Bawiec, W.J., ed., Geology, geochemistry, geophysics, mineral occur¬rences and mineral resource assessment for the Commonwealth of Puerto Rico: U.S. Geological Survey Open-File Report 98–038, p. 59–71, accessed February 28, 2020, at https://pubs.usgs.gov/of/1998/of98-038/. Bessette-Kirton, E.K., Cerovski-Darrian, C., Schulz, W.H., Coe, J.A., Kean, J.W., Godt, J.W., Thomas, M.A. and Hughes, K.S., 2019, Landslides triggered by Hurricane Maria: Assessment of an extreme event in Puerto Rico: GSA Today, v. 29, no. 6. Bessette-Kirton, E.K., Coe, J.A., Godt, J.W., Kean, J.W., Rengers, F.K., Schulz, W.H., Baum, R.L., Jones, E.S., and Staley, D.M., 2017, Map data showing concentration of landslides caused by Hurricane Maria in Puerto Rico: U.S. Geological Survey data release, https://doi.org/10.5066/F7JD4VRF. Quantum Spatial, Inc., 2017 FEMA PR Imagery: https://s3amazonaws.com/fema-cap-imagery/Others/Maria (accessed October 2017). U.S. Geological Survey, 2018, USGS NED Original Product Resolution PR Puerto Rico 2015: http://nationalmap.gov/elevation.html (accessed October 2018). U.S. Geological Survey, 2020, USGS NED Original Product Resolution PR Puerto Rico 2015: http://nationalmap.gov/elevation.html (accessed October 2018)