In late September 2017, intense precipitation associated with Hurricane Maria caused extensive landsliding across Puerto Rico. Much of the Las Marias 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, two 2.5-km2 study areas were selected and all landslides within were manually 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 qualitative attributes interpreted from the aerial imagery (such as geomorphic setting and impact of human activity) and qualitative attributes extracted from the geospatial data (such as source area length, width, and depth), as well as attributes extracted from other sources (such as geology and soil properties). 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 MarÃa) (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

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 brought intense rainfall and caused widespread landsliding throughout Puerto Rico during September 2017. Previous detailed landslide inventories following the hurricane include Bessette-Kirton et al. (2017, 2019). Here we continue that work with an in-depth look at two areas in San Lorenzo, which is a municipality in the east-central part of the main island. To study a characteristic sample of landslides in San Lorenzo, we mapped all visible landslides in two physiographically diverse areas, but all within the San Lorenzo Formation. 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 two-dimensional aerial photos. These difficulties with aerial imagery were partially resolved using the lidar. The map data comprises 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 ArcGIS Pro v. 3.0.36056 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 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 brought intense rainfall and caused widespread landsliding throughout Puerto Rico during September 2017. Previous detailed landslide inventories following the hurricane include Bessette-Kirton et al. (2017, 2019). Here we continue that work with an in-depth look at two areas in San Lorenzo, which is a municipality in the east-central part of the main island. To study a characteristic sample of landslides in San Lorenzo, we mapped all visible landslides in two physiographically diverse areas, but all within the San Lorenzo Formation. 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 two-dimensional aerial photos. These difficulties with aerial imagery were partially resolved using the lidar. The map data comprises 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 ArcGIS Pro v. 3.0.36056 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 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 during September 2017. Previous detailed landslide inventories following the hurricane include Bessette-Kirton et al. (2017, 2019). Here we continue that work with an in-depth look at a portion of northwest Naranjito, which is a municipality in the northeastern part of the main island. To study a characteristic sample of landslides in Naranjito, we mapped all visible individual landslides in an approximately triangular area 2.3 km wide by 1.9 km long. The boundary of our mapping was defined by previous studies (Bessette-Kirton et al., 2019). 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 comprises 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 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 during September 2017. Previous detailed landslide inventories following the hurricane include Bessette-Kirton et al. (2017, 2019). Here we continue that work with an in-depth look at a portion of northwest Naranjito, which is a municipality in the northeastern part of the main island. To study a characteristic sample of landslides in Naranjito, we mapped all visible individual landslides in an approximately triangular area 2.3 km wide by 1.9 km long. The boundary of our mapping was defined by previous studies (Bessette-Kirton et al., 2019). 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 comprises 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 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)

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)