This data release includes time-series, qualitative descriptions, and laboratory testing data from two monitoring stations installed in Puerto Rico following Hurricane Maria in 2017, which led to tens of thousands of landslides across the island (Bessette-Kirton et al., 2017). The stations were installed to investigate subsurface hydrologic response to rainfall and develop a quantitative link between rainfall and landsliding. The Toro Negro site is located within the state protected Toro Negro rainforest near 18°10’N, 66°34’W and the Utuado site is located outside the city of Utuado near 18°17’N, 66°39’W. The soil found at the Toro Negro site is low-permeability, fine-grained and cohesive, and underlain by saprolite. In contrast, the soil found at Utuado has higher hydraulic conductivity, relatively incohesive, and shallowly underlain by granodioritic bedrock. Instrumentation was installed at each site to measure precipitation, air temperature, barometric pressure, volumetric water content, pore-water pressure, and soil matric potential, at 15-minute intervals. An electronics enclosure, rain gage, and an instrumented soil pit (SP1) comprised each site for continuous monitoring. Volumetric soil water content was measured at 5 depths below the ground surface in each pit, using ruggedized dielectric sensors (range of 0-0.64 volumetric water content in mineral soils). Soil matric potential was measured at each site with two tensiometers (-80 to 100 kilopascals [kPa]) and one dielectric ceramic disc sensor (-6 to -1000 kPa). Pore-water pressure was measured at two depths with vibrating-wire piezometers (0 to 70 kPa). Each pressure sensor has an integrated thermistor and the associated temperature readings are included. In October 2019 an additional soil-pit was established at Toro Negro (SP2) to clarify the signal of two existing volumetric water content sensors with questionable readings. The data released with this report have not undergone any significant alterations since being recorded by the datalogger and are subject to inaccuracies related to equipment failure or loss of calibration. Missing data is represented as “not a number” (NaN). Also, there are time periods where groundwater conditions are outside of the instruments’ measurement range and these clipped data have been left in the record. Additionally, the tensiometer data returns erroneous data once it cavitates, and a Boolean data quality flag (vector “tensiometerFlag”) has been added to show where the data are likely reliable (1) or not reliable (0). The vibrating-wire piezometers are equipped with low air-entry filter tips (50 micron) and allow limited suctional range and these values should be viewed with skepticism. All values recorded by the piezometer are dependent on filter-saturation and, consequently, readings will be invalid during and after long periods of drought, until the tip has become re-saturated. Soil samples were taken from the documented soil pits at the time of installation and their index properties were measured in the Unsaturated Soil Mechanics Laboratory at Colorado School of Mines. The properties measured include particle size distributions (ASTM-152H), Atterberg limits (ASTM D-4318), soil classifications (USCS), specific gravity (ASTM D-854), unsaturated and saturated soil hydraulic properties including hysteretic saturated hydraulic conductivities and unsaturated soil-water retention curves using the TRIM method (Wayllace and Lu, 2012), strength properties including cohesion and the angle of internal friction determined using direct shear tests on saturated samples (ASTM D-3080) that included modifications for measurements at relatively low effective stresses (i.e., 0.2-20 kPa) (Likos et al., 2010). An additional “monitoring.readme.pdf” file is included and contains these details along with naming conventions for the hydrologic monitoring data. Logs of the soil pits at Utuado and Toro Negro are documented in the “PR UTU-ELT Monitoring Site

During September 2017, Hurricane Maria caused widespread landsliding throughout mountainous regions of Puerto Rico, with more than 71,000 landslides being subsequently identified from aerial imagery (Hughes et al., 2019). Most landslides apparently mobilized as debris flows and occurred within soil (unconsolidated material overlying saprolite and bedrock) and saprolite overlying less-weathered rock (e.g., Bessette-Kirton et al., 2019a). To better understand the characteristics of Maria-triggered landslides, debris flows, and materials in which landslides occurred, we performed reconnaissance-level studies of 118 landslides, 46 soil exposures generally within landslide scars, 24 saprolite exposures, and 37 rock exposures. Results from these studies are provided herein. Landslides studied were mostly selected from aerial imagery collected between 9-15 October 2017 (Quantum Spatial, Inc., 2017), with emphasis placed on four study areas where landslides were particularly numerous, and which were previously studied (Bessette-Kirton et al., 2019b) using aerial imagery and lidar data. However, we also selected landslides for field study that were spread across the mountainous parts of the main island of Puerto Rico, and which occurred in geological formations in which Maria-induced landslides were numerous. Finally, we selected landslides for field study that were relatively easily accessible from roads, although we attempted to mostly evaluate landslides that did not have clear association with roadway construction or drainage. However, a previous study found that landslides in Puerto Rico are approximately five times more likely near roads compared to away from roads (Larsen and Parks, 1997). Field studies were performed sporadically between June 2018 and March 2022. Data provided with this release are in the form of a file geodatabase developed using ESRI ArcGIS and which comprises four point feature classes; Soil_Descriptions, Saprolite_Descriptions, Rock_Descriptions, and Source_Areas, with the latter describing locations from which landslides initiated. Measurements of landslide location and dimensions were made in the field using tape measures, laser rangefinders, hand levels, clinometers, pocket transits, geological compasses, and positioning systems on mobile devices (that is, cellular telephones, tablets) that utilize GPS techniques and distance from cellular antennas. Unconfined compressive strength and undrained shear strength under field conditions were approximated for some materials using hand penetrometers and hand vane-shear devices, respectively, with values provided in Consistency/Compactness (unconfined compressive strength) and Notes (undrained shear strength) fields. Soil and saprolite colors were visually estimated using Munsell color charts. Data were collected in the field on mobile devices running ESRI ArcGIS Collector software and subsequently compiled using ArcGIS desktop software. Photographs were collected of many (but not all) features, and these are provided also in the geodatabase. The following paragraphs describe the attributes of each of the four point feature classes. Additional information is provided in the accompanying metadata file. Soil_Descriptions OBJECTID – Unique number assigned by software to identify each point observation. Shape – Type of feature, in all cases “point.” Soil Type – Visually and manually estimated primary soil type modified by soil types present in lesser amounts, with soil types generally of “clay,” “silt,” “sand,” and “gravel.” “Clay” consists of particles 0.002 mm diameter and finer, “silt” consists of particles 0.002 mm - 0.075 mm diameter, “sand” consists of particles 0.075 mm - 4.75 mm diameter, and “gravel” consists of particles 4.75 mm - 75 mm diameter. Secondary soil type may modify primary type (for example, “silty clay”) if more than 30% of the secondary type is present. Non-primary soil type content also may be described as “trace” if 1% - 10% is present,

During September 2017, Hurricane Maria caused widespread landsliding throughout mountainous regions of Puerto Rico, with more than 71,000 landslides being subsequently identified from aerial imagery (Hughes et al., 2019). Most landslides apparently mobilized as debris flows and occurred within soil (unconsolidated material overlying saprolite and bedrock) and saprolite overlying less-weathered rock (e.g., Bessette-Kirton et al., 2019a). To better understand the characteristics of Maria-triggered landslides, debris flows, and materials in which landslides occurred, we performed reconnaissance-level studies of 118 landslides, 46 soil exposures generally within landslide scars, 24 saprolite exposures, and 37 rock exposures. Results from these studies are provided herein. Landslides studied were mostly selected from aerial imagery collected between 9-15 October 2017 (Quantum Spatial, Inc., 2017), with emphasis placed on four study areas where landslides were particularly numerous, and which were previously studied (Bessette-Kirton et al., 2019b) using aerial imagery and lidar data. However, we also selected landslides for field study that were spread across the mountainous parts of the main island of Puerto Rico, and which occurred in geological formations in which Maria-induced landslides were numerous. Finally, we selected landslides for field study that were relatively easily accessible from roads, although we attempted to mostly evaluate landslides that did not have clear association with roadway construction or drainage. However, a previous study found that landslides in Puerto Rico are approximately five times more likely near roads compared to away from roads (Larsen and Parks, 1997). Field studies were performed sporadically between June 2018 and March 2022. Data provided with this release are in the form of a file geodatabase developed using ESRI ArcGIS and which comprises four point feature classes; Soil_Descriptions, Saprolite_Descriptions, Rock_Descriptions, and Source_Areas, with the latter describing locations from which landslides initiated. Measurements of landslide location and dimensions were made in the field using tape measures, laser rangefinders, hand levels, clinometers, pocket transits, geological compasses, and positioning systems on mobile devices (that is, cellular telephones, tablets) that utilize GPS techniques and distance from cellular antennas. Unconfined compressive strength and undrained shear strength under field conditions were approximated for some materials using hand penetrometers and hand vane-shear devices, respectively, with values provided in Consistency/Compactness (unconfined compressive strength) and Notes (undrained shear strength) fields. Soil and saprolite colors were visually estimated using Munsell color charts. Data were collected in the field on mobile devices running ESRI ArcGIS Collector software and subsequently compiled using ArcGIS desktop software. Photographs were collected of many (but not all) features, and these are provided also in the geodatabase. The following paragraphs describe the attributes of each of the four point feature classes. Additional information is provided in the accompanying metadata file. Soil_Descriptions OBJECTID – Unique number assigned by software to identify each point observation. Shape – Type of feature, in all cases “point.” Soil Type – Visually and manually estimated primary soil type modified by soil types present in lesser amounts, with soil types generally of “clay,” “silt,” “sand,” and “gravel.” “Clay” consists of particles 0.002 mm diameter and finer, “silt” consists of particles 0.002 mm - 0.075 mm diameter, “sand” consists of particles 0.075 mm - 4.75 mm diameter, and “gravel” consists of particles 4.75 mm - 75 mm diameter. Secondary soil type may modify primary type (for example, “silty clay”) if more than 30% of the secondary type is present. Non-primary soil type content also may be described as “trace” if 1% - 10% is present,

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)

This data release provides topographic (horizontal and vertical) data for 78 sites, surveyed from November 2017 to July 2019 as part of documentation of flooding that occurred in Puerto Rico during and after Hurricane Maria (September to November 2017). Hurricane Maria made landfall the Island of Puerto Rico on September 20, 2017 and was one of the deadliest storms in U.S. history. The U.S. Geological Survey (USGS) personnel conducted topographic surveys at selected stream sites to facilitate hydraulic modeling of peak streamflows (or discharges) – termed indirect measurements – using published standard USGS methods and hydraulic modeling studies to establish new stage-discharge relations for sites at which flooding substantially changed the pre-existing relation. Indirect (post-flood) measurements are used to characterize flood peaks that could not be determined using direct methods (for example current-velocity meters, hydro-acoustic instruments or established stage-streamflow relations) because flood conditions exceeded the capabilities of those methods, streamgage sites could not be accessed during flooding, or safety issues precluded access by USGS personnel during flooding. The standard-step hydraulic method, often referred to as the step-backwater method, is a widely accepted one-dimensional hydraulic model to determine (theoretical) water-surface elevations at a location of interest for specified streamflows.

This Data Release includes information to support the characterization of surface/near-surface infiltration rates of selected landslide source area materials following Hurricane Maria across Puerto Rico, USA. The dataset includes comma-delimited measurements of field-saturated hydraulic conductivity (Kfs) collected over two field campaigns (Fall 2018 and Spring 2019) as well as laboratory-derived measurements of soil/saprolite texture. The Kfs experiments were conducted within (or in the vicinity of) landslide source areas across the three primary geologic terranes on the island (Bawiec, 1998), including intrusive, volcaniclastic, and submarine basalt/chert lithologies. Depending on site conditions and the hydrologic conditions of interest, a Soil Moisture Inc. Guelph Permeameter (Soil Moisture, Inc. 2012), Decagon DualHead Infiltrometer (Decagon Devices, Inc. 2016), Decagon Mini-Disk Infiltrometer (Decagon Devices, Inc. 2018), or bottomless bucket approach (Nimmo et al. 2009) was used. Timing, location, and material information are provided for each Kfs measurement. All Kfs measurements (see Kfs-PR.csv) include a field-based texture estimate, and select measurements include quantitative texture estimates based on a Beckman Coulter particle size analyzer (Gee & Or 2002). Dianne Brien, Lindsay Davis, and William Schulz provided invaluable field assistance during this infiltration measurement campaign. Ben Mirus and Eric Jones provided constructive reviews for an earlier version of this Data Release. The following citations relate to this Data Release: Bawiec, W.J. (1998). Geology, Geochemistry, Geophysics, Mineral Occurrences and Mineral Resource Assessment for the Commonwealth of Puerto Rico (Open-File Report 98-38). Reston, VA: U.S. Geological Survey. https://doi.org/10.3133/ofr9838 Beckman Coulter, Inc. (2011). LS 13 32 Laser Diffraction Particle Size Analyzer Operator’s Manual. Brea, CA: Beckman Coulter, Inc. Available at: https://www.beckmancoulter.com/wsrportal/techdocs?docname=B05577AB.pdf Decagon Devices, Inc.; now Meter Environment Group, Inc. (2016). DualHead Infiltrometer Operator’s Manual. Pullman, WA: Decagon Devices, Inc. Available at: http://manuals.decagon.com/Retired%20and%20Discontinued/Manuals/14968_DHInfiltr ometer_Web.pdf Decagon Devices, Inc.; now Meter Environment Group, Inc. (2018). Mini Disk Infiltrometer Operator’s Manual. Pullman, WA: Decagon Devices, Inc. Available at: http://library.metergroup.com/Manuals/10564_Mini%20Disk%20Infiltrometer_Web.pdf Gee G.W., & Or D. (2002). Particle size analysis. In: Dane, J.H., & Topp, G.C. (Eds), Methods of Soil Analysis, Part 4-Physical Methods, Soil Science Society of America Book Series, Volume 5 (pp. 255-293). Madison, WA: Soil Science Society of America. Nimmo, J.R., Schmidt, K.M., Perkins, K.S., & Stock, J.D. (2009). Rapid measurement of field- saturated hydraulic conductivity for areal characterization. Vadose Zone Journal, 8(1), 142-149. https://doi.org/10.2136/vzj2007.0159 Soil Moisture, Inc. (2012). 2800K1 Guelph Permeameter Operating Instructions. Santa Barbara, CA: Soil Moisture, Inc. Available at: https://www.soilmoisture.com

This data release provides datasets supporting research on landslides triggered by a series of storms in the San Francisco Bay Area, California, that occurred between December 25, 2022, through January 19, 2023. During this period, eight atmospheric river storms delivered intense and prolonged rainfall across the region, leading to significant hydrological responses and widespread landsliding. Statewide, over 700 landslides were initially reported, with the total count likely exceeding 10,000. This release includes detailed observations from three landslide-prone monitoring sites in urbanized areas of the San Francisco Bay Area, capturing high-resolution time series data on rainfall, soil moisture, and subsurface pore water pressure. These datasets can be used for analyzing the subsurface hydrologic conditions preceding and during landslide events, thereby providing insight into storm-induced landslide triggers and informing future landslide prediction models. The data also support ongoing efforts to improve early warning systems for rainfall-induced landslides in California and other vulnerable regions. The data package includes the following files: 1. CSV Files: Time-series data capturing rain, soil moisture and piezometer measurements for each monitoring site. These files track key variables such as soil volumetric water content and pore water pressure, essential for understanding the hydrologic triggers of landslides. 2. PDF: Geotechnical Properties of Sites: This document presents detailed results from soil classification and hydromechanical testing conducted at each site. The data provide the physical properties of the soil, such as its composition and behavior under stress, which influence landslide susceptibility. 3. PDF: Site Images and Cross-Sections: This file contains a photograph and cross-sectional diagram of each site, offering visual context and structural details for the monitoring locations. These diagrams help illustrate the subsurface geological characteristics relevant to landslide risks. 4. PDF: Location Maps: This document includes a map showing the precise geographic location of each monitoring site within the San Francisco Bay Area, providing context for the collected data regarding regional landslide hazards. 5. CSV Files: Landslide location data for landslides mapped to have occurred during the December 25, 2022, to January 19, 2023, storm sequence near the BALT1 (East Bay), BALT2 (Marin County), and BALT3 (San Francisco Peninsula) monitoring sites. Each dataset includes a unique landslide identifier and the corresponding easting and northing referenced to the North American Datum of 1983 (NAD83) (EPSG:26910) and projected to Universal Transverse Mercator (UTM) Zone 10 North coordinates. These locations represent general landslide locations and should not be misconstrued as the precise headscarp location.

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 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