Pluvials can have dramatic impacts on the shoreline bluffs of Lake Michigan due to increases in both shallow subsurface moisture conditions related to the prolonged wet weather pattern and wave erosion as the lake level rises. These changes can result in an increased frequency and magnitude of slope failures. During the most recent pluvial, the monthly average level of Lake Michigan rose 1.9 m from a record low in January 2013 to a near record high in June-July 2020. To assess the impacts on coastal bluffs from slope failures during the recent pluvial, an inventory of landslides was completed, including slope failures active during the early part of the pluvial, on the coastal bluffs of North Manitou Island, part of the Sleeping Bear Dunes National Lakeshore in Michigan. Landslides were mapped using high-resolution orthoimagery, collected in April 2012, and high-resolution topography derived from a LiDAR data set, collected in December 2014. This data release presents geographic information system (GIS) data, provided as line and polygon shapefiles (.shp), depicting landslides and related landforms and features. Polygon map data delineates the areas of deposits, source areas, and related landforms (such as alluvial fans and colluvial aprons). Scarps (such as headscarps and minor scarps) are presented as hachured line data. An attribute file is included providing a definition of the mapped units and a brief description of the approach used in the mapping.

Pluvials can have dramatic impacts on the shoreline bluffs of Lake Michigan due to increases in both shallow subsurface moisture conditions related to the prolonged wet weather pattern and wave erosion as the lake level rises. These changes can result in an increased frequency and magnitude of slope failures. During the most recent pluvial, the monthly average level of Lake Michigan rose 1.9 m from a record low in January 2013 to a near record high in June-July 2020. To assess the impacts on coastal bluffs from slope failures during the recent pluvial, an inventory of landslides was completed, including slope failures active during the early part of the pluvial, on the coastal bluffs of South Manitou Island, part of the Sleeping Bear Dunes National Lakeshore in Michigan. Landslides were mapped using high-resolution orthoimagery, collected in April 2012, and high-resolution topography derived from two LiDAR data sets, the first collected in December 2014 and the second collected between November 2015 and March 2016. This data release presents geographic information system (GIS) data, provided as line and polygon shapefiles (.shp), depicting landslides and related landforms and features. Polygon map data delineates the areas of deposits, source areas, and related landforms (such as alluvial fans and colluvial aprons). Scarps (such as headscarps and minor scarps) are presented as hachured line data. An attribute file is included providing a definition of the mapped units and a brief description of the approach used in the mapping.

Pluvials can have dramatic impacts on the shoreline bluffs of Lake Michigan due to increases in both shallow subsurface moisture conditions related to the prolonged wet weather pattern and wave erosion as the lake level rises. These changes can result in an increased frequency and magnitude of slope failures. During the most recent pluvial, the monthly average level of Lake Michigan rose 1.9 m from a record low in January 2013 to a near record high in June-July 2020. To assess the impacts on coastal bluffs from slope failures during the recent pluvial, an inventory of landslides was completed, including slope failures active during the early part of the pluvial, on the coastal bluffs of South Manitou Island, part of the Sleeping Bear Dunes National Lakeshore in Michigan. Landslides were mapped using high-resolution orthoimagery, collected in April 2012, and high-resolution topography derived from two LiDAR data sets, the first collected in December 2014 and the second collected between November 2015 and March 2016. This data release presents geographic information system (GIS) data, provided as line and polygon shapefiles (.shp), depicting landslides and related landforms and features. Polygon map data delineates the areas of deposits, source areas, and related landforms (such as alluvial fans and colluvial aprons). Scarps (such as headscarps and minor scarps) are presented as hachured line data. An attribute file is included providing a definition of the mapped units and a brief description of the approach used in the mapping.

Pluvials can have dramatic impacts on the shoreline bluffs of Lake Michigan due to increases in both shallow subsurface moisture conditions related to the prolonged wet weather pattern and wave erosion as the lake level rises. These changes can result in an increased frequency and magnitude of slope failures. During the most recent pluvial, the monthly average level of Lake Michigan rose 1.9 m from a record low in January 2013 to a near record high in June-July 2020. To assess the impacts on coastal bluffs from slope failures during the recent pluvial, an inventory of landslides was completed, including slope failures active during the early part of the pluvial, on the coastal bluffs of South Manitou Island, part of the Sleeping Bear Dunes National Lakeshore in Michigan. Landslides were mapped using high-resolution orthoimagery, collected in April 2012, and high-resolution topography derived from two LiDAR data sets, the first collected in December 2014 and the second collected between November 2015 and March 2016. This data release presents geographic information system (GIS) data, provided as line and polygon shapefiles (.shp), depicting landslides and related landforms and features. Polygon map data delineates the areas of deposits, source areas, and related landforms (such as alluvial fans and colluvial aprons). Scarps (such as headscarps and minor scarps) are presented as hachured line data. An attribute file is included providing a definition of the mapped units and a brief description of the approach used in the mapping.

The effects of climate change have the potential to impact slope stability. Negative impacts are expected to be greatest at high northerly latitudes where degradation of permafrost in rock and soil, debuttressing of slopes as a result of glacial retreat, and changes in ocean ice-cover are likely to increase the susceptibility of slopes to landslides. In the United States, the greatest increases in air temperature and precipitation are expected to occur in Alaska. In order to assess the impact that these environmental changes will have on landslide size (magnitude), mobility, and frequency, inventories of historical landslides are needed. These inventories provide baseline data that can be used to identify changes between historical and future landslide magnitude, mobility, and frequency. This data release presents GIS and attribute data for an inventory of rock avalanches in a 5000 sq. km area of western Glacier Bay National Park and Preserve, Alaska. We created the inventory from 30 m resolution Landsat imagery acquired from June 1984 to September 2016. For each calendar year, we visually examined a minimum of one Landsat image obtained between the months of May and October. We examined a total of 104 Landsat images. The contrast between the spectral signatures of freshly exposed rock avalanche source areas and deposits and surrounding, undisturbed snow and ice is typically significant enough to detect surficial changes. We identified and mapped rock avalanches by locating areas with 1) high contrast compared to surrounding snow and ice, 2) different spectral signatures between successive Landsat images, and 3) lobate forms typical of rock-avalanche deposits. Using these criteria, we mapped a total of 24 rock avalanches ranging in size from 0.1 to 22 km2. Attribute data for each rock avalanche includes: a date, or range in possible dates, of occurrence; the name of the Landsat image(s) used to identify and map the avalanche; the total area covered by the rock avalanche (including the source area and deposit); the maximum travel distance measured along a curvilinear centerline (L); and the change in elevation between the start and end points of the centerline (H). We also include a table containing a list of all the Landsat images examined. We acknowledge that our mapped polygons will be different, and less accurate, than polygons that could be mapped from higher-resolution satellite, aerial, and hand-held imagery. We specifically chose not to use high resolution imagery because we desired a long-term historical inventory that was unbiased by changes in image resolution. Eventually, new mapping should be done to create an inventory that fully utilizes recently available high-resolution imagery. Data included in this release form the basis of an interpretive paper available in the conference proceedings of the 3rd North American Symposium on Landslides held in Roanoke, Virginia in June, 2017.

The effects of climate change have the potential to impact slope stability. Negative impacts are expected to be greatest at high northerly latitudes where degradation of permafrost in rock and soil, debuttressing of slopes as a result of glacial retreat, and changes in ocean ice-cover are likely to increase the susceptibility of slopes to landslides. In the United States, the greatest increases in air temperature and precipitation are expected to occur in Alaska. In order to assess the impact that these environmental changes will have on landslide size (magnitude), mobility, and frequency, inventories of historical landslides are needed. These inventories provide baseline data that can be used to identify changes between historical and future landslide magnitude, mobility, and frequency. This data release presents GIS and attribute data for an inventory of rock avalanches in a 5000 sq. km area of western Glacier Bay National Park and Preserve, Alaska. We created the inventory from 30 m resolution Landsat imagery acquired from June 1984 to September 2016. For each calendar year, we visually examined a minimum of one Landsat image obtained between the months of May and October. We examined a total of 104 Landsat images. The contrast between the spectral signatures of freshly exposed rock avalanche source areas and deposits and surrounding, undisturbed snow and ice is typically significant enough to detect surficial changes. We identified and mapped rock avalanches by locating areas with 1) high contrast compared to surrounding snow and ice, 2) different spectral signatures between successive Landsat images, and 3) lobate forms typical of rock-avalanche deposits. Using these criteria, we mapped a total of 24 rock avalanches ranging in size from 0.1 to 22 km2. Attribute data for each rock avalanche includes: a date, or range in possible dates, of occurrence; the name of the Landsat image(s) used to identify and map the avalanche; the total area covered by the rock avalanche (including the source area and deposit); the maximum travel distance measured along a curvilinear centerline (L); and the change in elevation between the start and end points of the centerline (H). We also include a table containing a list of all the Landsat images examined. We acknowledge that our mapped polygons will be different, and less accurate, than polygons that could be mapped from higher-resolution satellite, aerial, and hand-held imagery. We specifically chose not to use high resolution imagery because we desired a long-term historical inventory that was unbiased by changes in image resolution. Eventually, new mapping should be done to create an inventory that fully utilizes recently available high-resolution imagery. Data included in this release form the basis of an interpretive paper available in the conference proceedings of the 3rd North American Symposium on Landslides held in Roanoke, Virginia in June, 2017.

Landslide hazard susceptibility mapping in Homer, Alaska, Report of Investigation 2024-3, provides a map and database of historical and prehistoric slope failures, maps of shallow and deep-seated landslide susceptibility, and a map of simulated debris flow runouts for the City of Homer, Alaska and nearby populated areas including Kachemak City and Millers Landing. The landslide inventory map integrates existing maps of landslides caused by the 1964 Great Alaska Earthquake and newly mapped slope failures identified in sequences of aerial photos since 1950 and high-resolution light detection and ranging (lidar) data collected for this project. The Alaska Division of Geological & Geophysical Surveys (DGGS) staff created a shallow landslide susceptibility map following protocols like those developed by the Oregon Department of Geology and Mineral Industries, which includes incorporating landslide inventory data, geotechnical soil properties, and lidar-derived topographic slope to calculate the Factor of Safety (FOS), which serves as a proxy for landslide susceptibility. Debris flow runout extents were generated using the model Laharz, which simulates runout extents based on catchment-specific physical parameters (e.g., hypothetical sediment volumes). Data from these analyses are collectively intended to depict locations where landslides are relatively more likely to occur or are relatively more likely to travel. The results provide important hazard information that can help guide planning and future risk investigations. The maps are not intended to predict slope failures and are site-specific; detailed investigations should be conducted before development in vulnerable areas. Results are for informational purposes and are not intended for legal, engineering, or surveying uses. These data and the interpretive maps and report are available from the DGGS website: http://doi.org/10.14509/31155.

This dataset contains an inventory of landslides in many of the most landslide-prone parts of Minnesota. This project was created to improve our understanding of the landslide hazard in Minnesota and to provide a nearly statewide base map of landslide data. The mapping was performed by geologists from the U.S. Geological Survey, the Freshwater Society, and several academic institutions where undergraduate students, graduate students and faculty performed mapping. Contributing academic institution include the University of Minnesota Duluth, the University of Minnesota Twin Cities, the University of Wisconsin-Superior, Gustavus Adolphus College, Winona State University, Minnesota State University, Mankato, St. Thomas University, and North Dakota State University. These landslides were identified using several methods. These include analysis of historical records, direct field observation, location using satellite or aerial imagery, and identification in topographic data products derived from the statewide lidar data coverage. Most of the mapped landslides were identified using lidar derivatives and have not been evaluated in the field by geologists or engineers. These data should be considered a preliminary survey and are not intended to represent a complete and accurate inventory of landslides for these areas. There may be a range in the accuracy, detail, and completeness with which landslides are mapped, and in the information associated with a given landslide; however, all mapped landslides were reviewed by USGS personnel and the senior project members. Mapping procedures including the assignment of numerical values for confidence follow guidelines found in DOGAMI Special Paper 42: https://www.oregongeology.org/pubs/sp/p-SP-42.htm. Site-specific investigations should be completed before using these data for land development or management decisions. This Data Release consists of: 1) Minnesota_Landslides_v1_1.gdb.zip which contains the landslide inventory mapping data and the areas that were mapped, to be used in a GIS, 2) Minnesota_Landslides_v1_3.sd which is an ESRI service layer definition file that enables use of the data in online and offline GIS, 3) MN_Landslide_Photos.zip that contains a collection of geotagged photos showing landslides; these can be imported into a GIS, and 4) metadata.xml which contains metadata for all included files. Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

A hydrologic monitoring network was installed to investigate landslide hazards affecting the railway corridor along the eastern shore of Puget Sound between Seattle and Everett, near Mukilteo, Washington. During the summer of 2015, the U.S. Geological Survey installed instrumentation at four sites to measure rainfall and air temperature every 15 minutes. Two of the four sites are installed on contrasting coastal bluffs, one landslide scarred and one vegetated. At these two sites, in addition to rainfall and air temperature, volumetric water content, pore pressure, soil suction, soil temperature (via hydrologic instrumentation), and barometric pressure were measured every 15 minutes. The instrumentation was designed to supplement landslide-rainfall thresholds developed by the U.S. Geological Survey with a long-term goal of advancing the understanding of the relationship between landslide potential and hydrologic forcing along the coastal bluffs. Additionally, the system was designed to function as a prototype monitoring system to evaluate criteria for site selection, instrument selection, and placement of instruments. Two files are included with this release. A comma separated value (csv) file contains monitoring data for the time-periods described by its name, for example 20150711_20160809.csv contains data for the period between July 11, 2015 and August 9, 2016. A read-me file (readme.doc) describes the sensor naming convention used for column names in the data files. The following citation relates to a report that provides background information and is intended to accompany this data release. Smith, J.B.; Baum, R.L.; Mirus, Benjamin B.; Michel, Abigail R.; Stark, Ben, 2017, Results of Hydrologic Monitoring on Landslide-Prone Coastal Bluffs Near Mukilteo Washington: U.S. Geological Survey Open-File Report 2017-1095, 50 p., http://dx.doi.org/10.3133/ofr20171095

This data release includes time-series data and qualitative descriptions from a monitoring station on a steep, landslide-prone slope above the City of Sitka, Alaska. On August 18, 2015, heavy rainfall triggered around 60 landslides in and around Sitka. These landslides moved downslope rapidly; several were damaging, and one demolished a home on South Kramer Avenue and killed three people. On September 16-18, 2019, the U.S. Geological Survey installed instrumentation at a site near the initiation zones of these landslides and other previous landslides on the west face of Harbor Mountain. The station consists of an electronics enclosure, a mounted rain gage, and two instrumented soil pits. Instruments record continuous measurements of precipitation, air temperature, volumetric water content, pore-water pressure, soil temperature, and soil matric potential at five-minute intervals. Soil pits were dug as deep as possible into the soil mantle for installation of the hydrologic monitoring instruments. Extensive probing with a 1.2-m-long piece of rebar to the point of refusal confirmed that the bottom of each hole was near the top of bedrock or compact till. The first soil pit (SP1), located at N 57.08551, W 135.35936, is about 1 m downslope from the north rim of the drainage hollow. SP1 is about 60 cm deep with the upper 12-15 cm in dark brown, moist, silty sand with large concentration of plant roots. Below 15 cm, to bottom of hole, consists of abundant gray sandstone clasts in silty sand matrix, which ranges in color from orange-brown, brown, to gray. The SP1 sensor array consists of a water potential sensor and soil moisture sensor at 25 cm depth, a second soil moisture sensor at 50 cm depth, and a pressure transducer near bottom of hole with a port at ~55 cm depth. The second soil pit (SP2), located at N 57.08548, W 135.35933, is about 5 m downslope from the north rim of the drainage hollow and is 65 cm deep. The top of hard material (bedrock or till) was about 70 cm deep, but there was free water at a depth of about 50-55 cm. Material throughout the depth of the hole was moist sandy silty clay of a gelatinous consistency. Color ranged from orange-brown to dark brown. Very few stones were present. These soils were interpreted as transported/mixed, weathered volcanic ash (Jacqueline Foss, USDA Forest Service, personal communication, 2019). The SP2 sensor array consists of soil moisture sensors at 25 and 40 cm depth, and a pressure transducer lying on the bottom of the hole, with a port at about 60 cm depth. A Campbell Scientific CR1000 datalogger is used to collect continuous data from these sensors. The datalogger and modem are contained in a sealed, weather-resistant fiberglass enclosure. The CR1000 datalogger contains an internal thermistor that continuously measures temperature. Additionally, an air temperature sensor was installed to collect continuous air temperature data. A tipping bucket rain gage installed in a clearing about 10 m northwest of the logger enclosure collects precipitation data. The maximum resolution of the rain gauge is 0.2 mm; that is, one tip of the bucket represents 0.2 mm. Four METER ECH20 EC-5 sensors are used to collect soil moisture data. Pore-water pressures are measured using two Campbell Scientific CS-451 pressure transducers. A METER MPS-6 water potential sensor in SP1 is used to collect soil matric potential. This sensor’s measurements range from -100,000 to -9 kPa was exceeded for the duration of the monitoring period. Recorded values appear to hover around the sensor’s upper limit (-9 kPa), with the exception of September 2019 when the station was first installed and a few brief periods in July 2022 when conditions were sufficiently dry for matric potentials to drop below -9 kPa. The water potential sensor and pressure sensors have integrated thermistors and the associated temperature readings are included. Several factors that may influence data consistency and/or quality should be