Debris flow hazard evaluations for multi-hazard risk mapping in Sitka, Alaska

Hubbard, T.D.Daanen, R.P.Stevens, D.S.P.2025Debris flow hazard evaluations for multi-hazard risk mapping in Sitka, Alaskashapefile, rasterReport of InvestigationRI 2024-2Fairbanks, Alaska, United StatesAlaska Division of Geological & Geophysical Surveys14 p., 6 sheets, scale 1:20,000https://doi.org/10.14509/30187Debris flow hazard evaluations for multi-hazard risk mapping in Sitka, Alaska, Report of Investigation 2024-2, provides digital data used to develop interpretive maps and a geospatial database of historical debris flows, shallow-debris flow susceptible slopes, and simulated debris flow runouts for the City of Sitka, Alaska. On August 18, 2015, heavy rainfall and wind resulted in numerous debris flows in and around Sitka, Alaska. Four debris flows impacted roads and infrastructure in Sitka, and the southernmost of two flows at Kramer Avenue took the lives of three residents. In response to these events, the Alaska Division of Geological & Geophysical Surveys (DGGS), through a Community and Technical Partners Grant with the Federal Emergency Management Agency (FEMA), initiated a multi-hazard assessment of the Sitka area. The objective was to help better understand debris flow hazards, inform mitigation efforts, guide future development activities, and protect public safety in and around Sitka. We emphasize this study is a regional evaluation, not a site-specific assessment. It would be inappropriate to use the results for site-specific decision-making. Our final products show what computer models predict could occur based on the selected inputs and with the idealized assumption that the simplified input conditions occur, unvaryingly, throughout the entire model area. The reality is much more complex and can only be addressed with detailed site-specific studies. Our model is intentionally conservative to not underestimate the potential risk to life and safety, and this can be seen in areas where modeled potential debris flow runouts are longer than those observed from historic debris flows. These data and the interpretive maps and report are available from the DGGS website: http://doi.org/10.14509/30187.This study provides a regional evaluation of debris flow hazards in and around Sitka. The study aims to help the community better understand debris flow hazards, inform mitigation efforts, guide future development activities, and protect public safety.>watershed_size: A polygon shapefile outlining watershed areas used for debris flow runout simulations. These polygons represent debris contribution areas in modeled simulations. >landslide_inventory: Feature class containing polygons outlining the extent of landslide deposits identified in the 2018 high-resolution lidar data. >scenario1_modeled_debris_flow_runout: Raster image data depicting the modeled extent of debris flow runout zones simulated using LaharZ (Iverson and others, 1988; Schilling, 1998). Volumes for runout zone modeling were estimated based on known debris flows; the 2014 Starrigavan debris flow [(high volume; 11,000m3 (388,461ft3)], 2015 South Kramer debris flow [(medium volume; volume; 48,000m3 (1,695104ft3)], and 2015 Silver Baby debris flow [(low volume; 9,000m3 (31783 ft3)]. In this scenario debris flow runout is modeled using the best fitting parameters calibrated on the South Kramer debris flow. >scenario2_modeled_debris_flow_runout: Raster image data depicting the modeled extent of debris flow runout zones simulated using LaharZ (Iverson and others, 1988; Schilling, 1998). Volumes for runout zone modeling were estimated based on known debris flows; the 2014 Starrigavan debris flow [(high volume; 48,000m3 (388,461ft3)], 2015 South Kramer debris flow [(medium volume; volume; 11,000m3 (1,695104ft3)], and 2015 Silver Baby debris flow [(low volume; 9,000m3 (31783 ft3)]. In this scenario debris flow runout is modeled using the best fitting parameters calibrated on the North Kramer debris flow. >slope_susceptibility: Raster image data model of the relationship between shear forces acting to move material downslope and forces acting to resist downslope movement. This is a representation of the Factor of Safety (FOS).2016ground conditionCompleteNone planned-135.409699-135.08744357.16715856.977999ISO 19115 Topic CategorygeoscientificInformationAlaska Division of Geological & Geophysical SurveysCoastalCoastal and RiverColluviumDebris AvalancheDebris FlowDebris Flow DepositEarthquake Related Slope FailureEngineeringEngineering GeologyEnvironmentEnvironmentalGeologicGeologic HazardsGeological ProcessGeologyGeomorphologyGeotechnicalHazardsHydrology and Surficial GeologyLandslideLiDARModelingRaster ImageSlopeSlope InstabilitySurfaceSurface WaterSurficialSurficial GeologyAlaska Division of Geological & Geophysical SurveysBear CoveBeaver LakeBlue LakeCascade CreekCastle HillCrescent BayGranite CreekGreen LakeHalibut PointHalibut Point RoadHarbor Mountain RoadHarbor PointHeart LakeHerring CoveIndian RiverIndian River RoadIndian River ValleyJamestown BayMaksoutoff RoadMedvejie LakeSawmill CoveSawmill CreekSawmill Creek RoadSealing CoveSilver BaySitkaSitka Ferry Terminal RoadSitka HarborSoutheast AlaskaStarrigavan BaySwan LakeThimbleberry BayThimbleberry LakeThomsen HarborVodopad RiverWhiting HarborWalker, J.D., Geissman, J.W., Bowring, S.A, and Babcock, L.E., comp., 2012, Geologic Time Scale v. 4.0: Geological Society of AmericaHoloceneThis report, map, and/or dataset is available directly from the State of Alaska, Department of Natural Resources, Division of Geological & Geophysical Surveys (see contact information below).Any hard copies or published datasets utilizing these datasets shall clearly indicate their source. If the user has modified the data in any way, the user is obligated to describe the types of modifications the user has made. The user specifically agrees not to misrepresent these datasets, nor to imply that changes made by the user were approved by the State of Alaska, Department of Natural Resources, Division of Geological & Geophysical Surveys. The State of Alaska makes no express or implied warranties (including warranties for merchantability and fitness) with respect to the character, functions, or capabilities of the electronic data or products or their appropriateness for any user's purposes. In no event will the State of Alaska be liable for any incidental, indirect, special, consequential, or other damages suffered by the user or any other person or entity whether from the use of the electronic services or products or any failure thereof or otherwise. In no event will the State of Alaska's liability to the Requestor or anyone else exceed the fee paid for the electronic service or product.Alaska Division of Geological & Geophysical SurveysMetadata Managermailing and physical

3354 College Road

FairbanksAK99709-3707USA(907)451-5020(907)451-5050dggspubs@alaska.gov8 am to 4:30 pm, Monday through Friday, except State holidaysPlease view our website (https://www.dggs.alaska.gov) for the latest information on available data. Please contact us using the e-mail address provided above when possible.We want to thank Jacqueline Foss from the U.S. Forest Service for her helpful discussions about the data and the time she spent sharing her knowledge of debris flows in Southeast Alaska. We appreciate Brinnen Carter from the Sitka National Historical Park for his help with logistics and insights about debris flows in Sitka. Staff with the City and Borough of Sitka were extremely helpful in tracking down data and providing important input. We wish to acknowledge Cynthia McCoy from FEMA for her guidance during the completion of the project, as well as Dennis Staley at the USGS and Bill Burns at DOGAMI for reviews that helped to improve the quality of this product. We also thank Patricia Ekberg and Amy Macpherson for GIS and cartographic support. This project was funded by the FEMA Cooperating Technical Partners Program (grant number EMS-2016-CA-00006). Lidar data collection was partially funded by a Cooperative Agreement between the National Park Service and DGGS (grant number P17AC00903).Daanen, R.P.Wolken, G.J.Herbst, A.M.2020Lidar-derived elevation data for Sitka, AlaskaRaw Data FileRDF 2020-13Fairbanks, Alaska, United StatesAlaska Division of Geological & Geophysical Surveys11 phttps://doi.org/10.14509/30531Larsen, M.C.Nicolazzo, J.A.Athey, J.E.2023Landslide hazards in AlaskaInformation CircularIC 96Fairbanks, Alaska, United StatesAlaska Division of Geological & Geophysical Surveys2 phttps://doi.org/10.14509/31100The debris flow inventory, factor of safety, debris flow runout models, and integrated results maps were developed using the best available data; however, there are inherent limitations. The intended use of these data products is to help identify the relative debris flow risk in and around Sitka, provide a basis for regional planning and increased resiliency, and help identify localities where more detailed debris flow mapping is warranted. Maps are not intended for use at scales other than the published map data scale (1:20,000). The accompanying report provides a complete list of specific limitations and potential sources of error.Not applicableThis data release is complete.In order to produce bare-earth DEMs with sufficient ground point density for mapping debris flows, DGGS conducted a new lidar survey in May 2018 (Daanen and others, 2020). We processed the data in-house and produced ground-modeled surfaces. On steep, densely vegetated slopes, we were able to obtain ~3.5 ground-classified points per 1 m2, with approximately 100 non-ground classified points for every ground-classified point. Our resulting modeled ground surfaces proved excellent for identifying debris flow deposits in this complex terrain. We used lidar derived hillshade and slopeshade images along with 10-meter-interval contours derived from lidar DEMs, in combination with other available imagery, to identify debris flow deposits based on their geomorphic characteristics. The positional accuracy the debris flow inventory, factor of safety, debris flow runout models were developed using the best available data; however, there are inherent limitations to the calculated and interpreted feature extents. The accompanying report provides a complete discussion of specific limitations and potential sources of error.Debris Flow Inventory - To develop a comprehensive debris flow inventory, DGGS (1) collected and organized existing information about previously identified debris flows; (2) obtained remotely sensed data to evaluate the accuracy, extent, and location of identified debris flows; (3) acquired and processed high-resolution lidar (light detection and ranging) elevation data; (4) identified additional historical debris flow areas using lidar data; (5) compiled all debris flow information, with appropriate attribute information, into a geodatabase; and (6) generated a debris flow inventory map. Further detail and a more comprehensive reference list of data sources can be found in the accompanying report.2016Debris Flow Susceptible Slopes - DGGS developed a factor of safety map (FOS) for the Sitka area using methods modified from Burns and others (2012), who describe the protocol for shallow-debris flow susceptibility mapping used by the State of Oregon. DGGS classified geospatial soil data (primarily from the U.S. Department of Agriculture (USDA, 2018)) according to the parent material (geologic unit) and then used the associated geotechnical information for each soil unit to calculate a representative saturated soil density for each geologic unit. To be conservative and anticipate worst-case scenarios, we used the highest value of dry bulk density to calculate saturated bulk density. The debris flows in this area are shallow, so the depth to failure was estimated as the depth to bedrock using USDA data. To anticipate worst-case scenarios, and because debris flows often occur during heavy rain events, we assumed the groundwater depth ratio to be one (implying fully saturated conditions). DGGS developed a factor of safety map (FOS) for the Sitka area using methods modified from Burns and others (2012), who describe the protocol for shallow-debris flow susceptibility mapping used by the State of Oregon. DGGS classified geospatial soil data (primarily from the U.S. Department of Agriculture (USDA, 2018)) according to the parent material (geologic unit) and then used the associated geotechnical information for each soil unit to calculate a representative saturated soil density for each geologic unit. To be conservative and anticipate worst-case scenarios, we used the highest value of dry bulk density to calculate saturated bulk density. The debris flows in this area are shallow, so the depth to failure was estimated as the depth to bedrock using USDA data. To anticipate worst-case scenarios, and because debris flows often occur during heavy rain events, we assumed the groundwater depth ratio to be one (implying fully saturated conditions). Geotechnical properties were assumed constant for each geologic unit. However, slope varies, so FOS was calculated independently for ranges of slope within each geologic unit. To display the FOS results in map space, geologic parent material vector polygons derived from the USDA data were converted to raster format. Raster cell values were assigned based on the attribute value of the geologic parent material polygon at the center of each cell. A series of operations were then performed to create a new raster in which each 1 m (~3.3 ft) cell was assigned a FOS value dependent on the type of geologic material and the slope within that raster cell (obtained from lidar elevation data). Based on the work of Burns and others (2012), areas with FOS values greater than 1.5 are classified as having little to no debris flow susceptibility, FOS values from 1.25 to 1.5 are classified as having moderate debris flow susceptibility, and FOS values less than 1.25 are classified as having high debris flow susceptibility. our parameters are deliberately conservative, and site-specific investigations by qualified engineers are needed due to the generalized nature of our analysis. Further detail and a more comprehensive reference list of data sources can be found in the accompanying report.2016Simulating Debris Flow Runout - We used LaharZ, a computer model developed by Schilling (1998) for the U.S. Geological Survey, to simulate the behavior and forecast areas likely to be inundated by hypothetical future debris flow events. We used debris flow volumes ranging from 900 m3 to 48,000 m3 (31,781 ft3 to 1,695,104 ft3) for our modeled catchments. These volumes represent two historical debris flows that occurred in the Sitka area: the Silver Bay debris flow of 2015 and the Starrigavan debris flow of September 2014, respectively. For simulations of medium-size catchments, we used the 11,000 m3 (388,461 ft3) volume of the South Kramer debris flow as the upper volume limit. None of the catchments were scaled individually for debris volume, but catchment volume scaling was accomplished using three parameters: (1) catchment area, (2) catchment mean slope, and (3) catchment maximum range in elevation. The starting points of debris flows were chosen based on geomorphological evidence of debris accumulation along a drainage. In some areas where we simulated debris flows, we needed to hydro-flatten or hydro-enforce our DEM model to ensure the modeled debris flows moved in appropriate directions. In some cases, this required filling in man-made trenches or removing small edges on road embankments that would potentially curb water movement but not a debris flow. The next step in the process was to smooth the simulated debris flow extents to eliminate artifacts generated by small variations in the elevation model. We used the ArcGIS focal statistics and conditional tools to create our final debris flow zones. Further detail and a more comprehensive reference list of data sources can be found in the accompanying report.2016raster0.0000010.000001decimal degreesNAD83GRS 806378137298.257222101ri2024-2-watershed_size.shpA polygon shapefile outlining watershed areas used for debris flow runout simulations. These polygons represent debris contribution areas in modeled simulations.DGGSwatershed_sizeCatchSizeVolume-scaled catchment size. The values are based on catchment area, catchment mean slope, and catchment elevation range. For each of the three parameters, cutoffs were chosen to divide the catchments into two groups of roughly the same number. The cutoffs chosen were a 40,000 m2 (430,556 ft2) area, a 35-degree mean slope, and a 250 m (~820 ft) elevation range. Scores of one or zero were then assigned to catchments with greater and lesser values, respectively, of the parameter. The three individual scores for each catchment were then added to determine catchment size categories: "Large" (value=3), "Medium" (value =2), and "Small" (values 1 and zero).this reporttextri2024-2-landslide_inventory.shpFeature class containing polygons outlining the extent of landslide deposits identified in the 2018 high-resolution lidar data.DGGSlandslide_inventoryOuadLocatName of the USGS 1:63,360 in which the landslide deposit occurs.this reporttextDateMoveEstimated year of occurrence of mapped landslide deposits, if applicable. ("yyyy" format). The specific date is given when known. ("mm/dd/yyyy" format). "Unknown" is used when the specific date and year of occurrence are unknown.this reporttextFeatNameThe name of the landslide deposit classified according to it type of movement. "Kramer South" and "Kramer North". Blank if unknown.this reporttextMoveClassThe type of landslide deposit and movement. "Debris Flow" or "Complex-Rock Fall"this reporttextAgeCatThe estimated time when the landslide identified in the polygon occurred. "Historic (less than 50 yrs)" are landslides that occurred within the last 50 years. "Pre-Historic (greater than 50 yrs) are landslides that occurred more than 50 years ago. Estimates are based on historical information and/or imagery interpretation.this reporttextri2024-2-scenario1_modeled_debris_flow_runout.tifRaster image data depicting the modeled extent of debris flow runout zones simulated using LaharZ (Iverson and others, 1988; Schilling, 1998). Volumes for runout zone modeling were estimated based on known debris flows; the 2014 Starrigavan debris flow [(high volume; 11,000m3 (388,461ft3)], 2015 South Kramer debris flow [(medium volume; volume; 48,000m3 (1,695104ft3)], and 2015 Silver Baby debris flow [(low volume; 9,000m3 (31783 ft3)]. In this scenario debris flow runout is modeled using the best fitting parameters calibrated on the South Kramer debris flow.DGGSscenario1_modeled_debris_flow_runoutri2024-2-scenario1_modeled_debris_flow_runout.tifRaster image data depicting the modeled extent of debris flow runout zones simulated using LaharZ (Iverson and others, 1988; Schilling, 1998). Volumes for runout zone modeling were estimated based on known debris flows; the 2014 Starrigavan debris flow [(high volume; 48,000m3 (388,461ft3)], 2015 South Kramer debris flow [(medium volume; volume; 11,000m3 (1,695104ft3)], and 2015 Silver Baby debris flow [(low volume; 9,000m3 (31783 ft3)]. In this scenario debris flow runout is modeled using the best fitting parameters calibrated on the North Kramer debris flow.DGGSscenario2_modeled_debris_flow_runoutri2024-2-slope_susceptibility.tifRaster image data model of the relationship between shear forces acting to move material downslope and forces acting to resist downslope movement. This is a representation of the Factor of Safety (FOS).DGGSslope_susceptibilityAlaska Division of Geological & Geophysical SurveysMetadata Managermailing and physical

3354 College Road

FairbanksAK99709-3707USA(907)451-5020(907)451-5050dggspubs@alaska.gov8 am to 4:30 pm, Monday through Friday, except State holidaysFGDC Content Standard for Digital Geospatial MetadataFGDC-STD-001-1998If the user has modified the data in any way they are obligated to describe the types of modifications they have performed in the supporting metadata file. User specifically agrees not to imply that changes they made were approved by the Alaska Department of Natural Resources or Division of Geological & Geophysical Surveys.https://dggs.alaska.gov/metadata/dggs.extdggs metadata extensions