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                              <gco:Date>2020-01-01</gco:Date>
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                                 <gmd:country>
                                    <gco:CharacterString>USA</gco:CharacterString>
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                  <gco:CharacterString>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.</gco:CharacterString>
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                  <gco:CharacterString>This lineage was generated by the FGDC CSDGM to 19139 transformation</gco:CharacterString>
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                     <gmd:description>
                        <gco:CharacterString>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.</gco:CharacterString>
                     </gmd:description>
                     <gmd:dateTime>
                        <gco:DateTime>2016-01-01T00:00:00</gco:DateTime>
                     </gmd:dateTime>
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               </gmd:processStep>
               <gmd:processStep>
                  <gmd:LI_ProcessStep>
                     <gmd:description>
                        <gco:CharacterString>Debris 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.</gco:CharacterString>
                     </gmd:description>
                     <gmd:dateTime>
                        <gco:DateTime>2016-01-01T00:00:00</gco:DateTime>
                     </gmd:dateTime>
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               </gmd:processStep>
               <gmd:processStep>
                  <gmd:LI_ProcessStep>
                     <gmd:description>
                        <gco:CharacterString>Simulating 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.</gco:CharacterString>
                     </gmd:description>
                     <gmd:dateTime>
                        <gco:DateTime>2016-01-01T00:00:00</gco:DateTime>
                     </gmd:dateTime>
                  </gmd:LI_ProcessStep>
               </gmd:processStep>
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         <gmd:otherConstraints>
            <gco:CharacterString>Metadata Use Constraints: If 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 &amp; Geophysical Surveys.</gco:CharacterString>
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            <gco:CharacterString> This metadata was automatically generated from the FGDC Content Standard for Digital Geospatial Metadata standard (version FGDC-STD-001-1998) using the 2019-08-12T11:11:00 version of the FGDC CSDGM to ISO 19139 transform.</gco:CharacterString>
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                  <gco:CharacterString>Simone Montayne</gco:CharacterString>
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                  <gco:CharacterString>Alaska Division of Geological &amp; Geophysical Surveys</gco:CharacterString>
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                              <gco:CharacterString>(907)451-5020</gco:CharacterString>
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                              <gco:CharacterString>3354 College Road</gco:CharacterString>
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