Jeffrey J. Duda Jill M. Hardiman 20231128 spreadsheet https://doi.org/10.5066/P9MKQ2UK We assessed habitat suitability for salmonids across selected tributaries upstream from three hydroelectric dams on the upper Skagit River in Whatcom County, northern Washington. We used NetMap, a commercial toolset within the ArcMap geographic information system (GIS), to analyze stream attributes based upon a synthetic stream channel network derived from digital elevation models. The GIS-derived stream attributes—including gradient, bankfull width, valley width index, elevation, and stream flow—allowed us to examine the spatial distribution and relative quality of spawning and rearing habitat for salmonids based on existing intrinsic potential (IP) models. As a first step, we created maps of potential anadromous fish distribution by identifying potential migration barriers within the synthetic stream network. Next, we applied a suite of existing IP models for steelhead, Coho, and Chinook Salmon (Oncorhynchus mykiss, O. kisutch, and O. tshawytscha, respectively) to estimate low, medium, and high IP habitat for each species. Three different IP models were used for each species, based on species preference curves from populations from coastal Oregon, northern California, Alaska, and western Washington. Data were collected to help assess the feasibility of anadromous salmon introductions upstream of three hydroelectric dams on the mainstem of the Skagit River. These summaries allow estimates of the amount, location, and potential quality of habitat in mainstrem and tributary habitats upstream of the dams. 20220101 ground condition Complete None planned Upper Skagit Watershed -121.2386 -120.8249 49.2239 48.6964 ISO 19115 Topic Category biota USGS Thesaurus habitat suitability indices USGS Metadata Identifier USGS:65651951d34e3aa43a43d03c Common geographic areas North Cascades National Park None Chinook salmon coho salmon steelhead Kingdom Animalia Subkingdom Bilateria Infrakingdom Deuterostomia Phylum Chordata Subphylum Vertebrata Infraphylum Gnathostomata Superclass Actinopterygii Class Teleostei Superorder Protacanthopterygii Order Salmoniformes Family Salmonidae Subfamily Salmoninae Genus Oncorhynchus Species Oncorhynchus tshawytscha TSN: 161980 Species Oncorhynchus kisutch TSN: 161977 Species Oncorhynchus mykiss TSN: 161989 None. Please see 'Distribution Info' for details. None. Users are advised to read the dataset's metadata thoroughly to understand appropriate use and data limitations. Jeffrey J. Duda mailing and physical

6505 NE 65th Street

Seattle WA 98115 206-526-2532 Seattle City Light No formal attribute accuracy tests were conducted No formal logical accuracy tests were conducted Data set is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully for additional details. No formal positional accuracy tests were conducted No formal positional accuracy tests were conducted Terrainworks 20230101 application/service Digital and/or Hardcopy 20220101 publication date NetMap Synthetic stream network and stream reach attribute data We used a synthetic watershed approach (that is, a watershed with stream segments composed within a GIS based on DEM data; Benda and others, 2016) to assess the potential distribution of anadromous salmon in the Skagit River Basin upstream from the dams. NetMap (Terrainworks, 2023) is a commercial software system available as an add-on analysis toolkit for ArcGIS (ESRI, 2023) for use in watershed analyses (Benda and others, 2007). At the start of the project, an existing NetMap model for the upper Skagit River watershed up to the Canadian border was based on a 10-m DEM. Terrainworks extended the model into the remaining watershed in Canada using a 20-m DEM, which was the highest resolution publicly available DEM. Terrainworks merged the two DEMs together to create a seamless DEM that could be packaged into NetMap as a dataset for further analysis and estimates of fish distribution and IP modeling. References: ESRI, 2023, ArcGIS Pro version 3.1, accessed April 14, 2023, at h ttps://www .esri.com/ en- us/ arcgis/ products/ arcgis- pro/ overview. Terrainworks, 2023, NetMap tools, accessed April 14, 2023, at https ://terrain works.com/ get- netmap- virtual- watersheds- and- tools. 20220101 As a first analysis step, we used the hydrographic representation of the upper Skagit River study area in NetMap to identify the upper extent of potential anadromous salmonid distribution in each tributary upstream from the dams. We did this by applying two criteria related to fish passage: changes to gradient that would limit anadromous fish passage, and the presence of natural waterfalls and other water surface drops that could serve as anadromous fish barriers. Definitions of both criteria were obtained from the anadromous fish passage assessment protocols for the State of Washington (Washington Department of Fish and Wildlife, 2019). The first criterion to assess the extent of potential habitat was a maximum of 20-percent gradient over a 160-m reach, which studies have shown to be impassable for upstream migrating salmonids (Washington Department of Fish and Wildlife, 2019). Although some studies have used lower gradient thresholds (such as, 16 percent over 300 m for Fraser River salmonids; Finn and others, 2021), we used the standard for upstream migration barriers as set by the State of Washington.The second criterion was waterfalls or geomorphic features with vertical drops greater than or equal to 3.7 m. We assessed such drops using the NetMap node-based stream layer, which represents the synthetic stream network at the grain of the DEM that is 10 m for reaches in the United States and 20 m for reaches in Canada. NetMap identifies a node drop value for a reach based on differences in elevation between adjacent nodes. Potential upstream distribution was stopped at nodes that equaled or exceeded 3.7 m. In most cases, because of elevation changes, several nodes would exceed the drop height threshold and be obvious areas of steep gradient and vertical waterfalls or cascades. We cross-referenced known barriers and waterfalls with the node drop values in NetMap as verification that the tool was accurately depicting known barriers. Because of the remote nature of the watershed and few existing on-the-ground surveys of potential barriers, most of the potential barriers stopping upstream anadromous fish distribution were identified only from the NetMap environment. However, there were a few cases where single or small clusters of adjacent nodes that exceeded the height threshold were surrounded by several hundred meters of habitat upstream and downstream from the impacted reach.To be conservative with identifying potential barriers, in these cases we by-passed such “spurious nodes,” which had drops ranging from 3.7 to 8.8 m, and assumed that fish passage was possible. There were only a few such cases in Canyon Creek (one node), Castle Fork Creek (six nodes in three reaches), Devils Creek (eight nodes in four reaches), Granite Creek (three nodes in two reaches), Lightning Creek (two nodes in one reach), McMillan Creek (two nodes in one reach), Nepopekum Creek (eight nodes in five reaches), and Snass Creek (eight nodes in five reaches). Given the above two decision rules, we identified the potential areas of anadromous fish distribution within GIS for use in IP modeling References: Finn, R.J.R., Chalifour, L., Gergel, S.E., Hinch, S.G., Scott, D.C., and Martin, T.G., 2021, Quantifying lost and inaccessible habitat for Pacific salmon in Canada’s lower Fraser River: Ecosphere, v. 12, no. 7, p. e03646. Washington Department of Fish and Wildlife, 2019, Fish passage inventory, assessment, and prioritization manual: Olympia, Washington, Washington Department of Fish and Wildlife, 284 p. [Also available at https://wdfw.wa.gov/ publications/ 02061.] 20220101 For the next step, we used several existing species-specific IP models to estimate the geomorphically based habitat suitability for Chinook Salmon, Coho Salmon, and steelhead. There were two approaches to calculate the IP depending on the model and species. The first approach was the original one described by Burnett and others (2007) where IP for each reach is calculated based upon species suitability curves for each of three habitat variables so that for each reach, habitat scores for the species would range from 1 (highest preference) to 0 (not suitable). An IP score based on the geometric mean from the three variables of interest (for example, gradient, valley confinement, and width) is calculated for each reach, ranging from 1 to 0. The second approach was a categorically based one, where low, medium, and high IP values were assigned to each reach according to ranges of values for the 3 variables of interest. For example, reaches with a bankfull width between 3.8 and 25 m, a gradient from 0 to 0.5 percent, and a moderate valley width ratio would receive a relative IP score of “medium.” To compare model-derived IP scores and maps across species, we assigned reaches from numerically derived models as low (score less than or equal to 0.25), medium (greater than 0.25 or less than or equal to 0.75), and high (greater than 0.75) IP. All calculations were made in GIS using the NetMap tool applied to the fish distribution described above. References: Burnett, K.M., Reeves, G.H., Miller, D.J., Clarke, S., Vance-Borland, K. and Christiansen, K., 2007, Distribution of salmon‐habitat potential relative to landscape characteristics and implications for conservation: Ecological Applications, v. 17, no. 1, p. 66-80. 20220101 Upper Skagit Intrinsic Potential results.csv Comma Separated Value (CSV) file containing data. Producer Defined ID A unique identifier code generated by NetMap software for each of the synthetic stream reaches in the hydrography Producer Defined 1262 69674 Stream_Name Name of stream Producer Defined None An unnamed stream that does not have an official geographic name Producer defined Names of streams Elevation_m Elevation above sea level in meters Producer Defined 267.45706 1582.46375 LENGTH_M Length of stream reach in meters Producer Defined 8.6176 226.76111 GRADIENT Difference in elevation from the start to the end of the reach as a percentage Producer Defined 0.0 0.19746 STRM_ORDER Reach stream size based on classification described in Strahler (1957). Strahler, A.N., 1957, Quantitative analysis of watershed geomorphology: EOS, Transactions of the American Geophysical Union, v. 38, no. 6, p. 913-920. Producer Defined 1 6 MEANANNCMS Average annual flow calculated for the reach Producer Defined 0.0002 20.64 WIDTH_M Width of stream reach in meters Producer Defined 0.1649 28.7047 DEPTH_M Average depth of stream reach in meters Producer Defined 0.0115 0.8034 VWI Valley Width Index as calculated by NetMap Producer Defined 0.239999995 273.2099915 IPChinookConnor Intrinsic Potential score for Chinook salmon based model described in report by Connor et al. (2015). Connor, E., Lowry, E., Smith, D., Ramsden, K., Barkdull, B., Warinner, B., Hartson, R., and Brocksmith, R., 2015, Tributary assessment for potential Chinook salmon rearing habitat and recommendations for prioritizing habitat protection and restoration: Skagit Watershed Council Report, accessed November 22, 2021, at https://www. skagitwatershed.org/ wp- content/ uploads/ Tier- 2- Tributary- composite- 2015- 02- 04_ final- draft- 2.pdf. Producer Defined High Intrinsic potential score greater than 0.75. Producer defined Medium Intrinsic potential score greater than or equal to 0.25 and less than or equal to 0.75. Producer defined Low Intrinsic potential score less than 0.25. Producer defined IPChinookBusch Intrinsic Potential score for Chinook salmon based model described in publication by Busch et al. (2013). Busch, D.S., Sheer, M., Burnett, K., McElhany, P., and Cooney, T., 2013, Landscape-level model to predict spawning habitat for lower Columbia River fall Chinook salmon (Oncorhynchus tshawytscha): River Research and Applications, v. 29, no. 3, p. 297–312. Producer Defined High Intrinsic potential score greater than 0.75. Producer defined Medium Intrinsic potential score greater than or equal to 0.25 and less than or equal to 0.75. Producer defined Low Intrinsic potential score less than 0.25. Producer defined IPChinookCnH Intrinsic Potential score for Chinook salmon based model described in report by Cooney and Holzer (2006). Cooney, T.D., and Holzer, D.M., 2006, Appendix C—Interior Columbia basin stream type Chinook salmon and steelhead populations—Habitat intrinsic potential analysis: Interior Columbia Basin Technical Recovery Team (ICTRT), Viability criteria review draft: Seattle, Washington, Northwest Fisheries Science Center, 17 p. Producer Defined Medium Intrinsic potential score ranked as medium Producer defined Low Intrinsic potential score ranked as low Producer defined High Intrinsic potential score ranked as high Producer defined IPCohoBurnett Intrinsic Potential score for Coho salmon based model described in publication by Burnett et al. (2007). Burnett, K.M., Reeves, G.H., Miller, D.J., Clarke, S., Vance-Borland, K., and Christiansen, K., 2007, Distribution of salmon-habitat potential relative to landscape characteristics and implications for conservation: Ecological Applications, v. 17, no. 1, p. 66–80. Producer Defined High Intrinsic potential score greater than 0.75. Producer defined Medium Intrinsic potential score greater than or equal to 0.25 and less than or equal to 0.75. Producer defined Low Intrinsic potential score less than 0.25. Producer defined IPCohoAgrawal Intrinsic Potential score for Coho salmon based model described in report by Cooney and Holzer (2006). Agrawal, A., Schick, R.S., Bjorkstedt, E.P., Szerlong, R.G., Goslin, M.N., Spence, B.C., Williams, T.H., and Burnett, K.M., 2005, Predicting the potential for historical coho, Chinook, and steelhead habitat in northern California: Santa Cruz, California, National Oceanic and Atmospheric Administration, Technical Memorandum NOAA-TM-NMFS-SWFSC-379, 34 p. Producer Defined High Intrinsic potential score greater than 0.75. Producer defined Medium Intrinsic potential score greater than or equal to 0.25 and less than or equal to 0.75. Producer defined Low Intrinsic potential score less than 0.25. Producer defined IPCohoRomey Intrinsic Potential score for Coho salmon based model available in NetMap software. Terrainworks, 2023, NetMap tools, accessed April 14, 2023, at https ://terrain works.com/ get- netmap- virtual- watersheds- and- tools. Producer Defined High Intrinsic potential score greater than 0.75. Producer defined Medium Intrinsic potential score greater than or equal to 0.25 and less than or equal to 0.75. Producer defined Low Intrinsic potential score less than 0.25. Producer defined IPSteelheadTRT Intrinsic Potential score for steelhead based model described in report by Steelhead Technical Recovery Team. Hard, J.J., Myers, J.M., Connor, E.J., Hayman, R.A., Kope, R.G., Lucchetti, G., Marshall, A.R., Pess, G.R., and Thompson, B.E., 2015, Viability criteria for steelhead within the Puget Sound distinct population segment: National Oceanic and Atmospheric Administration Technical Memorandum NMFS-NWFSC-129, 367 p. Producer Defined Low Intrinsic potential score ranked as low Producer defined Medium Intrinsic potential score ranked as medium Producer defined High Intrinsic potential score ranked as high Producer defined IPSteelheadBurnett Intrinsic Potential score for steelhead based model described in publication by Burnett et al. (2007). Burnett, K.M., Reeves, G.H., Miller, D.J., Clarke, S., Vance-Borland, K., and Christiansen, K., 2007, Distribution of salmon-habitat potential relative to landscape characteristics and implications for conservation: Ecological Applications, v. 17, no. 1, p. 66–80. Producer Defined Medium Intrinsic potential score greater than or equal to 0.25 and less than or equal to 0.75. Producer defined High Intrinsic potential score greater than 0.75. Producer defined Low Intrinsic potential score less than 0.25. Producer defined IPSteelheadAgrawal Intrinsic Potential score for steelhead based model described in report by Cooney and Holzer (2006). Agrawal, A., Schick, R.S., Bjorkstedt, E.P., Szerlong, R.G., Goslin, M.N., Spence, B.C., Williams, T.H., and Burnett, K.M., 2005, Predicting the potential for historical coho, Chinook, and steelhead habitat in northern California: Santa Cruz, California, National Oceanic and Atmospheric Administration, Technical Memorandum NOAA-TM-NMFS-SWFSC-379, 34 p. Producer Defined Medium Intrinsic potential score greater than or equal to 0.25 and less than or equal to 0.75. Producer defined High Intrinsic potential score greater than 0.75. Producer defined Low Intrinsic potential score less than 0.25. Producer defined We assessed habitat suitability for salmonids across selected tributaries upstream from three hydroelectric dams on the upper Skagit River in Whatcom County, northern Washington. We used NetMap, a commercial toolset within the ArcMap geographic information system (GIS), to analyze stream attributes based upon a synthetic stream channel network derived from digital elevation models. The GIS-derived stream attributes—including gradient, bankfull width, valley width index, elevation, and stream flow—allowed us to examine the spatial distribution and relative quality of spawning and rearing habitat for salmonids based on existing intrinsic potential (IP) models. As a first step, we created maps of potential anadromous fish distribution by identifying potential migration barriers within the synthetic stream network. Next, we applied a suite of existing IP models for steelhead, coho, and Chinook salmon (Oncorhynchus mykiss, O. kisutch, and O. tshawytscha, respectively) to estimate low, medium, and high IP habitat for each species. Three different IP models were used for each species, based on species preference curves from populations from coastal Oregon, northern California, Alaska, and western Washington. Duda, J.J., and Hardiman, J.M., 2023, Applying intrinsic potential models to evaluate salmon (Oncorhynchus spp.) introduction into main-stem and tributary habitats upstream from the Skagit River Hydroelectric Project, northern Washington: U.S. Geological Survey Open-File Report 2023–1077, 44 p., https://doi.org/ 10.3133/ ofr20231077. GS ScienceBase U.S. Geological Survey mailing address

Denver Federal Center, Building 810, Mail Stop 302

Denver CO 80225 United States 1-888-275-8747 sciencebase@usgs.gov 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. Digital Data https://doi.org/10.5066/P9MKQ2UK None 20231128 Jeff Duda U.S. Geological Survey, NW-PACIFIC ISLAND REG RESEARCH ECOLOGIST mailing address

6505 NE 65Th Street

Seattle WA 98115 US 206-526-2532 206-526-6654 jduda@usgs.gov FGDC Biological Data Profile of the Content Standard for Digital Geospatial Metadata FGDC-STD-001.1-1999