Hopkins, Anders L. Jaenicke, Margaret E. Bozimowski, Alexandra (Sasha) A. Kowalski, Kurt P. Sanocki, Chris A. 2024 raster digital data Reston, VA U.S. Geological Survey Hopkins, A.L., Jaenicke, M.E., Bozimowski, A.A., Kowalski, K.P., and Sanocki, C.A., 2024, Connecting River Systems Restoration Assessment, Index Composite (version 2.0): U.S. Geological Survey data release, https://doi.org/10.5066/P9SD4LHS. https://doi.org/10.5066/P9SD4LHS U.S. Geological Survey 2016 vector and raster digital data Reston, VA U.S. Geological Survey https://doi.org/10.5066/P9UAA4HY These datasets are part of the U.S. Geological Survey (USGS) Great Lakes Coastal Wetland Restoration Assessment (GLCWRA) initiative and were used to assess the restorability of areas that historically supported coastal wetlands along the rivers and lake connecting Lake Huron to Lake Erie. The GLCWRA initiative uses principles of geodesign to identify coastal wetland areas that have the greatest potential for habitat restoration. The data model uses the following seven primary parameters to identify and rank wetland restoration areas. The parameters are: Parameter 0: Water, 10-meter Parameter 1: Hydroperiod, 10-meter Parameter 2: Wetland Soils, 10-meter Parameter 3: Flowlines, 10-meter Parameter 4: Conservation and Recreation Lands, 10-meter Parameter 5: Impervious Surfaces, 30-meter Parameter 6: Land Use, 30-meter The resulting composite index raster can be used by ecological managers and planners to assist with the identification and selection of wetland for restoration initiatives. For more information on these parameters, see the full data release documentation and the GLCWRA webpage: https://glcwra.wim.usgs.gov/. The purpose of the 10-meter Index Composite gridded dataset is to highlight coastal areas where the potential for wetland restoration is highest. This analysis is intended to be a decision making aid for land managers and restoration practitioners, with the hope that coastal wetland restoration projects be more efficient, less costly, and more successful. The purpose for releasing version 2.0 of these datasets is that they were created with more recent and more accurate input data. For changes between versions 1.0 and 2.0, please see the Release History text file. 2024 publication date Complete None planned -83.3918 -82.3643 43.2349 42.0117 ISO 19115 Topic Category environment geoscientificInformation inlandWaters Marine Realms Information Bank (MRIB) keywords lake lakeshore wetland wetland restoration stream river hydrology resource management USGS Thesaurus natural resource management water quality remediation wetland ecosystems Holocene Common geographic areas Great Lakes Lake Huron Lake Erie Lake St. Clair St. Clair St. Clair-Detroit 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. Kurt P Kowalski U.S. Geological Survey, MIDCONTINENT REGION Research Ecologist mailing address

1451 Green Rd

Ann Arbor MI 48105 US 734-214-9308 734-994-8780 kkowalski@usgs.gov The GLCWRA was produced with funding provided by the Great Lakes Restoration Initiative (GLRI) and in collaboration with the New College of Florida. Digital datasets obtained from public repositories and datasets including the National Hydrography Dataset (NHD), National Wetland Index (NWI), 3D Elevation Program (3DEP), U.S. Census Bureau, National Geospatial Program, U.S. Fish and Wildlife Service, National Oceanic and Atmospheric Association (NOAA), Protected Areas Database of the U.S. (PAD-US), National Land Cover Database (NLDC), Gridded Soil Survey Geographic Database (gSSURGO), Coastal Change Analysis Program (C-CAP) U.S. Geological Survey 2016 application/service Geonarrative map website for GLCWRA study https://glcwra.wim.usgs.gov/ The attribute values of this dataset were inspected and confirmed to be within the proper range of values. The attribute values were reviewed, and spatial data was visually inspected to verify the that the data values and their locations accurately describe the purpose of the data. This dataset is considered complete for the information presented, as described in the abstract. This dataset provides coverage for the entire Connecting River Systems Restoration Assessment study area. 100% of the spatial data was visually inspected to verify the positional accuracy of its features. The review consisted of comparing the spatial data to its input datasets and to underlying GIS basemaps. While this data has been reviewed, the spatial data is only as spatially accurate as the input data sources. This dataset does not have a vertical component, so testing for vertical accuracy was not necessary. U.S. Geological Survey 2022 raster digital data Tiles: USGS_1M_17_x26y463_OH_Statewide_Phase1_2019_B19.tif, USGS_one_meter_x26y463_MI_31Co_Lenawee_2017.tif, USGS_one_meter_x26y463_OH_LowerMaumee_B16_2016.tif, USGS_one_meter_x26y464_MI_31Co_Lenawee_2017.tif, USGS_one_meter_x26y465_MI_31Co_Lenawee_2017.tif, USGS_one_meter_x26y466_MI_31Co_Lenawee_2017.tif, USGS_one_meter_x26y467_MI_31Co_Lenawee_2017.tif, USGS_one_meter_x26y467_MI_31Co_Washtenaw_2016.tif, USGS_one_meter_x27y463_MI_31Co_Lenawee_2017.tif, USGS_one_meter_x27y463_MI_31Co_Monroe_2016.tif, USGS_one_meter_x27y463_OH_LowerMaumee_B16_2016.tif, USGS_one_meter_x27y464_MI_31Co_Lenawee_2017.tif, USGS_one_meter_x27y464_MI_31Co_Monroe_2016.tif, USGS_one_meter_x27y465_MI_31Co_Lenawee_2017.tif, USGS_one_meter_x27y465_MI_31Co_Monroe_2016.tif, USGS_one_meter_x27y466_MI_31Co_Lenawee_2017.tif, USGS_one_meter_x27y466_MI_31Co_Monroe_2016.tif, USGS_one_meter_x27y467_MI_31Co_Lenawee_2017.tif, USGS_one_meter_x27y467_MI_31Co_Monroe_2016.tif, 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USGS_one_meter_x34y476_MI_31Co_StClair_2016.tif, USGS_one_meter_x34y477_MI_31Co_StClair_2016.tif, USGS_one_meter_x34y478_MI_31Co_StClair_2016.tif, USGS_one_meter_x34y479_MI_31Co_Sanilac_2017.tif, USGS_one_meter_x34y479_MI_31Co_StClair_2016.tif, USGS_one_meter_x34y480_MI_31Co_Sanilac_2017.tif, USGS_one_meter_x35y472_MI_31Co_Macomb_2016.tif, USGS_one_meter_x35y472_MI_31Co_StClair_2016.tif, USGS_one_meter_x35y473_MI_31Co_Macomb_2016.tif, USGS_one_meter_x35y473_MI_31Co_StClair_2016.tif, USGS_one_meter_x35y474_MI_31Co_Macomb_2016.tif, USGS_one_meter_x35y474_MI_31Co_StClair_2016.tif, USGS_one_meter_x35y475_MI_31Co_Macomb_2016.tif, USGS_one_meter_x35y475_MI_31Co_StClair_2016.tif, USGS_one_meter_x35y476_MI_31Co_Macomb_2016.tif, USGS_one_meter_x35y476_MI_31Co_StClair_2016.tif, USGS_one_meter_x35y477_MI_31Co_StClair_2016.tif, USGS_one_meter_x35y478_MI_31Co_StClair_2016.tif, USGS_one_meter_x35y479_MI_31Co_Sanilac_2017.tif, USGS_one_meter_x35y479_MI_31Co_StClair_2016.tif, USGS_one_meter_x35y480_MI_31Co_Sanilac_2017.tif, USGS_one_meter_x36y471_MI_31Co_StClair_2016.tif, USGS_one_meter_x36y472_MI_31Co_StClair_2016.tif, USGS_one_meter_x36y473_MI_31Co_Macomb_2016.tif, USGS_one_meter_x36y473_MI_31Co_StClair_2016.tif, USGS_one_meter_x36y474_MI_31Co_StClair_2016.tif, USGS_one_meter_x36y475_MI_31Co_StClair_2016.tif, USGS_one_meter_x36y476_MI_31Co_StClair_2016.tif, USGS_one_meter_x36y477_MI_31Co_StClair_2016.tif, USGS_one_meter_x36y478_MI_31Co_StClair_2016.tif, USGS_one_meter_x36y479_MI_31Co_Sanilac_2017.tif, USGS_one_meter_x36y479_MI_31Co_StClair_2016.tif, USGS_one_meter_x36y480_MI_31Co_Sanilac_2017.tif, USGS_one_meter_x37y472_MI_31Co_StClair_2016.tif, USGS_one_meter_x37y473_MI_31Co_StClair_2016.tif, USGS_one_meter_x37y474_MI_31Co_StClair_2016.tif, USGS_one_meter_x37y475_MI_31Co_StClair_2016.tif, USGS_one_meter_x37y476_MI_31Co_StClair_2016.tif, USGS_one_meter_x37y477_MI_31Co_StClair_2016.tif, USGS_one_meter_x37y478_MI_31Co_StClair_2016.tif, USGS_one_meter_x37y479_MI_31Co_Sanilac_2017.tif, USGS_one_meter_x37y479_MI_31Co_StClair_2016.tif, USGS_one_meter_x37y480_MI_31Co_Sanilac_2017.tif, USGS_one_meter_x38y474_MI_31Co_StClair_2016.tif, USGS_one_meter_x38y475_MI_31Co_StClair_2016.tif, USGS_one_meter_x38y476_MI_31Co_StClair_2016.tif, USGS_one_meter_x38y477_MI_31Co_StClair_2016.tif, USGS_one_meter_x38y478_MI_31Co_StClair_2016.tif https://www.sciencebase.gov/catalog/item/543e6b86e4b0fd76af69cf4c Digital 2015 2019 observed 1 meter Digital Elevation Model The DEM tiles of the source data were mosaiced together to create a continuous raster providing full coverage for the study area and the surrounding areas. U.S. Fish and Wildlife Service 20230501 vector digital data https://www.fws.gov/program/national-wetlands-inventory/download-state-wetlands-data 24000 Digital 19770101 20230501 publication date NWI Identified the extent, approximate location and type of wetlands in the CRSRA Study Area. U.S. Geological Survey 20200819 vector digital data http://nhd.usgs.gov/ 24000 Digital 20160630 observed NHD 0408 The NHD were used to identify functioning wetlands within the study area. U.S. Geological Survey 20200825 vector digital data http://nhd.usgs.gov/ 24000 Digital 20160630 observed NHD 0409 The NHD were used to identify functioning wetlands within the study area. U.S. Geological Survey 20200819 vector digital data http://nhd.usgs.gov/ Digital 20160630 observed NHD 0410 The NHD were used to identify functioning wetlands within the study area. National Oceanic and Atmospheric Administration (NOAA) 20230801 tabular digital data Hourly water level data collected from 1990-01-01 through 2020-12-31 https://tidesandcurrents.noaa.gov/stationhome.html?id=9075002 Digital 19900101 20201231 ground condition NOAA Water Levels Lakeport, MI These water level data were used to create the study area and the hydroperiod layers. National Oceanic and Atmospheric Administration (NOAA) 20230801 tabular digital data Hourly water level data collected from 1990-01-01 through 2020-12-31 https://tidesandcurrents.noaa.gov/stationhome.html?id=9014090 Digital 19900101 20201231 ground condition NOAA Water Levels Mouth of the Black River, MI These water level data were used to create the hydroperiod layer. National Oceanic and Atmospheric Administration (NOAA) 20230801 tabular digital data Hourly water level data collected from 1990-01-01 through 2020-12-31 https://tidesandcurrents.noaa.gov/stationhome.html?id=9014080 Digital 19900101 20201231 ground condition NOAA Water Levels St. Clair State Police, MI These water level data were used to create the hydroperiod layer. National Oceanic and Atmospheric Administration (NOAA) 20230801 tabular digital data Hourly water level data collected from 1990-01-01 through 2020-12-31 https://tidesandcurrents.noaa.gov/stationhome.html?id=9014070 Digital 19900101 20201231 ground condition NOAA Water Levels Algonac, MI These water level data were used to create the hydroperiod layer. National Oceanic and Atmospheric Administration (NOAA) 20230801 tabular digital data Hourly water level data collected from 1990-01-01 through 2020-12-31 https://tidesandcurrents.noaa.gov/stationhome.html?id=9034052 Digital 19900101 20201231 ground condition NOAA Water Levels St. Clair Shores, MI These water level data were used to create the hydroperiod layer. National Oceanic and Atmospheric Administration (NOAA) 20230801 tabular digital data Hourly water level data collected from 1990-01-01 through 2020-12-31 https://tidesandcurrents.noaa.gov/stationhome.html?id=9044049 Digital 19900101 20201231 ground condition NOAA Water Levels Windmill Point, MI These water level data were used to create the hydroperiod layer. National Oceanic and Atmospheric Administration (NOAA) 20230801 tabular digital data Hourly water level data collected from 1990-01-01 through 2020-12-31 https://tidesandcurrents.noaa.gov/map/index.html?id=9044030 Digital 19900101 20201231 ground condition NOAA Water Levels Wyandotte, MI These water level data were used to create the hydroperiod layer. National Oceanic and Atmospheric Administration (NOAA) 20230801 tabular digital data Hourly water level data collected from 1990-01-01 through 2020-12-31 https://tidesandcurrents.noaa.gov/stationhome.html?id=9044020 Digital 19900101 20201231 ground condition NOAA Water Levels Gibraltar, MI These water level data were used to create the hydroperiod layer. Army Corps of Engineers Calappi, Tim 2023 vector digital data The two step plane shapefiles were provided by Tim Calappi of the USACE via personal communication in August of 2023. Digital 2020 observed Step Planes The step planes helped to determine where to divide up the study area into different analytical zones. USDA National Resource Conservation Service 20220907 vector digital data Soil Survey Staff. Gridded Soil Survey Geographic (gSSURGO) Database for Michigan. United States Department of Agriculture, Natural Resources Conservation Service. Available online at http://datagateway.nrcs.usda.gov/. 20220907 (202210 official release). https://nrcs.app.box.com/v/soils/ Digital 20220907 publication date SSURGO Identified soils with wetland properties in the CRSRA Study Area. NOAA Office for Coastal Management 20130521 raster digital data Office for Coastal Management, 2023: NOAA's Coastal Change Analysis Program (C-CAP) 2016 Regional Land Cover Data - Coastal United States, https://www.fisheries.noaa.gov/inport/item/48336. https://coast.noaa.gov/dataviewer/#/landcover/search/-9330820.916828036,5109458.609850191,-9127804.169702608,5385039.588268601 Digital 2013 observed C-CAP The NLCD dataset was used to identify land cover types. U.S. Geological Survey (USGS) Gap Analysis Project (GAP) 20220131 vector digital data withheld withheld withheld Digital 2005 2021 observed PADUS This dataset provided the locations and layouts of publicly own and protected land. U.S. Geological Survey Jon Dewitz 20230630 raster digital data https://doi.org/10.5066/P9JZ7AO3 https://www.mrlc.gov/data https://www.mrlc.gov/data-services-page Digital 2001 2021 observed NLCD Identified the areas with impervious surfaces within the study area. International Boundary Commission 20180420 vector digital data https://www.internationalboundarycommission.org/en/maps-coordinates/coordinates.php Digital 2018 publication date US Canada Border This shapefile of the international boundary between the US and Canada was used to shape the eastern edge of the study area. The Index Composite was created by staff at the USGS Upper Midwest Water Science Center in the following manner: The seven parameters (Parameters 0 - 6: Parameter 0: Mask, Parameter 1: Hydroperiod, Parameter 2: Wetland Soils, Parameter 3: Flowlines, Parameter 4: Conservation and Recreation Lands, Parameter 5: Impervious Surfaces, and Parameter 6: Land Use) were combined through a Python script to produce the raw Index Composite. This script treats each parameter like a variable in an equation, and by performing the equation, the Index Composite in the form of a new raster is calculated. 20231016 The raw Index Composite was then generalized using several ArcGIS Pro tools. First, the raster was reclassified so that any values of 1 or greater became 1, and any values of 0 or less became zero. Next, the tools "Shrink" (Number of Cells = 1, Zone values = 1, Shrink Method = Morphological) and "Expand" (Number of Cells = 1, Zone values = 1, Expand Method = Morphological) were run on this binary raster which represents the footprint of the positive Index Composite values. Finally, the shrunk and expanded binary raster was multiplied by the raw Index Composite to create the final output raster. The purpose of this generalization method is to clean up some of the scattered and isolated composite values without changing any of the cells actual values. 20231016 Lastly, the Index Composite was visually inspected by USGS ecologists from the Great Lakes Science Center to verify the accuracy of the index composite values. These specialists compared the cells values to satellite imagery in ArcGIS Pro's basemaps as well as Google Maps. Where areas of false positive cell values were found, these index composite cell values were changed to 0. The purpose of this expert visual inspection was to ensure that there were no areas erroneously labelled as highly restorable. 20231020 Raster Grid Cell 13411 8132 1 Universal Transverse Mercator 17 0.9996 -81.0 0.0 500000.0 0.0 row and column 10.0 10.0 meters North_American_Datum_1983 GRS 1980 6378137.0 298.2572221010042 CRSRA_IndexComposite.tif A raster dataset (in geoTIFF format) illustrating the restorability of coastal wetlands within the CRSRA study area. Producer Defined Value Values in this raster represent a metric of restorability of coastal wetlands along the shoreline of the Great Lakes. High values mean a higher change of restorability and lower values represent a lesser change of restorability. Producer Defined -128 Outside of the study area. Producer defined 0 100 CRSRA_IndexComposite.zip contains a TIF raster image (CRSRA_IndexComposite.tif) and associated metadata (CRSRA_IndexComposite.tif.xml). NA 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 on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. Digital Data https://doi.org/10.5066/P9SD4LHS None 20240515 Kurt P Kowalski U.S. Geological Survey, MIDCONTINENT REGION Research Ecologist mailing address

1451 Green Rd

Ann Arbor MI 48105 US 734-214-9308 734-994-8780 kkowalski@usgs.gov FGDC Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998 20250801 10490500 TRUE