Jaenicke, Margaret E. Hopkins, Anders L. Kowalski, Kurt P. Bozimowski, Alexandra A. Sanocki, Chris A. 202406 raster digital data Reston, VA U.S. Geological Survey https://doi.org/10.5066/P9OS796H U. S. Geological Survey 2016 vector and raster digital data Reston, VA U. S. Geological Survey https://doi.org/10.5066/P9UAA4HY The U.S. Geological Survey (USGS) created the Index Composite data was the final result of the Western Lake Erie Restoration Assessment (WLERA). This dataset represents the restorability of wetland habitats along the western shoreline of Lake Erie. This study is a part of the Great Lakes Coastal Wetland Restoration Assessment (GLCWRA) initiative in which coastal wetlands around the Great Lakes were identified and ranked for potential wetland restoration. The WLERA Index Composite was created by an algorithm which takes in 7, 10-meter raster input datasets (Parameters 0-6) and evaluates an area's restorability on a per-pixel basis. Each parameter represents an land surface quality that either contributes to or detracts from that area's potential for wetland restoration. Please refer to the process steps and https://glcwra.wim.usgs.gov/ for further explanation on the methods. Version 2.0 of this dataset was created with more recently released input data. For information on the changes between versions 1.0 and 2.0, please see the Release History text file. The purpose of the Index Composite is to highlight coastal areas where the potential for wetland restoration is highest. This analysis is intended to be a decision-making tool for land managers and restoration practitioners, with the hope that coastal wetland restoration projects be more efficient, less costly, and more successful. The datasets in WLERA Composite 2024 (Jaenicke et al., 2024) were created with more recently published datasets than the WLERA Composite 2016 release (Saarinen, 2016). The foremost among these is the digital elevation model (DEM) which was used to create the study area boundary, the hydroperiod, the dikes and the index composite layers. The elevation data was created from 3DEP DEM tiles published between 2015 and 2019. Updated versions of the following datasets were also used in the creation of WLERA Composite 2024: National Wetland Inventory (NWI) data published in 2023, National Oceanic and Atmospheric Association (NOAA) water level measurements from 1993-2023, Soil Survey Geographic Database (SSURGO) data published in 2022, National Hydrology Dataset (NHD) published in 2023, Protected Areas Database (PAD-US) data published in 2022 and National Land Cover Database (NLCD) data published in 2021. The methodology of the project has largely stayed the same between the WLERA Composite 2016 and WLERA Composite 2024 data release. The notable exception is that there were additional steps used to create the index composite. After the index composite was created using the input parameters and the composite model, a boundary cleaning routine was used to reduce the amount of data noise or lone grid cells present within the index composite. The index composite layer was also reviewed by USGS ecologists after the model was executed and before the data were published, to ensure the accuracy of the results. Based on this review, index composite values returned from the model that did not highlight restorable wetlands were removed from the final dataset. 202406 publication date Complete None planned -83.7447 -82.0833 42.1282 41.2615 ISO 19115 Topic Category environment geoscientificInformation inlandWaters Marine Realms Information Bank (MRIB) keywords wetland wetland restoration hydrology resource management water quality lake lakeshore stream river USGS Thesaurus hydrology natural resource management water quality remediation wetland ecosystems Common geographic areas Great Lakes Ohio Michigan Lake Erie Western Lake Erie 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), Transportation Investment Generating Economic Recovery (TIGER) dataset U.S. Geological Survey 2016 application/service Geonarrative map website for GLCWRA study https://glcwra.wim.usgs.gov/ Kurt P Kowalski Justin A. Saarinen 2016 dataset https://www.sciencebase.gov U.S. Geological Survey Previous release of data. See "Supplemental Information" for more context. https://doi.org/10.5066/f74t6ghx 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 Western Lake Erie 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. ESRI 20210405 remote-sensing image https://services.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer Digital 20210405 publication date ESRI World Imagery Satellite imagery from ESRI was used to select functioning wetlands from the NWI dataset. Google Maps 2023 remote-sensing image https://www.google.com/maps/@41.7410276,-82.990953,134833m/data=!3m1!1e3?entry=ttu Digital 2023 publication date Google Satellite Imagery Satellite imagery from Google Maps was used to select functioning wetlands from the NWI dataset. These dataset were used during the month of October 2023. NOAA Tides and Currents 20230324 tabular digital data Hourly data: 3/24/1993 - 8/31/1996 6-min data: 9/1/1996 - 3/24/2023 https://tidesandcurrents.noaa.gov/waterlevels.html?id=9063085&type=Tide+Data&name=Toledo&state=OH Digital 19930324 20230324 ground condition NOAA Water Levels These data were used to reclassify the DEM and create the hydroperiod. U.S. Geological Survey 2011 tabular digital data ned19_n41x25_w082x25_oh_north_2006 ned19_n41x25_w082x50_oh_north_2006 ned19_n41x25_w082x75_oh_north_2006 ned19_n41x25_w083x00_oh_north_2006 ned19_n41x25_w083x25_oh_north_2006 ned19_n41x25_w083x50_oh_north_2006 ned19_n41x25_w083x75_oh_north_2006 ned19_n41x25_w084x00_oh_north_2006 ned19_n41x25_w084x25_oh_north_2006 ned19_n41x25_w084x50_oh_north_2006 ned19_n41x25_w084x75_oh_north_2006 ned19_n41x50_w082x00_oh_north_2006 ned19_n41x50_w082x25_oh_north_2006 ned19_n41x50_w082x50_oh_north_2006 ned19_n41x50_w082x75_oh_north_2006 ned19_n41x50_w083x00_oh_north_2006 ned19_n41x50_w083x25_oh_north_2006 ned19_n41x50_w083x50_oh_north_2006 ned19_n41x50_w083x75_oh_north_2006 ned19_n41x50_w084x00_oh_north_2006 ned19_n41x50_w084x25_oh_north_2006 ned19_n41x50_w084x50_oh_north_2006 ned19_n41x75_w082x00_oh_north_2006 ned19_n41x75_w082x25_oh_north_2006 ned19_n41x75_w082x75_oh_north_2006 ned19_n41x75_w083x00_oh_north_2006 ned19_n41x75_w083x25_oh_north_2006 ned19_n41x75_w083x50_mi_monroeco_2009 ned19_n41x75_w083x50_oh_north_2006 ned19_n41x75_w083x75_mi_monroeco_2009 ned19_n41x75_w083x75_oh_north_2006 ned19_n41x75_w084x00_mi_3southerncounties_2010 ned19_n41x75_w084x00_mi_monroeco_2009 ned19_n41x75_w084x00_oh_north_2006 ned19_n41x75_w084x25_mi_3southerncounties_2010 ned19_n41x75_w084x25_oh_north_2006 ned19_n41x75_w084x50_mi_3southerncounties_2010 ned19_n41x75_w084x50_oh_north_2006 ned19_n42x00_w083x25_mi_monroeco_2009 ned19_n42x00_w083x50_mi_monroeco_2009 ned19_n42x00_w083x75_mi_3southerncounties_2010 ned19_n42x00_w083x75_mi_monroeco_2009 ned19_n42x00_w084x00_mi_3southerncounties_2010 ned19_n42x00_w084x00_mi_monroeco_2009 ned19_n42x25_w083x25_mi_monroeco_2009 ned19_n42x25_w083x25_mi_wayneco_2009 ned19_n42x25_w083x50_mi_monroeco_2009 ned19_n42x25_w083x50_mi_wayneco_2009 ned19_n42x25_w083x75_mi_monroeco_2009 ned19_n42x25_w083x75_mi_washtenawco_2009 ned19_n42x25_w083x75_mi_wayneco_2009 ned19_n42x25_w084x00_mi_3southerncounties_2010 ned19_n42x25_w084x00_mi_monroeco_2009 ned19_n42x25_w084x00_mi_washtenawco_2009 ned19_n42x25_w084x25_mi_3southerncounties_2010 ned19_n42x25_w084x25_mi_washtenawco_2009 ned19_n42x25_w084x50_mi_3southerncounties_2010 Digital 2005 2010 observed WLERA DEM The DEM data provided an elevation raster from which the hydroperiod dataset was created. United States Fish and Wildlife Service 20230501 vector digital data https://www.fws.gov/program/national-wetlands-inventory/data-download 24000 Digital 20230501 publication date MI NWI Identified coastal wetlands habitats. United States Fish and Wildlife Service 20230501 vector digital data https://www.fws.gov/program/national-wetlands-inventory/data-download 24000 Digital 20230501 publication date OH NWI Identified coastal wetlands habitats. National Oceanic and Atmospheric Administration (NOAA) 2016 raster digital data The CCAP data was downloaded in UTM Zone 17N coordinate reference system. This was done by using the 'Custom Download Method' from the website listed above. https://coast.noaa.gov/digitalcoast/data/ccapregional.html Digital 2016 publication date C-CAP Identified land use types. United States Department of Agriculture (USDA) Natural Resources Conservation Service 20220829 raster digital data Counties used: Monroe County, MI (MI115) Erie, OH (OH043) Lorain, OH (OH093) Lucas, OH (OH095) Ottawa, OH (OH123) Sandusky, OH (OH143) Wood, OH (OH173) https://websoilsurvey.sc.egov.usda.gov/App/WebSoilSurvey.aspx https://websoilsurvey.sc.egov.usda.gov/ Digital 20220829 publication date SSURGO Identified the soil types of the assessment area. Data was downloaded by county from the 'Download Soils Data' tab. U. S. Geological Survey 20230307 vector digital data Hydrological Units (HU) used: 8 - 04100001 (NHD_H_04100001_HU8_GDB) 8 - 04100002 (NHD_H_04100002_HU8_GDB) 8 - 04100009 (NHD_H_04100009_HU8_GDB) 8 - 04100010 (NHD_H_04100010_HU8_GDB) 8 - 04100011 (NHD_H_04100011_HU8_GDB) 8 - 04100012 (NHD_H_04100012_HU8_GDB) 8 - 04100013 (NHD_H_04100013_HU8_GDB) 8 - 04110001 (NHD_H_04110001_HU8_GDB) 8 - 04260000 (NHD_H_04160000_HU8_GDB) https://www.usgs.gov/national-hydrography/national-hydrography-dataset Digital 20230307 publication date NHD Flowlines Identified the centerlines of stream segments or reaches that make up the nation's surface water drainage system. NHDFlowline features consist of categories stream/river, artificial path, canal/ditch, pipeline, connector, underground conduit, and coastline. U. S. Geological Survey (USGS) Gap Analysis Project (GAP) 20220131 3.0 tabular digital data https://www.sciencebase.gov/catalog/item/61794fc2d34ea58c3c6f9f69 Digital 20220131 publication date PAD-US Region 3 Identifies protected areas as they would be more suitable for wetland restoration. U.S. Geological Survey Dewitz, J. 20210604 2.0 raster digital data nlcd_2019_impervious_l48_20210604 https://www.sciencebase.gov/catalog/item/604a500ed34eb120311b006c Digital 2001 2019 observed NLCD Impervious Products The impervious surface data are used to show where there are impervious surfaces within the study area, because those areas are not suitable for wetland restoration. 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. 20230908 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. This reinforces contiguous areas (most desirable for wetland restoration) while de-emphasizing isolated areas or areas generated accidentally through random data noise. 20231015 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 9327 13625 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 WLERA_IndexComposite.tif A raster dataset (in geoTIFF format) illustrating the relative restorability of coastal wetlands within the WLERA study area. Producer Defined Value Values in this raster represent a metric of restorability of coastal wetlands along the shoreline of Western Lake Erie. High values mean a higher chance of restorability and lower values represent less possibility for restoration. Producer Defined 255 Outside of the WLERA study area. Producer defined 0 100 WLERA_IndexComposite.zip contains a TIF raster image (WLERA_IndexComposite.tif) and associated metadata (WLERA_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 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/P9OS796H 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 11224300 TRUE