Data Explorer

Data Sources

Data Categories

The data sources in this section are organized by the type of data they provide. There are currentlt 4 types of categories: biology, demographics, geography, and hydrology.

Biology Data

• Souce:

  Office for Coastal Management, 2024: NOAA C-CAP National Wetland Potential, https://www.fisheries.noaa.gov/inport/item/48357.

• Description from Data Owner:

  The probability rating which covers landcover mapping provides a continuum of wetness from dry to water. The layer is not a wetland classification but provides the wetland likelihood at a specific location. The rating was developed through a modelling process combining multiple GIS and remote sensing data sets including soil characteristics, elevation, existing wetland inventories, hydrographical extents and satellite imagery.

• Usage:

  This data is used in the NOAA Wetland Potential Model as one of the selection priorities.

• Souce:

  The National Fish and Wildlife Foundation (NFWF): The Coastal Resilience Evaluation and Siting Tool (CREST), https://resilientcoasts.org/Home.

• Description from Data Owner:

  The Fish and Wildlife Index identifies areas on the landscape where species and their habitats are located, helping to understand areas where implementing nature-based solutions are likely to benefit federal- or state-designated species of concern. In continental U.S (CONUS), a Terrestrial and Aquatic Index are combined based principally on rarity-weighted richness. Importantly, in CONUS, the Aquatic Index includes both freshwater and marine species, but focuses almost exclusively on very shallow, nearshore areas.

• Usage:

  This data is used in the Habitat Protection Model as one of the selection priorities.

• Souce:

  The Global Biodiversity Information Facility (GBIF), https://www.gbif.org/.

• The downloads used in this project:

  1. IUCN Global Red List Category - Critically Endangered (CR) & Endangered (EN): GBIF.org (01 June 2024) GBIF Occurrence Download https://doi.org/10.15468/dl.sstbsa

  2. New York State List of Endangered Species: GBIF.org (01 June 2024) GBIF Occurrence Download https://doi.org/10.15468/dl.p54gzc

• Usage:

  This data is used in the Habitat Protection Model as one of the selection priorities.

• Auxiliary species activity range sources:

  IUCN Global Red List Category - Critically Endangered (CR) & Endangered (EN):

  1. Lake Sturgeon Acipenser fulvescens: U.S. Fish & Wildlife Species Description

  2. American Chestnut Castanea dentata: Lady Bird Johnson Wildflower Center Plant Database

  3. White Ash Fraxinus americana: USDA Forest Service Southern Research Station, Lady Bird Johnson Wildflower Center Plant Database

  4. Black Ash Fraxinus nigra: USDA Forest Service Southern Research Station, Lady Bird Johnson Wildflower Center Plant Database

  5. Green Ash Fraxinus pennsylvanica: USDA Forest Service Southern Research Station, Lady Bird Johnson Wildflower Center Plant Database

  6. Pumpkin Ash Fraxinus profunda: USDA Forest Service Southern Research Station, Lady Bird Johnson Wildflower Center Plant Database

  7. Butternut Juglans cinerea: USDA Forest Service Southern Research Station, Lady Bird Johnson Wildflower Center Plant Database

  8. European rabbit Oryctolagus cuniculus: iNaturalist Taxonomy Explorer

  9. American elm Ulmus americana: USDA Forest Service Southern Research Station, Lady Bird Johnson Wildflower Center Plant Database

  New York State List of Endangered Species:

  1. Shortnose Sturgeon Acipenser brevirostrum: NOAA Species Directory, New York State Department of Environmental Conservation

  2. Golden Eagle Aquila chrysaetos: U.S. Fish & Wildlife Species Description, New York State Department of Environmental Conservation

  3. Short-eared Owl Asio flammeus: U.S. Fish & Wildlife Species Description, New York State Department of Environmental Conservation

  4. Piping Plover Charadrius melodus: U.S. Fish & Wildlife Species Description, New York State Department of Environmental Conservation

  5. Black Tern Chlidonias niger: Birds of the World, New York State Department of Environmental Conservation

  6. Peregrine Falcon Falco peregrinus: Birds of the World, New York State Department of Environmental Conservation

  7. Loggerhead Shrike Lanius ludovicianus: Birds of the World, New York State Department of Environmental Conservation

• Souce:

  The Global Biodiversity Information Facility (GBIF), https://www.gbif.org/.

• The downloads used in this project:

  1. Phragmites adans: GBIF.org (31 May 2024) GBIF Occurrence Download https://doi.org/10.15468/dl.36h8qz

  2. Neogobius melanostomus: GBIF.org (31 May 2024) GBIF Occurrence Download https://doi.org/10.15468/dl.fvuaje

  3. Dreissena polymorpha: GBIF.org (31 May 2024) GBIF Occurrence Download https://doi.org/10.15468/dl.kdyzux

  4. Lythrum salicaria: GBIF.org (31 May 2024) GBIF Occurrence Download https://doi.org/10.15468/dl.hvsgze

  5. Cyprinus carpio: GBIF.org (31 May 2024) GBIF Occurrence Download https://doi.org/10.15468/dl.75uqcg

  6. Myriophyllum spicatum: GBIF.org (31 May 2024) GBIF Occurrence Download https://doi.org/10.15468/dl.94vshg

• Usage:

  This data is used in the Invasive Species Control Model as one of the selection priorities.

• Auxiliary species activity range sources:

  1. Common Reed Phragmites australis: USDA Plant Guide for common reed, USGS Nonindigenous Aquatic Species(NAS) Collection

  2. Round Goby Neogobius melanostomus: U.S. Fish & Wildlife Service Round Goby Ecological Risk Screening Summary, USGS Nonindigenous Aquatic Species(NAS) Collection

  3. Zebra Mussel Dreissena polymorpha: U.S. Fish & Wildlife Service Zebra Mussel Ecological Risk Screening Summary, USGS Nonindigenous Aquatic Species(NAS) Collection

  4. Purple Loosestrife Lythrum salicaria: New York Invasive Species Information, USGS Nonindigenous Aquatic Species(NAS) Collection

  5. Common Carp Cyprinus carpio: U.S. Fish & Wildlife Service Common Carp Ecological Risk Screening Summary, USGS Nonindigenous Aquatic Species(NAS) Collection

  6. Eurasian Watermilfoil Myriophyllum spicatum: U.S. Fish & Wildlife Service Eurasian Watermilfoil Ecological Risk Screening Summary, USGS Nonindigenous Aquatic Species(NAS) Collection

Demographic Data

• Souce:

  US Census Bureau, Geography Division: TIGER/Line® Shapefiles, https://www.census.gov/cgi-bin/geo/shapefiles/index.php.

• Description from Data Owner:

  The core TIGER/Line Files and Shapefiles do not include demographic data, but they do contain geographic entity codes (GEOIDs) that can be linked to the Census Bureau's demographic data.

• Usage:

  This data is used as a reference layer in the map interface for orientation.

• Souce:

  US Census Bureau, Geography Division: TIGER/Line® Shapefiles, https://www.census.gov/cgi-bin/geo/shapefiles/index.php.

• Description from Data Owner:

  The core TIGER/Line Files and Shapefiles do not include demographic data, but they do contain geographic entity codes (GEOIDs) that can be linked to the Census Bureau's demographic data.

• Usage:

  This data is used as a reference layer in the map interface for orientation.

• Souce:

  The National Fish and Wildlife Foundation (NFWF): The Coastal Resilience Evaluation and Siting Tool (CREST), https://resilientcoasts.org/Home.

• Description from Data Owner:

  The Community Exposure Index explores the relationship between potential flooding threats and the presence of community assets by combining two composite indices: the Threat Index and the Community Asset Index. The Threat Index utilizes landscape characteristics and flood-related data. The Community Asset Index helps to understand where critical infrastructure, facilities, and population are concentrated on the landscape. Together, these indices combine to identify areas where community assets overlap with flood threats, also known as exposure. While individual data inputs vary regionally, the Regional Coastal Resilience Assessments utilize standardized methods to calculate the Community Exposure Index.

• Usage:

  This data is used in the NFWF Social Vulnerability Model as one of the selection priorities.

Geography Data

• Souce:

  Office for Coastal Management, 2024: Great Lakes Hardened Shorelines Classification 2019, https://www.fisheries.noaa.gov/inport/item/59439.

• Description from Data Owner:

  The database consists of a single polyline feature class with segmented and classified by shoreline type. Each shoreline segment was initially determined to be either artificial or natural, then further classified by structure or natural feature type and the quality of its condition. The data were created by digitizing shoreline using NAIP imagery from 2014 through 2017, then compared with oblique imagery to determine structure condition and quality. This dataset creation was funded through the Great Lakes Restoration Initiative.

• Usage:

  This data is used in the Erosion Potential Model as one of the selection priorities.

• Souce:

  Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture. Web Soil Survey. Available online at the following link: http://websoilsurvey.sc.egov.usda.gov/.

• Description from Data Owner:

  Erosion factor K indicates the susceptibility of a soil to sheet and rill erosion by water. Factor K is one of six factors used in the Universal Soil Loss Equation (USLE) and the Revised Universal Soil Loss Equation (RUSLE) to predict the average annual rate of soil loss by sheet and rill erosion in tons per acre per year. The estimates are based primarily on percentage of silt, sand, and organic matter and on soil structure and saturated hydraulic conductivity (Ksat). Values of K range from 0.02 to 0.69. Other factors being equal, the higher the value, the more susceptible the soil is to sheet and rill erosion by water. "Erosion factor Kw (whole soil)" indicates the erodibility of the whole soil. The estimates are modified by the presence of rock fragments.

• Usage:

  This data is used in the Erosion Potential Model as one of the selection priorities.

Hydrology Data

• Souce:

  NYS GIS Clearinghouse: NYS Civil Boundaries, https://data.gis.ny.gov/maps/074d3456e5664f5e85d0fb251d05cc5b/about.

• Description from Data Owner:

  This feature service has polygon layers for the following boundary types: State, Counties, Cites, Towns, Cities and Towns combined, Villages, and Indian Territories. In addition, there are separate shoreline layers for the State layer and the County layer. Boundaries are at 1:24,000-scale positional accuracy except for the shoreline in State Shoreline and Counties Shoreline which is being adjusted to 1:24,000-scale positional accuracy as part of ongoing work.

• Usage:

  This data is used as the base of area of interest for all calculations.

• Souce:

  ArcGIS Online: Watershed Boundary Dataset HUC 10s - HU10.

• Usage:

  This data is used as a reference layer in the map interface for orientation.

• Souce:

  USGS: National Hydrography Dataset Plus High Resolution National Release 1 FileGDB, https://www.usgs.gov/national-hydrography/access-national-hydrography-products.

• Description from Data Owner:

  The NHDPlus HR is a geospatial dataset depicting the flow of water across the Nation's landscapes and through the stream network. The NHDPlus HR was built using the National Hydrography Dataset High Resolution data at 1:24,000 scale or more detailed, the 10 meter 3D Elevation Program data, and the nationally complete Watershed Boundary Dataset.

• Usage:

  This data is used as a reference layer in the map interface for orientation.

• Souce:

  US Army Corps of Engineers: Sediment Budgets for Lake Erie and Lake Ontario, https://usace.maps.arcgis.com/apps/MapSeries/index.html?appid=34476ea8c07a4111841d28a5d960cb02.

• Description from Data Owner:

  Sediment budget is a measure of sediment (usually sand) “sources” (inputs), “sinks” (outputs), and net change within a specified “control volume” (a cell or series of connecting cells) over a given period of time. It is a fundamental design tool for projects concerned with sediment transport, deposition, and erosion.

• Usage:

  This data is used in the Sediment Loss, Sediment Gain, and Erosion Potential Model as one of the selection priorities. It is also available as a reference layer in the map interface for orientation.

Documentation

Unit Generator Explanation

Purpose and Goal

Operational coastal units are seen as a relatively untested and novel approach to coastal analysis that could hold particular potential in decision-making processes that require considering both local conditions and regional transferability. At its most simplistic, the operational coastal unit method assumes that there is value in defining new boundaries for where particular strategies are or are not applicable based on the contextual conditions found in specific places. And perhaps most importantly, that these new boundaries need not respond to existing administrative jurisdictions. Underpinning this idea are two overarching goals, balanced with one another.

1. That local conditions and context are important culturally and ecologically. Decision-making regarding the shoreline should take into consideration the cultural values and knowledge that exist and have evolved in those specific areas. Additionally, the ecological context of any given location should be considered as important and special as opposed to generalized into a larger and more abstract idea of 'ecology'. In these ways, local conditions can be both acknowledged and formative in the decision-making processes to encourage the development of more sensitive and contextual outcomes. Put simply, the more local, the better.

2. That transferability is to some degree essential to the practicality of any formal coastal management decision. This does not mean that transferability is what is desired, but only that it is what is necessary to get projects funded and completed. In this way, projects should aim to be as contextually specific as possible while only being as transferable as necessary. However, in the correct circumstances, transferability could provide the opportunity for ideas to spread within areas of similar contextual conditions which does offer both cost savings and project innovation.

With these two balanced goals in mind, this project aims to establish a method for creating coastal units that are particular and appropriate for a given task. This specific project asks to look at the application of the operational coastal units in coastal management decisions that involve strategic sediment management and adaptability as a defining feature, although many other objectives could also be met with the coastal unit methods and efforts have been made to develop a more robust unit structure to accommodate some of these alternative objectives.

Steps Explanation

General

• How to use the buttons in each step:

  At the initial state, all the input boxes and buttons are locked except the Start/Reset button in step one. To start generating the units, click the Start/Reset button. For all the other buttons, they should be clicked in sequence. That is to say, after finishing choosing or typing the inputs, click the Generate/Reset button to preview the result, and if the result looks good, click the Next button in each step to unlock the input boxes and buttons in the following step.

• Vocabulary:

  • Resolution:

    Continuous small pieces with the same length. It is the product of dividing a longer line. This is the overall fidelity or detail of the unit generation process along the length of the selected study area. It is effectively the “cell size” that the selected area is split into before attributes are applied to them and they are then re-assembled into units. For example: If 50 meters is selected as the resolution, the selected study area will be split into 50-meter segments along its length and attributes of the following steps applied to those 50-meter segments.

  • Priority:

    List of criteria that will be used to weight the calculations. These are the topics of interest to the user that assemble different data sets together to be applied to the cells created in the previous step. For example: If “Erosion Potential” and “NFWF Habitat Protection” are selected, datasets will be assembled to create operational coastal units that prioritize coastal erosion first and habitat creation second. The outcome of this process is the application of a rating for each segment from 0-1, based on how it applies to the selected priorities. More details on what datasets are used for what priority are included below.

  • Group:

    The number of group types. A group means a group of resolution that has similar score from calculations. Multiple units may be in the same group. This indicates how many groups the user would like the prioritized cells to be assembled into. For example, in Figure 5 below, the user selected 3 groups, making values of 1-3 in the red group, 4-6 in the yellow group, and 7-10 in the blue group. As the example illustrates, 3 groups do not equal 3 units, (the example has 6 units) as the unit number will vary based on the distribution of priority ratings.

  • Unit:

    Final product of this tool with a unique ID.

• Summary of the working method for operational coastal unit delineation

  Figure 1. Divide: Coast divided into "cells" of set size, e.g. 10 meters.

  

  Figure 2. Match: Datasets are also divided and matched to cells.

  

  Figure 3. Class: Data in each cell is classed based on user priority.

  

  Figure 4. Score: Scores are assigned to each cell based on classification.

  

  Figure 5. Group: Cells are grouped into coastal units based on scores and desired group number.

  

Step by Step Instructions

Step 1: Choose your area

• Purpose:

  This step will help choose your area of interest along the Lake Erie shoreline of New York State, which will be the base of the calculations in the following steps and the area that will eventually be divided into units.

• Method:

  1. Click the Start/Reset button to start. After clicking it, you will see two pins appear at the two ends of the coastal line.
  2. Drag the two pins to the two ends of the area of interest along the coastline. The pins will automatically snap to the location of the coastline. You can drag and redrag for multiple times until you are satisfied with the result.
  3. After you drag the pins to the desired location, click the Next button to move to step 2, which will zoom the map to your selected location and unlock the input boxes in step 2.

• Input Description:

  There are no input boxes in this step: interact with the map to choose your area.

• Important:

  Remember to click the Start/Reset button to initialize this tool and click the Next button to move to step 2.

Step 2: Choose resolution

• Purpose:

  This step will divide the coastline you choose in step 1 to multiple small pieces with the same length, which is called Resolution.

• Method:

  1. In the input box below the Resolution, type the length (as numbers) you want to have for each resolution. You can choose the unit (e.g. feet, meter) in the dropdown box next to the input box.

• Input Description:

  • Resolutipn: The length that will be used to divide the coastline. For example, if you type "30" and choose "feet" as the unit type, the coastline chunk you selected in the previous step will be separated into small line segments that are 30 feet each.

Step 3: Choose priority

• Purpose:

  This step will do the scoring for each resolution based on the criteria you choose in the Priority dropdown box and color each resolution by its final score.

• Method:

  1. Pick the scoring criteria that you are interested in. In the dropdown box next to First, select the criterion that you care the most. The score for each resolution will be weighted based on the priority list you choose.
  2. After you fill in all the input boxes, click the Generate/Reset button to preview the result, which will display the score for each resolution in a color gradient. You can click the Generate/Reset for multiple times to change inputs (you can also change the resolution in step 2 at this point).
  3. After you are satisfied with the preview result, click the Next button to move to step 4, which will unlock the input boxes in step 4.

• Input Description:

  • Priority: In this part, you will specify the criteria you want to use to determine the final units of your selected coastline. The score calculation for each criterion behind the scenes is linked to a certain calculation of GIS layers related to that criteria, called the "criteria model". See the "Criteria Models Explanation" below for details about each calculation.

• Important:

  Remember to click the Start/Reset button to initialize this tool and click the Next button to move to step 4.

• Note:

  Because of the limitation of the original GIS layers, some GIS layers may not cover the whole coastline of Lake Erie. In that case, the final result of this tool may not be precise. See the "Criteria Models Explanation" below for details.

Step 4: Group into Final Units

• Purpose:

  This step will group the resolution with similar scores from step 3 into different groups and display the final units through a box around each unit.

• Method:

  1. In the input box below the Number of groups, type the number of groups (as numbers) you want to have for the final result. Multiple final units may be in the same group. The group stands for the number of group types that you want to have for the final result.
  2. Next, click the Generate/Reset button to preview the result, which will display the final units generated from the number of groups you specified above by circling them in boxes. When hover over each box, a pop-up will show the group type of that unit, and when you click each box, a panel will display more detailed information about each unit. You can click the Generate/Reset for multiple times.
  3. After you are satisfied with the preview result, click the Next button to move to step 5, which will unlock the input boxes in step 5.

• Input Description:

  Number of groups: The number of unit types you want to have for the final units based on the score of the resolution. Resolutions with similar final scores will be grouped together to form the final unit. Each group may belong to multiple units. It is also possible that one or multiple group types are not connected to any units.

• Important:

  Remember to click the Start/Reset button to initialize this tool and click the Next button to move to step 5.

Step 5: Download

• Purpose:

  This step will let you download your result generated from the previous steps and use it elsewhere.

• Method:

  1. In the input box below the Download Units, choose the type of file that you want to generate. Currently, this step accepts geoJSON and shapefile.
  2. Next, click the Download button and a pop-up window will allow you the select the saving location.

Similarity Finder Explanation

Purpose and Goal

This component functions as a supplementary tool for the unit generator, enhancing its capabilities and broadening its application. Instead of selecting an area of interest and receiving a collection of units based on their priorities, this new tool allows users to identify similar areas along the entire coastline based on their priorities on a point of interest. By analyzing various geographical and environmental parameters, the tool can highlight regions with comparable characteristics.

This project aims to leverage the calculation algorithms embedded within the unit generator to assist users in identifying areas with similar conditions. Coastal management professionals can use the results from this tool to draw parallels between different regions, enabling them to apply successful strategies from one area to another with similar conditions. This not only improves the efficiency of coastal management efforts but also helps in the sustainable development and preservation of coastal regions.

Steps Explanation

General

• How to use the buttons in each step:

  At the initial state, all the input boxes and buttons are locked except the Start/Reset button in step one. To start finding similar areas, click the Start/Reset button. For all the other buttons, they should be clicked in sequence. That is to say, after finishing choosing or dragging the inputs, click the Generate/Reset button to preview the result, and if the result looks good, click the Next button in each step to unlock the input boxes and buttons in the following step.

• Vocabulary:

  • Priority:

    List of criteria that will be used to weight the calculations. These are the topics of interest to the user that assemble different data sets together to be applied to the whole coastal area. For example: If “Erosion Potential” and “NFWF Habitat Protection” are selected, datasets will be assembled to prioritize erosion potential score first and habitat protection score second. The outcome of this process is the application of a rating for each segment from 0-1, based on how it applies to the selected priorities. More details on what datasets are used for what priority are included below.

  • Similarity:

    The similarity value displays an area's similarity to the selected point based on the selected priorities. It measures the abosulute difference of the final score, which means an area 0.1 above or below the selected point's final score will have the same similarity value.

Step by Step Instructions

Step 1: Choose point location

• Purpose:

  This step will help pick a point along the Lake Erie shoreline of New York State, which will be the base of the calculations in the following steps.

• Method:

  1. Click the Start/Reset button to start. After clicking it, you will see one pins appear at the middle of the coastal line.
  2. Drag the pin to a point of interest along the coastline. The pin will automatically snap to the location of the coastline. You can drag and redrag for multiple times until you are satisfied with the result.
  3. After you drag the pin to the desired location, click the Next button to move to step 2, which will unlock the input boxes in step 2.

• Input Description:

  There are no input boxes in this step: interact with the map to choose your point.

• Important:

  Remember to click the Start/Reset button to initialize this tool and click the Next button to move to step 2.

Step 2: Choose priority

• Purpose:

  This step will do the scoring of every 4000 ft lengeth along the whole coastline based on the criteria you choose in the Priority dropdown box and color each 4000 ft chunk by its final score.

• Method:

  1. Pick the scoring criteria that you are interested in. In the dropdown box next to First, select the criterion that you care the most. The score for each 4000 ft chunk will be weighted based on the priority list you choose.
  2. After you fill in all the input boxes, click the Generate/Reset button to preview the result, which will display the score for each 4000 ft chunk in a color gradient. You can click the Generate/Reset for multiple times to change inputs.
  3. After you are satisfied with the preview result, click the Next button to move to step 3, which will unlock the input boxes in step 3 and display the final score of your selected area on the range.

• Input Description:

  • Priority: In this part, you will specify the criteria you want to use to determine the similarity between your selected point and the whole coastline. The score calculation for each criterion behind the scenes is linked to a certain calculation of GIS layers related to that criteria, called the "criteria model". See the "Criteria Models Explanation" below for details about each calculation.

• Important:

  Remember to click the Start/Reset button to initialize this tool and click the Next button to move to step 3.

• Note:

  Because of the limitation of the original GIS layers, some GIS layers may not cover the whole coastline of Lake Erie. In that case, the final result of this tool may not be precise. See the "Criteria Models Explanation" below for details.

Step 3: Find similar area

• Purpose:

  This step will filter the final scores of all the 4000 ft chunks along the shoreline and highlight the ones with a final score that sits within your selected range. The range bar can be dragged to change the location of the circle buttons or you can click the arrows below the bar for more precise manipulation.

• Method:

  1. In the range bar below the Range of search, specify the minimum and maximum values you want for the final similarity dataset. This range of search includes both boundary values, and the range should include the final score of your selected point.
  2. Next, click the Generate/Reset button to preview the result, which will highlight all the 4000 ft chunks that have a final score within your selected range. When hover over each chunk, a pop-up will show the similarity between this chunk and your selected point, and when you click each chunk, a panel will display more detailed information. You can click the Generate/Reset for multiple times.
  3. After you are satisfied with the preview result, click the Next button to move to step 4, which will unlock the input boxes in step 4.

• Input Description:

  Range of search: The minimum and maximum values you want to have for the similarity dataset. Both boundary values are included in the range

• Important:

  Remember to click the Next button to move to step 4.

Step 4: Download

• Purpose:

  This step will let you download your result generated from the previous steps and use it elsewhere.

• Method:

  1. In the input box below the Download Units, choose the type of file that you want to generate. Currently, this step accepts geoJSON and shapefile.
  2. Next, click the Download button and a pop-up window will allow you the select the saving location.

Criteria Models Explanation

General

• What's the weight for each priority:

  First priority 50%, second priority 30%, third priority 20%.

• How to assign value from GIS layer to each resolution:

  The resolution line will be offset for a certain distance (default is 200 meters) in both directions to create a bounding box. The values in each GIS layer will be assigned to the resolution if they overlap with the bounding box of the resolution line. If more than one value overlaps with the bounding box, the values will be averaged. If no value overlaps with the bounding box, the value for the resolution will be the value of the nearest GIS object.

Sediment Loss Model

• Description:

  This model focuses on the actual sediment loss amount along the shoreline. Higher score means higher amount of sediment loss.

• GIS Data:

  1. Sediment budget: The Sediment Budget, produced by the United States Army Corps of Engineers for both Lake Erie and Lake Ontario covers the large extent of our study area. The sediment budget estimates the gain, loss, and movement of sediment across the lake shoreline, through a series of cells, each measuring approximately a half mile in length.

     Coarse out: Coarse Material leaving the cell for open water.

     

     Bypass: Material moved manually from one cell to another.

     

     Downdrift Loss: Material leaving cell to adjacent cell by downdrift.

     

     Fines out: Fine Material leaving cell for open water.

     

     Littoral coarse out: Coarse Material leaving cell to open water via littoral transport.

     

• Calculating Algorithm:

  1. For each sediment budget cell, sediment loss = Coarse out + Bypass + Downdrift loss + Fines out + Littoral coarse out
  2. Rescale: the actual sediment loss value of all the resolution will be projected to a scale of 0 to 1, where closer to 0 means less loss and closer to 1 means more loss.

• Note:

  The sediment budget layer does not have values for the area near City of Buffalo. If the selected coastline includes those areas, the result may not be precise.

Sediment Gain Model

• Description:

  This model focuses on the actual sediment gain amount along the shoreline. Higher score means higher amount of sediment gain.

• GIS Data:

  1. Sediment budget: The Sediment Budget, produced by the United States Army Corps of Engineers for both Lake Erie and Lake Ontario covers the large extent of our study area. The sediment budget estimates the gain, loss, and movement of sediment across the lake shoreline, through a series of cells, each measuring approximately a half mile in length.

     Bluff in: Erosion from Bluff added to cell.

     

     Tributary bedload: Material added to cell via river input.

     

     Downdrift gain: Material added to cell from adjacent cell by downdrift.

     

     Littoral coarse in: Coarse Material added to cell from open water via littoral transport.

     

• Calculating Algorithm:

  1. For each sediment budget cell, sediment gain = Bluff in + Tributary bedload + Downdrift gain + Littoral coarse in
  2. Rescale: the actual sediment gain value of all the resolution will be projected to a scale of 0 to 1, where closer to 0 means less gain and closer to 1 means more loss.

• Note:

  The sediment budget layer does not have values for the area near City of Buffalo. If the selected coastline includes those areas, the result may not be precise.

Erosion Potential Model

• Description:

  This model focuses on the erosion potential of the coastal edge. Higher score means higher erosion potential.

• GIS Data:

  1. Retreat rate: The retreat rate is one of the properties of the sediment budget, which is produced by the United States Army Corps of Engineers for both Lake Erie and Lake Ontario. Similar to the other values from the sediment budget that are used in the sediment loss/gain model, the retreat rate value is recorded through a series of cells, each measuring approximately a half mile in length. Specifically, the retreat rate is the rate at which the shoreline is eroding, measured in feet per year.
  2. Shoreline type: The shoreline type GIS data contains baseline hardened shoreline classification, which specifies the current shoreline condition along the study area. The original data was created for the National Oceanic and Atmospheric Administration's Office for Coastal Management, in partnership with the United States Army Corps of Engineers, and funded through the Great Lakes Restoration Initiative to support detailed shoreline mapping.
  3. Soil erosion K factor: The soil K factor used in this model is from the USDA Natural Resources Conservation Service's Web Soil Survey. Based on their description, the K factor indicates the susceptibility of soil to sheet and rill erosion by water. The estimates are based primarily on the percentage of silt, sand, and organic matter and on soil structure and saturated hydraulic conductivity (Ksat). Values of K range from 0.02 to 0.69. Other factors being equal, the higher the value, the more susceptible the soil is to sheet and rill erosion by water. Specifically, we are using the K factor for the whole soil, which includes modifications based on the presence of rock fragments.

• Calculating Algorithm:

  1. Weight among all GIS data: erosion potential for each resolution = 0.5 * retreat rate + 0.3 * shoreline type + 0.2 * soil K factor
  2. Rescale: the actual retreat rate value of all the resolution will be projected to a scale of 0 to 1, where closer to 0 means less shoreline erosion and closer to 1 means more shoreline erosion.
  3. Rescale: the actual soil K factor value of all the resolution will be projected to a scale of 0 to 1, where closer to 0 means less soil erosion and closer to 1 means more soil erosion.
  4. Because the shoreline type is categorical data, we assign a value of 0 to 5 to each category based on the category's eroding characteristics, and then rescale it to a scale of 0 to 1. Higher value means more related to erosion. The categories associated with each value are shown below:
     • 0: 'Bedrock (Resistant) no overburden', 'Artificial Good Quality Well Engineered'
     • 1: 'Bedrock (Resistant) with glacial overburden', 'Artificial Moderate Quality Moderately Engineered', 'Open Shore Wetlands'
     • 2: 'Bedrock (Erosive) no overburden', 'Bedrock (Erosive) with glacial Overburden', 'Open Shoreline Wetlands', 'Composite Low Bank / Low Plain'
     • 3: 'Artificial Poor Quality Poorly Engineered', 'Pocket Beach', 'Artificial Depositional (e.g., jetty, groin fill)', 'Bedrock (Resistant) with sand overburden'
     • 4: 'Bedrock (Erosion) with sand overburden', 'Baymouth - Barrier (fronting wetlands or shallow embayments, estuaries)', 'Low Bank', 'Natural Depositional (areas with active supply/deposition)'
     • 5: 'Rivermouth / Sheltered Wetlands', 'Sandy Low Bank / Low Plain', 'Sandy Beach / Dune (relict deposits, areas with no new deposition)', 'Cohesive Bluffs (composition unknown)', 'Sandy Beach / Dune Complex', 'Coarse Beaches', 'Sand or Cohesive Bluffs (Till or Lacustrine)', 'Composite Bluffs (sand content 20-50%)', 'Composite Bluffs (sand content >50%)', 'Gravel Beaches'

• Note:

  The sediment budget layer does not have values (retreat rate) for the area near City of Buffalo. Besides, the soil K factor also has many null values near City of Buffalo. If the selected coastline includes those areas, the result may not be precise.

Invasive Species Control Model

• Description:

  This model identifies the diversity of invasive species existing in one area. Higher scores represent areas that contain varies of different invasive species and thus facing higher invasive species threats.

• GIS Data:

  1. Diversity of Invasive Species:

     This data is based on the species occurrences data from Global Biodiversity Information Facility (GBIF), which includes multiple datasets such as iNaturalist Research-grade Observations and USGS Nonindigenous Aquatic Species Database.

     The invasive species used here are typical invasive species in the Great Lakes Area based on U.S. Fish and Wildlife Service or USDA reports.

• Calculating Algorithm:

  1. The diversity calculation of invasive species is based on each species' movability. Each occurrence's data will form a buffer area based on the species' activity range. Then each resolution will count the number of buffers that overlap with it and one species is only counted once. The diversity score of each resolution will be the number of species that exist in each resolution. This number will then be rescaled on a 0 to 1 range.

     For the estimated activity range of each species, they are based on each species' characteristics in the species description from multiple sources (see Data Explorer for more details). For plants or non-moveable species, its activity range is calculated from its spreading ability and coordinate precision of the observation points.

     • Round Goby Neogobius melanostomus: 200 m.
     • Eurasian watermilfoil Myriophyllum spicatum: 100 m.
     • Purple Loosestrife Lythrum salicaria: 100 m.
     • Zebra Mussel Dreissena polymorpha: 1 km.
     • Common Carp Cyprinus carpio: 1.5 km.
     • Common Reed Phragmites australis: 100 m.

Habitat Protection Model

• Description:

  This model identifies critical habitat and protected areas for terrestrial and aquatic species of conservation concern. Higher scores represent areas that are important for numerous species of concern, where implementing a restoration project would likely provide the greatest benefit to wildlife.

• GIS Data:

  1. Fish and Wildlife Index:

     This data is based on the Fish and Wildlife Index developed by the National Fish and Wildlife Foundation. This index is the sum of the terrestrial and aquatic indexes, also developed by the NFWF, plus protected areas.

     The terrestrial index identifies terrestrial wildlife species of greatest conservation need from state wildlife action plans, federally listed species, and GLIFWC harvest regulations. It then identifies suitable habitat for each species and sums them by taxonomic group, using the following datasets: USGS GAP Analysis, Critical Habitat, Important Bird Areas & Key Biodiversity Areas, and Coastal Bluffs and Dunes.

     Using similar methodology, the aquatic index identifies species of concern in riverine and lacustrine habitats from the same three sources as the terrestrial index. It then identifies areas of suitable habitat for each species using the following datasets: Species Habitat Distribution, Critical Habitat, Spawning and Reef Locations, and the Great Lakes Eastern Brook Trout Conservation Portfolio.

     Protected areas included in the model were sourced from the USGS Gap Analysis Project, Protected Areas Database of the United States (PAD-US) 2.1: U.S. Geological Survey data release, NOAA National Marine Sanctuaries, and ProtectedSeas.

  2. Diversity of Endangered Species:

     This data is based on the species occurrences data from Global Biodiversity Information Facility (GBIF), which includes multiple datasets such as iNaturalist Research-grade Observations, EOD - eBird Observation Dataset, and Diveboard - Scuba Diving Citizen Science Observations.

     The endangered species used here are from two sources: the Critically Endangered (CR) & Endangered (EN) categories of the IUCN Global Red List and the New York State's local list of endangered species, which includes species that may not be endangered globally but is endangered in Ner York State specifically.

• Calculating Algorithm:

  1. Weight among all GIS data: habitat protection score for each resolution = 0.7 * NFWF Fish and Wildlife Index + 0.3 * Diversity of Endangered Species score

  2. Detailed methodology for calculating the Fish and Wildlife Index, including a complete list of species included in the analysis, input data sources, and geoprocessing steps, can be found in the following report:

     Dobson, J.G., Johnson, I.P., Orlando, J.L., Lussier, B.C., and Byler, K.A. (2023) U.S. Great Lakes Coastal Resilience Assessment (Appendix A.3-5, F-G). UNC Asheville National Environmental Modeling and Analysis Center, Asheville, NC. Prepared for the National Fish and Wildlife Foundation.

  3. The diversity calculation of endangered species is based on each species' movability. Each occurrence's data will form a buffer area based on the species' activity range. Then each resolution will count the number of buffers that overlap with it and one species is only counted once. The diversity score of each resolution will be the number of species that exist in each resolution. This number will then be rescaled on a 0 to 1 range.

     For the estimated activity range of each species, they are based on each species' characteristics in the species description from multiple sources (see Data Explorer for more details) and the clustering patterns of the observation points (if any) for solitary organisms. For plants or non-moveable species, its activity range is calculated from its spreading ability and coordinate precision of the observation points.

     • Lake Sturgeon Acipenser fulvescens: 10 km.
     • American Chestnut Castanea dentata: 100 m.
     • White Ash Fraxinus americana: 100 m.
     • Black Ash Fraxinus nigra: 100 m.
     • Green Ash Fraxinus pennsylvanica: 100 m.
     • Pumpkin Ash Fraxinus profunda: 100 m.
     • Butternut Juglans cinerea: 100 m.
     • European rabbit Oryctolagus cuniculus: 2 km.
     • American elm Ulmus americana: 100 m.
     • Shortnose Sturgeon Acipenser brevirostrum: 10 km.
     • Golden Eagle Asio flammeus: 20 km.
     • Short-eared Owl Acipenser brevirostrum: 15 km.
     • Piping Plover Charadrius melodus: 1 km.
     • Black Tern Chlidonias niger: 1.5 km.
     • Peregrine Falcon Lanius ludovicianus: 1 km.
     • Loggerhead Shrike Acipenser brevirostrum: 10 km.

NOAA Wetland Protection/Restoration Model

• Description:

  This model highlights the potential for land to be a wetland in order to identify areas for restoration. The model does not represent the current landcover condition of wetlands, but rather the likelihood that a wetland would occur at a specific location given its soil characteristics, elevation, and existing landcover. Higher values represent a high likelihood of wetland, and low values represent areas where wetland establishment is very unlikely.

• GIS Data:

  1. NOAA C-CAP National Wetland Potential:

     This model is based on the NOAA C-CAP National Wetland Potential dataset by the NOAA Office for Coastal Management. Input data includes the National Wetland Inventory (NWI), Soil Survey Geographic (SSURGO) database, National Hydrography Dataset (NHD), National Elevation Dataset (NED), and Landsat Satellite Imagery.

• Calculating Algorithm:

  1. The wetland potential values are defined in the following manner:
     • 0: No data
     • 1 - 9: Extremely low likelihood of wetness to extremely high likelihood of wetness
     • 10: Open water

  2. Detailed methodology and metadata for this dataset can be found at:

     Office for Coastal Management, 2024: NOAA C-CAP National Wetland Potential from 2010-06-15 to 2010-08-15. NOAA National Centers for Environmental Information.

NFWF Community Exposure Model

• Description:

  This model identifies the spatial distribution of the potential exposure of community's assets to flooding threats by using the Threat Index and Community Asset Index. It focuses on incorporating flood-related threats due to the lake level variation in the Great Lakes Area, which is differnt from the saltwater coastal regionss. Higher scores represent areas with higher prevalence of threats and higher presence of community assets.

• GIS Data:

  1. Community Exposure Index:

     This model is based on the Community Exposure Index developed by the National Fish and Wildlife Foundation. This index is the multiplication of the Threat Index and Community Asset Index.

     The threat index includes threats of costal flood, erosion, and severe strom hazards. It ranks the input from low to high and reclassifies the ranked result into 10 classes using percentile distribution. It includes datasets such as FEMA National Flood Hazard, USDA-NRCS gSSURGO and gNATSGO, USGS National Elevation Dataset, NOAA Lidar Digital Elevation Model data and C-CAP land cover, EPA EnviroAtlas, etc..

     Using similar methodology, the community asset index quantify the number of asset present and incldes data such as population density, social bulnerability, critical facilities, and critical infrastructure.

     The multiplication of the two indexes above can display the exposure, which is the relationship between potential threats and the presence of community asset

• Calculating Algorithm:

  1. Detailed methodology for calculating the Community Exposure Index, including a complete list of input data sources and geoprocessing steps can be found in the following report:

     Dobson, J.G., Johnson, I.P., Orlando, J.L., Lussier, B.C., and Byler, K.A. (2023) U.S. Great Lakes Coastal Resilience Assessment (Appendix A.3-5, F-G). UNC Asheville National Environmental Modeling and Analysis Center, Asheville, NC. Prepared for the National Fish and Wildlife Foundation.

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