Coastal Hazards & Sea-Level Rise Asset Vulnerability Assessment Protocol: Updated Project Description & Methodology

Abstract

The National Park Service (NPS), in collaboration with the Program for the Study of Developed Shorelines at Western Carolina University, developed a standard natural hazards and climate change vulnerability assessment methodology for infrastructure in coastal national parks. The Coastal Hazards & Sea-Level Rise Asset Vulnerability Assessment Protocol evaluates the vulnerability of NPS buildings and transportation assets to sea-level rise, flooding, and shoreline change. This document describes the developmental history of this protocol and provides a detailed description of the methodology.

Full text

Coastal Hazards & Sea-Level Rise Asset
Vulnerability Assessment Protocol
Updated Project Description & Methodology
Natural Resource Report NPS/NRSS/CCRP/NRR—2022/2427
National Park Service
U.S. Department of the Interior
Natural Resource Stewardship and Science

ON
THIS PAGE
F
ire Island Lighthouse at Fire Island National Seashore
©PROGRAM FOR THE STUDY OF DEVELOPED SHORELINES
, WESTERN CAROLINA UNIVERSITY
ON THE COVER
Fort Jefferson at Dry Tortugas National
Park
©PROGRAM FOR THE STUDY OF DEVELOPED SHORELINES, WESTERN CAROLINA UNIVERSITY

Coastal Hazards & Sea-Level Rise Asset
Vulnerability Assessment Protocol
Updated Project Description & Methodology
Natural Resource Report NPS/NRSS/CCRP/NRR—2022/2427
Katie McDowell Peek1, Blair R. Tormey1, Holli L. Thompson1, Robert S. Young1
1Western Carolina University
Old Student Union
Cullowhee, North Carolina 28723
July 2022
U.S.
Department of the Interior
National Park Service
Natural Resource
Stewardship and Science
Fort Collins, Colorado

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The National Park Service, Natural Resource Stewardship and Science office in Fort Collins,
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This report is available in digital format from the Natural Resource Publications Management
website. If you have difficulty accessing information in this publication, particularly if using assistive
technology, please email irma@nps.gov.
Please cite this publication as:
Peek, K. M., B. R. Tormey, H. L. Thompson, and R. S. Young. 2022. Coastal hazards & sea-level
rise asset vulnerability assessment protocol: Updated project description & methodology. Natural
Resource Report NPS/NRSS/CCRP/NRR—2022/2427. National Park Service, Fort Collins,
Colorado. https://doi.org/10.36967/2293653.
NPS 999/182813, July 2022

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Contents
Page
Figures .................................................................................................................................................... v
Tables ................................................................................................................................................... vii
Executive Summary .............................................................................................................................. ix
Acknowledgments ...............................................................................................................................xiii
Acronyms ............................................................................................................................................xiii
Background & Objectives ...................................................................................................................... 1
Development of the Coastal Hazards & SLR Asset Vulnerability Assessment
Protocol .......................................................................................................................................... 1
Review of Previous Infrastructure VA Approach (2012–2013) ................................................ 2
Creation of a New Coastal VA Protocol for Infrastructure (2014) ........................................... 2
Pilot of the Coastal Hazards & SLR Asset Vulnerability Assessment Protocol
(2015) ........................................................................................................................................ 3
VA Protocol Expansion & Collaboration (2016–2017) ............................................................ 4
Continued Protocol Implementation & Collaboration (2017–present) ..................................... 4
Protocol Methodology ........................................................................................................................... 5
Asset Data Preparation ................................................................................................................... 5
Road & Trail Segmentation ....................................................................................................... 6
Exposure Mapping & Analysis ...................................................................................................... 7
1. Flooding Potential Indicator .................................................................................................. 9
2. Shoreline Change Indicator ................................................................................................. 11
3. SLR Inundation Indicator .................................................................................................... 14
4. Extreme Event Flooding Indicator ...................................................................................... 15
5. Reported Coastal Hazards Indicator .................................................................................... 16
Quality Control ........................................................................................................................ 16
Exposure Calculations & Final Scoring .................................................................................. 18
Sensitivity Analysis ...................................................................................................................... 20

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Contents (continued)
Page
1. Flood Damage Potential ...................................................................................................... 23
2. Storm Resistance & Condition ............................................................................................ 23
3. Historical Damage ............................................................................................................... 23
4. Protective Engineering ........................................................................................................ 24
Additional Bridge Indicators ................................................................................................... 25
Quality Control ........................................................................................................................ 25
Sensitivity Calculations & Final Scoring ................................................................................ 25
Vulnerability Calculations ............................................................................................................ 27
Products & Deliverables ............................................................................................................... 28
Summary & Conclusions ..................................................................................................................... 31
Literature Cited .................................................................................................................................... 33
Appendix A: FMSS Descriptions ........................................................................................................ 37
Appendix B: Accessible Figures & Tables .......................................................................................... 41

v
Figures
Page
Figure 1. Flowchart of the primary steps within the Coastal Hazards & SLR Asset
Vulnerability Assessment Protocol. ....................................................................................................... 5
Figure 2. Example of road segmentation process for East End Road at Virgin Islands
National Park. ........................................................................................................................................ 7
Figure 3. A portion of an Exposure Analysis Data Results Excel sheet, showing an
example of scoring for each exposure indicator. ................................................................................... 8
Figure 4. FEMA flood zones for Everglades National Park, showing the D zone. ............................ 10
Figure 5. Example of the Shoreline Change Hazard Zone and associated data at Cape
Hatteras National Seashore. ................................................................................................................. 13
Figure 6. NOAA SLR viewer local scenario and NPS-specific SLR projections for
George Washington Birthplace National Monument. .......................................................................... 15
Figure 7. Aerial imagery highlighting barrier island geomorphology and infrastructure
changes at Gulf Islands National Seashore. ......................................................................................... 17
Figure 8. Example of an Exposure Analysis Data Results Excel sheet, showing all
scoring columns, including individual indicator scoring, intermediate calculations, and
final exposure results. .......................................................................................................................... 19
Figure 9. Example of a Sensitivity Analysis Data Results Excel sheet, showing all scoring
columns, including individual indicator scoring, intermediate calculations, and final
sensitivity results. ................................................................................................................................. 22
Figure 10. Example of a Vulnerability Assessment Data Results Excel sheet, showing all
scoring columns, including exposure and sensitivity scoring, intermediate calculations,
and final vulnerability results. .............................................................................................................. 28

vii
Tables
Page
Table 1. Exposure indicators and common data sources. ...................................................................... 8
Table 2. Shoreline change increment groups and buffer distances. .................................................... 13
Table 3. Exposure scoring summary. .................................................................................................. 20
Table 4. Sensitivity indicators and common data sources. .................................................................. 21
Table 5. Additional bridge sensitivity indicators and scoring. ............................................................ 25
Table 6. Sensitivity scoring summary. ................................................................................................ 26
Table 7. Sensitivity scoring summary for bridges with NBI data. ...................................................... 27
Table 8. Vulnerability scoring summary. ............................................................................................ 28
Table A-1. Table of Asset Code and Facility Type descriptions from FMSS. ................................... 37
Table B-1. Accessible version of Figure 3. ......................................................................................... 41
Table B-2. Accessible version of inset table in Figure 6. NPS-specific SLR projections
for George Washington Birthplace National Monument from Caffrey et al. 2018. ............................ 41
Table B-3. Accessible version of Figure 8. ......................................................................................... 42
Table B-4. Accessible version of Figure 9. ......................................................................................... 43
Table B-5. Accessible version of Figure 10. ....................................................................................... 45

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Executive Summary
The National Park Service (NPS), in collaboration with the Program for the Study of Developed
Shorelines at Western Carolina University, developed a standard natural hazards and climate change
vulnerability assessment methodology for infrastructure in coastal national parks. The Coastal
Hazards & Sea-Level Rise Asset Vulnerability Assessment Protocol evaluates the vulnerability of
NPS buildings and transportation assets to sea-level rise, flooding, and shoreline change (note that
underground utilities are not included as of this writing due to incomplete data regarding location,
condition, and other attributes). The fundamental purpose of vulnerability assessments is to
understand the degree to which resources and assets are “susceptible to harm from direct and indirect
effects of climate change, including variability and extremes” (NPS 2021a). This document describes
the developmental history of this protocol and provides a detailed description of the methodology.
Over the past decade, the Coastal Hazards & Sea-Level Rise Asset Vulnerability Assessment
Protocol was developed and refined in phases, including 1) a review of infrastructure vulnerability
assessment approaches within the NPS (2012–2013); 2) creation of a new coastal vulnerability
assessment protocol for infrastructure (2014); 3) pilot application of the protocol in seven park units
(2015); 4) protocol application in 12 additional parks and partnership with parallel projects (2016–
2017); and 5) continued protocol implementation and collaboration (2017–present). The protocol has
been used to assess more than 40 parks (as of March 2022).
Exposure is commonly evaluated when conducting vulnerability assessments for infrastructure, but
sensitivity is not, particularly at the asset level (Peek et al. 2022). This vulnerability assessment
protocol evaluates both the exposure and sensitivity of individual NPS assets. The protocol focuses
on buildings and transportation assets listed in the NPS Facility Management Software System
database and has three primary steps: 1) Exposure Analysis and Mapping, 2) Sensitivity Analysis,
and 3) Vulnerability Calculations.
The first step in this vulnerability assessment protocol is to analyze the exposure of NPS assets to
five exposure hazards/indicators: flooding potential, shoreline change, sea-level rise inundation,
extreme event flooding, and reported coastal hazards. All exposure hazards are assessed to the year
2050, which is a reasonable timeframe for park planning. Evaluating exposure is primarily an
exercise of data gathering and geospatial analysis, as exposure is directly dependent on location and
site elevation. We use data from nationally available and consistent sources to facilitate direct
comparison of exposure scores from one site to another.
The second step is to analyze the sensitivity of each asset. Sensitivity is not location dependent, and
sensitivity analysis includes examining all features of an asset (e.g., materials, age, construction, or
design elements) that will improve or degrade its performance when exposed to a hazard. The
primary data source for the sensitivity analysis is an asset-specific questionnaire typically completed
by knowledgeable park staff, along with additional sensitivity data obtained from the National Bridge
Inventory, aerial imagery, and site visits. The final vulnerability score is a simple mathematical
combination of exposure and sensitivity.

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Quality control is a key component throughout this vulnerability assessment process. Expert
verification of exposure data is crucial to provide accurate and meaningful results, as hazard data are
often incomplete, can contain errors, and do not always accurately model dynamic coastal
environments. As part of this protocol, we carefully review all exposure hazard data and employ our
understanding of coastal geomorphological processes and past events to identify model errors or
inaccuracies. When necessary and available, we verify hazard data by consulting various
supplemental data sources (often historical aerial imagery, storm records, and other hazard maps).
The sensitivity analysis process relies heavily on park institutional knowledge regarding the physical
attributes of assets. We review the responses for each sensitivity indicator for consistency and verify
the results as needed (e.g., through further discussion, aerial imagery, or other documentation).
Data gathered using this protocol can directly inform decision-making, be integrated into quantitative
risk evaluations, and inform asset-level planning. Vulnerability is assessed at the individual park
level, and results can be combined into regional or national summaries because scores are
comparable from site to site, enabling the NPS to identify highly vulnerable assets across all coastal
parks regardless of differences in geography or asset type. The protocol also collates the vulnerability
results with input data from the Facility Management Software System and other sources. Core
protocol products include these data reformatted in spreadsheets and geodatabases (e.g., ArcGIS and
Google Earth files, NPS Coastal Vulnerability Data Viewer) and summarized in a final report.

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Flooded boardwalk at Jean Lafitte National Historical Park and Preserve (©PROGRAM FOR THE STUDY
OF DEVELOPED SHORELINES, WESTERN CAROLINA UNIVERSITY).

xiii
Acknowledgments
We would like to thank the following people: Amanda Babson, Rebecca Beavers, Andy Coburn,
Alan Ellsworth, John Gross, Cat Hawkins Hoffman, Matt Holly, and Shawn Norton.
Acronyms
CFP—Climate Friendly Parks
FEMA—Federal Emergency Management Agency
FIRM—Flood Insurance Rate Map
FMSS—Facility Management Software System
GIS—Geographic Information Systems
NBI—National Bridge Inventory
NOAA—National Oceanic and Atmospheric Administration
NPS—National Park Service
RCP—Representative Concentration Pathway
SFHA—Special Flood Hazard Area
SLOSH—Sea, Lake, and Overland Surges from Hurricanes
SLR—Sea-Level Rise
VA—Vulnerability Assessment
WCU—Western Carolina University

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Background & Objectives
In 2013, the National Park Service (NPS) initiated a project with the Program for the Study of
Developed Shorelines at Western Carolina University (WCU) to develop a climate change
vulnerability assessment (VA) protocol for infrastructure in coastal national parks. The Coastal
Hazards & Sea-Level Rise Asset Vulnerability Assessment Protocol evaluates the vulnerability of
NPS buildings and transportation assets to sea-level rise (SLR), flooding, and shoreline change.
Underground utilities are not included as of this writing due to incomplete data regarding location,
condition, and other attributes. The overarching purpose of climate change VAs within the NPS is to
better understand the degree to which park resources and assets are “susceptible to harm from direct
and indirect effects of climate change, including variability and extremes” (NPS 2021a).
Vulnerability assessments help identify why, where, when, and which resources may be most at risk
(NPS 2021b) and help prepare managers for effective adaptation planning (Beavers et al. 2016). The
purpose of this document is to provide the developmental history of this protocol and a detailed
description of the methodology.
Major project goals:
● Establish a standard methodology and set of best practices for conducting VAs in the built
environment to allow managers to compare the vulnerability of coastal park infrastructure
assets across the NPS.
● Provide VA results that are detailed, asset specific, and integrated with other NPS asset
management data.
● Evaluate asset vulnerability as a function of exposure and sensitivity.
● Use widely available, established data sources to evaluate asset exposure.
● Provide a comprehensive and objective evaluation of asset sensitivity.
Development of the Coastal Hazards & SLR Asset Vulnerability Assessment Protocol
Assessing vulnerability is the first step in climate change and hazard adaptation, as this information
is critical to sensibly managing and planning for resources and assets. The NPS and WCU developed
the Coastal Hazards & SLR Asset Vulnerability Assessment Protocol, which creates a standard
VA methodology for coastal park assets.
Multiple NPS-specific studies use exposure, sensitivity, and adaptive capacity to evaluate resource
vulnerability (e.g., Amberg et al. 2012; Barrows et al. 2014; Handler et al. 2020; Hansen et al. 2013;
Ricci et al. 2019a; b; Stroh et al. 2016). Exposure is a measure of the character, magnitude, and rate
of changes a target may experience (e.g., from the impacts of climate change or a natural hazard;
NPS 2021a), while sensitivity reflects the degree to which a resource is affected by the exposure.
Adaptive capacity is the ability of a resource to accommodate or cope with the impacts (Beavers et
al. 2016; Glick et al. 2011; NPS 2021a).
The discussion below includes a timeline for the development of the current VA protocol including
improvements in the procedure and participating NPS units that contributed to the evolution of the
current product.

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Review of Previous Infrastructure VA Approach (2012–2013)
The NPS Sustainable Operations and Climate Change Branch (now the Sustainable Operations and
Maintenance Branch) of the Facilities Management Division (in collaboration with the NPS Climate
Change Response Program) piloted an effort called “Facilities Adaptation to Climate Change
Impacts in Coastal Areas” (ICF International 2012a; b). This work developed a park-level risk
screening approach to assess vulnerability of park facilities to selected coastal hazards using the three
metrics of vulnerability. This approach used mapped landscape and geomorphologic data to evaluate
exposure, and Facility Management Software System (FMSS) data to evaluate and score sensitivity
and adaptive capacity (Facility Condition Index and Current Replacement Value attributes,
respectively). The NPS piloted this approach in two parks (Pu`uhonua O Hōnaunau National Historic
Park and Assateague Island National Seashore) and completed final reports in 2012 (ICF
International 2012a; b). In 2013, the NPS partnered with WCU to expand and improve this VA
approach (ICF International 2012a; b).
Goals for next steps in the protocol development were:
● Consider additional stressors (SLR and coastal flooding).
● Explore which indicators of vulnerability are most useful and appropriate to support park
planning.
Creation of a New Coastal VA Protocol for Infrastructure (2014)
We presented an updated version of this protocol (ICF International 2012a; b) during the Climate
Friendly Parks (CFP) Workshop at Cape Hatteras National Seashore, Wright Brothers National
Memorial, and Fort Raleigh National Historic Site in November of 2014. Participant feedback
indicated that Facility Condition Index and Current Replacement Value do not accurately represent
an asset’s sensitivity and adaptive capacity to coastal hazards and SLR. Park staff expressed
hesitation about using FMSS data to evaluate and score physical vulnerability, as FMSS attributes
can be subjective (depending on current priorities and staffing) and are calculated for specific
management or maintenance purposes.
Workshop participants further discussed the complexities of evaluating adaptive capacity for
infrastructure, noting that indicators for this component of vulnerability are numerous, hard to
quantify, and can change frequently. Infrastructure cannot inherently adapt to climate change or other
hazards, while “living” resources often can (e.g., salt marsh plants can naturally adapt to SLR by
migrating upland, whereas a building, on its own, cannot). Some organizations and agencies include
extrinsic circumstances as part of adaptive capacity for infrastructure. These include a wide variety of
organizational, social, political, or economic factors that would (or would not) enable relocation or
modification of infrastructure to avoid harm from the effects of SLR, storms, and other stressors.
However, the subjective, ever-changing, and often transient nature of these extrinsic factors makes
them difficult to evaluate and quantify in a meaningful or consistent way.
Based on feedback from this workshop and follow-up discussions, we drafted a new VA protocol for
coastal infrastructure, including these key features.

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Key protocol features:
● Standardized exposure and sensitivity indicators (i.e., representative proxy factors) for
evaluating the vulnerability of coastal hazards and SLR.
● Data collection that is as objective and uniform as possible.
● Exposure indicators that include multiple coastal hazards and climate change factors, and use
well-established, widely available, and georeferenced data (where available). These indicators
include flooding potential, shoreline change, SLR inundation, extreme event flooding, and
reported coastal hazards.
● Sensitivity indicators that include inherent properties, construction types/methods, or other
characteristics of the asset, and no longer use FMSS attributes. These indicators include flood
damage potential, storm resistance/condition, historical damage, and protective engineering.
● Adaptive capacity is not included in an asset’s vulnerability score.
Pilot of the Coastal Hazards & SLR Asset Vulnerability Assessment Protocol (2015)
In 2015, we piloted the Coastal Hazards & SLR Asset Vulnerability Assessment Protocol in
seven park units in the NPS Southeast Region: Big Cypress National Preserve, Biscayne National
Park, Fort Sumter and Fort Moultrie National Historical Park, Gulf Islands National Seashore, Cape
Hatteras National Seashore, Wright Brothers National Memorial, and Fort Raleigh National Historic
Site. Feedback obtained during CFP and Integrated Park Improvement workshops led to further
refinement of the protocol. During this pilot period, the NPS also established additional protocol
standards related to methods, data, and scoring. These standards were based on NPS planning needs
and concurrent climate change and vulnerability projects.
Key protocol developments:
● Creation of the asset-level survey tool and methods for data about each sensitivity indicator,
recognizing that park personnel with institutional knowledge have the most accurate and
current information regarding these indicators. While facility databases may contain some data
related to these indicators (e.g., condition), in most cases these data are not appropriate for this
protocol.
● Incorporating precise threshold elevation data for buildings (when available) into the
sensitivity analysis to increase accuracy of the indicator on flood damage potential.
Standards we adopted in the 2015 protocol revision include:
● 2050 planning horizon for the VAs as requested by the NPS.
● Scoring structure compatible with the Federal Highway Administration Vulnerability
Assessment Scoring Tool (ICF 2015) as requested by the NPS.
● Use of SLR projections from the United Nations Intergovernmental Panel on Climate Change
and storm surge scenarios from National Oceanic and Atmospheric Administration (NOAA)
models (Caffrey et al. 2018), if available, within the exposure analysis.

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VA Protocol Expansion & Collaboration (2016–2017)
Throughout 2016 and 2017, the project expanded to include additional park units across all coastal
NPS regions (18 for an exposure analysis and 12 for a full VA). This required protocol modifications
for areas with different geomorphology (e.g., cliffed shorelines) and areas with additional hazards
(e.g., tsunami).
In addition to participating in CFP workshops at Jean Lafitte National Historical Park and Preserve
and Canaveral National Seashore, we provided VA results for Colonial National Historical Park to be
included in the Integrated Coastal Climate Change Vulnerability Assessment methodology piloted by
the NPS Northeast Region and the University of Rhode Island (Ricci et al. 2019b). We also provided
vulnerability data for incorporation into a cultural resources adaptation pilot study of historic
buildings at Cape Lookout National Seashore (Fatorić and Seekamp 2017).
Key protocol developments:
● Addition of region-specific coastal hazards, such as tsunamis in the NPS Alaska Region and
cliff retreat in the NPS Pacific West Region.
● Incorporation of a minimal (or no) exposure category to score assets more accurately across all
coastal parks.
Continued Protocol Implementation & Collaboration (2017–present)
Over the next several years, we completed VAs at multiple parks using the Coastal Hazards & SLR
Asset Vulnerability Assessment Protocol and assisted with the integration of these results into
ongoing collaborative projects. We provided infrastructure vulnerability data for the Integrated
Coastal Climate Change Vulnerability Assessment methodology at Fire Island National Seashore and
George Washington Birthplace National Monument, as well as for the Facility Management
Environmental Hazard Assessment, Adaptation and Resilience Planning Workshop at Fort Pulaski
National Monument. We also presented vulnerability assessment results at the CFP workshop at New
Bedford Whaling National Historical Park and Roger Williams National Memorial.
In 2020, the NPS Climate Change Response Program tasked WCU to complete VAs for all
remaining NPS Southeast Region coastal units to provide a region-wide analysis and summary of
vulnerability. The only major development to the protocol during this period was the added
evaluation of a higher SLR scenario in response to updated projections in 2017 from NOAA (Sweet
et al. 2017). This higher SLR scenario was included only as a comparison for these NPS Southeast
Region park units.
In 2022, NOAA updated their SLR projections again (Sweet et al. 2022). These projections will
determine the SLR scenario used in the protocol, and this scenario will be used to assess exposure
results going forward pending additional, future SLR projection updates. The next phase of this
project (which began in 2022) will complete VAs for all remaining NPS Northeast Region coastal
units. To date, WCU has used the protocol to evaluate vulnerability at more than 40 park units.
Key (2022) protocol developments:
● Evaluation of a higher SLR scenario in response to updated projections from NOAA.

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Protocol Methodology
The Coastal Hazards & SLR Asset Vulnerability Assessment Protocol evaluates the vulnerability
of NPS assets to SLR, flooding, and shoreline change approximately to the year 2050, with a focus
on buildings and transportation assets listed in the NPS FMSS database. This protocol has three
primary steps: 1) Exposure Mapping and Analysis, 2) Sensitivity Analysis, and 3) Vulnerability
Calculations. Figure 1 summarizes the key steps within this VA protocol.
Figure 1. Flowchart of the primary steps within the Coastal Hazards & SLR Asset Vulnerability
Assessment Protocol.
Asset Data Preparation
Due to the quantity of information within FMSS, we prepare the asset data by completing these
initial steps.
1. Filter park-specific FMSS Location Hierarchy Report to extract buildings (or
structures) and transportation-related assets. The most common asset types evaluated are
roads, parking areas, trails, buildings, marinas, and fortifications. Asset types commonly not
evaluated include maintained landscapes, major systems (e.g., water, electrical, waste,
communications), archaeological sites, and interpretive media. Underground utilities are not
included as of this writing due to incomplete data regarding location, condition, and other
attributes. All evaluated FMSS Asset Types and Facility Types are found in Appendix A.

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2. Obtain existing asset geographic location data. Many FMSS-listed assets lack geospatial
coordinates. We request the relevant geospatial data from archives maintained at the regional
and national levels (typically in a geodatabase format).
3. Associate geospatial data with FMSS records. As required, we associate geospatial data
(provided by the NPS) with the correct FMSS records.
4. Identify the latitude and longitude of assets without geographic location data. We most
commonly use imagery (aerial or ground), site visits, and correspondence with park staff to
obtain the geographic location data of assets.
5. Convert FMSS asset data into a geospatial format. To prepare the FMSS asset data for
exposure analysis, we initially convert location data to point shapefiles (linear and polygon
features are digitized during the exposure analysis in most parks).
Road & Trail Segmentation
We represent roads and trails geospatially as continuous linear features (i.e., polylines). However,
since exposure varies with location, we divide long roads and trails into smaller segments to assess
vulnerability more accurately and at a finer scale. Typically, we segment these assets based on
exposure variability, length, road intersections, and/or geographical features. We then evaluate and
score each segment individually for exposure, sensitivity, and vulnerability (Figure 2). We also
assign each segment a modified location code (e.g., 12345_1, 12345_2, 12345_3); however, all
segments share the same FMSS attributes.

7
Figure 2. Example of road segmentation process for East End Road at Virgin Islands National Park. The
primary segmentation factor for this road was level of exposure (the most extensive hazard zone for this
area is shown by grey hatched areas). Road intersections, geography, and segment length were also
used as criteria to determine break points between segments.
Exposure Mapping & Analysis
This VA protocol includes five coastal hazard and climate change exposure indicators: flooding
potential, shoreline change, SLR inundation, extreme event flooding, and reported coastal hazards
(Table 1). Evaluating exposure is primarily a geospatial exercise, as exposure is directly dependent
on location (i.e., whether the area surrounding an asset has experienced, or has the potential to
experience, a hazard). Although we complete much of the exposure analysis in Geographic
Information Systems (GIS), we record all calculations and results in the Exposure Analysis Data
Results Excel sheet (Figure 3). In most cases, this protocol relies on existing exposure data. Only in
rare instances do we create unique exposure maps or data.

8
Table 1. Exposure indicators and common data sources.
Indicator
Description
Common Data Sources
Flooding Potential
1% annual flood chance ±
velocity/waves
Federal Emergency Management Agency flood zones
(VE or AE); digital elevation models
Shoreline Change
Erosion, coastal proximity,
or cliff retreat
Shoreline buffers derived from USGS, state, or NPS
shoreline change data; cliff retreat rate buffers;
shoreline proximity buffers
SLR Inundation
2050 scenarios
NPS-specific SLR modeling A; NOAA SLR projections
& inundation model B; digital elevation models
Extreme Event Flooding
Storm surge, tsunami, or
extreme high water
NPS-specific storm surge projections & models A;
digital elevation models; NOAA SLOSH models C;
digital elevation models
Reported Coastal Hazards
Historic flooding, visible
slope instability
Park surveys/questionnaire results; storm imagery &
reconnaissance
A Caffrey et al. 2018
B Sweet et al. 2017; 2022
C SLOSH - Sea, Lake, and Overland Surges from Hurricanes; Zachry et al. 2015
Figure 3. A portion of an Exposure Analysis Data Results Excel sheet, showing an example of scoring for
each exposure indicator (columns 1a–1f). Columns 1a. and 1b. together represent the flooding potential
indicator, and column 1f. Historic Flood Score is the reported coastal hazards indicator in this example.
See Table B-1 for an accessible version.
In general, we assign each asset a score of 4 or 1 for each of the five exposure indicators (Table 1).
We assign a score of 4 to assets located within an exposure indicator zone (or with a record of past
coastal hazards), and a score of 1 to assets that are outside the indicator zone. At the outset of the
protocol development, the NPS requested that our scoring structure be compatible with the Federal

9
Highway Administration Vulnerability Assessment Scoring Tool, which uses a 1–4 numeric scoring
system. In general, the scoring structure represents a binary scoring method where 4 is unfavorable
and 1 is favorable (as higher scores overall mean higher exposure). We calculate a raw total indicator
score for each asset and then categorize (bin) those scores into one of four exposure rankings (based
on the number of hazard zones): minimal or none (asset is not within any mapped zone), low (1
zone), moderate (2–3 zones), and high (4–5 zones). Specific methods for scoring each exposure
indicator are described in the following sections.
1. Flooding Potential Indicator
The flooding potential indicator describes hazards related to the 1% annual flood chance, including
waves and water velocity. The following sections describe the data sources, data preparation steps,
methods, and scoring for this exposure indicator.
Data Sources & Preparation
Federal Emergency Management Agency (FEMA) Flood Insurance Rate Maps (FIRMs, also known
as flood maps, see Box 1) are the most common data source for the flooding potential exposure
indicator (Table 1). Relevant FEMA flood zones include those within the Special Flood Hazard Area
(SFHA), most commonly the VE and AE zones (less commonly the A, AO, or AH). FEMA defines
the VE zone as “areas subject to inundation by the 1-percent-annual-chance flood event, with
additional hazards due to storm-induced velocity wave action,” and the AE zone as “areas subject to
inundation by the 1-percent-annual-chance flood event” (determined by detailed methods; FEMA
Glossary 2021). One caveat is that these data do not currently address any projections of climate
change. For a further description of the FEMA flood zones, see Box 1.
Box 1. FEMA Flood Zones and Special Flood Hazard Area Definitions
Flood hazard areas identified on the Flood Insurance Rate Map are identified as a Special Flood
Hazard Area (SFHA). SFHA are defined as the area that will be inundated by the flood event
having a 1-percent chance of being equaled or exceeded in any given year. The 1-percent annual
chance flood is also referred to as the base flood or 100-year flood. SFHAs are labeled as Zone
A, Zone AO, Zone AH, Zones A1-A30, Zone AE, Zone A99, Zone AR, Zone AR/AE, Zone
AR/AO, Zone AR/A1-A30, Zone AR/A, Zone V, Zone VE, and Zones V1-V30.
Glossary Section: NFIP - National Flood Insurance Program

10
FEMA data preparation steps:
1. Download digital FIRMs using FEMA’s National Flood Hazard Layer Viewer. Most
coastal areas have digital data. For an area without digital data, we consult the PDF FIRMs.
2. Insert digital FEMA data into a GIS software program. Since the beginning of this
project, we have used numerous versions of ESRI’s ArcMap software (most recently version
10.8).
3. Select and extract the VE and AE (or alternate A) zones. This step reduces the overall
size of the data used for the GIS analysis and is not always necessary for smaller parks.
4. Clip the FEMA data to the extent of the park. This step further reduces the overall size of
the data used for the GIS analysis and is not always necessary.
Most parks evaluated thus far have FEMA digital flood map coverage. In the few exceptions where
detailed FEMA data are lacking, we use a variety of data sources (e.g., digital elevation models,
records of past flood extent, other hazard maps) and geologic expertise to make an informed
decision. For example, FEMA designated a portion of Everglades National Park within a D zone,
which is defined as “possible but undetermined flood hazards” (Figure 4). Based on the elevation
data, other storm surge maps, adjacent flood zones, and record of past flooding, we determined that
assets in this area should be scored within FEMA’s A zone.
Figure 4. FEMA flood zones for Everglades National Park, showing the D zone (possible but
undetermined flood hazards). In this zone, we used elevation data, storm surge maps, past flooding, and
adjacent flood zones to evaluate the flooding potential indicator.

11
Analysis & Scoring
The following scoring methodology for the flooding potential exposure indicator applies to units with
full FEMA FIRM coverage. For parks where FEMA data are lacking, we score assets directly based
on alternate data sources and geologic expertise.
Flooding Potential Indicator scoring methodology:
1. Map the location of park assets and compare to the extent of the FEMA flood zones.
Using ArcMap, we compare the location of park assets to the extent of the FEMA SFHA.
2. Score assets within the FEMA VE flood zone. We assign assets located within the VE zone
(the highest hazard zone) a score of 4 within the 1a. FEMA VE Zone Score column in the
Exposure Analysis Data Results Excel sheet (Figure 3).
3. Score assets within the FEMA AE flood zone. We assign assets located within the AE (or
other A) zone a score of 4 within the 1b. FEMA AE Zone Score column in the Exposure
Analysis Data Results Excel sheet (Figure 3).
4. Score assets outside of FEMA flood zones. We assign assets located outside of both the
FEMA flood zones (VE or A) a score of 1 within the 1b. FEMA AE Zone Score column in the
Exposure Analysis Data Results Excel sheet (Figure 3).
2. Shoreline Change Indicator
The shoreline change exposure indicator represents hazards such as beach and dune erosion, cliff
retreat, and coastal proximity. We most commonly evaluate shoreline erosion and coastal proximity
hazards for this indicator. The following sections describe the data sources, data preparation steps,
methods, and scoring for this exposure indicator.
Data Sources & Preparation
We commonly acquire shoreline change (erosion/accretion rate and cliff retreat) data from the U.S.
Geological Survey (Coastal Change Hazards Portal), state coastal management programs (e.g., North
Carolina Division of Coastal Management), or from the NPS Inventory and Monitoring Program
(Table 1). In most cases, these shoreline-change data are calculated and displayed along evenly
spaced transects (commonly 50 meters apart) along the oceanfront. This protocol uses the short-term
shoreline change rates (calculated using data ranging from the 1970’s to present day) when available.
We recognize that with continuing SLR, these numbers may eventually underestimate shoreline
retreat in many areas.
Using these data, we create Shoreline Change Hazard Zones to represent the land area potentially
exposed to shoreline change to the year 2050. For parks evaluated prior to 2020, shoreline change
analyses were based on a 35-year timeline. Parks evaluated after 2020 use a 30-year timeline.

12
Shoreline Change Hazard Zone GIS steps:
1. Obtain or digitize a shoreline adjacent to park assets. A digital shoreline is required as a
baseline to create shoreline change hazard zones. Recent digital shorelines are sometimes
available, particularly for parks with property along the oceanfront. When no shorelines are
available, we manually digitize a shoreline based on the most recent aerial imagery using the
wet/dry line for oceanfront or sandy shorelines (Figure 5), and the water’s edge for other
estuarine, soundside, and urban shorelines. We consult aerial imagery from multiple
timeframes when using the water’s edge for digitization, in particular in areas with a high
tidal range.
2. Group and display shoreline change data. In most cases, we group and display (color
code) shoreline-change data transects in 1- or 2-meter increment groups (Table 2, and
Figure 5). In limited cases, especially shorelines with cliff retreat, it may be necessary to
group transects into more detailed increments (Peek et al. 2017).
3. Interpret shoreline segments. We divide the digital shoreline into segments based on our
interpretation of patterns in the color-coded shoreline change transects (Table 2, and
Figure 5).
4. Buffer shoreline segments with shoreline change data. We buffer the shoreline segments
based on the increment groups and corresponding buffer distance (Table 2, and Figure 5).
Buffer distances are a function of the shoreline change increment and the study timeline (e.g.,
either 30 or 35 years).
5. Buffer shorelines without shoreline change data. We use a standard buffer distance for
shorelines without shoreline change data (e.g., estuarine, sound, marsh, or urban; Table 2,
and Figure 5). This creates a shoreline proximity buffer, which assumes areas closest to the
coast generally have a higher exposure to erosion and other hazards. Final buffers represent
the shoreline change hazard zones (Figure 5).

13
Figure 5. Example of the Shoreline Change Hazard Zone and associated data at Cape Hatteras National
Seashore. Shoreline change rate transects are perpendicular to the shoreline and color coded by
increment group (Table 2). We segmented and buffered the digitized oceanfront shoreline (dotted black
line) by interpreting the general patterns in the transect rates. Note that we generalized the three 2 to 4
meters of erosion per year transects (orange) on the west portion of the shoreline to be within the 60-
meter buffer zone (1 to 2 meters of erosion per year). As a rule, we only generalize up to 3 consecutive
transects within another zone.
Table 2. Shoreline change increment groups and buffer distances.
Increment Groups
Description
Buffer Distance A
1 meter / year
<1 meters of erosion per year (accretion or no data)
30 meters
2 meter / year
1 to 2 meters of erosion per year
60 meters
4 meter / year
2 to 4 meters of erosion per year
120 meters
6 meter / year B 4 to 6 meters of erosion per year 180 meters
A Using a 30-year timeline (parks evaluated after 2020).
B Continued 2-meter increments as needed.
Analysis & Scoring
Using ArcMap, we compare the location of park assets to the extent of the shoreline change hazard
zones. We assign assets located within any of the mapped shoreline change hazard zones a score of 4
within the 1c. Shoreline Change Score column in the Exposure Analysis Data Results Excel sheet,
and those outside the zone a score of 1 (Figure 3).

14
3. SLR Inundation Indicator
The SLR inundation indicator describes hazards related to the potential inundation by SLR by the
year 2050. The following sections describe the data sources, data preparation steps, methods, and
scoring for this exposure indicator.
Data Sources & Preparation
The data for this exposure indicator are SLR projections and inundation models (Caffrey et al. 2018)
developed specifically for NPS units (Table 1). This study estimated inundation extent using a
modified bathtub approach developed by NOAA, which attempts to account for local and regional
tidal variability and hydrological connectivity. The authors developed inundation scenarios for the
years 2050 and 2100, based on the moderate (4.5) Representative Concentration Pathway (RCP) and
the most extreme (8.5) RCP. One caveat of these data is that the model does not incorporate local
land level change (subsidence or uplift). For many parks this is not a problem, as this change is
relatively small compared to the amount of predicted water level rise. However, the SLR data in
parks with high rates of subsidence (parks in southern Louisiana) or uplift (many Alaska parks) will
require adjustment.
In 2020, we revised the SLR data used in the protocol to reflect more closely the updated (and in
most cases higher) projections from NOAA (Sweet et al. 2017). For parks with VAs initiated in 2020
or 2021 (primarily NPS Southeast Region parks), these new data were only provided for comparison
and were not incorporated into the final exposure results. We will consult NOAA’s most recent
projections (Sweet et al. 2022) on all VAs initiated in 2022 (and later) to determine the scenario used
within the protocol and will incorporate this scenario into the exposure results going forward.
Methods for incorporating higher SLR projections:
1. Consult NOAA SLR data and projections. We consult NOAA’s Sea Level Rise Viewer to
identify the closest location (to the evaluated park) with updated SLR projections. We use the
2050 intermediate and intermediate-high SLR scenarios (Figure 6).
2. Consult SLR data tables and projections from the NPS-specific SLR study. We consult
data tables from Caffrey et al. (2018) to identify the SLR values for each year and scenario
(Figure 6, inset).
3. Select the NPS-specific projection that best matches the NOAA projections. The selected
SLR value (and associated inundation maps) serves as a proxy for the updated NOAA
projections for 2050 (Figure 6).

15
Figure 6. NOAA SLR viewer local scenario (background image) and NPS-specific SLR projections (inset
table) for George Washington Birthplace National Monument. We selected the 2100 4.5 RCP SLR value
(0.63 meters) because it best matches the NOAA intermediate and intermediate high scenarios (0.52
meters and 0.7 meters respectively). See Table B-2 for an accessible version of the inset.
Analysis & Scoring
Using ArcMap, we compare the location of park assets to the extent of the SLR inundation hazard
zone. We assign assets located within this hazard zone a score of 4 within the 1d. SLR Score column
in the Exposure Analysis Data Results Excel sheet, and those outside the zone a score of 1 (Figure 3).
4. Extreme Event Flooding Indicator
The extreme event flooding indicator describes hazards related to storm surge, tsunami, or other
extreme high-water events. The following sections describe the data sources, data preparation steps,
methods, and scoring for this exposure indicator.
Data Sources & Preparation
Storm surge is the primary extreme flooding event that occurs within parks along the Atlantic Ocean
and Gulf of Mexico coast of the U.S. The most common data source for this exposure indicator is
storm surge inundation models (Caffrey et al. 2018) developed specifically for NPS units using
NOAA’s SLOSH (Sea, Lake, and Overland Surges from Hurricanes) methodology (see NOAA for
more information). We selected the category 3 storm surge inundation maps for use within this
analysis (Table 1). We also consult NOAA’s National Storm Surge Hazard Maps - Version 2
(SLOSH) and/or digital elevation models as part of the analysis, primarily for comparison or when
other data do not exist.
For parks not commonly impacted by tropical storms and surge (e.g., parks in the NPS Pacific West
and Alaska regions), we evaluate other extreme event flooding hazards, commonly either modeled
extreme high-water events (from Caffrey et al. 2018) or tsunami hazard zones. The source of the
tsunami hazard data is variable, but commonly comes from state agencies or universities.

16
Analysis & Scoring
Using ArcMap, we compare the location of park assets to the extent of the extreme event flooding
hazard zone. We assign assets located within this hazard zone a score of 4 within the 1e. Extreme
Event Flooding Score column in the Exposure Analysis Data Results Excel sheet, and those outside
the zone a score of 1 (Figure 3). Some parks have more than one extreme event flooding dataset
available (e.g., Caribbean parks often have both tsunami and storm surge inundation maps). In these
cases, we assign an asset a score of 4 if located within any of the available inundation datasets.
5. Reported Coastal Hazards Indicator
The reported coastal hazards exposure indicator captures how past events (e.g., historic flooding and
visible slope instability) impacted individual assets. We include this indicator to add additional
weight to locations that have experienced previous flooding (primarily) and to provide some
verification of the four indicators above, which are largely predictive.
Data Sources & Preparation
We commonly obtain historical flooding and visible slope instability information from park staff
through a questionnaire (WCU 2022) and subsequent discussions, as well as imagery and
reconnaissance visits.
Analysis & Scoring
We assign assets with reported coastal hazards a score of 4 within the 1f. Reported Coastal Hazards
column in the Exposure Analysis Data Results Excel sheet, and those without reported coastal
hazards a score of 1 (Figure 3).
Quality Control
The exposure analysis process is primarily a geospatial exercise; however, expert verification is
crucial to provide the most accurate and meaningful results. Hazard data are often incomplete within
national parks, can contain errors, and do not always accurately model dynamic coastal
environments. While the exposure analysis in this protocol uses the most current and reliable datasets
available, extreme events often can completely reconfigure a coastal shoreline and topography in a
short period of time (Figure 7). As a result, exposure data sources can quickly become inaccurate,
especially inundation models, which have the potential to underestimate flooding in areas recently
overwashed by hurricanes (Figure 7).

17
Figure 7. Aerial imagery highlighting barrier island geomorphology and infrastructure changes at Gulf
Islands National Seashore. A) 2003 infrastructure configuration in the Opal Beach area. B) Overwash
from storms in 2004 covering Opal Beach infrastructure. C) New infrastructure configuration at Opal
Beach with overwash from storms in 2006. D) Storm surge category 3 inundation model (green shading)
and recent (2019) Opal Beach infrastructure configuration. Due to the changing shoreline and topography
at this location, the surge model does not show inundation of the oceanfront infrastructure, despite
multiple overwash episodes.
As part of this protocol, we carefully review all exposure hazard data and use our knowledge of
coastal geomorphology and past events to identify possible errors or inaccuracies. When necessary,
we verify hazard data by consulting multiple other data sources (often historical aerial imagery,
storm records, and other existing hazard maps). This quality control is imperative, particularly for
studies that utilize hazard model datasets.
Because assets are represented in GIS by points and lines, we examine aerial imagery to observe the
exact geographic footprint of each asset relative to the hazard zones. This is completed during the
initial exposure analysis and scoring, and then repeated (often multiple times, by multiple personnel)
as a final quality control check on the results.

18
Exposure Calculations & Final Scoring
After individual scores are determined for each exposure indicator (as described in the previous
sections), we complete several additional steps to obtain a final, comprehensive exposure score and
rank that incorporates all hazards (see Box 2). We record all exposure calculations and scoring within
the Exposure Analysis Data Results Excel sheet (Figure 8).
Box 2. Exposure Rank Descriptions
This VA evaluates the exposure of assets to multiple coastal hazards combined (Table 1).
Assets with High Exposure are within at least four exposure indicator hazard zones.
Assets with Moderate Exposure are within two or three exposure indicator hazard zones.
Assets with Low Exposure are within only one exposure indicator hazard zone. The asset could
still be seriously impacted by this hazard.
Assets with Minimal Exposure are not in any exposure indicator hazard zone. This does not
mean that the asset has no exposure to coastal hazards, but it is not within the exposure hazard
data used in this study.

19
Figure 8. Example of an Exposure Analysis Data Results Excel sheet, showing all scoring columns, including individual indicator scoring,
intermediate calculations, and final exposure results. Data columns not shown include multiple attributes taken directly from FMSS, and any final
notes related to exposure analysis and scoring. See Table B-3 for an accessible version.

20
Final exposure scoring steps:
1. Calculate the raw scores. We calculate raw scores as a sum of the exposure indicator scores
(using an Excel formula) within the Step 2: Raw Score from Step 1 column (Figure 8).
2. Bin the raw scores. We bin (group) the raw exposure scores into categories based on the
natural breaks shown in Table 3 and record these scores within the Step 3: Binned Raw Score
column (Figure 8).
Table 3. Exposure scoring summary.
Exposure Zones A
Raw Score
Exposure Score
Exposure Rank
4 or 5
17–20
4
High
2 or 3
11–14
3
Moderate
1
8
2
Low
0
5
1
Minimal B
A Number of exposure indicator zones an asset is within (assigned a 4).
B Minimal (or no), as some assets may not have exposure to the hazards evaluated.
3. Record assets within the FEMA VE Zone. Within this protocol, we automatically assign
any asset within FEMA’s highest hazard zone (VE) a high exposure score overall. We also
assign any assets with a score of 4 in the 1a. FEMA VE Zone column a score of 4 in the Step
4. VE Auto High column (Figure 8).
4. Update the exposure score. We assign a score of 4 within the Step 5. Exposure Score
Unmodified column for any asset assigned a score of 4 within the previous step. Scores for all
other assets are carried over directly from Step 3: Binned Raw Score (Figure 8).
5. Add comments if needed. We add any notes related to the exposure analysis or provided by
park staff to the final column of the Exposure Analysis Data Results Excel sheet and flag
these assets within the Step 6. Flagged Asset column (Figure 8).
6. Assign assets a final exposure score and rank. We carry over final exposure scores directly
from the Step 5. Exposure Score Unmodified column into Step 7: Exposure Score, and we
assign the corresponding exposure ranks in the Step 7: Exposure Rank column (Table 3,
Figure 8, and Box 2).
Sensitivity Analysis
The second step in this VA protocol is to analyze the sensitivity of NPS assets to coastal hazards and
SLR. We do not analyze the sensitivity of assets with minimal exposure; these assets are
automatically assigned a minimal ranking for vulnerability. To be vulnerable to a hazard, an asset
must first be exposed (regardless of sensitivity).
Similar to exposure, this protocol includes a set of indicators for asset sensitivity (Table 4).
Sensitivity is not location dependent, and the scoring is more subjective. Sensitivity is related to how

21
an asset is likely to fare when exposed to a hazard, which is a function of its inherent properties,
construction, and level of engineered protection. In this protocol, the sensitivity indicators for
buildings and transportation assets are the same; however, the manner in which sensitivity is
addressed during design and construction can be different (e.g., storm resistance of a road versus a
building).
Table 4. Sensitivity indicators and common data sources.
Indicator
Common Data Sources
Flood Damage Potential
Questionnaire & discussions; threshold elevation measurements; WCU
field assessments
Storm Resistance & Condition
Questionnaire & discussions; imagery; WCU field assessments
Historical Damage
Questionnaire & discussions; storm imagery
Protective Engineering
Questionnaire & discussions; imagery; NPS engineering inventory; WCU
field assessments
Because digital sensitivity data are not typically available for infrastructure, the primary data source
for the sensitivity analysis is an asset-specific questionnaire (Box 3; WCU 2022 and Appendix B)
and feedback from park staff (Table 4). The asset questionnaire addresses each of the various
sensitivity indicators (e.g., asking if a building is elevated to evaluate flood damage potential) and is
distributed to appropriate personnel (typically individuals with institutional memory of park
infrastructure). Where appropriate, we obtain sensitivity data from field assessments, asset databases
(e.g., property records, maintenance reports, site surveys, etc.), the National Bridge Inventory (NBI),
and aerial imagery.
In general, we score assets either favorably or unfavorably for each of the evaluated sensitivity
indicators (Table 4). We assign assets with an unfavorable sensitivity result a score of 4 for that
indicator, and assets with a favorable result a score of 1 (Figure 9). We calculate a raw total indicator
score for each asset and then categorize (bin) these scores into one of three sensitivity rankings
(reflective of the number of unfavorable indicators): low (0 or 1 unfavorable indicators), moderate (2
unfavorable indicators), and high (3 or 4 unfavorable indicators). Finally, we modify the scoring
methodology and groupings if additional bridge indicators are necessary (see Sensitivity Calculations
& Final Scoring section).

22
Figure 9. Example of a Sensitivity Analysis Data Results Excel sheet, showing all scoring columns, including individual indicator scoring,
intermediate calculations, and final sensitivity results. Scoring columns with blue headers contain the threshold elevation data and analysis. Data
columns not shown include multiple attributes taken directly from FMSS, and any final notes related to sensitivity analysis and scoring. See
Table B-4 for an accessible version.

23
1. Flood Damage Potential
The flood damage potential sensitivity indicator represents how likely an asset is to be inundated if
the surrounding land area is flooded. Assets constructed at least 5 feet above local ground level on
elevated stilts/pilings or artificial fill material are less likely to be inundated by floods. A height of 5
feet can be easily estimated and highlights assets that were intentionally elevated higher than a
typical crawlspace. We commonly obtain this information through the questionnaire (Box 3; WCU
2022), feedback from park staff, imagery, or visual inspection during field assessments (Table 4).
We assign assets elevated at least 5 feet above local ground level a score of 1 for the Flood Damage
Potential column of the Sensitivity Analysis Data Results Excel sheet. If the asset is not elevated, we
assign it a score of 4 (Figure 9).
Threshold Elevation Data
When available, we incorporate building threshold elevation data collected by the NPS Information
System Services into analysis of the Flood Damage Potential sensitivity indicator. These data
(collected at a handful of parks thus far) are acquired with sub-centimeter Global Positioning System
equipment to record accurate threshold (first floor) elevations. For buildings within a FEMA SFHA,
we compare the precise threshold elevation to the local Base Flood Elevation (level the water is
anticipated to reach during a base flood). If the threshold elevation of a building is above the Base
Flood Elevation, we assign it a score of 1 for the Flood Damage Potential indicator. If the threshold
is below the Base Flood Elevation, we assign the building a score of 4 (Figure 9).
2. Storm Resistance & Condition
The storm resistance and condition sensitivity indicator represents how well an asset will resist
damage from coastal hazards and SLR. Assets built to storm-resistant standards, with quality
construction, or in good condition are less likely to be damaged by coastal hazards. We commonly
obtain this information through the questionnaire (Box 3; WCU 2022), feedback from park staff,
imagery, or field assessments (Table 4).
This sensitivity indicator is scored as a combination of storm resistance and condition. We assign
assets determined to be storm resistant a score of 1 for the Storm Resistance column of the Sensitivity
Analysis Data Results Excel sheet, and assets that are not storm resistant a score of 4. We assign
assets determined to be in poor condition a score of 4 for the Condition column of the Sensitivity
Analysis Data Results Excel sheet, and assets not in poor condition a score of 1 (Figure 9).
3. Historical Damage
The historical damage sensitivity indicator represents if an asset has been damaged by coastal
hazards in the past, as assets that have been previously damaged are more likely be damaged in the
future. This is similar to the reported coastal hazards exposure indicator (Table 1), but instead of
focusing on the site or area around an asset, this indicator is focused on wave or water damage to the
asset itself. We commonly obtain this information through the questionnaire (Box 3; WCU 2022),
feedback from park staff, or imagery (Table 4).

24
We assign assets with historical damage a score of 4 for the Historical Damage column of the
Sensitivity Analysis Data Results Excel sheet, and assets with no historical damage a score of 1
(Figure 9).
4. Protective Engineering
The protective engineering sensitivity indicator represents if an asset is protected by engineering
including hard structures (e.g., seawalls, bulkheads) or landscape modifications (e.g., significant
drainage alteration, major restored landscape). This indicator assumes assets protected with
engineering are less likely to be damaged by coastal hazards. Data sources for this indicator
commonly include the asset questionnaire (Box 3; WCU 2022), feedback from park staff, imagery, or
the NPS Coastal Engineering Inventory (Table 4).
We assign assets protected by engineering structures a score of 1 for the Protective Engineering
column of the Sensitivity Analysis Data Results Excel sheet, and assets not protected by engineering
a score of 4 (Figure 9).
Box 3. Common Sensitivity Indicator Questions
Flood Damage Potential: Are any of the following assets elevated at least 5 feet above local
ground level (including critical utilities)? Examples include: 1) assets on stilts or pilings, or 2)
assets built on artificial fill material above local ground level. Note: if elevated, but not quite 5
feet, indicate in comments.
Storm Resistance: Are any of the following assets built to specific storm-resistant standards,
engineering codes, or inherently resistant to other forms of damage or deterioration (e.g.,
fortifications).
Condition: Are any of the following assets particularly vulnerable to flood/wave damage due to
condition? In other words, is the asset in poor condition due to deterioration, lack of
maintenance, etc.? Do not consider the location of the asset (even if it is near the water or
commonly flooded), only consider the physical condition of the asset itself.
Historical Damage: Have any of the following assets been significantly damaged in previous
storm/flooding events (water/waves only)?
Protective Engineering: Are any of the following assets currently being protected by an
engineered structure (e.g., seawall, bulkhead) or other major engineering features (e.g., drainage,
major landscape modification, major restored landscape)?

25
Additional Bridge Indicators
Bridges are included as transportation infrastructure within this VA but have specific structural
characteristics that we incorporate into the sensitivity analysis. Compared to other transportation
assets (e.g., roads, trails, parking lots), bridges have a different design, purpose, and response to
coastal hazards and SLR, highlighting the need for additional sensitivity indicators. For example,
bridge clearance height can be extremely significant during flooding, as low bridge clearance can
result in damage to the bridge deck, or even dislocation from its foundation. We evaluate four
additional sensitivity indicators for bridges included in the NBI (Table 5). The four sensitivity
indicators selected had the most consistent and widely available NBI data.
Table 5. Additional bridge sensitivity indicators and scoring.
Indicator
Data Source
Scoring
Clearance
NBI (item 39)
Feet of clearance: > 15 ft = 1; 9–15 ft = 2; 1–8 ft = 3; 0 ft = 4
Scour Rating
NBI (item 113)
Rating: n/a = 1; low (T, 9) & stable (5–8) = 2; stable (4) = 3; critical (0–3) = 4
Condition
NBI (items 58–60)
Condition Rating (average A): n/a = 1; 0–3 = 2; 4–6 = 3, 7–9 = 4
Age NBI (item 27);
FMSS database
Age (in years): 0–25 yrs = 1; 26–50 yrs = 2; 51–75 yrs = 3; > 75 yrs = 4
A Each available condition rating is first scored individually and then averaged.
Quality Control
The sensitivity analysis process relies heavily on park institutional knowledge regarding the physical
attributes of assets. The level of institutional knowledge at each park varies depending on staff
turnover and the asset portfolio. Although responses from the sensitivity questionnaire and
discussions are relatively accurate, inaccuracies due to human error are always possible. We carefully
review the responses for each sensitivity indicator for anomalies and verify the results as needed
(e.g., through further discussion, aerial imagery, field assessments, or other documentation).
Sensitivity Calculations & Final Scoring
After scores are assigned for each sensitivity indicator (as described in the above sections), we
complete several additional steps to obtain a final sensitivity score and rank. We record all
calculations and scoring in the Sensitivity Analysis Data Results Excel sheet (Figure 9).
Final sensitivity scoring steps:
1. Add comments if needed. We add any notes related to the sensitivity analysis or provided
by park staff to the final column of the Sensitivity Analysis Data Results Excel sheet and flag
these assets within the Step 2. Flagged Asset column (Figure 9).
2. Calculate the raw scores. We calculate raw scores as a sum of the sensitivity indicator
scores (as an Excel formula) within the Step 3: Raw Score (sum step 1) column (Figure 9).
We combine the Storm Resistance and Condition indicator columns to represent one indicator
(scores are summed and then divided by 2 as part of the raw score).

26
3. Calculate raw scores for bridges with additional NBI indicators. For bridges with data for
the additional NBI indicators, we complete the sensitivity indicator scoring separately from
the other assets. If a bridge has a NBI condition score (from Table 5), we use this score in
place of the value in the Condition column of the Sensitivity Analysis Data Results Excel
sheet. We sum all other indicators (as an Excel formula) within the Step 3: Raw Score
column (Figure 9).
4. Bin the raw scores. We bin (group) the raw exposure scores into categories based on the
breaks shown in Table 6, and we record these scores within the Step 4: Sensitivity Score
column (Figure 9).
a. Bin the raw scores for bridges with additional NBI indicators. For bridges with
data for the additional NBI indicators, we bin (group) the raw exposure scores into
categories based on the number of indicators and natural breaks in the values
(Table 7). Because the calculated NBI condition score is used in place of the original
condition value (Table 4), the number of other available NBI indicators determines
the binning (grouping) of the raw scores (Table 7).
5. Assign assets a final rank. We assign final sensitivity ranks in the Step 4: Sensitivity Rank
column (Table 6, Figure 9, and Box 3).
Table 6. Sensitivity scoring summary (with only 4 indicators and no NBI data).
# Indicators
Raw Score
Sensitivity Score
Sensitivity Rank
4
≥ 13
4
High
≥ 8 and ≤ 12
3
Moderate
≥ 4 and < 8
2
Low

27
Table 7. Sensitivity scoring summary for bridges with NBI data.
Total Indicators (NBI Indicators)
Raw Score
Sensitivity Score
Sensitivity Rank
4 (bridges with only NBI condition)
≥ 13
4
High
≥ 8 and ≤ 12
3
Moderate
≥ 4 and < 8
2
Low
5 (bridges with one NBI indicator A)
≥ 16
4
High
≥ 11 and < 16
3
Moderate
< 11
2
Low
6 (bridges with two NBI indicators A)
≥ 18
4
High
≥ 12 and < 18
3
Moderate
< 12
2
Low
7 (bridges with three NBI indicators A)
≥ 22
4
High
≥ 14 and < 22 3 Moderate
< 14
2
Low
A Number of NBI indicators (not counting NBI condition). NBI condition may or may not be present. If available,
NBI condition is used in place of the original Condition score (Table 4; Figure 9).
Vulnerability Calculations
After final exposure and sensitivity scores are determined for each asset (as described in the above
sections), we calculate raw scores using a simple summation formula in the Raw Score column of the
Vulnerability Assessment Data Results Excel sheet (Figure 10). We bin (group) the raw vulnerability
scores into final scores and rankings based on natural breaks (Table 8), and we report these within
the Final Score and Final Rank columns of the Vulnerability Assessment Data Results Excel sheet
(Figure 10).

28
Figure 10. Example of a Vulnerability Assessment Data Results Excel sheet, showing all scoring
columns, including exposure and sensitivity scoring, intermediate calculations, and final vulnerability
results. Data columns after the vulnerability results are a sample of those provided and are mined directly
from FMSS. See Table B-5 for accessible version.
Table 8. Vulnerability scoring summary.
Raw Score
Scoring Options
Final Score
Final Rank
7–8
high (4) exposure + high (4) sensitivity; high (4) exposure +
moderate (3) sensitivity; moderate exposure (3) + high (4) sensitivity
4
High
6 high (4) exposure + low (2) sensitivity; moderate (3) exposure +
moderate (3) sensitivity; low (2) exposure + high (4) sensitivity
3 Moderate
4–5
moderate (3) exposure + low (2) sensitivity; low (2) exposure +
moderate (3) sensitivity; low (2) exposure + low (2) sensitivity
2
Low
1
minimal (1) exposure + null sensitivity
1
Minimal A
A Assets with a vulnerability score of 1 are those with minimal (or no) exposure.
Products & Deliverables
After completing a draft of each VA, we send all results (in Excel and/or geospatial format) to
appropriate park staff. Parks review all steps and calculations and the resulting exposure, sensitivity,
and vulnerability of each asset, and follow-up with any clarifications or questions. Results are final
after this review period.
For each park, we deliver the final asset-specific exposure, sensitivity, and vulnerability results to the
NPS in multiple formats, including 1) Excel spreadsheets, 2) ArcMap map package (.mpk) and
shapefiles (.shp), and 3) Google Earth Keyhole Markup Language (.kml or .kmz). We also provide
all geospatial exposure hazard data to the NPS as ArcMap shapefiles. The NPS displays the
geospatial results for each park within the NPS Coastal Vulnerability Data Viewer.

29
Finally, we deliver a short summary of the VA results as a written report. This report includes a brief
discussion of the exposure, sensitivity, and vulnerability results, as well as a list of data sources and
any unique or park-specific factors (see Peek et al. 2018).

31
Summary & Conclusions
This report describes the developmental history and detailed methodology for the Coastal Hazards
& SLR Asset Vulnerability Assessment Protocol. The protocol is unique in that the vulnerability
of each park asset is assessed and scored individually as a combination of exposure and sensitivity.
Sensitivity is not commonly evaluated within infrastructure VAs (Peek et al. 2022); this protocol
creates a standardized method for evaluating and scoring the sensitivity of NPS assets. This approach
began over a decade ago as a series of pilot projects in coastal parks. It has evolved to allow the NPS
to integrate scientific data for exposure with the condition and characteristics of NPS buildings and
transportation assets to evaluate vulnerability. Application of this methodology consistently across all
coastal parks enables the NPS to identify highly vulnerable areas and assets. The exposure and
sensitivity data compiled for each park can also serve as a starting point for subsequent or parallel
assessment efforts. These data are being made available through web-based distribution within the
NPS intranet. This is critical information to support science-based decisions for adaptation and
resilience planning in parks affected by coastal hazards and SLR. Finally, the protocol is flexible in
that additional hazards or updated hazard data can be easily integrated into a completed VA to refine
or update the scoring as needed. The protocol can also be used to examine different time horizons if
needed.

33
Literature Cited
Amberg, S., K. Kilkus, S. Gardner, J. E. Gross, M. Wood, and B. Drazkowski. 2012. Badlands
National Park: Climate change vulnerability assessment. Natural Resource Report
NPS/BADL/NRR. National Park Service, Fort Collins, Colorado.
Barrows, C. W., J. Hoines, K. D. Fleming, M. S. Vamstad, M. Murphy-Mariscal, K. Lalumiere, and
M. Harding. 2014. Designing a sustainable monitoring framework for assessing impacts of
climate change at Joshua Tree National Park, USA. Biodiversity and Conservation 23:3263–
3285.
Beavers, R. L., A. L. Babson, and C. A. Schupp (editors). 2016. Coastal adaptation strategies
handbook. NPS 999/134090. National Park Service, Washington, D.C.
Caffrey, M. A., R. L. Beavers, and C. H. Hoffman. 2018. Sea level rise and storm surge projections
for the National Park Service. Natural Resource Report NPS/NRSS/NRR—2018/1648. National
Park Service, Fort Collins, Colorado.
Fatorić, S., and E. Seekamp. 2017. Assessing historical significance and use potential of buildings
within historic districts: An overview of a measurement framework developed for climate
adaptation planning. AG-832. NC State Extension, Raleigh, North Carolina.
FEMA Glossary, 2021. Terms frequently used by FEMA website. Available at:
https://www.fema.gov/about/glossary (accessed 16 February 2022).
Glick, P., B. A. Stein, and N. A. Edelson (editors). 2011. Scanning the conservation horizon: A guide
to climate change vulnerability assessment. National Wildlife Federation, Washington, D.C.
Handler, S., P. Burkman, J. Van Stappen, S. E. Johnson, E. Epstein, R. O’Connor, G. W. Schuurman,
A. Prosperi, L. R. Briley, D. Cooper, M. J. Cooper, R. Croll, U. Gafvert, L. R. Iverson, B.
Krumenaker, B. M. Lofgren, N. I. Montano, H. Panci, D. Panek, L. R. Parker, M. P. Peters, K.
M. Schmitt, C. W. Swanston, and N. Tillison. 2020. Climate change vulnerability assessment for
terrestrial ecosystems at Apostle Islands National Lakeshore. Natural Resource Report.
NPS/APIS/NRR—2020/2121. National Park Service, Fort Collins, Colorado.
Hansen, A. J., N. Piekielek, C. Davis, J. Haas, D. M. Theobald, J. E. Gross, W. B. Monahan, T.
Olliff, and S. W. Running. 2013. Exposure of U.S. National Parks to land use and climate change
1900–2100. Ecological Applications 24:484–502.
ICF International. 2012a. Facilities adaptation to climate change impacts in coastal areas: workshop
findings for Pu`uhonua O Hōnaunau National Historic Park, September 2012. ICF International,
Washington, D.C.
ICF International. 2012b. Facilities adaptation to climate change impacts in coastal areas: workshop
findings for Assateague Island National Seashore, December 2012. ICF International,
Washington, D.C.

34
ICF International. 2015. U.S. DOT Vulnerability Assessment Scoring Tool User’s Guide. ICF
International, Washington, D.C.
National Park Service (NPS). 2021a. Coming to terms with climate change: Working definitions.
National Park Service Climate Change Response Program, Fort Collins, Colorado.
National Park Service (NPS). 2021b. Climate Change: Assess Vulnerability website. Available at:
https://www.nps.gov/subjects/climatechange/vulnerability.htm (accessed 18 November 2021).
Peek, K. M., B. R. Tormey, H. L. Thompson, R. S. Young, S. Norton, and R. Scavo. 2017. Cabrillo
National Monument Coastal Hazards & Climate Change Asset Exposure Analysis & Case Study:
Cliff Retreat Exposure. NPS 342/154057. National Park Service, Washington, D.C.
Peek, K. M., B. R. Tormey, H. L. Thompson, R. S. Young, S. Norton, J. McNamee, and R. Scavo.
2018. Fire Island National Seashore Coastal Hazards & Sea-Level Rise Asset Vulnerability
Assessment. NPS 615/154058. National Park Service, Washington, D.C.
Peek, K. M., B. R. Tormey, H. L. Thompson, R. L. Beavers, A. C. Ellsworth, and C. H. Hoffman
(editors). 2022. Climate change vulnerability assessments in the National Park Service: An
integrated review for infrastructure, natural resources, and cultural resources. Natural Resource
Report NPS/NRSS/CCRP/NRR—2022/2404. National Park Service, Fort Collins, Colorado.
Ricci, G., D. D. Robadue Jr., P. Rubinoff, A. Casey, and A. L. Babson. 2019a. Integrated Coastal
Climate Change Vulnerability Assessment: Colonial National Historical Park. Natural Resource
Report NPS/COLO/NRR—2019/1945. National Park Service, Fort Collins, Colorado.
Ricci, G., D. D. Robadue Jr., P. Rubinoff, A. Casey, and A. L. Babson. 2019b. Method for Integrated
Coastal Climate Change Vulnerability Assessment. Natural Resource Report NPS/NER/NRR—
2019/1933. National Park Service, Fort Collins, Colorado.
Stroh, E. D., M. A. Struckhoff, D. Shaver, and K. A. Karstensen. 2016. Vulnerabilities of national
parks in the American Midwest to climate and land use changes. Scientific Investigations Report
2016–5057. United Stated Geological Survey, Reston, Virginia.
Sweet, W. V., R. E. Kopp, C. P. Weaver, J. Obeysekera, R. M. Horton, E. R. Theiler, and C. Zervas.
2017. Global and Regional Sea Level Rise Scenarios for the United States. NOAA Technical
Report NOS CO-OPS 083. National Oceanic and Atmospheric Administration, National Ocean
Service, Silver Spring, Maryland.
Sweet, W. V., B. D. Hamlington, R. E. Kopp, C. P. Weaver, P. L. Barnard, D. Bekaert, W. Brooks,
M. Craghan, G. Dusek, T. Frederikse, G. Garner, A. S. Genz, J. P. Krasting, E. Larour, D. Marcy,
J. J. Marra, J. Obeysekera, M. Osler, M. Pendleton, D. Roman, L. Schmied, W. Veatch, K. D.
White, and C. Zuzak. 2022. Global and Regional Sea Level Rise Scenarios for the United States:
Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines. NOAA
Technical Report NOS 01. National Oceanic and Atmospheric Administration, National Ocean
Service, Silver Spring, Maryland.

35
Western Carolina University (WCU). 2022. Questionnaire Templates for Coastal Hazards Protocol
(2022). https://irma.nps.gov/DataStore/Reference/Profile/2293481.
Zachry, B. C., W. J. Booth, J. R. Rhome, and T. M. Sharon. 2015. A National View of Storm Surge
Risk and Inundation. Weather, Climate, and Society, 7(2), 109–117. DOI:
http://dx.doi.org/10.1175/WCAS–D–14–00049.1

37
Appendix A: FMSS Descriptions
Asset Code & Facility Type in FMSS
Table A-1. Table of Asset Code and Facility Type descriptions from FMSS (provided by NPS Information
System Services).
Asset Code
Facility Type
1100 - Road
1110 - Paved
1100 - Road
1120 - Unpaved
1100 - Road
1130 - Both
1300 - Parking Area
1310 - Paved
1300 - Parking Area
1320 - Unpaved
1300 - Parking Area
1330 - Both
1700 - Road Bridge 1710 - Road Bridge
1800 - Road Tunnel
1810 - Road Tunnel
2100 - Trail
2110 - Class I
2100 - Trail 2120 - Class II
2100 - Trail
2130 - Class III
2100 - Trail
2140 - Class IV
2100 - Trail
2150 - Class V
2200 - Trail Bridge (Substantial)
2210 - Trail Bridge
2300 - Trail Tunnel (Substantial)
2310 - Trail Tunnel
3100 - Maintained Landscapes A
3110 - Maintained Landscapes
3800 - Boundary A
3810 - Boundary
4100 - Building*
4110 - Office
4100 - Building*
4114 - Post Office
4100 - Building*
4123 - School
4100 - Building*
4129 - Other Institutional Uses
4100 - Building**
4130 - Housing
4100 - Building**
4131 - Dormitories/Barracks
4100 - Building*
4141 - Warehouses
4100 - Building*
4150 - Industrial
4100 - Building*
4160 - Service
A Asset categories not typically evaluated with this protocol, also shown in bold text.

38
Table A-1 (continued). Table of Asset Code and Facility Type descriptions from FMSS (provided by NPS
Information System Services).
Asset Code
Facility Type
4100 - Building*
4172 - Communications Systems
4100 - Building*
4173 - Navigation and Traffic Aids
4100 - Building*
4174 - Laboratories
4100 - Building*
4180 - All Other
5100 - Water System A
5110 - Non-potable
5100 - Water System
A
5120 - Potable
5200 - Waste Water System A
5210 - Municipal Wastewater
5200 - Waste Water System A
5220 - Stormwater
5300 - Heating & Cooling Plant 5310 - Heating
5300 - Heating & Cooling Plant
5320 - Cooling
5300 - Heating & Cooling Plant
5330 - Both
5400 - Electrical System A
5410 - Water (Hydro)
5400 - Electrical System A
5420 - Solar
5400 - Electrical System A
5430 - Fossil Fuel
5400 - Electrical System A
5440 - Geothermal
5400 - Electrical System A
5450 - Public
5400 - Electrical System A
5460 - Wind
5400 - Electrical System A
5470 - Fuel Cell
5500 - Communication Systems A
5510 - Phone Systems
5500 - Communication Systems A
5520 - IT Systems
5500 - Communication Systems A
5530 - Radio Systems
5700 - Fuel System
5710 - Fuel for Transportation
5700 - Fuel System
5720 - Fuel for Utilities
5800 - Solid Waste/Recycling System
5810 - Municipal Solid Waste System
5800 - Solid Waste/Recycling System
5820 - Recycling System
5800 - Solid Waste/Recycling System
5830 - Hazardous Waste System
6100 - Dam/Levee/Dike
6110 - Dams/Levees/Dikes
6200 - Constructed Waterway
6210 - Constructed Waterways
6300 - Marina/Waterfront System 6310 - Recreational
A Asset categories not typically evaluated with this protocol, also shown in bold text.

39
Table A-1 (continued). Table of Asset Code and Facility Type descriptions from FMSS (provided by NPS
Information System Services).
Asset Code
Facility Type
6300 - Marina/Waterfront System
6320 - Protection
6400 - Aviation System
6410 - Controlled
6400 - Aviation System
6420 - Uncontrolled
6500 - Railroad System
6510 - Railroad System
7100 - Monuments
7110 - Memorial Complex
7100 - Monuments 7120 - Commemorative Objects
7100 - Monuments
7130 - Headstone / Marker
7100 - Monuments
7140 - Large Interpretive Objectives
7200 - Maintained Archeological Sites
A
7210 - Maintained Archeological Sites
7300 - Fortification
7310 - Earthwork
7300 - Fortification
7320 - Masonry
7300 - Fortification
7330 - Wood
7300 - Fortification
7340 - Other Construction
7400 - Towers/Missile Silos
7410 - Silo
7400 - Towers/Missile Silos
7420 - Tower
7500 - Interpretive Media A
7510 - Exhibit System
7500 - Interpretive Media A
7520 - Wayside System
7900 - Amphitheaters
7910 - Amphitheaters
8999 - Fleet A
–
9999 - No Asset Code Available A
–
A Asset category not typically evaluated with this protocol, also shown in bold text.

41
Appendix B: Accessible Figures & Tables
Table B-1. Accessible version of Figure 3. A portion of an Exposure Analysis Data Results Excel sheet,
showing an example of scoring for each exposure indicator (columns 1a–1f). Columns 1a. and 1b.
together represent the flooding potential indicator, and column 1f. Historic Flood Score is the reported
coastal hazards indicator in this example.
ID Location
1a. FEMA
VE Zone
Score
1b. FEMA A
Zone Score
1c. Shoreline
Change
Score
1d. SLR
Score
1e. Extreme
Flooding
Score
1f. Historic
Flood
Score
1
Asset Name
n/a
4
1
1
4
4
2
Asset Name
n/a
1
1
1
4
1
3
Asset Name
n/a
4
1
1
4
1
4
Asset Name
n/a
4
1
1
4
4
5
Asset Name
4
n/a
1
1
4
4
6
Asset Name
n/a
4
4
1
4
4
7
Asset Name
n/a
4
4
1
4
4
8
Asset Name
4
n/a
1
1
4
4
9
Asset Name
n/a
4
4
1
4
4
Table B-2. Accessible version of inset table in Figure 6. NPS-specific SLR projections for George
Washington Birthplace National Monument from Caffrey et al. 2018. Only the 4.5 and 8.5 RCP scenario
were mapped.
Year
2.6 RCP
4.5 RCP
6 RCP
8.5 RCP
2030
0.15 m
0.15 m
0.15 m
0.14 m
2050
0.26 m
0.27 m
0.26 m
0.28 m
2100
0.53 m
0.63 m
0.66 m
0.8 m

42
Table B-3. Accessible version of Figure 8. Example of an Exposure Analysis Data Results Excel sheet, showing all scoring columns, including
individual indicator scoring, intermediate calculations, and final exposure results. Data columns not shown include multiple attributes taken directly
from FMSS, and any final notes related to exposure analysis and scoring.
Location
1a.
FEMA
VE
Zone
Score
1b.
FEMA A
Zone
Score
1c.
Shoreline
Change
Score
1d.
SLR
Score
1e.
Extreme
Flooding
Score
1f.
Historic
Flood
Score
Step 2.
Raw
Score
from Step
1
Step 3.
Binned
Raw
Score
Step 4.
VE
Auto
High
Step 5.
Exposure
Score
Unmod.
Step 6.
Flagged
Asset
Step 7.
Exposure
Score
Step 7.
Exposure
Rank
Asset 1
n/a
4
1
1
4
4
14
3
n/a
3
see notes
3
moderate
Asset 2
n/a
1
1
1
4
1
8
2
n/a
2
n/a
2
low
Asset 3
n/a
4
1
1
4
1
11
3
n/a
3
n/a
3
moderate
Asset 4
n/a
4
1
1
4
4
14
3
n/a
3
see notes
3
moderate
Asset 5
4
n/a
1
1
4
4
14
3
4
4
see notes
4
high
Asset 6
n/a
4
4
1
4
4
17
4
n/a
4
n/a
4
high
Asset 7
n/a
4
4
1
4
4
17
4
n/a
4
n/a
4
high
Asset 8
4
n/a
1
1
4
4
14
3
4
4
see notes
4
high
Asset 9
n/a
4
4
1
4
4
17
4
n/a
4
see notes
4
high
Asset 10
n/a
4
1
1
4
4
14
3
n/a
3
n/a
3
moderate
Asset 11
n/a
4
1
4
4
1
14
3
n/a
3
see notes
3
moderate
Asset 12
n/a
4
1
4
4
4
17
4
n/a
4
n/a
4
high
Asset 13
n/a
4
1
1
4
4
14
3
n/a
3
n/a
3
moderate
Asset 14 n/a 4 1 4 4 4 17 4 n/a 4 n/a 4 high
Asset 15
n/a
4
1
4
4
4
17
4
n/a
4
see notes
4
high
Asset 16
n/a
4
1
4
4
4
17
4
n/a
4
see notes
4
high
Asset 17 n/a 4 1 4 4 4 17 4 n/a 4 see notes 4 high
Asset 18
n/a
4
1
1
4
4
14
3
n/a
3
n/a
3
moderate

43
Table B-4. Accessible version of Figure 9. Example of a Sensitivity Analysis Data Results Excel sheet, showing all scoring columns, including
individual indicator scoring, intermediate calculations, and final sensitivity results. Scoring columns with blue headers contain the threshold
elevation data and analysis. Data columns not shown include multiple attributes taken directly from FMSS, and any final notes related to sensitivity
analysis and scoring.
Location
Flood
Damage
Potential
BFE (m,
NAVD88)
Threshold
Elev (m,
NAVD88)
Above
or
Below
BFE
Storm
Resistance Condition
Historical
Damage
Protective
Engineering
Step 2.
Flagged
Asset
Step 3.
Raw
Score
(sum
step 1)
Step 4.
Sensitivity
Score
Step 5.
Sensitivity
Rank
Asset 1
4
1.83
n/a
n/a
4
1
1
4
n/a
11.5
3
moderate
Asset 2
1
n/a
n/a
n/a
4
4
1
4
see
notes
10.0
3
moderate
Asset 3
4
1.83
1.498
Below
4
4
1
4
n/a
13.0
4
high
Asset 4
1
n/a
n/a
n/a
4
4
1
4
see
notes
10.0
3
moderate
Asset 5 1 1.83 n/a n/a 4 1 1 4 see
notes
8.5 3 moderate
Asset 6
1
1.83
n/a
n/a
1
1
1
4
see
notes
7.0
2
low
Asset 7
4
1.83
n/a
n/a
4
4
4
4
n/a
16.0
4
high
Asset 8
1
1.52
n/a
n/a
4
1
1
4
n/a
8.5
3
moderate
Asset 9
1
1.52
2.386
Above
4
1
1
4
see
notes
8.5
3
moderate
Asset 10
1
1.52
n/a
n/a
4
1
1
4
n/a
8.5
3
moderate
Asset 11
1
1.52
n/a
n/a
4
1
1
4
see
notes
8.5
3
moderate
Asset 12
1
1.52
n/a
n/a
4
1
1
4
n/a
8.5
3
moderate
Asset 13
1
1.52
2.986
Above
4
1
1
4
see
notes
8.5
3
moderate

44
Table B-4 (continued). Accessible version of Figure 9. Example of a Sensitivity Analysis Data Results Excel sheet, showing all scoring columns,
including individual indicator scoring, intermediate calculations, and final sensitivity results. Scoring columns with blue headers contain the
threshold elevation data and analysis. Data columns not shown include multiple attributes taken directly from FMSS, and any final notes related to
sensitivity analysis and scoring.
Location
Flood
Damage
Potential
BFE (m,
NAVD88)
Threshold
Elev (m,
NAVD88)
Above
or
Below
BFE
Storm
Resistance Condition
Historical
Damage
Protective
Engineering
Step 2.
Flagged
Asset
Step 3.
Raw
Score
(sum
step 1)
Step 4.
Sensitivity
Score
Step 5.
Sensitivity
Rank
Asset 14
1
1.52
2.997
Above
4
1
1
4
see
notes
8.5
3
moderate
Asset 15
4
1.52
n/a
n/a
4
1
1
4
see
notes
11.5
3
moderate
Asset 16 4 1.52 n/a n/a 4 1 1 4 see
notes
11.5 3 moderate
Asset 17
1
1.52
1.737
Above
1
1
1
4
see
notes
7.0
2
low

45
Table B-5. Accessible version of Figure 10. Example of a Vulnerability Assessment Data Results Excel sheet, showing all scoring columns,
including exposure and sensitivity scoring, intermediate calculations, and final vulnerability results. Data columns after the vulnerability results are
a sample of those provided and are mined directly from FMSS.
Location Exposure Sensitivity Raw Score Final Score Final Rank
Location
Code Asset Code Facility Type
Asset 1
3
3
6
3
moderate
43987
4100
4124
Asset 2
2
3
5
2
low
44098
4100
4160
Asset 3
3
4
7
4
high
44100
4100
4141
Asset 4
2
3
5
2
low
44097
4100
4123
Asset 5
3
3
6
3
moderate
114197
4100
4141
Asset 6
3
2
5
2
low
114199
4100
4141
Asset 7
4
4
8
4
high
43997
4100
4180
Asset 8
4
3
7
4
high
98245
4100
4141
Asset 9
4
3
7
4
high
44094
4100
4123
Asset 10
4
3
7
4
high
98239
4100
4110
Asset 11
4
3
7
4
high
114210
4100
4141
Asset 12
1
n/a
1
1
minimal
98246
4100
4141
Asset 13
1
n/a
1
1
minimal
98241
4100
4131
Asset 14
4
3
7
4
high
98242
4100
4131
Asset 15 3 3 6 3 moderate 44091 4100 4141
Asset 16
3
3
6
3
moderate
106670
4100
4129

The Department of the Interior protects and manages the nation’s natural
resources and cultural heritage; provides scientific
and other information about
those resources; and honors its special responsibilities to American Indians, Alaska Natives, and
affi
liated Island Communities.
NPS 999/182813, July 2022

National Park Service
U.S. Department of the Interior
Natural Resource Stewardship and Science
1201 Oakridge Drive, Suite 150
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