Enhancing Coastal Resilience on Virginia s Eastern Shore: Application of the Sea-Level Affecting Marshes Model

Transcription

1 Enhancing Coastal Resilience on Virginia s Eastern Shore: Application of the Sea-Level Affecting Marshes Model Prepared for: The Nature Conservancy Charlottesville, VA Gwynn Crichton Sr. Project Scientist Prepared by: Warren Pinnacle Consulting, Inc. Waitsfield, VT Jonathan Clough President Amy Polaczyk Research Associate Marco Propato Research Associate November 2015

2 NOTICE This report was prepared by Warren Pinnacle Consulting, Inc. in the course of performing work contracted for and sponsored by The Nature Conservancy (hereafter TNC). The opinions expressed in this report do not necessarily reflect those of TNC, and reference to any specific product, service, process, or method does not constitute an implied or expressed recommendation or endorsement of it. Further, TNC and the contractor make no warranties or representations, expressed or implied, as to the fitness for particular purpose or merchantability of any product, apparatus, or service, or the usefulness, completeness, or accuracy of any processes, methods, or other information contained, described, disclosed, or referred to in this report. TNC and the contractor make no representation that the use of any product, apparatus, process, method, or other information will not infringe privately owned rights and will assume no liability for any loss, injury, or damage resulting from, or occurring in connection with, the use of information contained, described, disclosed, or referred to in this report.

3 Table of Contents Table of Contents... iii Figure Listing... iv Table Listing... v Acronyms and Abbreviations List... vi 1 Background Model Summary Methods Study Area Input Raster Preparation Elevation Data Elevation transformation Wetland Layers and translation to SLAMM Dikes and Impoundments Percent Impervious Model Timesteps Sea Level Rise Scenarios Tide Ranges Elevations expressed in half tide units (HTU) Wetland Boundary Elevation Wetland Elevation-Change Rates Tidal Salt Marsh Elevation-Change Rates of other Wetland Types Erosion Rates Barrier Islands Migration Model Calibration Model Setup Results Conclusions Literature Cited Appendix A: NWI Classes to SLAMM 6 Categories Appendix B: SLAMM Codes Appendix C: Great Diurnal Tide Ranges in Study Area (m) Application of SLAMM to the Eastern Shore of VA iii

4 Appendix D: Comprehensive Tables of Input Parameters Appendix E: Full Study Area Result Maps Figure Listing Figure 1. Study Area shown by yellow boundary Figure 2. Areas in light blue were flown at high tide Figure 3. VDATUM-derived correction values... 6 Figure 4. NWI photo dates for the study area Figure 5. Initial land cover... 8 Figure 6. Sea Level Rise Scenarios Figure 7. Relationship between tides, wetlands, and reference elevations for an example estuarine shore profile Figure 8. RFM and IFM Elevation Distribution Figure 9. Measured data for marsh elevation change rates as function of the marsh platform elevation Figure 10. RFM and IFM SLAMM Elevation-Change Models Figure 11. Overlay of Long-term Regression Rate (LRR) data from McLoughlin et al on SLAMM initial condition map Figure 12. Delineation of mainland and lagoonal marshes Figure 13. Marsh near Deep Creek, VA Figure 14. Input subsites Figure 15. Initial condition of sandbar near Smith Beach, VA Figure 16. Crabbing Marsh, VA Initial condition Figure 17. Crabbing Marsh, Historic SLR scenario 2011 ( time-zero ) Figure 18. Crabbing Marsh, VA, Historic SLR scenario Figure 19. Cedar Island, initial condition Figure 20. Cedar Island, Historic SLR scenario 2011 ( time-zero ) Figure 21. Cedar Island, Historic SLR scenario Figure 22. Crabbing Marsh, VA, Low SLR scenario Figure 23. Crabbing Marsh, VA, Low SLR scenario Figure 24. Cedar Island, Low SLR scenario Figure 25. Cedar Island, Low SLR scenario Figure 26. Crabbing Marsh, VA, High SLR scenario Figure 27. Crabbing Marsh, VA, High SLR scenario Figure 28. Cedar Island, High SLR scenario Figure 29. Cedar Island, High SLR scenario Figure 30. Crabbing Marsh, VA, Highest SLR scenario Figure 31. Crabbing Marsh, VA, Highest SLR scenario Figure 32. Crabbing Marsh, VA, Highest SLR scenario Figure 33. Crabbing Marsh, VA, Highest SLR scenario Figure 34. Cedar Island, Highest SLR scenario Figure 35. Cedar Island, Highest SLR scenario Figure 36. Cedar Island, Highest SLR scenario Figure 37. Cedar Island, Highest SLR scenario Figure 38. Great diurnal tide ranges (m) in the eastern Shore of VA (m) Application of SLAMM to the Eastern Shore of VA iv

5 Figure 39. Initial condition Figure 40. Time zero (2011) Figure 41. Historic Figure 42. Historic Figure 43. Historic Figure 44. Historic Figure 45. Low Figure 46. Low Figure 47. Low Figure 48. Low Figure 49. High Figure 50. High Figure 51. High Figure 52. High Figure 53. Highest Figure 54. Highest Figure 55. Highest Figure 56. Highest Table Listing Table 1. Land cover categories for ESVA study area... 9 Table 2. Accelerated SLR by scenario for each timestep Table day inundation analysis results at NOAA gauge stations Table 4. SET data database for Delmarva Peninsula Table 5. Migration rate and heading applied to barrier islands Table 6. Default minimum wetland elevations in SLAMM conceptual model Table 7. Dates and tide ranges for subsites Table 8. Time-Zero Results (acres) Table 9. Land cover changes for each SLR scenario at Table 10. Acreage at 2065 for tidal marshes Table 11. Acres at each time step for Historic SLR scenario (32 cm by 2065 and 48 cm by 2100) Table 12. Acres at each time step for Low SLR scenario (47 cm by 2065 and 79 cm by 2100) Table 13. Acres at each time step for High SLR scenario (79 cm by 2065 and 1.49 m by 2100) Table 14.Acres at each time step for Highest SLR scenario (1.15 m by 2065 and 2.29 m by 2100) Application of SLAMM to the Eastern Shore of VA v

6 Acronyms and Abbreviations List DEM Digital Elevation Map FEMA US Federal Emergency Management Agency GIS Geographic Information Systems GT Great Diurnal Tide Range HTU Half-Tide Units (highest tide each day minus the mean tide level) IFM Irregularly-Flooded Marsh LiDAR Light Detection and Ranging method to produce elevation data LRR Linear Regression Rate m Meters MEM Marsh Equilibrium Model MHHW Mean Higher High Water (average highest tide each day) MLLW Mean Lower Low Water (average lowest tide each day) MTL Mean Tide Level NAVD88 North American Vertical Datum of 1988 NED USGS National Elevation Dataset NLD National Levee Database from the U.S. Army Corps of Engineers NOAA United States National Oceanic and Atmospheric Administration NWI National Wetlands Inventory RFM Regularly-Flooded Marsh RMSE Root Mean Standard Error SD Standard Deviation SLAMM Sea-level Affecting Marshes Model SLR Sea-Level Rise STORET EPA Data Warehouse TSS Total Suspended Solids USFWS United States Fish and Wildlife Service USGS United States Geological Survey UTM Universal Transverse Mercator (UTM) conformal projection VDATUM NOAA Product for converting vertical datums WBE Wetland Boundary Elevation (coastal-wetland to dry land boundary) WPC Warren Pinnacle Consulting, Inc. Application of SLAMM to the Eastern Shore of VA vi

7 1 Background This application of the Sea-Level Affecting Marshes Model (SLAMM) is part of a project titled: Enhancing Coastal Resilience on Virginia s Eastern Shore funded by the National Fish and Wildlife Foundation and carried out by The Nature Conservancy in partnership with the Accomack-Northampton Planning District Commission, the Virginia Coast Reserve Long-Term Ecological Research site, NASA, and the Chincoteague National Wildlife Refuge. The goal of the project is to enhance coastal resilience in the Eastern Shore of Virginia (Accomack and Northampton counties on the Delmarva Peninsula) by providing the scientific framework, decision-making support tools, and examples of real world, naturebased solutions needed to avoid costly, ineffective, management decisions that could have unintended consequences for this unique stretch of coast. Task 2 of this endeavor aims to Gather the best available science and data to populate, test, and run models that simulate the effects of physical processes (i.e., sea-level rise, storm surge, and marsh migration) on the coastal system with the goal of enabling stakeholders to evaluate a range of future management options. SLAMM provides data useful to a range of project stakeholders by creating projections of the potential effects of accelerated sea-level rise on coastal ecosystems. Tidal marshes are dynamic ecosystems that provide significant ecological and economic value. Given that tidal marshes are located at the interface between land and water, they can be among the most susceptible ecosystems to climate change, especially accelerated sea-level rise (SLR). Numerous factors can affect marsh fate including the elevation of marshes relative to the tides, marshes frequency of inundation, the salinity of flooding waters, the biomass of marsh platforms, land subsidence, marsh substrate, and the settling of suspended sediment into the marshes. Because of these factors, a simple calculation of current marsh elevations as compared to future projections of sea level does not provide an adequate estimation of wetland vulnerability. SLAMM is widely recognized as an effective model to study and predict wetland response to long-term sea-level rise (Park et al. 1991) and has been applied in every coastal US state (Craft et al. 2009; Galbraith et al. 2002; Glick et al. 2007, 2011; National Wildlife Federation and Florida Wildlife Federation 2006; Park et al. 1993; Titus et al. 1991). 1.1 Model Summary SLAMM predicts when marshes are likely to be vulnerable to SLR and defines locations where marshes may migrate upland in response to changes in water levels. The model attempts to simulate the dominant processes that affect shoreline modifications during long-term sea-level rise and uses a complex decision tree incorporating geometric and qualitative relationships to predict changes in coastal land cover classes. Application of SLAMM to the Eastern Shore of VA 1

8 SLAMM is not a hydrodynamic model. Rather, SLAMM predicts long term shoreline and habitat class changes based upon a succession of equilibrium states with sea level. Model outputs include mapped distributions of wetlands at different time steps in response to sea level rise changes as well as tabular and graphical data. The model s relative simplicity and modest data requirements allow its application at a reasonable cost. Mcleod and coworkers wrote in their review of sea-level rise impact models that... the SLAMM model provides useful, high-resolution, insights regarding how sea-level rise may impact coastal habitats (Mcleod et al. 2010). SLAMM assumes that wetlands inhabit a range of vertical elevations that is a function of the tide range. These assumptions are verified with local data for each site modeled. The model then computes relative (local) sea level rise for each cell at each time step. For this project, relative sea-level rise was provided, taking into account local subsidence rates (section 2.4). SLAMM can also calculate relative SLR as a function of global SLR scenarios offset by local factors such as subsidence and isostatic adjustment. Sea level rise is offset by marsh accretion and other factors affecting marsh surface elevation. When the model is applied, each study site is divided into cells of equal area (20 20 ft 2 for these simulations) that are treated individually. The conversion from one land cover class to another is computed by considering the new cell elevation at a given time step with respect to the class in that cell and its inundation frequency. Assumed wetland elevation ranges may be estimated as a function of tidal ranges or may be entered by the user if site-specific data are available. The connectivity module determines salt water paths under normal tidal conditions. In general, when a cell s elevation falls below the minimum elevation of the current land cover class and is connected to open water, then the land cover is converted to a new class according to a decision tree. In addition to the effects of inundation represented by the simple geometric model described above, the model can account for second order effects that may occur due to changes in the spatial relationships among the coastal elements. In particular, SLAMM can account for exposure to wave action and its erosion effects, overwash of barrier islands where beach migration and transport of sediments are estimated, saturation allowing coastal swamps and fresh marshes to migrate onto adjacent uplands as a response of the fresh water table to rising sea level close to the coast, and marsh accretion. Marsh accretion is the process of wetland elevations changing due to the accumulation of organic and inorganic matter. Accretion is one of the most important processes affecting marsh capability to respond to SLR. The SLAMM model was one of the first landscape-scale models to incorporate the effects of vertical marsh accretion rates on predictions of marsh fates, including this process since the mid-1980s (Park et al. Application of SLAMM to the Eastern Shore of VA 2

9 1989). Since 2010, SLAMM has incorporated dynamic relationships between marsh types, marsh elevations, tide ranges, and predicted rates of change in wetland elevations. The SLAMM application presented here utilizes a feedback between marsh elevations and elevation-change rates derived from local data sets. This feedback is also supported by similar results from mechanistic accretion and shallowsubsidence models (e.g. Morris 2013; Morris et al. 2002). As with any numerical model, SLAMM has important limitations. As mentioned above, SLAMM is not a hydrodynamic model. Therefore, cell-by-cell water flows are not predicted as a function of topography, diffusion and advection. Furthermore, there are no feedback mechanisms between hydrodynamic and ecological systems. Solids in water are not accounted for via mass balance which may affect accretion (e.g. local bank sloughing does not affect nearby sedimentation rates). The erosion model is also very simple and does not capture more complicated processes such as nick-point channel development. SLAMM has the capability to apply a salt-wedge model in an estuary and an overwash model for barrier islands. However, each of these model processes is rather simple and has not been applied in these simulations. As a potential follow up to this project, the confidence of model results could be evaluated and quantified with the built-in SLAMM uncertainty-analysis module. Using Monte-Carlo simulations, the SLAMM model is run iteratively, with model inputs randomly drawn from distributions representing input uncertainty. Each model realization represents one possible future for the studied area. All model realizations are then assembled into probability distributions of wetland coverage reflecting the effect of input data/model uncertainties on prediction results. When uncertainty-analysis is incorporated, the relative simplicity of the SLAMM model becomes a useful compromise that allows for an efficient characterization of uncertainties without excessive computational time. In addition, all model uncertainties can be summarized in a single map such as the percent likelihood of a coastal marsh for each modeled cell at a given date. In this manner, a complex uncertainty analysis can actually simplify the presentation of model results. A more detailed description of model processes, underlying assumptions, and equations can be found in the SLAMM Technical Documentation (available at Application of SLAMM to the Eastern Shore of VA 3

10 2 Methods 2.1 Study Area The study area was limited to Accomack and Northampton Counties in Virginia. Figure 1. Study Area shown by yellow boundary. 2.2 Input Raster Preparation SLAMM is a raster-based model meaning that input cells are equally-sized squares arranged in a grid. The cell size was m or 20 ft. This section describes these critical data sources and the steps used to process the data for use in SLAMM. Data types reviewed here include elevation, wetland land cover, impervious land cover, dikes and impoundments. Application of SLAMM to the Eastern Shore of VA 4

11 2.2.1 Elevation Data High vertical-resolution elevation data may be the most important SLAMM data requirement. Elevation data when combined with tidal data are used to determine the extent and frequency of saltwater inundation. For the study area the elevation layer was obtained by processing the data of 2010 VITA-VGIN Lidar: Eastern Shore of VA. However, some areas were flown at high tide, see Figure 2. Figure 2. Areas in light blue were flown at high tide. Marshes, in red, have no elevation assigned from the LiDAR (Satellite Imagery Data: Google, TerraMetrics). Since the flooded marshes in these areas were showing as "open water", their elevation was not recorded. Additional analysis was required to model these regions, therefore, and this work was carried out by John Porter of University of Virginia. LANDSAT images from , acquired at different tidal phases, were used to estimate missing marsh elevations. A multiple linear regression model was fit to 8,643 randomly chosen (with a minimum separation distance of 60 m) marsh points for which LiDAR-based elevations were available. The model explained 54% of the variance in elevation. The regression equation was then used to calculate estimated elevation values for the marshes that were missing in the original LiDAR acquisition. Application of SLAMM to the Eastern Shore of VA 5

12 The resulting elevation data set was tested against the SLAMM conceptual model that links wetland elevations, tide ranges, and wetland maps. Ultimately, the combined elevation data conformed well within the SLAMM conceptual model (see section 2.9 for calibration details.) Slope Layer. Slope rasters were derived from the DEMs described above using QGIS software. Accurate slopes of the marsh surface are an important SLAMM consideration as they are used in the calculation of the fraction of a wetland that is lost (transferred to the next class) Elevation transformation NOAA s VDATUM version 3.2 (NOS 2013) was utilized to convert elevation data from the NAVD88 vertical datum to Mean Tide Level (MTL), which is the vertical datum used in SLAMM. This is required as coastal wetlands inhabit elevation ranges in terms of tide ranges as opposed to geodetic datums (McKee and Patrick 1988). VDATUM does not provide vertical corrections over dry land; dry-land elevations were corrected using the VDATUM correction from the nearest open water. Corrections in the study areas ranged from approximately m to 0.05 m. A spatial map of corrections is shown in Figure 3. Figure 3. VDATUM-derived correction values Application of SLAMM to the Eastern Shore of VA 6

13 2.2.3 Wetland Layers and translation to SLAMM Wetland rasters were created from the National Wetlands Inventory (NWI) surveys with dates ranging as shown in Figure 4. Figure 4. NWI photo dates for the study area. Application of SLAMM to the Eastern Shore of VA 7

14 NWI land coverage codes were translated to SLAMM codes using Table 4 of the SLAMM Technical Documentation as produced with assistance from Bill Wilen of the National Wetlands Inventory (Clough et al. 2012) and included in Appendix A. Since dry land (developed or undeveloped) is not classified by NWI, SLAMM classified cells as dry land if they were initially blank but had an elevation assigned. The resulting raster was checked visually to make sure the projection information is correct, has a consistent number of rows and columns as the other rasters in the project area, and to ensure that the data looked complete based on the source data. The resulting land cover for the area is shown in Figure 5. Figure 5. Initial land cover Application of SLAMM to the Eastern Shore of VA 8

15 Table 1 shows the current land coverage for the entire study area. Of the nearly half-million acres that represent the study area, 31% is occupied by dry land (developed and undeveloped) and 42% by estuarine and oceanic open water. The remaining 27% includes over 126,000 acres of wetland, and over 6,000 acres of beaches and tidal flats. Table 1. Land cover categories for ESVA study area* Estuarine Open Water Estuarine Undeveloped Dry Land Undeveloped Regularly-Flooded Marsh Regularly-Flooded Swamp Swamp Irreg.-Flooded Irreg.-Flooded Marsh Open Open Ocean Developed Developed Dry Land Inland-Fresh Inland-Fresh Marsh Tidal Tidal Swamp Trans. Trans. Salt Marsh Tidal Tidal Flat Ocean Ocean Beach Inland Inland Open Water Estuarine Estuarine Beach Tidal-Fresh Tidal-Fresh Marsh Rocky Rocky Intertidal Inland Inland Shore Land cover type Area (acres) Percentage Open Water 183,090 37% Dry Land 144,992 29% Marsh 41,981 9% 40,072 8% Marsh 27,997 6% Ocean 23,324 5% Dry Land 7,455 2% Marsh 6,919 1% Swamp 4,745 1% Salt Marsh 3,370 1% Flat 2,594 1% Beach 2,459 < 1% Open Water 1,358 < 1% Beach 1,067 < 1% Marsh 736 < 1% Intertidal 249 < 1% Shore 28 < 1% Total (incl. water) 492, % *A table to identify SLAMM categories from the raster map codes is provided in Appendix B These categories are combined as other nontidal wetlands in the TNC Coastal Resilience Tool Like all input data, there is some uncertainty in land-cover inputs. For example, some areas designated as Swamp and Inland Fresh based on NWI to SLAMM conversions may not always be wetlands. Some of the PEM categories are coded by NWI as emergent because they were recently cut at the time of the NWI imagery (Personal Communication, Chris Bruce, July 2, 2015). For this reason, TNC visualization tools tend to code these categories as other nontidal wetlands. Application of SLAMM to the Eastern Shore of VA 9

16 2.2.4 Dikes and Impoundments Dike rasters were created using NWI data sources: All NWI wetland polygons with the diked or impounded attribute h were selected from the original NWI data layer and these lands were assumed to be permanently protected from flooding. This procedure has the potential to miss dry lands that are protected by dikes and seawalls as contemporary NWI data contains wetlands data only Percent Impervious The split of dry land into developed and undeveloped categories was performed using percent impervious data from the 2011 National Land Cover Dataset (NLCD) (Xian et al. 2011). The cell size was resampled from the original 30 m resolution to a 20-foot resolution in order to match the cell resolution of the other rasters in the project. A threshold of 25% impervious was used to classify areas as developed. Because the NLCD impervious data do not often capture secondary roads, these data were supplemented with road centerline data from the Virginia Geographic Information Network. 2.3 Model Timesteps SLAMM simulations were run from 2011, the newest initial wetland cover layer, to 2100 with modelsolution time steps at 2025, 2040, 2055, 2065, 2085 and Sea Level Rise Scenarios The accelerated sea-level rise (SLR) scenarios used in this analysis are based on the SLR projections from the report produced by Virginia Institute of Marine Science Center for Coastal Resources Management (VIMS CCRM) describing how the Commonwealth can best respond to the ongoing challenges that high tides, storm surge, intense rain storms, sinking land, and rising sea level pose to residents and localities along Virginia s Chesapeake Bay and Atlantic shorelines (Mitchell et al. 2013). VIMS CCRM adjusted the 2014 National Climate Assessment (Parris et al. 2012) sea-level rise curves by adding an average regional rate of subsidence based on a recent USGS publication (Eggleston and Pope 2013) and a 1974 survey of changes in benchmark elevation (Holdahl and Morrison 1974). While the USGS publication suggests that the average rate of subsidence for tidewater Virginia is 3.1 mm/yr, this rate is high for the Eastern Shore area. Instead a more conservative estimate for the Eastern Shore of 2.7 mm/year was used based on both the USGS report and more recent elevation survey data from the Shore. The four relative sea-level rise curves that project sea-level rise under different emission scenarios used for these simulations are summarized in Table 2 and Figure 6. Application of SLAMM to the Eastern Shore of VA 10

17 Table 2. SLR by scenario for each reporting timestep SLR Beyond 1992 levels (m) Historic Low High Highest Sea Level Rise (mm) Historic Low High Highest Sea Level Rise (inch) Year 0 Figure 6. Sea Level Rise Scenarios. 2.5 Tide Ranges SLAMM requires the great diurnal tide range (GT) 1 as an input. The GT data were collected from NOAA tide stations and tide prediction tables for These data are provided directly by the NOAA Tides & Currents website ( Overall, GT values in the project area varied from a minimum of 0.26 m at Franklin City up to 1.58 m in Upshur Neck, south end. A map of GT data throughout the study area is provided in Appendix C. 1 GT - Difference between the mean higher high (MHHW) and mean lower low water (MLLW) levels. Application of SLAMM to the Eastern Shore of VA 11

18 2.5.1 Elevations expressed in half tide units (HTU) In general, wetlands inhabit a range of vertical elevations that is a function of the tide range (Titus and Wang 2008) - one conceptual example of this is shown in Figure 7. Because of this, rather than expressing marsh elevation in absolute values (e.g. meters, feet, cm, etc.), SLAMM uses units relative to the local tide range or half-tide units. A half-tide unit is defined as half of the great diurnal tide range (GT/2). A numerical example follows: If a marsh elevation is X meters above MTL, its elevation in half tide units (HTU) is given by X/(GT/2). For example, consider a marsh with an elevation 1 m above MTL, with a tide range (GT) of 1.5 m. The height of the marsh in HTU is equal to 1/(1.5/2)=1.33 HTU. This set of units is straightforward to understand if you consider that, mean tide level is defined as 0.0 HTU, high tide (MHHW) is defined as 1.0 HTU, and low tide (MLLW) is defined as -1.0 HTU. A marsh with an elevation above 1.0 HTU falls above the high tide line regardless the absolute value of the tide. Figure 7. Relationship between tides, wetlands, and reference elevations for an example estuarine shore profile. Source (Titus and Wang 2008) 2.6 Wetland Boundary Elevation The wetland boundary elevation (WBE) parameter in SLAMM defines the boundary between coastal wetlands and dry lands (including non-tidal wetlands). This elevation, relative to mean-tide level, is Application of SLAMM to the Eastern Shore of VA 12

19 determined through analysis of higher high water levels in NOAA tide records. In practice, we have found that the elevation that differentiates coastal wetlands and dry lands is approximately the height inundated once every 30 days. Therefore, the 30-day inundation level was determined for the two locations in the Eastern Shore of Virginia with NOAA verified water-level data available: Kiptopeke and Wachapreague. An additional data point was analyzed outside the study area Bishops Head, MD. A minimum of five years of data were analyzed in order to characterize this relationship in each location. The elevations of the 30-day inundation expressed as function of MHHW are summarized in Table 3. These relationships were used to derive sitespecific WBEs based on the available local measured GT applied: WBE=1.5*GT on the ocean side and WBE=1.72*GT on the bay side of the peninsula. Table day inundation analysis results at NOAA gauge stations Station Name NOAA Years of data % of MHHW Station ID analyzed Kiptopeke, VA Wachapreague, VA Bishops Head, MD Wetland Elevation-Change Rates A literature search was conducted to collect relevant data on accretion rates and wetland-elevation change rates. In addition, data from members of the project advisory committee were used to determine models of wetland elevation-change rates for the study area Tidal Salt Marsh The current SLAMM application attempts to account for what are potentially critical feedbacks between tidal-marsh surface elevation change rates and SLR (Kirwan et al. 2010). In tidal marshes, increasing inundation can lead to additional deposition of inorganic sediment that can help tidal wetlands keep pace with rising sea levels (Reed 1995). In addition, salt marshes will often grow more rapidly at lower elevations allowing for further inorganic sediment trapping (Morris et al. 2002). There are two primary coastal marsh types within our modeling that are subject to these feedbacks, generally defined here: Regularly Flooded Marsh (RFM) includes low to mid elevation marshes. Roughly speaking, these are marshes that are inundated by tidal water at least once per day. Irregularly Flooded Marsh (IFM) includes high elevation marshes. These marshes are inundated by tidal water once per day or less. Application of SLAMM to the Eastern Shore of VA 13

20 The persistence and conversion dynamics of RFM and IFM in SLAMM are summarized as follows: SLAMM assumes that wetlands will inhabit a range of vertical elevations that is a function of the tide range and the mean-tide level (Titus and Wang 2008). When irregularly-flooded marsh (e.g., the IFM platform) falls below the modeled minimum elevation, then the land cover is converted to regularly-flooded marsh (RFM). When RFM fall below the modeled minimum elevation, generally below mean-tide level, then the land cover is assumed converted to non-vegetated tidal flats. The elevation intervals of existence (relative to tide ranges) can be adjusted by the user to reflect local conditions. Note: The upper elevation boundaries are not critical to the model; SLAMM does not predict any conversion to irregularly-flooded marsh or dry-land production above these elevations. However, examining these boundaries are important to validate the consistency of model assumptions with regards to observed wetland coverage, elevations, and tide data. The distributions of RFM and IFM elevations in Half Tide Units (HTU) 2 in the study area are summarized in Figure 8. 2 A half-tide unit is defined as half of the great diurnal tide range (GT/2). MHHW is defined as 1.0 HTU, MTL (mean-tide level) is defined as 0.0 HTU, and MLLW as -1.0 HTU. HTU are used because wetlands inhabit a range of vertical elevations that is a function of the tide range. Therefore, rather than expressing marsh elevation in absolute values (e.g. meters, feet, cm, etc.), SLAMM uses units relative to the local tide range or half-tide units. Application of SLAMM to the Eastern Shore of VA 14

21 Figure 8. RFM and IFM Elevation Distribution From the histogram above one can observe that: The RFM distribution extends well below MTL (0 HTU) The IFM distribution extends below MHHW (1 HTU). The RFM and IFM distributions overlap in an elevation region (approximately the elevation range between 0.5 to 2 HTU) where both marsh types exist according to the land cover data. Similar marsh elevation distributions have been observed in many other regional studies performed. In addition, a review study by McKee and Patrick (1988) founds that the lower and upper bound limits of RFM (Spartina Alterniflora) can be described as a linear function of the tidal range and may extend below MTL (lower bound) and above MHHW (upper bound). Some of the extreme values within the tails of the distributions shown in Figure 8 are likely a function of horizontal error between data sets or finer-scale LiDAR data coupled with coarser scale NWI maps (see (see Figure 13 for example). Observed data. Site-specific elevation-change data is available primarily within the mainland marshes on the eastern shore of the Delmarva Peninsula. Without site-specific data available for other locations, a Application of SLAMM to the Eastern Shore of VA 15

22 single accretion-to-elevation relationship is assumed throughout the study area, but this is varied as a function of tide range. The available elevation-change data are summarized in Table 4. Figure 9 plots available marsh elevation-change rates as function of the marsh-platform elevation. Table 4. SET data database for Delmarva Peninsula. Site ID Geomorphic classification Zone Plant community Lat. Long. Elev (m above msl ± SE, NAVD 88) years of measure -ment Elev. rate change (mm yr - 1 ± SE) TSS adjacent water body (mg L -1 ± SE) 1A headward eroding tidal creek bottom (Upper Phillips Creek marsh) tidal creek bottom bare bottom present ± 40.5 (n=105) 1C valley marsh (Upper Phillips Creek marsh) marsh platform short-form S. alterniflora present ± 40.5 (n=105) 1D valley marsh (Upper Phillips Creek marsh) marsh platform short-form S. alterniflora present ± 40.5 (n=105) 1E 2A 2B 2C 3A 3B valley marsh (Upper Phillips Creek marsh) valley marsh (Upper Phillips Creek marsh) valley marsh (Upper Phillips Creek marsh) valley marsh (Upper Phillips Creek marsh) valley marsh (Upper Phillips Creek marsh) valley marsh (Upper Phillips Creek marsh) marsh platform marsh platform marsh platform marsh platform marsh platform marsh platform short-form S. alterniflora short-form S. alterniflora short-form S. alterniflora shortform S. alterniflora S. patens + D. spicata and J. roemerian us S. patens + D. spicata and J. roemerian us present present present present present present ± 40.5 (n=105) ± 40.5 (n=105) ± 40.5 (n=105) ± 40.5 (n=105) ± 40.5 (n=105) ± 40.5 (n=105) Application of SLAMM to the Eastern Shore of VA 16

23 Site ID 3C Geomorphic classification valley marsh (Upper Phillips Creek marsh) Zone marsh platform Plant community S. patens + D. spicata and J. roemerian us Lat Long Elev (m above msl ± SE, NAVD 88) years of measure -ment present Elev. rate change (mm yr - 1 ± SE) 4.69 TSS adjacent water body (mg L -1 ± SE) ± 40.5 (n=105) 4A 4B 4C valley marsh (Upper Phillips Creek marsh) valley marsh (Upper Phillips Creek marsh) valley marsh (Upper Phillips Creek marsh) marsh platform marsh platfor m marsh platfor m S. patens + D. spicata S. patens + D. spicata S. patens + D. spicata E present present present ± 40.5 (n=105) ± 40.5 (n=105) ± 40.5 (n=105) Figure 9. Measured data for marsh elevation change rates as function of the marsh platform elevation.vertical lines are the approximated vegetation boundaries Application of SLAMM to the Eastern Shore of VA 17

24 Marsh elevation change rate models. Based on the available data and the observations above, two parabolic feedback curves are proposed to model the accretion response of RFM and IFM marshes. The curves are reported in Figure 10. Because tidal ranges vary throughout the study area, the elevation-change curves shown above are reported with elevations expressed relative to half-tide units (HTU). This allows these curves to be applied throughout the study area regardless of the tidal range differences 3. The assumption that coastal marshes have strong relationships between their elevations in the tidal frame and their accretion rates is strongly supported by mechanistic modeling in the literature (Kirwan et al. 2010). The parabolic shape of the curves is often driven by parabolic biomass density curves for each marsh type (Morris et al. 2002). Elevation change rate (mm yr -1 ) y = x x R² = MHHW Overlap y = x x R² = IFM RFM IFM LB 0 UB UB RFM LB Elevation (HTU) Figure 10. RFM and IFM SLAMM Elevation-Change Models.Blue vertical lines define RFM boundaries (LB = lower bound, UB = upper bound) while IFM ones are in orange. (The horizontal lines of the elevation-change curves are both set to 2 mm/yr) 3 For the accretion data considered here, a GT=1.5 m is used. This is derived from an equation provided by Christiansen (1998) and Turaski (2002) to calculate tide levels for Phillips Creek from nearby NOAA tide stations tidal ranges. Application of SLAMM to the Eastern Shore of VA 18

25 Based on the elevation distributions within available site-specific data, and the literature we set wetland boundaries as follows: o RFM Boundaries Lower Bound to be -0.3 HTU which is compatible with the observed elevation/land cover data and the relationship provided by McKee and Patrick (1988). Upper Bound to be 1.2 HTU which is compatible with elevation/land cover data and McKee and Patrick (1988). l o IFM Boundaries Lower Bound to be 0.75 HTU based on the elevation distribution and the available data. IFM Upper Bound to be 1.7 HTU based on the observation about the marsh upland transition in this area and the elevation distribution for undeveloped dry land. These two curves were produced by estimating the best fit with a quadratic polynomial where measured data are available. Rather than extrapolating these curves beyond measured data, we are assuming a constant 2 mm/yr elevation change where data are not available (toward the boundaries of the overlapping regions). In other words, regularly-flooded marsh habitat that occurs near or above high tide is assumed to accumulate less sediment and to have a relatively low elevation change (2 mm/year) compared to irregularly-flooded marshes that occur at or near mean-tide level. Irregularly-flooded marsh habitat that starts to fall below the high tide level is also assumed to have a lower elevation change (2 mm/year). The fact that both of these marsh types will have lower rates of elevation change at their boundary is supported by the three relatively-low elevation change data points at the center of Figure 10. The lower boundary point for RFM (-0.3, 2) is used to fit the curve for this marsh type and similarly the upper boundary point for IFM (1.7, 0) is also used as a data point to fit the curve. Summary. The feedback relations y=f(x) with y=elevation change rate and x=platform elevation in HTU, applied everywhere are: RFM: y = x x if f(x)>2; y=2 otherwise; IFM: y = x x if x>0.82; y=2 otherwise Elevation-Change Rates of other Wetland Types The Inland-fresh Marsh elevation-change rate was set to 1 mm/yr. Studies of fens and freshwater marshes in Michigan and Georgia (Craft and Casey 2000; Graham et al. 2005) suggest this to be an appropriate value based on 210 Pb measurements. Tidal Fresh Marsh accretion was set to 5 mm/yr based on data presented by Neubauer (Neubauer 2008; Neubauer et al. 2002). Tidal-fresh marsh accounts for only one Application of SLAMM to the Eastern Shore of VA 19

26 half of one percent of coastal wetlands in the study area. Accretion feedbacks were not used for tidal-fresh marshes due to a lack of site-specific data. Lacking site-specific data, values of 1.6 mm/yr and 1.1 mm/yr were assigned for swamp and tidal swamp elevation-change rates, respectively which were measured in Georgia by Dr. Christopher Craft (Craft 2008, 2012). Beach sedimentation was set to 0.5 mm/yr, a commonly used value in SLAMM applications. Average beach sedimentation rates are assumed to be lower than marsh-accretion rates due to the lack of vegetation to trap suspended sediment, though it is known to be highly spatially variable. In addition, it is worth noting that future beach nourishment, should it occur within the study area, is not accounted for in these SLAMM simulations. 2.8 Erosion Rates SLAMM models erosion as additive to inundation; horizontal wetland erosion is assumed to be the effect of wave action. Marsh or swamp erosion is only assumed to occur when the wetland type in question is directly exposed to open water with a sufficient fetch which is the open-water region over which waves can set up. The fetch that triggers horizontal erosion in marshes is typically set to 9 km, though this parameter was varied within the current application. In SLAMM, within a given portion of the map, average erosion rates are entered for marshes, swamps, tidal flats, and beaches. Marshes were assumed to erode directly to open water in this model application. Different erosion models were applied to three different portions of the study area: For the mainland marshes of Virginia Coast Reserve (Figure 12), erosion rates were derived using directly measured data of marsh retreat (McLoughlin et al. 2011). For the lagoonal marshes on the eastern shore, a reduced fetch requirement was applied and specific wind-directions were examined based on Mariotti and Fagherazzi (2013). For the rest of the study area on the western shorelines, marsh erosion rates were applied based on the standard SLAMM erosion model and the data of Rosen (1980). McLoughlin and coworkers (2011) created maps of erosion rates on the mainland marshes of the Virginia Coast Reserve (Figure 11). Areas with positive net erosion rates (marshes retreating) were found to be highly clustered and average erosion rates were calculated within each of these clusters. Areas with zero erosion or with observed aggradation were modeled with no erosion since SLAMM does not account for aggradation processes. The fetch requirement was turned off for the mainland marshes. Locations with positive historical-erosion rates were assumed to continue eroding at that rate and locations with no erosion (white dots in Figure 11) were not assumed to undergo any erosion. Application of SLAMM to the Eastern Shore of VA 20

27 Figure 11. Overlay of Long-term Regression Rate (LRR) data from McLoughlin et al on SLAMM initial condition map. White dots along the mainland shoreline indicate locations where no erosion was observed. The lagoonal marshes on the eastern shore have also been extensively studied. Based on Figure 4 in (Mariotti and Fagherazzi 2013) the maximum fetch requirement before applying erosion was decreased to 1 Km. Based on Figure 11 of (Mariotti et al. 2010) wind directions were limited to the most frequent observed, generally north to northeast (between 330 and 60 degrees) and to the south to southwest ( degrees). The combination of reducing the fetch requirement but adding specificity of dominant wind directions results in site-appropriate predictions of eroding shorelines. Marshes that met the erosional requirements based on fetch and wind direction were assumed to erode at 0.45 m/yr based on (Rosen 1980). This erosion rate is essentially the same as the average rate of eroding mainland marshes (0.53 m/year) measured by (McLoughlin et al. 2011). Application of SLAMM to the Eastern Shore of VA 21

28 Lagoonal Marsh Mainland Marsh Figure 12. Delineation of mainland and lagoonal marshes. Marshes in black outlined areas are designated as mainland marsh while all marshes to the east of the mainland marsh area were designated as lagoonal. For the remaining marshes, marsh erosion rates were applied based on data of Rosen (1980). At the time of the research, marsh margins comprised 20% of the Virginia Chesapeake Bay shoreline. Rosen determined the average marsh shoreline retreat to be 0.54 m/yr, with the eastern shore having a lower erosion rate (0.45 m/yr) than the western shore (0.64 m/yr, Rosen 1980). As noted above, the eastern rates were used for marshes in the lagoonal portion of the SLAMM runs. The rate of 0.64 m/yr was applied for marshes on the western portion of the study area. In the current publicly-available version of SLAMM the same erosion rate is applied to tidal flats and beaches. However, in the current application, separate erosion rates were applied for beaches and tidal flats in order to differentiate between the beaches exposed to open ocean and the lagoonal tidal flats that are protected from open ocean waves. In order to parameterize the tidal flats/beach erosion rates required by SLAMM, we relied on two main sources of data: The previously mentioned work of Rosen (1980) and the USGS National Assessment of Shoreline Change (Hapke et al. 2011). An erosion rate of 0.89 m/yr was Application of SLAMM to the Eastern Shore of VA 22

29 applied to the Bay-side beaches and tidal flats and the lagoonal tidal flats on the Ocean-side of the peninsula based on Rosen s work (1980). This value is the average of erosion rates observed weighted by the prevalence of the beach type where each rate was measured. Although collected for the Bay-side beaches of the peninsula, it was also applied to the tidal flats in the lagoon due to a lack of site-specific erosion data. For the beaches on the Atlantic side of the study area, the USGS reference was used (Hapke et al. 2011). In this study, transects along the Atlantic coast were characterized to determine the long-term rate of shoreline change (from the1800 s to the latest LiDAR date). Long term regression rates (LRRs) were calculated by fitting a regression line to all shoreline points for a particular transect. The Long-term (linear) regression rate is the slope of the best-fit regression line determined by minimizing the sum of the squared residuals (Hapke et al. 2011). In the northeastern portion of the study area the LRR showed positive shoreline movement, indicating aggradation. In these areas erosion rates were set to zero. In areas where shorelines had negative LRRs, the derived rate was applied. The specific rates beach erosion applied ranged from 0 to 5.5 m/year. For beaches on barrier islands, erosion was turned off as these islands were allowed to migrate over time (see next section). 2.9 Barrier Island Migration For barrier islands, a new module was developed in SLAMM to account for their migration as a result of winds and increased inundation. During barrier island migration, sand from the eroding shoreface is transported to the dunes and swales, so that they increase in elevation over time. As sea level rise rates increase, the shoreface is eroded faster, providing more sediment to building the barrier island habitats higher. Eastern shore barrier islands have been successfully migrating for around 6000 years (3+ m of SLR). The new SLAMM module is a simple model that incorporates historical migration rates for barrier islands. In practice, the area of the barrier island is specified by drawing a polygon around it. The model accepts data about the migration rate in meters per year and the heading in degrees for each polygon. All land in the barrier island that is not a regularly-flooded marsh migrates at a rate equal to the average observed migration rate and this migration replaces regularly-flooded marshes in its path. (An open ocean cell with no-data elevation is left in the wake of the migrating island.) The migration direction is determined based on the heading value applied, and elevations of all barrier island lands are assumed to be maintained relative to sea-level. Migration rates were obtained from Matt Kirwan (personal communication) while heading values were determined assuming that the island will migrate perpendicular to the shoreline. The set of migration rates used for these simulations is reported in Table 5. Application of SLAMM to the Eastern Shore of VA 23

30 Table 5. Migration rate and heading applied to barrier islands Island Name Migration rate (m/yr) Heading (degrees) Smith Myrtle Ship Shoal Wreck Cobb Hog Parramore Cedar Metompkin Assawoman+Wallops Fisherman s Island NA* NA* *A migration rate of m/yr for Fisherman s Island has been observed but it was not applied since this island is prograding seaward Application of SLAMM to the Eastern Shore of VA 24

31 2.10 Model Calibration In order to test the consistency of key SLAMM modeling inputs, such as current land cover, elevations, modeled tidal ranges and hydraulic connectivity, SLAMM is run at time zero in which tides are applied to the study area but no sea-level rise, accretion or erosion are considered. Because of DEM and NWI uncertainty, local factors such as variability in the water table, and simplifications within the SLAMM conceptual model, some cells may initially be below their lowest allowable elevation land cover category and are immediately converted by the model to a different land cover category. For example, an area classified in the wetland layer as fresh-water swamp subject to regular saline tides, according to its elevation and tidal information, would be converted by SLAMM to a tidal swamp at time zero. Where model calibration results in significant land-cover changes, additional investigation is required to confirm that the current land cover of a particular area is correctly represented by time-zero conversion results. If not, it may be necessary to better calibrate data layers and model inputs to the actual observed conditions. Land-coverage conversion maps at time zero are always reviewed to identify any initial problems, and to make necessary adjustments to correct them. In some cases the initial land cover re-categorization by SLAMM better describes the current coverage of a given area. For example, the high horizontal resolution of the elevation data can allow for a more refined wetland map than the original NWI-generated shapefiles used in this project. The standard mapping protocol for the NWI maps is to include wetlands with an area of 0.5 acres (2023 m 2 ). In addition, long, narrow rectangles, such as those following drainage-ways and stream corridors may or may not be mapped, depending on project objectives (Federal Geographic Data Committee, 2009). With a ~6m cellsize, SLAMM is able to discern wetlands of 37 m 2. Therefore, time zero maps sometimes provide a refinement to the initial wetland layers, as shown in Figure 13 and these type of initial land cover conversion are then accepted without any further investigation. Application of SLAMM to the Eastern Shore of VA 25

32 Estuarine Open WateEstuarine Open Water Irreg.-Flooded Marsh Irreg.-Flooded Marsh Regularly-Flooded M Regularly-Flooded Marsh Initial wetland layer Time-zero predicted wetland layer Satellite Imagery (from Google Earth) Figure 13. Marsh near Deep Creek, VA Another issue encountered during model calibration was the immediate flooding of some cell areas covered by developed land. Most often these areas were bridges and piers areas that are represented as development in the wetland layer but whose elevations are not included in the bare-earth elevation layer. Obviously, these land cover conversion were deemed acceptable. However, occasionally SLAMM predicts some low-lying residential areas to be flooded at least once every 30 days based on tide data. These occurrences were investigated on a case-by-case basis by examining satellite imagery from Google Earth and Bing Maps and performing web searches for any public records of flooding issues. In most cases the main reason for these initial land cover conversions is the native resolution of the impervious cover layer determining developed areas, which is 30x30 m 2, compared to the higher resolution of the elevation layer, resampled at 6.096x6.096 m 2 for this project. Similar to the calibration results shown in Figure 13, the higher resolution elevation data can allow the model to better define this wet to dry land interface at time zero. Application of SLAMM to the Eastern Shore of VA 26

33 Initial inundation of dry land could not always be explained by the low resolution of the impervious layer. Sometimes, initial inundation of dry land was due to an assigned wetland-boundary elevation ( WBE parameter) that was too high for the area in question. Because of the lack of fine-scale spatial data and the inherent uncertainty of the wetland-boundary elevation estimates, adjustments were sometimes required on a site by site basis to correct initial dry land conversion. The occurrence of tidal-freshwater wetlands in riverine environments, such as tidal swamps and tidal-fresh marshes, is generally found to be more closely correlated with the salinity content in the water than the marsh platform elevation. However, the SLAMM salinity submodel was not used in these simulations because of the model s data requirements (often the required data, such as up-river bathymetry and salinity, were not available) and the significant time required for model calibration. The simplified model concept used here is that water salinity is correlated with marsh elevation on an estuary-specific basis. To implement this assumption, the minimum allowable elevations for these tidal-freshwater habitats were set to heights based on the measured marsh elevations using site-specific LiDAR data. Table 6 presents the minimum elevations applied for the study area. Table 6. Default minimum wetland elevations in SLAMM conceptual model. SLAMM Category Min Elev. Min Unit Undeveloped Dry Land 1 WBE Developed Dry Land 1 WBE Swamp 1 WBE Ocean Beach -1 HTU Inland-Fresh Marsh 1 WBE Tidal Flat -1 HTU Regularly-Flooded Marsh -0.3 HTU Riverine Tidal 1 WBE Irreg.-Flooded Marsh 0.75 HTU Inland Open Water 1 WBE Trans. Salt Marsh 1 HTU Tidal Swamp* 1.04 HTU Tidal-Fresh Marsh* 0.64 HTU Estuarine Beach -1 HTU Rocky Intertidal -1 HTU Inland Shore -1 HTU Ocean Flat -1 HTU *For these marsh habitats lower-boundary elevations are assumed to be highly dependent on freshwater flow and therefore are generally set based on site-specific data (see text for more detailed discussion). Application of SLAMM to the Eastern Shore of VA 27

34 As inundated developed land is unlikely to immediately convert to a coastal wetland, a new landcover category was included in SLAMM: Flooded Development. This category occurs when developed dry land is inundated by salt water at least once every 30 days. Flooded developed land is not subject to additional land-cover conversions. There is some uncertainty as to whether a marsh could inhabit this land cover, so the model is likely somewhat conservative with respect to marsh transgression in these locations. Several iterations of layer refinement were necessary in order to get an acceptable calibrated model to the initial conditions. After each step, time zero maps were compared to the initial condition maps using GIS software and annotating where large conversions of wetlands were observed. These issues were consequently explained or fixed by additional calibration or layer refinement. Any calibrations or allowable time zero changes were quality assured by an independent team member. Model projections are reported from time-zero forward so that the projected land cover changes are only due to SLR and not due to initial model calibration. Application of SLAMM to the Eastern Shore of VA 28

35 2.11 Model Setup The study area was divided into several input subsites to represent the diversity in input data layer dates, tide ranges, and wetland elevations. The input subsite boundaries are shown in Figure 14. Corresponding photo dates and tide ranges are presented in Table Global 10 2 Figure 14. Input subsites. Application of SLAMM to the Eastern Shore of VA 29

36 Table 7. Dates and tide ranges for subsites. Subsite NWI Photo Date DEM Date GT Great Diurnal Tide Range (m) Global Once parameterized the model was calibrated by analyzing the time-zero model results. Through this step, subsite 17 was added to allow the tidal-swamps in the two estuaries in the northwest of the study area to extend lower in the tidal frame. The calibration step indicated that tidal swamps in this area were converting to irregularly-flooded marsh at time zero, however, this was not supported by the available aerial (Google Earth) imagery. Therefore, the model code was changed to allow tidal swamps to extend lower in the tidal frame in this area, in an attempt to capture the effects of freshwater influence in these estuaries. Another observation made in the time-zero calibration step was the loss of tidal flats and sand bars along the Bay side of the peninsula. The calibrated model indicated several small areas where more loss was occurring in the SLAMM model that was observed in the most recent aerial imagery. Examination of the spatial data indicated that the wetland layer and elevation layer did not place these features in the same location (although surrounding landforms did line up well). This indicated that tidal flats had shifted between the collection of the wetland data and the LiDAR data. An example of this is presented in Figure 15. This analysis illustrates the difficulties and uncertainties in accurately modeling tidal flats and mud Application of SLAMM to the Eastern Shore of VA 30

37 flats accurately down to mean lower low water (MLLW). It should also be noted that NWI data layers are not generally tidally coordinated down to the MLLW level. A B C D Figure 15. Initial condition of sandbar near Smith Beach, VA Initial condition is in panel (A) and time-zero prediction (B). Elevation data layer (C) shows the sandbar elevation/location does not align well with the NWI location in panel A. Google Earth Imagery indicates the sandbar may have undergone changes in morphology since NWI data was derived (D) Table 8 presents a comparison between the initial acreage of each land cover type and its acreage at timezero (2011) along with the corresponding change in acreage between the two. Application of SLAMM to the Eastern Shore of VA 31

38 Table 8. Time-Zero Results (acres) Initial 2011 Change Estuarine Open Water 183, ,740 1,650 Undeveloped Dry Land 144, ,838-1,154 Regularly-Flooded Marsh 41,981 41, Swamp 40,072 40, Irreg.-Flooded Marsh 27,997 25,407-2,591 Open Ocean 23,324 23, Developed Dry Land 7,455 7, Inland-Fresh Marsh 6,919 6, Tidal Swamp 4,745 4, Trans. Salt Marsh 3,370 4, Tidal Flat 2,594 4,263 1,669 Ocean Beach 2,459 2, Inland Open Water 1,358 1, Estuarine Beach 1, Tidal-Fresh Marsh Rocky Intertidal Inland Shore Flooded Developed Dry Land Total (incl. water) 492, ,435 0 Application of SLAMM to the Eastern Shore of VA 32

39 3 Results This section presents numerical and graphical results for the Eastern Shore of Virginia study area. Tables are included of land-cover acreage at each time step (2025, 2040, 2065, and 2100) for each SLR scenario simulated (Historic = 32 cm, Low = 47 cm, High = 79 cm, and Highest = 1.15 m by 2065). Table 9 presents a summary table showing the percentage loss or gain for selected land-cover types (negative numbers represent acreage losses). It is important to note that changes presented in Table 9 are calculated starting from to the 2011 time-zero result and represent projected land-cover changes as a result of sealevel rise excluding any predicted changes that occur when the model is applied to initial-condition data, as discussed in the Model Calibration and Model Setup sections of this report. Land cover category Table 9. Land cover changes for each SLR scenario at 2065 Acres in 2011 Percentage Land cover change from 2011 to 2065 Historic Low High Highest Undeveloped Dry Land 143, % -2.5% -6.4% -11.0% Regularly-Flooded Marsh 41, % 5.1% 53.1% -23.5% Swamp 40, % -2.5% -10.0% -16.5% Irreg.-Flooded Marsh 25, % -8.7% -85.2% -92.6% Developed Dry Land 7,350-1% -3% -9% -16% Inland-Fresh Marsh 6, % -2.9% -12.0% -20.7% Tidal Swamp 4,641-7% -24% -69% -86% Trans. Salt Marsh 4, % 17.6% 64.5% 74.9% Tidal Flat 4,270-37% -34% 67% 747% Ocean Beach 2, % -1.9% -1.0% -1.7% Inland Open Water 1,340-6% -12% -19% -26% Estuarine Beach % -22.5% -31.0% -38.6% Tidal-Fresh Marsh % -4.4% -22.0% -55.3% Flooded Developed Dry Land % 180.9% 602.9% % Only modest losses are predicted under the historic rate of SLR, with tidal flats and estuarine beaches being most vulnerable. Under accelerated SLR scenarios, SLAMM results suggest that more land-cover categories are vulnerable. In particular, irregularly-flooded marshes (higher elevation marshes) are predicted to sustain serious losses in acreage, from almost 9% loss under the Low SLR scenario to more than 90% under the Highest SLR scenario. Regularly-flooded (low) marsh appears resilient to lower rates of SLR. However, covered area losses of this marsh are limited because IFM convert to RFM which may actually lead to a net gain of RFM by 2065 as projected under Low and High SLR scenarios. For the High and Highest scenarios RFM are not capable to keep up with SLR and overall coverage is predicted to be reduced by 2065 under the Highest SLR scenario and by 2100 under the High SLR scenario. Application of SLAMM to the Eastern Shore of VA 33

40 Losses of currently-existing IFM will be balanced to some extent by the creation of new marshes through upland migration. In SLAMM, dry lands are predicted to convert to the transitional marsh category when regularly inundated. (There is generally overlap in elevation ranges for irregularly-flooded marsh and the transitional marsh/scrub shrub categories, and therefore uncertainty as to which habitat will emerge.) Overall, however, predicted losses of irregularly-flooded marsh are greater than predicted gains in transitional marsh coverage, resulting in a net loss of high-elevation marshes (irregularly-flooded marshes) as shown in Table 9. Overall, by 2065 overall acreage of tidal marsh is predicted to be maintained or slightly increased up to the High SLR scenario, while marshes will be lost under the Highest SLR scenario. However, if the time horizon is set to 2100, then marsh net losses are observed for SLR higher than the Low SLR scenario. Table 10. Acreage at 2065 for tidal marshes. Acres in 2011 Historic Low High Highest Irreg.-Flooded Marsh 25, ,204-21,545-23,305 Trans. Salt Marsh 4, ,889 3,429 Regularly-Flooded Marsh 41,947-1,161 2,159 22,302-9,918 Total 71,746-1, ,646-29,794 In addition to losses in marshes, important reductions in tidal swamp and tidal fresh marsh are projected from these SLAMM simulations. Losses increase with increasing SLR by 2065 and, in the case of tidal swamp, culminate in a near complete loss of this land-cover type by Estuarine beaches are another category that are predicted to sustain serious losses, while ocean beaches are predicted to be maintained as barrier islands were modeled resilient to SLR. Results for each of the sea-level rise scenarios examined are presented in the following section. Tabular results are presented for each SLR scenario along with maps for two select areas within the study region: the Crabbing Marsh near Machipongo, VA and Cedar Island. Appendix E presents maps of the entire study area and GIS ASCII Raster maps are available the entire study area for each scenario at Application of SLAMM to the Eastern Shore of VA 34

41 Table 11. Acres at each time step for Historic SLR scenario (32 cm by 2065 and 48 cm by 2100) Estuarine Open Water Estuarine Undeveloped Dry Land Undeveloped Regularly-Flooded Marsh Regularly-Flooded Swamp Swamp Irreg.-Flooded Marsh Irreg.-Flooded Open Open Ocean Developed Developed Dry Land Inland-Fresh Inland-Fresh Marsh Tidal Tidal Swamp Trans. Trans. Salt Marsh Tidal Tidal Flat Ocean Ocean Beach Inland Inland Open Water Estuarine Estuarine Beach Tidal-Fresh Tidal-Fresh Marsh Flooded Developed Flooded Dry Land Rocky Rocky Intertidal Inland Inland Shore Open Water 184, , , , ,226 Dry Land 143, , , , ,052 Marsh 41,947 41,574 41,035 40,786 42,182 40,008 39,981 39,918 39,708 39,225 Marsh 25,413 25,054 24,782 24,536 24,588 Ocean 23,518 23,631 24,416 25,940 27,149 Dry Land 7,350 7,332 7,308 7,255 7,148 Marsh 6,901 6,886 6,865 6,825 6,690 Swamp 4,641 4,606 4,549 4,308 3,704 Salt Marsh 4,386 4,109 4,306 4,519 4,992 Flat 4,270 3,470 2,988 2,710 2,278 Beach 2,383 2,356 2,348 2,337 2,329 Open Water 1,340 1,309 1,304 1,261 1,172 Beach Marsh Developed Dry Land Intertidal Shore Total (incl. water) 492, , , , ,435 Application of SLAMM to the Eastern Shore of VA 35

42 Figure 16. Crabbing Marsh, VA Initial condition Figure 17. Crabbing Marsh, Historic SLR scenario 2011 ( time-zero ) Application of SLAMM to the Eastern Shore of VA 36

43 Figure 18. Crabbing Marsh, VA, Historic SLR scenario Application of SLAMM to the Eastern Shore of VA 37

44 Figure 19. Cedar Island, initial condition Application of SLAMM to the Eastern Shore of VA 38

45 Figure 20. Cedar Island, Historic SLR scenario 2011 ( time-zero ) Application of SLAMM to the Eastern Shore of VA 39

46 Figure 21. Cedar Island, Historic SLR scenario Application of SLAMM to the Eastern Shore of VA 40

47 Table 12. Acres at each time step for Low SLR scenario (47 cm by 2065 and 79 cm by 2100) Estuarine Open Water Estuarine Undeveloped Dry Land Undeveloped Regularly-Flooded Marsh Regularly-Flooded Swamp Swamp Irreg.-Flooded Marsh Irreg.-Flooded Open Open Ocean Developed Developed Dry Land Inland-Fresh Inland-Fresh Marsh Tidal Tidal Swamp Trans. Trans. Salt Marsh Tidal Tidal Flat Ocean Ocean Beach Inland Inland Open Water Estuarine Estuarine Beach Tidal-Fresh Tidal-Fresh Marsh Flooded Developed Flooded Dry Land Rocky Rocky Intertidal Inland Inland Shore Open Water 184, , , , ,461 Dry Land 143, , , , ,298 Marsh 41,971 41,680 41,474 44,130 64,229 40,001 39,944 39,749 39,000 36,362 Marsh 25,377 24,841 24,415 23,173 9,574 Ocean 23,518 23,633 24,420 25,948 27,178 Dry Land 7,349 7,324 7,276 7,158 6,656 Marsh 6,900 6,875 6,837 6,697 6,109 Swamp 4,637 4,580 4,398 3,546 1,571 Salt Marsh 4,417 4,222 4,576 5,196 7,417 Flat 4,285 3,571 3,235 2,813 2,142 Beach 2,383 2,354 2,345 2,339 2,360 Open Water 1,340 1,307 1,265 1,185 1,084 Beach Marsh Developed Dry Land Intertidal Shore Total (incl. water) 492, , , , ,435 Application of SLAMM to the Eastern Shore of VA 41

48 Figure 22. Crabbing Marsh, VA, Low SLR scenario Figure 23. Crabbing Marsh, VA, Low SLR scenario Application of SLAMM to the Eastern Shore of VA 42

49 Figure 24. Cedar Island, Low SLR scenario Application of SLAMM to the Eastern Shore of VA 43

50 Figure 25. Cedar Island, Low SLR scenario Application of SLAMM to the Eastern Shore of VA 44

51 Table 13. Acres at each time step for High SLR scenario (79 cm by 2065 and 1.49 m by 2100) Estuarine Open Water Estuarine Undeveloped Dry Land Undeveloped Regularly-Flooded Marsh Regularly-Flooded Swamp Swamp Irreg.-Flooded Marsh Irreg.-Flooded Open Open Ocean Developed Developed Dry Land Inland-Fresh Inland-Fresh Marsh Tidal Tidal Swamp Trans. Trans. Salt Marsh Tidal Tidal Flat Ocean Ocean Beach Inland Inland Open Water Estuarine Estuarine Beach Tidal-Fresh Tidal-Fresh Marsh Flooded Developed Flooded Dry Land Rocky Rocky Intertidal Inland Inland Shore Open Water 184, , , , ,825 Dry Land 143, , , , ,174 Marsh 42,029 42,003 44,550 64,331 17,409 39,995 39,798 39,165 36,012 31,401 Marsh 25,292 24,356 22,363 3,747 1,684 Ocean 23,518 23,637 24,428 25,978 27,366 Dry Land 7,347 7,299 7,204 6,699 5,805 Marsh 6,900 6,852 6,736 6,073 4,971 Swamp 4,627 4,470 3,740 1, Salt Marsh 4,477 4,521 5,149 7,366 12,019 Flat 4,324 3,921 3,828 7,206 47,648 Beach 2,383 2,350 2,342 2,359 2,405 Open Water 1,340 1,270 1,205 1, Beach Marsh Developed Dry Land ,650 Intertidal Shore Total (incl. water) 492, , , , ,435 Application of SLAMM to the Eastern Shore of VA 45

52 Figure 26. Crabbing Marsh, VA, High SLR scenario Figure 27. Crabbing Marsh, VA, High SLR scenario Application of SLAMM to the Eastern Shore of VA 46

53 Figure 28. Cedar Island, High SLR scenario Application of SLAMM to the Eastern Shore of VA 47

54 Figure 29. Cedar Island, High SLR scenario Application of SLAMM to the Eastern Shore of VA 48

55 Table 14.Acres at each time step for Highest SLR scenario (1.15 m by 2065 and 2.29 m by 2100) Estuarine Open Water Estuarine Undeveloped Dry Land Undeveloped Regularly-Flooded Marsh Regularly-Flooded Swamp Swamp Irreg.-Flooded Marsh Irreg.-Flooded Open Open Ocean Developed Developed Dry Land Inland-Fresh Inland-Fresh Marsh Tidal Tidal Swamp Trans. Trans. Salt Marsh Tidal Tidal Flat Ocean Ocean Beach Inland Inland Open Water Estuarine Estuarine Beach Tidal-Fresh Tidal-Fresh Marsh Flooded Developed Flooded Dry Land Rocky Rocky Intertidal Inland Inland Shore Open Water 184, , , , ,373 Dry Land 143, , , , ,941 Marsh 42,122 43,122 57,664 32,204 15,084 39,984 39,572 37,697 33,396 23,574 Marsh 25,175 23,307 9,677 1,870 1,394 Ocean 23,518 23,641 24,436 26,052 27,928 Dry Land 7,345 7,263 7,071 6,137 5,435 Marsh 6,886 6,815 6,507 5,460 3,277 Swamp 4,614 4,193 2, Salt Marsh 4,580 4,838 7,224 8,009 20,114 Flat 4,362 4,536 5,294 36,947 11,534 Beach 2,383 2,346 2,345 2,343 2,050 Open Water 1,339 1,261 1, Beach Marsh Developed Dry Land ,318 2,019 Intertidal Shore Total (incl. water) 492, , , , ,435 Application of SLAMM to the Eastern Shore of VA 49

56 Figure 30. Crabbing Marsh, VA, Highest SLR scenario 2011 Figure 31. Crabbing Marsh, VA, Highest SLR scenario 2025 Application of SLAMM to the Eastern Shore of VA 50

57 Figure 32. Crabbing Marsh, VA, Highest SLR scenario 2040 Figure 33. Crabbing Marsh, VA, Highest SLR scenario 2065 Application of SLAMM to the Eastern Shore of VA 51

58 Figure 34. Cedar Island, Highest SLR scenario 2011 Application of SLAMM to the Eastern Shore of VA 52

59 Figure 35. Cedar Island, Highest SLR scenario 2025 Application of SLAMM to the Eastern Shore of VA 53

60 Figure 36. Cedar Island, Highest SLR scenario 2040 Application of SLAMM to the Eastern Shore of VA 54

61 Figure 37. Cedar Island, Highest SLR scenario 2065 Application of SLAMM to the Eastern Shore of VA 55

62 4 Conclusions In this portion of the Enhancing Coastal Resilience on Virginia s Eastern Shore project, SLAMM was applied to assess the vulnerability and resilience of wet and dry lands to accelerated sea-level rise. This study combined the best available elevation and wetland spatial data sets combined with tidal data and site specific accretion and erosion rates. Moreover, an empirical data analysis was used to incorporate feedbacks between marsh-surface elevation and accretion rates. This study examined four SLR scenarios (Historic = 32 cm by 2065, Low = 47 cm by 2100, High = 1.49 m by 2100, and Highest = 2.29 m by 2100) and found the main wetland types of the Eastern Shore to be increasingly vulnerable to the effects of SLR as the rate of SLR increases. The irregularly-flooded marsh category was predicted to be especially vulnerable. In considering these results, it is important to bear in mind some limitations of the study. While SLAMM is a useful tool for visualizing potential effects of SLR, the model only predicts changes due to long-term changes in sea levels. Anthropogenic changes such as beach nourishment, shoreline armoring, or construction of levees are not included in the simulations presented here. In addition, the effects of large storms on landcover conversion and marsh losses are not directly considered in this report. Furthermore, SLAMM predicts that irregularly-flooded marsh habitat that is regularly flooded will successfully convert into a viable low-marsh habitat. In some cases, it is possible that adding significant salinity to irregularlyflooded marsh habitats will result in peat collapse and direct conversion of irregularly-flooded marshes into open water. Given that many of these changes or events can be injurious to marsh habitats, the predictions from this model application can be considered optimistic. Wetland elevation-change rates are critical input parameters for SLAMM. While considerable sitespecific data exist in this site, it is fair to say that there is considerable uncertainty in the precision of the empirical accretion model derived for this project (Figure 9). An important future step could be to evaluate overall model uncertainty including uncertainty in modeled accretion rates along with elevation-data and SLR-scenario uncertainties. In the immediate future, the modeling reported upon herein will provide base layers for upcoming storm surge modeling. The ADCIRC models generated will take into account the predicted absence or presence of coastal marshes modeled here and their effects on storm attenuation. The current modeling will also provide a set of maps and numerical results for examining which dry lands and wetlands are predicted to be most vulnerable to SLR and in what timeframe. This will facilitate the evaluation of policy and landmanagement options given the likely threat of accelerated sea-level rise in this region. Application of SLAMM to the Eastern Shore of VA 56

63 Literature Cited Christiansen, T. (1998). Sediment deposition on a tidal salt marsh. University of Virginia. Clough, J., Park, Richard, Marco, P., Polaczyk, A., and Fuller, R. (2012). SLAMM 6.2 Technical Documentation. Craft, C. (2008). Tidal Swamp Accretion. Craft, C. (2012). Personal Communication. Craft, C. B., and Casey, W. P. (2000). Sediment and nutrient accumulation in floodplain and depressional freshwater wetlands of Georgia, USA. Wetlands, 20(2), Craft, C., Clough, J. S., Ehman, J., Joye, S., Park, R. A., Pennings, S., Guo, H., and Machmuller, M. (2009). Forecasting the effects of accelerated sea-level rise on tidal marsh ecosystem services. Frontiers in Ecology and the Environment, 7(2), Dahl, T. E., Dick, J., Swords, J., and Wilen, B. O. (2009). Data Collection Requirements and Procedures for Mapping Wetland, Deep water and Related Habitats of the United States. Division of Habitat and Resource Conservation, National Standards and Support Team, Madison, WI, 85. Eggleston, J., and Pope, J. (2013). Land subsidence and relative sea-level rise in the southern Chesapeake Bay region. US Geological Survey. Federal Geographic Data Committee,. (2009). Wetlands Mapping Standard. Reston, VA. Fry, J., Xian, G., Jin, S., Dewitz, J., Homer, C., Yang, L., Barnes, C., Herold, N., and Wickham, J. (2011). Completion of the 2006 National Land Cover Database for the Conterminous United States, PE&RS, Vol. 77 (9): Websites: mrlc. gov/nlcd2006. php, and mrlc. gov/downloadfile2. php. Galbraith, H., Jones, R., Park, R., Clough, J., Herrod-Julius, S., Harrington, B., and Page, G. (2002). Global Climate Change and Sea Level Rise: Potential Losses of Intertidal Habitat for Shorebirds. Waterbirds, 25(2), 173. Glick, P., Clough, J., and Nunley, B. (2007). Sea-level Rise and Coastal Habitats in the Pacific Northwest: An Analysis for Puget Sound, Southwestern Washington, and Northwestern Oregon. National Wildlife Federation. Glick, P., Clough, J., Polaczyk, A., and Nunley, B. (2011). Sea-Level Rise and Coastal Habitats in Southeastern Louisiana: An Application of the Sea Level Affecting Marshes (SLAMM) Model. Draft Technical Report. National Wildlife Federation. Graham, S. A., Craft, C. B., McCormick, P. V., and Aldous, A. (2005). Forms and accumulation of soil P in natural and recently restored peatlands Upper Klamath Lake, Oregon, USA. Wetlands, 25(3), Hapke, C. J., Himmelstoss, E. A., Kratzmann, M. G., List, J. H., and Thieler, Er. (2011). National assessment of shoreline change; historical shoreline change along the New England and Mid-Atlantic coasts. U. S. Geological Survey. Application of SLAMM to the Eastern Shore of VA 57

64 Holdahl, S. R., and Morrison, N. L. (1974). Regional investigations of vertical crustal movements in the US, using precise relevelings and mareograph data. Tectonophysics, 23(4), Kirwan, M. L., Guntenspergen, G. R., D Alpaos, A., Morris, J. T., Mudd, S. M., and Temmerman, S. (2010). Limits on the adaptability of coastal marshes to rising sea level. Geophysical Research Letters, 37(23). Mariotti, G., and Fagherazzi, S. (2013). Critical width of tidal flats triggers marsh collapse in the absence of sea-level rise. Proceedings of the national Academy of Sciences, 110(14), Mariotti, G., Fagherazzi, S., Wiberg, P. L., McGlathery, K. J., Carniello, L., and Defina, A. (2010). Influence of storm surges and sea level on shallow tidal basin erosive processes. Journal of Geophysical Research: Oceans, 115(C11), C McKee, K., and Patrick. (1988). The Relationship of Smooth Cordgrass (Spartina alterniflora) to Tidal Datums: A Review. Estuaries, 11(3), Mcleod, E., Poulter, B., Hinkel, J., Reyes, E., and Salm, R. (2010). Sea-level rise impact models and environmental conservation: A review of models and their applications. Ocean & Coastal Management, 53(9), McLoughlin, S., McGlathery, K., and Wiberg, P. (2011). Quantifying Changes along Mainland Marshes in the Virginia Coast Reserve. University of Virginia, 60. Mitchell, M., Hershner, C., Herman, J., Schatt, D., Eggington, E., and Stiles, S. (2013). Recurrent Flooding Study for Tidewater Virginia. Virginia Senate Document nr. 3, Richmond, Virginia. Morris, J. (2013). Marsh Equilibrium Model Version 3.4. Morris, J. T., Sundareshwar, P. V., Nietch, C. T., Kjerfve, B., and Cahoon, D. R. (2002). Responses of coastal wetlands to rising sea level. Ecology, 83(10), National Wildlife Federation, and Florida Wildlife Federation. (2006). An Unfavorable Tide: Global Warming, Coastal Habitats and Sportfishing in Florida. Neubauer, S. C. (2008). Contributions of mineral and organic components to tidal freshwater marsh accretion. Estuarine, Coastal and Shelf Science, 78(1), Neubauer, S. C., Anderson, I. C., Constantine, J. A., and Kuehl, S. A. (2002). Sediment Deposition and Accretion in a Mid-Atlantic (U.S.A.) Tidal Freshwater Marsh. Estuarine, Coastal and Shelf Science, 54(4), NOS. (2013). VDATUM. Park, R. A., Lee, J. K., and Canning, D. J. (1993). Potential Effects of Sea-Level Rise on Puget Sound Wetlands. Geocarto International, 8(4), 99. Park, R. A., Lee, J. K., Mausel, P. W., and Howe, R. C. (1991). Using remote sensing for modeling the impacts of sea level rise. World Resources Review, 3, Park, R. A., Trehan, M. S., Mausel, P. W., and Howe, R. C. (1989). The Effects of Sea Level Rise on U.S. Coastal Wetlands. The Potential Effects of Global Climate Change on the United States: Appendix B - Sea Level Rise, U.S. Environmental Protection Agency, Washington, DC, 1 1 to Application of SLAMM to the Eastern Shore of VA 58

65 Parris, A., Bromerski, P, Burkett, V., Cayan, D., Culver, M., Hall, J., Horton, R., Knuuti, K., Moss, R., Obeysekera, J., Sallenger, A., and Weiss, J. (2012). Global Sea Level Rise Scenarios for the United States National Climate Assessment. National Oceanic and Atmospheric Administration, Silver Spring, MD, 37. Reed, D. J. (1995). The response of coastal marshes to sea-level rise: Survival or submergence? Earth Surface Processes and Landforms, 20(1), Rosen, P. S. (1980). Erosion susceptibility of the Virginia Chesapeake Bay shoreline. Marine Geology, 34(1), Titus, J. G., Park, R. A., Leatherman, S. P., Weggel, J. R., Greene, M. S., Mausel, P. W., Brown, S., Gaunt, C., Trehan, M., and Yohe, G. (1991). Greenhouse effect and sea level rise: the cost of holding back the sea. Coastal Management, 19(2), Titus, J. G., and Wang, J. (2008). Maps of Lands Close to Sea Level along the Middle Atlantic Coast of the United States: An Elevation Data Set to Use While Waiting for LIDAR. Background Documents Supporting Climate Change Science Program Synthesis and Assessment Product 4.1, J.G. Titus and E.M. Strange (eds.). EPA 430R U.S. Environmental Protection Agency, Washington, DC. Turaski, S. J. (2002). Spatial and Temporal Controls on Saturated Overland Flow in a Regularly Flooded Salt Marsh. Master s Thesis, University of Virginia, Charlottesville. Xian, G., Homer, C., Dewitz, J., Fry, J., Hossain, N., and Wickham, J., The change of impervious surface area between 2001 and 2006 in the conterminous United States. Photogrammetric Engineering and Remote Sensing, Vol. 77(8): Application of SLAMM to the Eastern Shore of VA 59

66 Appendix A: NWI Classes to SLAMM 6 Categories SLAMM Code NWI code characters Name System Subsystem Class Subclass Water Regime Notes 1 Developed Dry Land (upland) U Undeveloped Dry land 2 (upland) U 3 Nontidal Swamp P NA FO, SS 1, 3 to 7, A,B,C,E,F,G,H,J,K None None or U 4 Cypress Swamp P NA FO, SS 2 A,B,C,E,F,G,H,J,K None or U 5 Inland Fresh Marsh P NA EM, f ** All A,B,C,E,F,G,H,J,K None None or U L 2 EM 2 E, F, G, H, K None None or U R 2, 3 EM 2 E, F, G, H, K None None or U 6 Tidal Fresh Marsh R 1 EM 2, None Fresh Tidal N, T P NA EM All, None Fresh Tidal S, R, T 7 Transitional Marsh / Scrub Shrub 8 Regularly Flooded Marsh (Saltmarsh) 9 Mangrove Tropical settings only, otherwise 7 10 Estuarine Beach old code BB and FL = US 11 Tidal Flat old code BB and FL =US 12 Ocean Beach old code BB and FL = US 13 Ocean Flat old code BB and FL = US E 2 SS, FO 1, 2, 4 to 7,None E 2 EM 1 None Source, Bill Wilen, National Wetlands Inventory. Tidal M, N, P None or U Tidal N None or U E 2 FO, SS 3 Tidal M, N, P None or U E 2 US 1,2 Important codes Tidal N, P SLAMM assumes developed land will be defended against sea-level rise. Categories 1 & 2 need to be distinguished manually. Palustrine Forested and Scrub- Shrub (living or dead) Needle-leaved Deciduous forest and Scrub-Shrub (living or dead) Palustrine Emergents; Lacustrine and Riverine Nonpersistent Emergents Riverine and Palustrine Freshwater Tidal Emergents Estuarine Intertidal, Scrub-shrub and Forested (ALL except 3 subclass) Only regularly flooded tidal marsh No intermittently flooded "P" water Regime Estuarine Intertidal Forested and Scrub-shrub, Broad-leaved Evergreen Estuarine Intertidal Unconsolidated Shores E 2 US None Tidal N, P Only when shores (need images or base map) E 2 US 3,4 Tidal M, N Estuarine Intertidal Unconsolidated None None or U Shore (mud or organic) and E 2 AB All Tidal M, N Aquatic Bed; Except 1 None or U Marine Intertidal Aquatic Bed E 2 AB 1 P Specifically, for wind driven tides on the south coast of TX M 2 AB 1, 3 Tidal M, N None None or U M 2 US 1,2 Tidal N, P Important M 2 US None Tidal P M 2 US 3,4 None Tidal M, N None or U Marine Intertidal Unconsolidated Shore, cobble-gravel, sand Marine Intertidal Unconsolidated Shore, mud or organic, (low energy coastline) Application of SLAMM to the Eastern Shore of VA 60

67 NWI code characters SLAMM System Subsystem Class Subclass Water Regime Notes Code Name 14 Rocky Intertidal M 2 RS All None Tidal M, N, P None or U Marine and Estuarine Intertidal Rocky Shore and Reef E 2 RS All None Tidal M,N, P None or U E 2 RF 2, 3 None Tidal M, N, P None or U E 2 AB 1 Tidal M, N None or U 15 Inland Open Water R 2 UB, AB All, None All, None Riverine, Lacustrine, and R 3 UB, AB, RB All, None All, None Palustrine Unconsolidated Bottom, old code OW = UB L 1, 2 UB, AB, RB All, None All, None and Aquatic Beds P NA UB, AB, RB All, None All, None R 5 UB All Only U 16 Riverine Tidal Open Water old code OW = UB 17 Estuarine Open Water (no h* for diked / impounded) R 1 All All None Source, Bill Wilen, National Wetlands Inventory Fresh Tidal S, R, T, V For more information on the NWI coding system see Appendix A of (Dahl et al. 2009) Riverine Tidal Open water Except EM Except 2 R1EM2 falls under SLAMM Category 6 E 1 All All None Tidal L, M, N, P Estuarine subtidal old code OW=UB 18 Tidal Creek E 2 SB All, Tidal M, N, P Estuarine Intertidal Streambed None Fresh Tidal R, S 19 Open Ocean old code OW = UB M 1 All All Tidal L, M, N, P Marine Subtidal and Marine Intertidal Aquatic Bed and Reef M 2 RF 1,3, None Tidal M, N, P None or U 20 Irregularly Flooded Marsh E 2 EM 1, 5 None P Irregularly Flooded Estuarine Intertidal Emergent marsh 21 Not Used 22 Inland Shore old code BB and FL = US E 2 US 2, 3, 4 None P Only when these salt pans are associated with E2EMN or P L 2 US, RS All All Nontidal Shoreline not pre-processed using Tidal Range Elevations P NA US All, None All Nontidal None or U R 2, 3 US, RS All, None All Nontidal None or U R 4 SB All, None All Nontidal None or U 23 Tidal Swamp P NA SS, FO All, None Fresh Tidal R, S, T Tidally influenced swamp * h=diked/impounded - When it is desirable to model the protective effects of dikes, an additional raster layer must be specified. ** Farmed wetlands are coded Pf All: valid components Water Regimes Nontidal A, B, C, E, F,G, J, K None: no Subclass or Water regime listed Saltwater Tidal L, M, N, P U: Unknown water regime Fresh Tidal R, S,T, V NA: Not applicable Note: Illegal codes must be categorize by intent. Old codes BB, FL = US DATE 1/14/12010 Old Code OW = UB Application of SLAMM to the Eastern Shore of VA 61

68 Appendix B: SLAMM Codes SLAMM Codes SLAMM Colors SLAMM Description 1 Developed Dry Land Developed Dry Land 2 Undeveloped Dry Land Undeveloped Dry Land 3 Swamp Swamp 4 Cypress Swamp Cypress Swamp 5 Inland Fresh Marsh Inland Fresh Marsh 6 Tidal Fresh Marsh Tidal Fresh Marsh 7 Transitional Salt Marsh Transitional Salt Marsh 8 Regularly-flooded Marsh Regularly-flooded Marsh 9 Mangrove Mangrove 10 Estuarine Beach Estuarine Beach 11 Tidal Flat Tidal Flat 12 Ocean Beach Ocean Beach 13 Ocean Flat Ocean Flat 14 Rocky Intertidal Rocky Intertidal 15 Inland Open Water Inland Open Water 16 Riverine Tidal Riverine Tidal 17 Estuarine Open Water Estuarine Open Water 18 Tidal Creek Tidal Creek 19 Open Ocean Open Ocean 20 Irregularly-flooded Marsh Irregularly-flooded Marsh 21 Tall Spartina Tall Spartina 22 Inland Shore Inland Shore 23 Tidal Swamp Tidal Swamp 24 Blank Blank 25 Flooded DevDry Flooded Developed Dry Land 26 Backshore Backshore Application of SLAMM to the Eastern Shore of VA 62

69 Appendix C: Great Diurnal Tide Ranges in Study Area (m) Figure 38. Great diurnal tide ranges (m) in the eastern Shore of VA (m) Application of SLAMM to the Eastern Shore of VA 63

70 Appendix D: Comprehensive Tables of Input Parameters (reference against map in Figure 14) Parameter/Subsite Global NWI Photo Date DEM Date Direction Offshore East East East West West West Historic Trend (mm/yr) Historic Eustatic Trend (mm/yr) GT Great Diurnal Tide Range (m) Salt Elev. (m above MTL) Marsh Erosion (horz. m /yr) Swamp Erosion (horz. m /yr) Beach Erosion (horz. m /yr) Tidal Flat Erosion (horz. m /yr) Tidal-Fresh Marsh Accr (mm/yr) Inland-Fresh Marsh Accr (mm/yr) Tidal Swamp Accr (mm/yr) Swamp Accretion (mm/yr) Beach Sed. Rate (mm/yr) Freq. Overwash (years) Use Elev Pre-processor [True,False] FALSE FALSE FALSE FALSE FALSE FALSE Reg Flood Max. Accr. (mm/year) Reg Flood Min. Accr. (mm/year) Reg Flood Elev a (mm/(year HTU^3)) Reg Flood Elev b (mm/(year HTU^2)) Reg Flood Elev c (mm/(year*htu)) Reg Flood Elev d (mm/year) Irreg Flood Max. Accr. (mm/year) Irreg Flood Min. Accr. (mm/year) Irreg Flood Elev a (mm/(year HTU^3)) Irreg Flood Elev b (mm/(year HTU^2)) Irreg Flood Elev c (mm/(year*htu)) Irreg Flood Elev d (mm/year) Parameter/Subsite NWI Photo Date DEM Date Direction Offshore West West West West West East Historic Trend (mm/yr) Application of SLAMM to the Eastern Shore of VA 64

71 Historic Eustatic Trend (mm/yr) GT Great Diurnal Tide Range (m) Salt Elev. (m above MTL) Marsh Erosion (horz. m /yr) Swamp Erosion (horz. m /yr) Beach Erosion (horz. m /yr) Tidal Flat Erosion (horz. m /yr) Tidal-Fresh Marsh Accr (mm/yr) Inland-Fresh Marsh Accr (mm/yr) Tidal Swamp Accr (mm/yr) Swamp Accretion (mm/yr) Beach Sed. Rate (mm/yr) Freq. Overwash (years) Use Elev Pre-processor [True,False] FALSE FALSE FALSE FALSE FALSE FALSE Reg Flood Max. Accr. (mm/year) Reg Flood Min. Accr. (mm/year) Reg Flood Elev a (mm/(year HTU^3)) Reg Flood Elev b (mm/(year HTU^2)) Reg Flood Elev c (mm/(year*htu)) Reg Flood Elev d (mm/year) Irreg Flood Max. Accr. (mm/year) Irreg Flood Min. Accr. (mm/year) Irreg Flood Elev a (mm/(year HTU^3)) Irreg Flood Elev b (mm/(year HTU^2)) Irreg Flood Elev c (mm/(year*htu)) Irreg Flood Elev d (mm/year) Application of SLAMM to the Eastern Shore of VA 65

72 Parameter/Subsite NWI Photo Date DEM Date Direction Offshore East East East East East West Historic Trend (mm/yr) Historic Eustatic Trend (mm/yr) GT Great Diurnal Tide Range (m) Salt Elev. (m above MTL) Marsh Erosion (horz. m /yr) Swamp Erosion (horz. m /yr) Beach Erosion (horz. m /yr) Tidal Flat Erosion (horz. m /yr) Tidal-Fresh Marsh Accr (mm/yr) Inland-Fresh Marsh Accr (mm/yr) Tidal Swamp Accr (mm/yr) Swamp Accretion (mm/yr) Beach Sed. Rate (mm/yr) Freq. Overwash (years) Use Elev Pre-processor [True,False] FALSE FALSE FALSE FALSE FALSE FALSE Reg Flood Max. Accr. (mm/year) Reg Flood Min. Accr. (mm/year) Reg Flood Elev a (mm/(year HTU^3)) Reg Flood Elev b (mm/(year HTU^2)) Reg Flood Elev c (mm/(year*htu)) Reg Flood Elev d (mm/year) Irreg Flood Max. Accr. (mm/year) Irreg Flood Min. Accr. (mm/year) Irreg Flood Elev a (mm/(year HTU^3)) Irreg Flood Elev b (mm/(year HTU^2)) Irreg Flood Elev c (mm/(year*htu)) Irreg Flood Elev d (mm/year) Application of SLAMM to the Eastern Shore of VA 66

73 Appendix E: Full Study Area Result Maps Figure 39. Initial condition Application of SLAMM to the Eastern Shore of VA 67

74 Figure 40. Time zero (2011) Application of SLAMM to the Eastern Shore of VA 68

75 Figure 41. Historic 2025 Application of SLAMM to the Eastern Shore of VA 69

76 Figure 42. Historic Application of SLAMM to the Eastern Shore of VA 70