modeled tradeoffs between developed land protection and tidal

RESEARCH ARTICLE

Modeled Tradeoffs between Developed Land

Protection and Tidal Habitat Maintenance

during Rising Sea Levels

Daniel Cadol1 Andrew J Elmore2 Steven M Guinn2 Katharina A M Engelhardt2

Geoffrey Sanders3

1 Earth and Environmental Science New Mexico Institute of Mining and Technology Socorro NM 87801

United States of America 2 Appalachian Laboratory University of Maryland Center for Environmental

Science Frostburg MD 21532 United States of America 3 Center for Urban Ecology National Park

Service Washington DC United States of America

dcadolnmtedu

Abstract

Tidal habitats host a diversity of species and provide hydrological services such as shore-

line protection and nutrient attenuation Accretion of sediment and biomass enables tidal

marshes and swamps to grow vertically providing a degree of resilience to rising sea lev-

els Even if accelerating sea level rise overcomes this vertical resilience tidal habitats have

the potential to migrate inland as they continue to occupy land that falls within the new tide

range elevations The existence of developed land inland of tidal habitats however may

prevent this migration as efforts are often made to dyke and protect developments To test

the importance of inland migration to maintaining tidal habitat abundance under a range of

potential rates of sea level rise we developed a spatially explicit elevation tracking and hab-

itat switching model dubbed the Marsh Accretion and Inundation Model (MAIM) which

incorporates elevation-dependent net land surface elevation gain functions We applied the

model to the metropolitan Washington DC region finding that the abundance of small

National Park Service units and other public open space along the tidal Potomac River sys-

tem provides a refuge to which tidal habitats may retreat to maintain total habitat area even

under moderate sea level rise scenarios (07 m and 11 m rise by 2100) Under a severe

sea level rise scenario associated with ice sheet collapse (17 m by 2100) habitat area is

maintained only if no development is protected from rising water If all existing development

is protected then 5 10 and 40 of the total tidal habitat area is lost by 2100 for the

three sea level rise scenarios tested

Introduction

Tidal marshes occur in a fixed band of elevations controlled by the local mean sea level andrange of tides [1] If mean sea level rises marshes may migrate landward to higher ground oraccumulate material and grow vertically to maintain elevation within the tidal frame otherwise

PLOS ONE | DOI101371journalpone0164875 October 27 2016 1 25

a11111

OPENACCESS

Citation Cadol D Elmore AJ Guinn SM

Engelhardt KAM Sanders G (2016) Modeled

Tradeoffs between Developed Land Protection and

Tidal Habitat Maintenance during Rising Sea

Levels PLoS ONE 11(10) e0164875 doi101371

journalpone0164875

Editor Maura (Gee) Geraldine Chapman University

of Sydney AUSTRALIA

Received May 1 2016

Accepted October 3 2016

Published October 27 2016

Copyright This is an open access article free of all

copyright and may be freely reproduced

distributed transmitted modified built upon or

otherwise used by anyone for any lawful purpose

The work is made available under the Creative

Commons CC0 public domain dedication

Data Availability Statement The MAIM model is

available from Github at httpsgithubcom

planetsandmanMAIM Input and output maps are

available from the National Park Service IRMA data

repository httpsirmanpsgovDataStore

ReferenceProfile2223826

Funding This research was funded by the National

Capital Region of the National Park Service through

cooperative agreement H3992060006 with the

University of Maryland Center for Environmental

Science (UMCES) Appalachian Laboratory (PI

Andrew J Elmore) and by the National Science

tidal marsh lands would be expected to transition into deeper water habitat classes such astidal flats with associated loss of ecosystem services [2] Marsh surface elevation change isaffected by processes which increase elevationndashsuch as mineral sediment deposition organicmatter deposition and root growthndashand those that decrease elevationndashsuch as sediment com-paction organic matter decay erosion and local or regional subsidence [3ndash8] Several of theseprocesses depend on the elevation of the marsh relative to mean sea level leading to both posi-tive and negative feedbacks on relative marsh elevation change [9ndash11] Additionally sea levelchange including both global eustatic components and regional gravitational or circulationeffects [12] alters the relative elevation of a marsh surfaceIn periods of rapidly rising sea levels such as those forecast for the coming decades [13ndash15]

marsh surface elevation gains may be overwhelmedby sea level rise (SLR) [16] In this caseimpediments to landward migration such as bluffs or levees will contribute to a reduction ofmarsh area and associated ecosystem services [17] This effect is expected to be particularlyprevalent becausemany engineering solutions that protect infrastructure from rising sea levelscreate just such a fixed landscape [18] Undeveloped coastal areas including managed openspace and agricultural land are less likely to be protected from SLR and may be crucial refugesto which marshes may migrateThe strategic management of coastal open space benefits frommarsh migration forecasts

[19] Models that track habitat class as a function of relative elevation (eg the Sea Level Affect-ing Marshes Model SLAMM [20]) have been effectively employed to this end Most attentionhowever has been focused on salt marshes [20 21] or large deltaic systems that cross fromfresh to brackish habitats [19 22ndash24] Frequently these models are applied over very largeareas at relatively coarse grids (30 m to 1 km cells) Without downplaying the importance ofsaltwater systems the research presented here seeks to extend this work to estuarine freshwatertidal marshes Freshwater marshes occur in most estuarine systems where tidal action extendsupstream along large river courses Though they are often smaller than salt marshes estuarinefreshwater marshes are important ecosystem components contributing disproportionately tonutrient retention and providing additional ecosystem services including flood control andhabitat for many rare threatened and endangered species [25ndash28] The work presented herewas conducted along the tidal Potomac River where numerous National Park Service (NPS)management units preserve open space in an elevation range relevant to marsh migrationwithin the next century (Fig 1) Because this work was conducted in a freshwater tidal environ-ment SLAMMhad some features that were not ideal (eg implicit salinity effects) and lackedother features that we deemed important (eg gradational vegetation zonation) From an eco-logical standpoint salt marshes have both an anoxic stress gradient (due to inundation time)and a salinity gradient (due to evaporation especially in warm environments) which togethercontribute to highly distinct zonation of species [1] Freshwater marshes only have the anoxicstress gradient and individual species are more loosely zoned with a great deal of elevationaloverlap between species [29]Therefore we developed and tested a spatially explicit elevation-tracking and vegetation

class-switching model which we designatedMarsh Accretion and Inundation Model (MAIM)Effort was made to develop a parsimonious model that also adequately captures elevation gainprocesses Other morphodynamicmodels have produced good results at similar scales [16 30]but these require data on vegetation biomass distribution sediment trapping efficiency andother highly variable parameters MAIM utilizes a consolidated empirical elevation gain func-tion to represent the biogeomorphic feedbacks that drive marsh elevation change in each vege-tation class Simplified process modeling or reduced-complexitymodeling such as thisattempts to optimize the trade-off between increased fidelity to physical processes andincreased uncertainty in the model parameters that describe these processes [31 32]

Modeled Tradeoffs between Developed Land and Tidal Habitat during Rising Sea Levels


Foundation through award DEB-0841394 (PI

Katharina A M Engelhardt)

Competing Interests The authors have declared

that no competing interests exist

Fig 1 Study area map The eleven sub-regions are outlined in red boxes and labeled in white Geographic

features in the sub-regions include PISC W- Marshall Hall PISC E- Mockly Point and Piscataway Creek GWMP

A- Little Hunting Creek and Mount Vernon GWMP B- Dyke Marsh GWMP C- Broad Creek GWMP D- Oxon

Creek GWMP E- Dangerfield Island and Reagan National Airport NACE B- Anacostia Park NACE A- Kingman

Lake and Kenilworth Marsh NACW A- Theodore Roosevelt Island and Columbia Island LFalls- Chain Bridge and

Little Falls Black italic labels in the location map identify significant points on the Potomac River downstream of the



Our implementation used control parameters calibrated for the freshwater tidal river reachof the Potomac estuary but to promote continued use in dissimilar systems MAIMwas inten-tionally programmed with a high degree of flexibility allowing for changes in the number ofhabitat classes their sequencing their elevation ranges and their elevation gain functions Inthis paper we briefly describe the model mechanics and calibration and then present modelresults for three SLR scenarios crossed with two lsquodevelopment protectionrsquo scenarios for whichMAIMwas initially run These six model runs enable an analysis of the interacting effects ofpotential SLR and development protection on marsh area loss

Methods

Study Area

We modeled 11 sub-regions of the upper tidal Potomac River system fromMount Vernon andPiscataway Park (38deg41rsquo N 77deg06rsquo W) at the downstream to Little Falls on the Potomac (38deg56rsquoN 77deg07rsquo W) and KenilworthMarsh on the Anacostia River (38deg55rsquo N 76deg57rsquo W) at theupstream (Fig 1) The sub-regions ranged in size from 10 to 20 km2 however only water andland below 5 m elevation (NAVD88) was modeled resulting in a total area of 6925 ha Of this4140 ha started as estuarine open water and 1315 ha was classified as developed The region isa highly fragmented patchwork of forest agriculture and developed land and contains a varietyof small NPS units that protect cultural and historic sites as well as natural resources includingfreshwater tidal marshes swamps and adjacent lands The disaggregated nature of the man-aged natural spaces creates management challenges but their relative abundance within anurban area also creates opportunities for innovative management approaches

Model Overview

MAIM requires six inputs a high-resolutionDEM an initial land classificationmap elevationlimits for each land class described as probability density functions land class switching rulessurface elevation change functions for each land class and a SLR scenario At each time stepMAIM (1) alters the surface elevation of each cell according to the elevation change rules pro-vided (2) changes sea level according to the SLR scenario provided (3) determines if the rela-tive elevation of any cells have left the range assigned to the land class of that cell and if so (4)switches the land class of those cells according to the user defined rules Land class elevationlimits are picked randomly for each model run from user defined probability density functionsWe designed the model to be run repeatedly in a Monte Carlo experiment with the most prob-able class of each cell and the probability of obtaining this class calculated at pre-selected out-put times Outputs for individual model runs are a DEM and a land classification rasterOutputs for Monte Carlo suites of runs also include a majority (or plurality) land classificationraster a majority class probability raster and summary statistics

Model Inputs

Elevation data For elevation we used an airborne LiDAR-derived DEM with 1 m cellsand 1 cm vertical precision (~ 2 cm vertical accuracy on hard surfaces) which was provided bythe NPS National Capital RegionGIS Office The LiDAR surveywas acquired between 10312008 and 12232008 The raw data are one meter posting first and last return and were usedprior to this project by technicians at Science Applications International Corporation to

study area MH- Marshall Hall IH- Indian Head Q- Quantico MP- Maryland Point and US 301- US Highway 301

Bridge

doi101371journalpone0164875g001



produce a 1-meter bare earth DEM Validation of the DEM is described in supporting sectionS1 File and S1 FigLand classes MAIM can accept any combination of user-defined land classes For this

application we included fourteen classes which were mapped using the LiDAR derivedDEMan NPS-commissioned vegetation classification polygon layer [33] a heads-up digitized(HUD) forest cover mask fromNational Agriculture Imagery Program (NAIP) imagery aHUD open water mask (representing the transition from tidal flat to open water) a weighteddistance from water map and a HUD developed land mask The mapping procedure isdescribed in supporting section S2 File and S2 Fig The validation of these maps using groundtruth points is described in supporting section S3 File and S1 TableThe fourteen land classes were Non-NPS DevelopedDry Land NPS DevelopedDry Land

Managed Open Space Agriculture Undeveloped Dry Land Undeveloped Dry LandWithSwamp SpeciesWetland Forest Irregularly FloodedForest Transitional Scrub EphemerallyFloodedMarsh Irregularly FloodedMarsh Regularly FloodedMarsh Tidal Flat and EstuarineOpenWater (Table 1) The first four classes were considered developed land though the devel-opment ranged from hard (eg pavement buildings) to soft (eg parks golf courses) Theonly agriculture in our area was the colonial era demonstration farm at Piscataway NationalPark These developed classes were mapped at all elevations and were given no lower elevationlimit in the scenarios that protected developmentUndeveloped dry land (and the developed classes in the unprotected scenarios) had a lower

elevation limit of 21 half tide units (HTUs) or approximately 1 m absolute elevation (all abso-lute elevations are reported in the NAVD88 datum) Half tide units (as used in SLAMM)record elevation relative to mean sea level (MSL) and each unit is half of the range between themean higher high water (MHHW) and mean lower low water (MLLW) Assuming symmetrichigh and low tides MSL will have an elevation of 0 HTU MHHWwill be at an elevation of 1HTU and MLLW will be at minus1 HTU Thus the lower limit of dry land was just over twice theelevation of MHHW an elevation for which there was no record of tidal flooding for at leasteight years preceding the study [34] When SLR caused dry land cells to drop below this lowerrelative elevation limit they either entered a marsh habitat track or a swamp forest habitattrack (Fig 2) To decide we defined the Undeveloped Dry LandWith Swamp Species categorywhich identifies upland cells that according to the NPS vegetationmaps contained tree speciescharacteristic of swamp forests that might persist if the land started to experience tidal flooding(Table 1 egAcer rubrum Fraxinus profunda Nyssa biflora etc)BothMSL and the tide range varied among our modeling sub-regions with MSL increasing

upstream due to fluvial influence and tide range increasing upstream due to narrowing of theestuary According to the National Oceanic and Atmospheric Administration data that weused in our model runs MSL increased from an absolute elevation of 0029 m at the lowermostsite (Piscataway) to 0048 m at the uppermost site (Little Falls) for the 1983ndash2001 epoch Tiderange increased from 0802 m to 0952 m at these same sites ConvertingHTU elevations toabsolute elevations at Piscataway for example minus1 HTU was equivalent to minus0372 m elevation0 HTU was equivalent to 0029 m and 1 HTU was equivalent to 0430 mClass switching rules All class-switching rules in MAIM are user defined In our imple-

mentation rising sea level caused cells to switch classes when relative elevation dropped belowthe lower limit for their current class Each class was assigned an upper and lower elevationlimit (Fig 2) Because the initial land class maps were not just elevation-based leading to poten-tial inconsistencies with these user defined elevation limits the first modeling step was a timezero (t0) correction This correction step also applied a small amount of sea level rise to accountfor the fact that the initial land classificationmap was developedwith 2008 data and the firstmodel time was 2010 This was the only modeling step where a cell may convert to a higher



elevation class Otherwise cell switching follows unidirectional class-dependent rules (Fig 2)Because our implementation of MAIM assumes only rising relative sea level cells identified asEstuarine OpenWater could not transition into anything else In addition to the base switchingrules we also created a second rule set that protected all development (hence two lsquoprotectionscenariosrsquo) This was accomplished by eliminating the lower elevation limit of the DevelopedDry Land (including NPS land) Managed Open Space and Agricultural classes to preventthem from ever switchingThe upper and lower elevation limits of each land class are an important source of uncer-

tainty in our model results We do not in fact expect the actual landscape to follow such strictelevation rules Therefore we attempted to model uncertainty by allowing the elevation limitsto vary between runs Each model scenario (SLR and protection scenario combination) wasrun 100 times with elevation limits picked randomly from Gaussian distributions centered onthe nominal limits (Table 1) The range of one standard deviation for each boundary is shownin Fig 2 These estimates of standard deviation were based on differences between the initialclassificationmap and field observations at three validation sites (S1 Table) In cases whereclass transitions are controlled by elevation the lower boundary value of one class must matchthe upper boundary of another class and in such cases they were varied togetherElevation changemodule MAIM changes the land surface elevation at each time step as

a representation of net accretion subsidence and erosion Any elevation change (ΔE) function

Table 1 Land class characteristics

Land Class Characteristics a Elevation Limits b Typical Inundation

Developed Dry Land (non-

NPS)

hard development (eg pavement buildings) with some associated landscaping none or gt21 HTU none

Developed Dry Land (NPS) hard development on NPS managed land none or gt21 HTU none

Managed Open Space soft development (eg pasture open space parks and golf courses) none or gt21 HTU none

Agriculture crop fields (primarily Piscataway Colonial Demonstration Farm in our study area) none or gt21 HTU none

Undeveloped Dry Land floodplain forest successional deciduous forest and mesic mixed hardwood gt21 HTU none

Undeveloped Dry Land with

Swamp Species

Upland forests that had some presence of Sweetgum (Liquidambar styraciflua)

Red Maple (Acer rubrum) Pumpkin Ash (Fraxinus profunda) Green Ash

(Fraxinus pennsylvanica) or Swamp Blackgum (Nyssa biflora)

gt21 HTU none

Wetland Forest maple-ash swamp and some oak floodplain swamp inundated by tidal events

above the typical spring high high water

13 to 21 HTU monthly to annually

Irregularly Flooded Forest tidal swamps such as swamp blackgum-ash and sweetgum-tuliptree temporarily

flooded forests

07ndash12 HTU 0ndash6 hours most

days

Transitional Scrub disturbed woody wetland successional herbaceous wetland vineland

shrubland and all marsh lands higher than 21 HTUs in spite of rare inundation

a hydrological influence from tides is expected

variable mostly

19ndash21 HTU

rarely large storms

or floods

Ephemerally Flooded Marsh highest marsh elevations Typha sp dominant but included some dogwood

stands

17ndash19 HTU several times per

year

Irregularly Flooded Marsh dominated by Typha sp Schoenoplectus sp and Impatiens sp with locally

dominant patches of Acorus sp and Peltandra sp


days

Regularly Flooded Marsh dominated by Nuphar sp and Peltandra sp -05ndash05 HTU 6ndash18 hours most

days

Tidal Flat may be vegetated with submerged aquatic vegetation (SAV) or may be barren

mud flat

-13 to -05 HTU 24 hours most days

Estuarine Open Water open water lt -13 HTU permanently

inundated

a Forest vegetation characteristics are based on an NPS-commissioned vegetation classification mapb Elevation limits are given in half tide units (HTUs) and are the means of the distributions from which model values were drawn Lower limit of developed

categories depends on the development protection scenario

doi101371journalpone0164875t001



that depends on elevation (E) can be applied in the code to each land class type (ie ΔE = f(Eclass)) Our implementation of MAIM does not attempt to model time-variant erosion orshoreline erosion Earlier work in DykeMarsh one of the tidal marshes in the study area ledto the development of the elevation change function utilized for marsh habitats [11] (Fig 3)

DE frac14 Snefrac12lnethE mnTHORN2

2s2n thorn Sl

1

1thorn e klethE mlTHORN 1

thorn Su1

1thorn e kuethE muTHORN 1

eth1THORN

where the first term on the right is a log-normal function and the second and third terms arelogistic sigmoidal functions that vertically (ΔE-dimension) shift the base log-normal functionin two zones This mathematical form was selected as a parsimonious method to fit observedtrends and parameter values were selected to minimize residuals This equation is likely over-parameterized and we do not suggest particular ecogeomorphicmeanings for the values butthe parameters were retained because each may have meaning The three S parameters are scal-ing values for each of the sub-functions Them parameters laterally shift (E-dimension) thesub-functions defining the midpoints of the sigmoids and mean of the log-normal The kterms control the spread of the two sigmoids and σn is the standard deviation of the log-nor-mal distribution which controls its spread The log-normal function is undefined if Emn inwhich case the accretion rate for tidal flats was applied The parameter values used to matchelevation change in the DykeMarsh calibration area are presented in Table 2Our elevation change functionwas derived by differencing two elevation surveys of Dyke

Marsh one conducted in 1992 [35] and one in 2012 [11] Systematic variation in elevationchange over these 20 years was observed as a function of original elevation which is repre-sented in Eq 1 (Fig 3) but there was a broad range of variability Eq 1 gives the best-fit meanelevation change rate for a given elevation but the 95 confidence interval for this mean

Fig 2 Elevation limits of land cover classes and class switching flow chart Whiskers on elevation ranges indicate one

standard deviation of the Gaussian distribution from which habitat transition elevations were selected in the repeated Monte Carol

runs




behavior extends plusmn 2 mmyr above and below the estimate and the 95 prediction interval forindividual points extends plusmn 5 mmyr The variability incorporates spatially discontinuous pro-cesses that occurred over the 20 years between surveys eg blow downs and storm overwashrelated erosion but it does not include tidal creek migration or shoreline erosion as pointsaffected by these processes were excluded from the analysis Because Eq 1 empirically incorpo-rates all processes that affected elevation of the marsh interior from 1992 to 2012 any futurechanges such as altered storm frequency and related overwash changes in nutrient or sedimentdelivery or invasion of new species would affect the reliability of this functionThe peak of our elevation change function is 6 mmyr occurring at ~ minus04 HTU (~ minus02 m

elevation) with a secondary peak of 4 mmyr at ~ 06 HTU This peak limits resilience to SLRany SLR rate greater than the peak will eventually lead to inundation an important consider-ation given the range of rates we modeled (4 to 9 mmyr for the lowest scenario and 10 to 21mmyr for the highest Table 3) Somemarsh points gained elevation at a greater rate but thefield data suggest that if recent elevation gain rates persist then there is a 95 probability thatthe limiting elevation gain rate is between 4ndash8 mmyr in DykeMarsh [11] and by extrapola-tion the full study region Suspended sediment supply has been shown to be a major control onaccretion rates [36 37] so changes relative to the 1992ndash2012 periodwould necessitate

Fig 3 Elevation gain vs elevation functions The empirical function utilized for marsh habitat elevation gain in the

simulations is shown in black (Eq 1) and two alternative elevation-gain functions that were used to test model sensitivity

to uncertainty in elevation gain rates are shown in gray Vertical lines show habitat zonation with dashed lines indicating

one standard deviation of the Gaussian distribution from which habitat transition elevations were selected


Table 2 Elevation change function (Eq 1) parameter values used

Log-Normal Curve Lower Sigmoid Upper Sigmoid

Parameter Value Parameter Value Parameter Value

σn 055 kl 65 ku 20

Sn 0008 Sl 00045 Su -00035

mn -15 ml -08 mu 023




modification of the function Suspended sediment concentration in the Potomac River atChain Bridge the upstream limit of our uppermost sub-region at Little Falls (Fig 1) from1985ndash1996 had a mean of 18 mgL and a median of 108 mgL with large flood events produc-ing concentrations as high as 2990 mgL [38] Annual suspended sediment loads from 1985 to2012 ranged from 03 to 58 millionmetric tons (mean 18 106 metric tons) though no trend isdiscernable [38 39] Tile sedimentation studies at DykeMarsh suggest that the total suspendedsediment load of the Potomac may be a better predictor of accretion than suspended sedimentconcentration [40]We applied Eq 1 to Ephemerally Irregularly and Regularly FloodedMarsh categories

which permits a smooth transition in elevation change across these boundaries (Fig 3) The ele-vation change characteristics of DykeMarsh [11] were assumed to be representative of allmarshes in the study region Surface elevation change rates for wetland forests tidal forestsand tidal flats were given fixed accretion values due to the paucity of data relating elevationchange to elevation in these habitat types [41 42] We elected to use elevation gain rates of00003 myr for wetland forests and 00011 myr for tidal forests (after [41]) and 0001 myrfor tidal flats These values are highly uncertain but we do expect accretion rates in these areasto be smaller than in nearby marshes [42] If the elevation gain rates used in the model arelower than reality then the results will overestimate area loss in these tidal habitat types Noelevation change was applied to dry land categories though their relative elevation did changeas sea level roseSea level rise scenarios Three SLR scenarios were adopted from work by the Scientific

and Technical Working Group of the Maryland Climate Change Commission (MCCC [43])The MCCC reported that projections provided by the Intergovernmental Panel on ClimateChange (IPCC) 4th Assessment Report were too conservative in that they did not include flowsfrom the polar ice sheets an evaluation consistent with other recent findings [13] and which isaddressed in the IPCC 5th Assessment Report [14] Further the MCCC report accounted forlocal variation in SLR due to regional ocean dynamics and land subsidence The MCCC didnot account for the potential for a weakening of the Gulf Stream which could raise local sealevels significantly (gt 020 cm) in the next decade [44] The MCCC estimates were notreported as an annual time series of SLR suitable for direct input to MAIM but rather as lowmedium and high projections of SLR to 2050 and 2100 (Table 3) We therefore incorporatedtemporal trends from the IPCC 5th Assessment Report RCP 60 scenario utilizing the IPCCcurve shape but scaling the 2100 value to match the MCCC estimates of total accumulated SLRthrough 2100 which were 07 11 and 17 m (Table 3) The curve shape is likely consistent

Table 3 Sea level rise scenarios

07 m Scenario 11 m Scenario 17 m Scenario

Year Sea level (m) SLR rate (mmyr) Sea level (m) SLR rate (mmyr) Sea level (m) SLR rate (mmyr)

2010 006 01 015

2020 01 4 016 6 025 10

2030 015 5 024 8 037 12

2040 022 7 034 10 052 15

2050 029 7 045 11 07 18

2060 036 7 057 12 088 18

2070 045 9 07 13 108 20

2080 053 8 084 14 129 21

2090 062 9 097 13 15 21

2100 07 8 11 13 17 20




between the two assessments because the scaled 2050 SLR values matched the MCCC projec-tions within 2 cm The highest MCCC projection includes 075 m of SLR related to rapid col-lapse of the Greenland and Antarctic ice sheets a poorly constrained process [45 46] TheMCCC projections are consistent with US Army Corps of Engineers guidance [47] and aNational Research Council report for the Pacific Coast of the United States [48] But otherregional forecasts of SLR to 2100 extend above and below the range projected by the MCCC[49] At the low end of the range the IPCC 5AR projects global SLR increases of 028ndash061 mby 2100 for a scenario in which CO2 emissions rapidly cease (RPC26) [14] The MCCC reportestimates 013ndash017 m of relative SLR due to land subsidence and 013ndash019 m of SLR due tochanges in regional ocean dynamics (not including changes to the Gulf Stream) suggesting thepossibility of local relative SLR as low as 054 m For comparison the historic SLR trend atWashington DC is 32 mmyr for the period 1924ndash2015 which when compared to globaleustatic SLR of 17 mmyr suggests regional subsidence of 15 mmyr Given the uncertaintyregarding many processes driving SLR and the range of values possible we do not intend tosuggest that the 11 m scenario is the most likely due to its central position in the three scenar-ios we tested

Results

Initial land class maps

The magnitude of the zero time (t0) correction that was applied to the initial land classificationmaps can be thought of as an indicator of the degree of agreement between our initial land clas-sification assumptions and our elevation limit estimates Because the elevation limits wereselected stochastically for each model run from a defined distribution the amount of changevaried For the case using the mean values of the elevation limits the largest changes orassumption disagreements were for wetland forest (+39 ha +105) irregularly flooded forest(minus71 ha minus37) irregularly floodedmarsh (+51 ha 38) tidal flats (minus35 ha minus18) regularlyfloodedmarsh (+13 ha +9) and ephemerally floodedmarsh (+8 ha +27) with other clas-ses changing 3 or less (S2 Table) The majority of these changes are irregularly flooded forestbeingmoved up to wetland forest or down to a marsh class a change that is consistent withour reliance on inputs other than elevation such as weighted distance to water and manuallydigitized forest in classifying tidal and wetland forests (S2 Fig) This extra information isexcluded in the habitat switching rules of MAIM which may result in less reliable mapping ofswamp forest categories One exception in the application of the t0 correctionwas for the devel-oped land classes Development was not converted to any other class until the first time step2020 As a result for the unprotected development scenarios there is a mass of land area thatgoes from developed land in 2010 to transitional scrub in 2020 to ephemerally floodedmarshin 2030 and on down the sequence (Fig 4) This migrating spike is an artifact of low-lyingdeveloped land only being permitted to drop one class per 10-year time step but the artifact iseliminated by 2070

Land cover distribution

Modeled trends of habitat class coverage through time depended heavily on both SLR scenarioand whether development was protected (Fig 4) The elevation dependent accretion ratesapplied to the marsh habitats contributed to complexity and divergence of results for the threeSLR scenarios For example in the protected development scenarios irregularly floodedmarshwas able to maintain position in the tidal frame and maintain its aerial coverage in the 07 mSLR scenario while it showed increasing losses for the 11 m and 17 m SLR scenariosWhendevelopment was allowed to convert to marsh irregularly floodedmarsh gained area in all



three SLR scenarios Regularly floodedmarsh experienced even greater area gains especially inthe 07 m SLR scenario because the peak of the elevation change function that fell within thisclass range was higher (Fig 3) But in the 11 m and 17 m SLR scenarios even regularly floodedmarsh began loosing area after 2050 and 2070 respectively with gains going to the tidal flatclass (Fig 4) Swamp forests saw significant declines in all scenarios though this may have beeninfluenced by the uncertainty in selecting accretion rates for these habitats Undeveloped dryland experienced the largest area losses of any land class and irregularly flooded forest lost thelargest percent of area Significant proportions of development managed open space and agri-culture converted to marsh habitats in the non-protection scenarios (25 35 and 45respectively in the 17 m SLR scenario 8 10 and 19 in the 07 m SLR scenario) Estuarineopen water gained the most area of any category in all scenariosLand class evolution followed patterns related to the two drivers included in the model

topography and habitat type In tidal habitats surrounded by steeply sloping valley walls strongdifferences in pattern were observed in the three SLR scenarios For example in the LittleHunting Creek area of the GWMP A sub-region (Fig 5) strips of marsh habitat as narrow as a

Fig 4 Habitat area through time for the 6 modeled scenarios Solid lines are the median values of the 100 Monte Carlo runs and the dashed lines are

the 1st and 3rd quartiles




Fig 5 Model results for the Little Hunting Creek area (GWMP A) Beginning model land classifications for 2010 (following the initial time correction) and

model results for the six scenarios




fewmeters wide were all that remained of the tidal habitat by 2100 in the highest 17 m SLRscenario But in the 07 m SLR scenario only tidal flats were lost to open water while marshhabitat persisted and expanded The effect of lower modeled accretion rates in swamp habitatsis observed in the conversion of these areas to tidal flat by 2100 in the 11 m SLR scenario evenas marshes remainedmarsh (Fig 5) In contrast where slopes near the river were gentle signifi-cant inland migration was observed In the DykeMarsh area of the GWMP B sub-regionswamps migrated inland and marshes persisted and expanded in the 07 m SLR scenario (Fig6) Most swamp was converted to marsh and nearly all marshland was restricted to regularlyfloodedmarsh (low marsh) by 2100 in the 11 m SLR scenario For the 17 m SLR scenario thevast majority of the marsh was inundated and converted to open water and if developmentwas not protected a large swath of a housing development converted to highmarsh by 2100(Fig 6) Just north is a golf course that experienced similar conversion to marsh if notprotectedLandward marsh migration into soft development was key to marsh habitat maintenance in

the Piscataway Park area the only sub-region with agricultural land especially in the 11 mand 17 m SLR scenarios (Fig 7) However expansion into managed open space was much lessimportant in the upper Anacostia River in spite of the fact that it is surrounded by develop-ment (Fig 8) The high banks limited dry land conversion to tidal habitat even in the highestSLR scenario and the greater tide range reduced the relative effect of SLR The differing degreesof landward marsh migration with different SLR rates were clearly visible on the gently slopingsurface of Broad Creekmarsh (Fig 9) Marsh habitat patches remained relatively narrow swathsin this area in all scenarios on the order of 10ndash50 m wide but migrated inland greater distancesin the 17 m SLR scenario

Effect of development protection

The model scenarios in which developed land was protected led to tidal habitat losses over themodeled period (2010ndash2100) while allowing developed land to convert to tidal habitat led tohabitat area gains even in the highest SLR scenario (Fig 10A) Tidal habitats here include cate-gories from wetland forest and transitional scrub down to tidal flats The greatest net gain oftidal habitat was for the 11 m SLR scenario with development not protected and the greatestgain excluding tidal flats was for the 07 m SLR scenario with development not protected Ifdevelopment was protected then about 10 of the tidal habitat area was lost by 2060 in the 17m SLR scenario accelerating to 40 loss by 2100 If developed land was sacrificed very little ofthat land became open water by 2100 even in the 17 m SLR scenario it nearly all contributedto tidal habitat Managed open space agriculture and NPS developed land made up 60 of thesacrificed land that converted to tidal habitat in the unprotected 17 m SLR scenario while 40was non-NPS hard development (Fig 10B) Thus according to the model results net tidal habi-tat loss by 2100 (either excluding or including tidal flats in that group) could be avoided in amedium SLR scenario if soft development was permitted to be flooded and habitat loss couldeven be avoided in a high SLR scenario if all development was unprotected

Elevation change

Equivalent habitat changes occurred later in lower SLR scenarios however the exact nature ofthe changes differed due to the interaction of elevation and elevation gain modeled in MAIMThe distribution of elevation gains varied greatly through time and among the SLR scenarios(Fig 11) The increasing open water gains in higher SLR scenarios are due to vertical land lossAlthough total tidal habitat area was maintained to 2100 under 17 m SLR without protection



Fig 6 Model results for the Dyke Marsh area (GWMP B) Beginning model land classifications for 2010 (following the initial time correction) and model

results for the six scenarios




Fig 7 Model results for the Piscataway Park area (PISC E) Beginning model land classifications for 2010 (following the initial time correction) and





Fig 8 Model results for the Anacostia RiverKenilworth Marsh area (NACE A) Beginning model land classifications for 2010 (following the initial time

correction) and model results for the six scenarios




Fig 9 Model results for the Broad Creek area (GWMP C) Beginning model land classifications for 2010 (following the initial time correction) and model





the resultant landscape had a smaller land area (less dry land especially at elevations near thetide level) and would be less robust to continued SLR than the lower SLR scenarios

Discussion and Conclusions

The complex biogeomorphic interplay between SLR and marsh accretion has long been recog-nized with local factors expected to cause great variation in response [50] And as expectedsome investigations of coastal response to SLR have forecast major habitat loss (eg [20])while others predict minimal change (eg [51]) Estimating the threshold SLR in each casebeyond which habitat loss accelerates can provide an appropriate metric for comparisonamong studies and help resolve conflicting interpretations [52] Several of our model scenariosresulted in increased tidal habitat in 2100 relative to 2010 All three SLR scenarios yielded

Fig 10 Year 2100 modeled habitat distributions (A) Tidal habitats only (B) All classes other than open water Low med and high refer

to the 07 m 11 m and 17 m SLR scenarios Protected development scenarios (ldquoprotrdquo) and non-protected development scenarios (ldquononrdquo)

are plotted for each SLR scenario




habitat gains when development was not protected The key influence of development protec-tion is in agreement with previous modeling work on the Gulf Coast of Texas [21] The resil-ience in our study also hinged on the elevation gain rates particularly the maximum gain ratein regularly floodedmarshes (Fig 3) though the local maximum in irregularly floodedmarsheswas important in the partitioning of land between these two classesTidal habitat gains were derived from a combination of elevation gains preserving existing

marsh and inundation converting dry land to marsh or swamp The land inundation is wellcontrolled although uncertainty remains regarding whether it will becomemarsh or swampOn the other hand marsh accretion rates are calibrated at a single area (DykeMarsh [11]) andtidal flat and tidal forest rates are estimates from prior studies in other regions [41] creating animportant source of uncertainty Elevation change data from sedimentation and erosion tableslocated in marshes on the Anacostia River fall in the range of those used in the model (NPSunpublished data) but it is not clear whether narrow marsh strips will behave similarly To testthe importance of accretion rate estimates we ran the model with two modified accretioncurves one in which the higher elevation peak reached 5 mmyr similar to the lower peak andthe other with a single broad peak up to 12 mmyr (Fig 3) The first modification caused areadistribution differences in 2100 of less than 1 for most classes but a 5 increase in irregularlyfloodedmarsh which was offset by less land lost to open water The secondmodificationwiththe highest peak led to a result in which only the high SLR with protected development sce-nario showed appreciable loss of tidal habitat emphasizing the potential for accretion to offsetSLR if the suspended sediment supply rate is high enough [53 54] We believe the modelwould be similarly sensitive to reductions in estimated accretion rates leading to habitat lossThese results emphasize two factors that contribute to marsh area change (1) inlandmigra-

tion and (2) verticalmaintenance vs submergence A third factorndashshoreline erosion vs progra-dationndashhas also been identified in previous modeling studies of marsh area change [55] Inlandmigration depends on SLR rate inland topography [22] and anthropogenic prevention ofinundation (eg levee construction) [21] High SLR rates and low-lying land areas will lead torapid inlandmigration of the marsh edge and if suspended sediment supplies and marsh

Fig 11 Hypsometric curves of the surface elevation distribution Data covers the entire study region and is separated among the 3

different sea level rise scenarios Cumulative area (x-axis) is the area with elevations exceeded by the corresponding elevation (y-axis) along

each curve




productivity are high enough to prevent loss of pre-existing marshland then total area willexpand Interaction among these three factors leads to four main cases (1) marsh expansionboth inland and seaward (2) marsh expansion while migrating inland (3) marsh contractionwhile migrating inland and (4) inundation [55] Because we do not include shoreline erosionor progradation in MAIM due to an inability to adequately model the processes our resultsare limited to the final three cases with shoreline retreat caused by point inundation alone Ingeneral we see case 2 for the unprotected 07 m and 11 m SLR scenarios and a mix of case 2and 3 for the unprotected 17 m SLR scenario (eg Figs 5 and 8) When development is pro-tected and inlandmigration is prevented in most areas we see case 3 for the 07 m and 11 mSLR scenarios and case 4 for the highest SLR (eg Figs 6 and 7)It is important to note that MAIM is not a hydrodynamic model capable of simulating spa-

tially variable sediment dynamics This is true for the deposition of sediment near tidal chan-nels [56] but also the erosion of sediment from shorelines [54 57 58] We know thatshorelines in some areas are receding due to wave action [59] but this process is far from uni-versal We mapped shoreline change between 1990 and 2012 using NAIP imagery but therates varied unpredictably in time and potential predictors such as fetch [60] had limitedexplanatory power possibly becausemuch of the shoreline is hardened Although past workhas emphasized the role of shoreline erosion in creating open water from tidal habitat [54]incorporating this process into a freshwater estuarine model like MAIM remains a future goalNonetheless because the elevation dependent elevation change rate approaches SLR rates (par-ticularly for the low SLR scenario) the primary constraint on habitat area is topography anddevelopment which we model effectivelySalinity effects are not characterized in this implementation of MAIM under the assump-

tion that the region will retain its freshwater characteristics through the end of the modelingperiod If brackish water were to intrude into the study area we would expect habitat loss forboth marshes [61] and swamps [62] Saline water dramatically changes biogeochemical pro-cesses in a marsh typically leading to decay of buried organic material and elevation collapse[63 64] According toWhitehead and colleagues [65] average salinity was very low (05 psu) inthe study area from 1986ndash2005 and extending as far as Maryland Point (Table 4 Fig 1) This isnear the center of the lsquohydrodynamic transition zonersquo in the Potomac recognized in 1984 byCallender and colleagues [66] extending fromQuantico VA to the US Highway 301 bridge(Fig 1) This zone does not appear to be migrating rapidly as Cornwell and colleagues [67]only saw salinity values above 1 psu beginning near Quantico during the summer low flow sea-son in 2010 and just above the Highway 301 bridge during spring high flows in 2011 (Table 4)If we assume that deliveries of freshwater down the Potomac River will remain stable through2100 then changes to cross sectional area of flow would be the dominant process by whichSLR may decrease downriver fluxes and velocities and promote upstream salt intrusion Atpresent the Potomac at Quantico has a cross sectional area of 16000 m2 while the cross sec-tion at Indian Head has an area of 11000 m2 and 6000 m2 at Marshall Hall in PiscatawayPark (in the lowermost sub-region PISCW Fig 1) If sea levels rise 17 m the highest we testedthe cross sectional areas at Indian Head and Piscataway would increase to 15000 m2 and 8000m2 respectively (Table 4) These values are lower than the present cross sectional areas atQuantico and points downstream [68] suggesting that salt intrusionmay not extend beyondIndian Point in our modeling scenarios although 2-D hydrodynamic modeling would berequired to test this more fullyModel validation was performed on synthetic landscapes and compared with SLAMM ver-

sion 6 with MAIM assumptions modified to match those of SLAMM (eg fewer land classessimplified class switching structure uniform or cubic accretion functions for each land class)Model outputs under these conditions agreed in all details Verification of the parameters



applied in this study however was not possible due to the lack of historical elevation data ofadequate precision to permit a hindcasting exercise Future workers may wish to verify resultsby analyzing the geospatial output to find locations that are predicted to switch class shortlyField observations from these zones that are near a threshold may show signs of pendingchange such as recent tree ring narrowing enhanced flooding soil alteration or establishmentof species associated with the anticipated future habitat classIn our analysis there was not always a monotonic relationship between increased SLR rate

and increased tidal habitat loss because the model included elevation dependent elevationgain In the unprotected development scenarios we observe an optimum SLR rate where areagain is maximized due to the tidal floodingof dry land in combination with marsh surfaceaccretion that maintains existing marsh analogous to the maxima modeled by Kirwan and col-leagues [55] and inferred by Morris and colleagues from biomass data in a South Carolina saltmarsh [69] In contrast protecting development always led to tidal habitat reduction It isunlikely that maximum development loss will be accepted by tidewater communities [21]however our results highlight an ability to alleviate habitat loss in high SLR scenarios by per-mitting some managed open space to be tidally inundated Soft development such as parks andgolf courses can be thought of as a habitat reserve but optimal management will need to beagile and responsive to actual SLR given the range of possible scenarios

Supporting Information

S1 Fig DEM elevations vs RTK-GPS survey elevations showing bias and systematic errorin the DEM used in this analysisRTK data from Feb 2012 1341 points(TIFF)

S2 Fig Decision tree used to generate the initial vegetation classification(TIFF)

S1 File DEM Validation Analysis of digital elevationmodel accuracy in marsh areas wherestanding biomass inhibits laser pulses from reaching the mineral sediment surface(DOCX)

S2 File Mapping procedureThe initial land classificationmap was based on a combinationof elevation data water and forest mapping from aerial photographs and pre-existing vegeta-tion community maps(DOCX)

Table 4 Potomac River salinity and cross sectional areas of flow

Location Distance

downstream of DC

(km)

Salinity regime

(~2010)aChannel

width (m)

x-section area (2010

estimate m2)

x-section area

(m2) 07 m SLR

x-section area

(m2) 11 m SLR

x-section area

(m2) 17 m SLR

US Hwy 301

Bridge

98 5ndash15 psu 3580 19000b - - -

Maryland

Point

75 05ndash5 psu 2190 14000 b - - -

Quantico 50 05ndash5 psu 2310 16000 17900 18800 20200

Indian Head 38 lt 05 psu 2100 11000 12500 13300 14600

Marshall Hall 25 lt 05 psu 6020 6000 7000 7500 8300

a Salinity data from Cornwall et al 2015b Lower river cross-sectional area values from Herman and Friedrichs 2010




S3 File Initial Map Validation The initial land classificationmaps were compared against246 field validation points collected in three sub-areas with diverse habitat classes and subduedelevation variation(DOCX)

S1 Table Field validation outcome of initial classificationmap(DOCX)

S2 Table Differences between initial classificationmap andmap adjusted for MAIM eleva-tion assumptions (t0 adjustment)(DOCX)

Acknowledgments

KHawkins digitized the Potomac Estuary shorelines J Cessna assisted with fieldworkM Kir-wan and an anonymous reviewer provided valuable recommendations to improve themanuscript

Author Contributions

ConceptualizationDCAE KE GS

Data curation SG DC

Formal analysisDC

Funding acquisitionAE KE GS

InvestigationDCAE GS KE

MethodologyDCAE SG

Project administrationGS KE AE

ResourcesGS AE KE

Software SG DC

SupervisionAE KE

Validation DCKE

VisualizationDC SG

Writing ndash original draftDC

Writing ndash reviewamp editingAE GS KE

References1 Bertness MD Ellison AM Determinants of Pattern in a New England Salt Marsh Plant Community

Ecological Monographs 1987 57(2)129ndash47 doi 1023071942621

2 Nicholls RJ Cazenave A Sea-Level Rise and Its Impact on Coastal Zones Science 2010 3281517ndash

20 doi 101126science1185782 PMID 20558707

3 Myrick RM Leopold LB Hydraulic geometry of a small tidal estuary US Geological Survey Profes-

sional Paper 442-B Washington DC US Government Printing Office 1963

4 Redfield AC Development of a New England salt marsh Ecological Monographs 1972 42(2)201ndash

37 doi 1023071942263



5 Pasternack GB Brush GS Sedimentation cycles in a river-mouth tidal freshwater marsh Estuaries

1998 21(3)407ndash15 doi 1023071352839

6 Zedler JB Callaway JC Desmond JS Vivian-Smith G Williams GD Sullivan G et al Californian Salt-

Marsh Vegetation An Improved Model of Spatial Pattern Ecosystems 1999 2(1)19ndash35 doi 10

1007s100219900055

7 Nyman JA Walters RJ Delaune RD Patrick WH Jr Marsh vertical accretion via vegetative growth

Estuarine Coastal and Shelf Science 2006 69(3ndash4)370ndash80 doi 101016jecss200605041

8 Neubauer SC Contributions of mineral and organic components to tidal freshwater marsh accretion

Estuarine Coastal and Shelf Science 2008 78(1)78ndash88 doi 101016jecss200711011

9 Kirwan ML Christian RR Blum LK Brinson MM On the Relationship Between Sea Level and Spartina

alterniflora Production Ecosystems 2012 15(1)140ndash7 doi 101007s10021-011-9498-7

10 Marani M Da Lio C DrsquoAlpaos A Vegetation engineers marsh morphology through multiple competing

stable states Proceedings of the National Academy of Sciences 2013 110(9)3259ndash63 doi 101073

pnas1218327110 PMID 23401529

11 Cadol D Engelhardt K Elmore A Sanders G Elevation-dependent surface elevation gain in a tidal

freshwater marsh and implications for marsh persistence Limnology and Oceanography 2014 59

(3)1065ndash80 doi 104319lo20145931065

12 Sallenger AH Doran KS Howd PA Hotspot of accelerated sea-level rise on the Atlantic coast of North

America Nature Clim Change 2012 2(12)884ndash8 doi 101038nclimate1597

13 Rahmstorf S A Semi-Empirical Approach to Projecting Future Sea-Level Rise Science 2007 315

(5810)368ndash70 doi 101126science1135456 PMID 17170254

14 IPCC Climate Change 2013mdashThe Physical Science Basis Working group I contribution to the fifth

assessment report of the IPCC In Stocker T Qin D Plattner G- K Tignor M Allen S Boschung J

et al editors 5 Cambridge University Press 2013 p 1535

15 Yin J Schlesinger ME Stouffer RJ Model projections of rapid sea-level rise on the northeast coast of

the United States Nature Geosci 2009 2(4)262ndash6

16 Schile LM Callaway JC Morris JT Stralberg D Parker VT Kelly M Modeling Tidal Marsh Distribution

with Sea-Level Rise Evaluating the Role of Vegetation Sediment and Upland Habitat in Marsh Resil-

iency PLoS ONE 2014 9(2)e88760 doi 101371journalpone0088760 PMID 24551156

17 Nicholls RJ Hoozemans FMJ Marchand M Increasing flood risk and wetland losses due to global

sea-level rise regional and global analyses Global Environmental Change 1999 9 Supplement 1

S69ndashS87 doi 101016S0959-3780(99)00019-9

18 Titus JG Hudgens DE Trescott DL Craghan M Nuckols WH Hershner CH et al State and local gov-

ernments plan for development of most land vulnerable to rising sea level along the US Atlantic coast

Environmental Research Letters 2009 4(4)044008

19 Costanza R Sklar FH White ML Modeling coastal landscape dynamics BioScience 1990 40(2)91ndash

107

20 Craft C Clough J Ehman J Joye S Park R Pennings S et al Forecasting the effects of accelerated

sea-level rise on tidal marsh ecosystem services Frontiers in Ecology and the Environment 2009 7

(2)73ndash8 doi 101890070219

21 Feagin RA Martinez ML Mendoza-Gonzalez G Costanza R Salt Marsh Zonal Migration and Ecosys-

tem Service Change in Response to Global Sea Level Rise A Case Study from an Urban Region

Ecology amp Society 2010 15(4)1ndash15 PMID 66785167

22 Brinson MM Christian RR Blum LK Multiple states in the sea-level induced transition from terrestrial

forest to estuary Estuaries 1995 18(4)648ndash59 doi 1023071352383

23 Reyes E White ML Martin JF Kemp GP Day JW Aravamuthan V LANDSCAPE MODELING OF

COASTAL HABITAT CHANGE IN THE MISSISSIPPI DELTA Ecology 2000 81(8)2331ndash49 doi 10

18900012-9658(2000)081[2331LMOCHC]20CO2

24 Martin JF Reyes E Kemp GP Mashriqui H Day JW Landscape Modeling of the Mississippi Delta

BioScience 2002 52(4)357ndash65

25 Costanza R drsquoArge R de Groot R Farber S Grasso M Hannon B et al The value of ecosystem ser-

vices putting the issues in perspective Ecological Economics 1998 25(1)67ndash72 doi 101016

S0921-8009(98)00019-6

26 Barbier EB Hacker SD Kennedy C Koch EW Stier AC Silliman BR The value of estuarine and

coastal ecosystem services Ecological Monographs 2010 81(2)169ndash93 doi 10189010-15101

27 Temmerman S Meire P Bouma TJ Herman PMJ Ysebaert T De Vriend HJ Ecosystem-based

coastal defence in the face of global change Nature 2013 504(7478)79ndash83 doi 101038

nature12859 PMID 24305151



28 Moller I Kudella M Rupprecht F Spencer T Paul M van Wesenbeeck BK et al Wave attenuation

over coastal salt marshes under storm surge conditions Nature Geosci 2014 7(10)727ndash31 doi 10

1038ngeo2251

29 Odum WE Comparative Ecology of Tidal Freshwater and Salt Marshes Annual Review of Ecology

and Systematics 1988 19147ndash76

30 Swanson KM Drexler JZ Schoellhamer DH Thorne KM Casazza ML Overton CT et al Wetland

Accretion Rate Model of Ecosystem Resilience (WARMER) and Its Application to Habitat Sustainabil-

ity for Endangered Species in the San Francisco Estuary Estuaries and Coasts 2013 37(2)476ndash92

doi 101007s12237-013-9694-0

31 Jerolmack DJ Paola C Complexity in a cellular model of river avulsion Geomorphology 2007 91(3ndash

4)259ndash70 doi 101016jgeomorph200704022

32 Murray AB Reducing model complexity for explanation and prediction Geomorphology 2007 90(3ndash

4)178ndash91 jgeomorph200610020

33 Hazler KG Fleming G Taverna K Teague J Smart L cartographers Draft vegetation classification

and maps for the National Park Service National Capital Region parks2012

34 Elmore AJ Engelhardt KAM Cadol D Palinkas CM Spatial patterns of plant litter in a tidal freshwater

marsh and implications for marsh persistence Ecological Applications 2015 doi 10189014-19701

35 Harper JD Heliotis FD Dyke Marsh hydrologic model George Mason University Fairfax VA 1992

36 Temmerman S Govers G Wartel S Meire P Modelling estuarine variations in tidal marsh sedimenta-

tion response to changing sea level and suspended sediment concentrations Marine Geology 2004

212(1ndash4)1ndash19 doi 101016jmargeo200410021

37 DrsquoAlpaos A Mudd SM Carniello L Dynamic response of marshes to perturbations in suspended sedi-

ment concentrations and rates of relative sea level rise Journal of Geophysical Research Earth Sur-

face 2011 116(F4)na-na doi 1010292011JF002093

38 Darrell LC Majedi BF Lizarraga JS Blomquist JD Nutrient and suspended-sediment concentrations

trends loads and yields from the nontidal part of the Susquehanna Potomac Patuxent and Choptank

rivers 1985ndash96 US Department of the Interior US Geological Survey 1999

39 Palinkas CM Engelhardt KAM Spatial and temporal patterns of modern (~100 yr) sedimentation in a

tidal freshwater marsh Implications for future sustainability Limnology and Oceanography 2016 61

(1)132ndash48 doi 101002lno10202

40 Palinkas CM Engelhardt KAM Cadol D Evaluating physical and biological influences on sedimenta-

tion in a tidal freshwater marsh with 7Be Estuarine Coastal and Shelf Science 2013 129(0)152ndash61

doi 101016jecss201305022

41 Craft C Freshwater input structures soil properties vertical accretion and nutrient accumulation of

Georgia and US tidal marshes Limnology and oceanography 2007 52(3)1220ndash30

42 Craft CB Tidal freshwater forest accretion does not keep pace with sea level rise Global Change Biol-

ogy 2012 18(12)3615ndash23 doi 101111gcb12009

43 Boesch DF Atkinson LP Boicourt WC Boon JD Cahoon DR Dalrymple RA et al Updating Mary-

landrsquos sea-level rise projections Report Cambridge MD 2013

44 Ezer T Atkinson LP Corlett WB Blanco JL Gulf Streamrsquos induced sea level rise and variability along

the US mid-Atlantic coast Journal of Geophysical Research Oceans 2013 118(2)685ndash97 doi 10

1002jgrc20091

45 Dutton A Carlson AE Long AJ Milne GA Clark PU DeConto R et al Sea-level rise due to polar ice-

sheet mass loss during past warm periods Science 2015 349(6244) doi 101126scienceaaa4019

PMID 26160951

46 Ritz C Edwards TL Durand G Payne AJ Peyaud V Hindmarsh RCA Potential sea-level rise from

Antarctic ice-sheet instability constrained by observations Nature 2015 528(7580)115ndash8 doi 10

1038nature16147 PMID 26580020

47 USACE Sea-Level Change Considerations in Civil Works Programs Department of the Army Engi-

neering Circular No 1165-2-212 1 October 2011 2011

48 Dalrymple RA Breaker L Brooks B Cayan D Griggs G Han W et al Sea-Level Rise for the Coasts

of California Oregon and Washington Past Present and Future National Research Council The

National Academies Press Washington DC 2012

49 Carson M Kohl A Stammer D A Slangen AB Katsman CA W van de Wal RS et al Coastal sea

level changes observed and projected during the 20th and 21st century Climatic Change 2016 134

(1)269ndash81 doi 101007s10584-015-1520-1

50 Reed DJ The response of coastal marshes to sea-level rise Survival or submergence Earth Surface

Processes and Landforms 1995 20(1)39ndash48 doi 101002esp3290200105



51 Kirwan ML Temmerman S Skeehan EE Guntenspergen GR Fagherazzi S Overestimation of marsh

vulnerability to sea level rise Nature Clim Change 2016 6(3)253ndash60 doi 101038nclimate2909

52 Kirwan ML Guntenspergen GR DrsquoAlpaos A Morris JT Mudd SM Temmerman S Limits on the adapt-

ability of coastal marshes to rising sea level Geophysical Research Letters 2010 37(23)L23401 doi

1010292010GL045489

53 Kirwan ML Murray AB Donnelly JP Corbett DR Rapid wetland expansion during European settle-

ment and its implication for marsh survival under modern sediment delivery rates Geology 2011 39

(5)507ndash10 doi 101130g317891

54 Fagherazzi S Mariotti G Wiberg P McGlathery K Marsh Collapse Does Not Require Sea Level Rise

Oceanography 2013 26(3)70ndash7 doi 105670oceanog201347

55 Kirwan ML Walters DC Reay WG Carr JA Sea level driven marsh expansion in a coupled model of

marsh erosion and migration Geophysical Research Letters 2016 43(9)4366ndash73 doi 101002

2016GL068507

56 Belliard JP Toffolon M Carniello L DrsquoAlpaos A An ecogeomorphic model of tidal channel initiation

and elaboration in progressive marsh accretional contexts Journal of Geophysical Research Earth

Surface 2015 120(6)1040ndash64 doi 1010022015JF003445

57 Marani M DrsquoAlpaos A Lanzoni S Santalucia M Understanding and predicting wave erosion of marsh

edges Geophysical Research Letters 2011 38(21)L21401 doi 1010292011GL048995

58 Mariotti G Fagherazzi S Critical width of tidal flats triggers marsh collapse in the absence of sea-level

rise Proceedings of the National Academy of Sciences 2013 110(14)5353ndash6 doi 101073pnas

1219600110 PMID 23513219

59 Litwin RJ Smoot JP Pavich MJ Oberg E Steury B Helwig B et al Rates and probable causes of

freshwater tidal marsh failure Potomac River estuary northern Virginia USA Wetlands 2013 33

(6)1037ndash61 doi 101007s13157-013-0461-6

60 Litwin RJ Smoot JP Pavich MJ Markewich HW Oberg E Helwig B et al Analysis of the deconstruc-

tion of Dyke Marsh George Washington Memorial Parkway Virginia Progression geologic and man-

made causes and effective restoration scenarios Open-File Report 2010ndash1269 Washington D

C2011 p 80

61 Weston NB Vile MA Neubauer SC Velinsky DJ Accelerated microbial organic matter mineralization

following salt-water intrusion into tidal freshwater marsh soils Biogeochemistry 2011 102(1)135ndash51

doi 101007s10533-010-9427-4

62 Hoeppner SS Rose KA Individual-based modeling of flooding and salinity effects on a coastal swamp

forest Ecological Modelling 2011 222(19)3541ndash58 doi 101016jecolmodel201107017

63 DeLaune R Nyman J Patrick W Jr Peat collapse ponding and wetland loss in a rapidly submerging

coastal marsh Journal of Coastal Research 19941021ndash30

64 Neubauer SC Ecosystem Responses of a Tidal Freshwater Marsh Experiencing Saltwater Intrusion

and Altered Hydrology Estuaries and Coasts 2013 36(3)491ndash507 doi 101007s12237-011-9455-x

65 Whitehead A Roach JL Zhang S Galvez F Genomic mechanisms of evolved physiological plasticity

in killifish distributed along an environmental salinity gradient Proceedings of the National Academy of

Sciences 2011 108(15)6193ndash8 doi 101073pnas1017542108 PMID 21444822

66 Callender E Carter V Hahl DC Hitt K Schultz BI A water-quality study of the tidal Potomac River and

Estuary an overview Report 1984 USGS Water Supply Paper 2233

67 Cornwell JC Owens MS Boynton WR Harris LA Sediment-Water Nitrogen Exchange along the Poto-

mac River Estuarine Salinity Gradient Journal of Coastal Research 2015 doi 102112

JCOASTRES-D-15-001591

68 Herman JD Friedrichs CT Estuarine Suspended Sediment Loads and Sediment Budgets in Tributar-

ies of Chesapeake Bay Phase 1 York Patuxent and Potomac Rivers Virginia Institute of Marine Sci-

ence 2010 Special Report in Applied Marine Science and Ocean Engineering No 420

69 Morris JT Sundareshwar PV Nietch CT Kjerfve B Cahoon DR Responses of coastal wetlands to ris-

ing sea level Ecology 2002 83(10)2869ndash77 doi 1018900012-9658(2002)083[2869rocwtr]20

co2



tidal marsh lands would be expected to transition into deeper water habitat classes such astidal flats with associated loss of ecosystem services [2] Marsh surface elevation change isaffected by processes which increase elevationndashsuch as mineral sediment deposition organicmatter deposition and root growthndashand those that decrease elevationndashsuch as sediment com-paction organic matter decay erosion and local or regional subsidence [3ndash8] Several of theseprocesses depend on the elevation of the marsh relative to mean sea level leading to both posi-tive and negative feedbacks on relative marsh elevation change [9ndash11] Additionally sea levelchange including both global eustatic components and regional gravitational or circulationeffects [12] alters the relative elevation of a marsh surfaceIn periods of rapidly rising sea levels such as those forecast for the coming decades [13ndash15]

marsh surface elevation gains may be overwhelmedby sea level rise (SLR) [16] In this caseimpediments to landward migration such as bluffs or levees will contribute to a reduction ofmarsh area and associated ecosystem services [17] This effect is expected to be particularlyprevalent becausemany engineering solutions that protect infrastructure from rising sea levelscreate just such a fixed landscape [18] Undeveloped coastal areas including managed openspace and agricultural land are less likely to be protected from SLR and may be crucial refugesto which marshes may migrateThe strategic management of coastal open space benefits frommarsh migration forecasts

[19] Models that track habitat class as a function of relative elevation (eg the Sea Level Affect-ing Marshes Model SLAMM [20]) have been effectively employed to this end Most attentionhowever has been focused on salt marshes [20 21] or large deltaic systems that cross fromfresh to brackish habitats [19 22ndash24] Frequently these models are applied over very largeareas at relatively coarse grids (30 m to 1 km cells) Without downplaying the importance ofsaltwater systems the research presented here seeks to extend this work to estuarine freshwatertidal marshes Freshwater marshes occur in most estuarine systems where tidal action extendsupstream along large river courses Though they are often smaller than salt marshes estuarinefreshwater marshes are important ecosystem components contributing disproportionately tonutrient retention and providing additional ecosystem services including flood control andhabitat for many rare threatened and endangered species [25ndash28] The work presented herewas conducted along the tidal Potomac River where numerous National Park Service (NPS)management units preserve open space in an elevation range relevant to marsh migrationwithin the next century (Fig 1) Because this work was conducted in a freshwater tidal environ-ment SLAMMhad some features that were not ideal (eg implicit salinity effects) and lackedother features that we deemed important (eg gradational vegetation zonation) From an eco-logical standpoint salt marshes have both an anoxic stress gradient (due to inundation time)and a salinity gradient (due to evaporation especially in warm environments) which togethercontribute to highly distinct zonation of species [1] Freshwater marshes only have the anoxicstress gradient and individual species are more loosely zoned with a great deal of elevationaloverlap between species [29]Therefore we developed and tested a spatially explicit elevation-tracking and vegetation

class-switching model which we designatedMarsh Accretion and Inundation Model (MAIM)Effort was made to develop a parsimonious model that also adequately captures elevation gainprocesses Other morphodynamicmodels have produced good results at similar scales [16 30]but these require data on vegetation biomass distribution sediment trapping efficiency andother highly variable parameters MAIM utilizes a consolidated empirical elevation gain func-tion to represent the biogeomorphic feedbacks that drive marsh elevation change in each vege-tation class Simplified process modeling or reduced-complexitymodeling such as thisattempts to optimize the trade-off between increased fidelity to physical processes andincreased uncertainty in the model parameters that describe these processes [31 32]



Foundation through award DEB-0841394 (PI

Katharina A M Engelhardt)

Competing Interests The authors have declared

that no competing interests exist










Methods

Study Area


Model Overview


Model Inputs



Bridge


















NPS)







Swamp Species




gt21 HTU none





flooded forests


days




variable mostly

19ndash21 HTU

rarely large storms

or floods


stands


year




days


days


mud flat



inundated








2s2n thorn Sl

1


thorn Su1


eth1THORN





runs














σn 055 kl 65 ku 20

Sn 0008 Sl 00045 Su -00035

mn -15 ml -08 mu 023










2010 006 01 015

2020 01 4 016 6 025 10

2030 015 5 024 8 037 12

2040 022 7 034 10 052 15

2050 029 7 045 11 07 18

2060 036 7 057 12 088 18

2070 045 9 07 13 108 20

2080 053 8 084 14 129 21

2090 062 9 097 13 15 21

2100 07 8 11 13 17 20





Results























Elevation change






































each curve


















Location Distance

downstream of DC

(km)

Salinity regime

(~2010)aChannel

width (m)


estimate m2)

x-section area

(m2) 07 m SLR

x-section area

(m2) 11 m SLR

x-section area

(m2) 17 m SLR

US Hwy 301

Bridge

98 5ndash15 psu 3580 19000b - - -

Maryland

Point

75 05ndash5 psu 2190 14000 b - - -











Acknowledgments




Data curation SG DC

Formal analysisDC



MethodologyDCAE SG


ResourcesGS AE KE

Software SG DC

SupervisionAE KE

Validation DCKE

VisualizationDC SG






20 doi 101126science1185782 PMID 20558707




37 doi 1023071942263




1998 21(3)407ndash15 doi 1023071352839



1007s100219900055









pnas1218327110 PMID 23401529



(3)1065ndash80 doi 104319lo20145931065















S69ndashS87 doi 101016S0959-3780(99)00019-9





107



(2)73ndash8 doi 101890070219








18900012-9658(2000)081[2331LMOCHC]20CO2





S0921-8009(98)00019-6










1038ngeo2251






doi 101007s12237-013-9694-0















face 2011 116(F4)na-na doi 1010292011JF002093






(1)132ndash48 doi 101002lno10202



doi 101016jecss201305022




ogy 2012 18(12)3615ndash23 doi 101111gcb12009





1002jgrc20091



PMID 26160951



1038nature16147 PMID 26580020








(1)269ndash81 doi 101007s10584-015-1520-1









1010292010GL045489



(5)507ndash10 doi 101130g317891





2016GL068507








1219600110 PMID 23513219



(6)1037ndash61 doi 101007s13157-013-0461-6




C2011 p 80



doi 101007s10533-010-9427-4




















co2












Methods

Study Area


Model Overview


Model Inputs



Bridge


















NPS)







Swamp Species




gt21 HTU none





flooded forests


days




variable mostly

19ndash21 HTU

rarely large storms

or floods


stands


year




days


days


mud flat



inundated








2s2n thorn Sl

1


thorn Su1


eth1THORN





runs














σn 055 kl 65 ku 20

Sn 0008 Sl 00045 Su -00035

mn -15 ml -08 mu 023










2010 006 01 015

2020 01 4 016 6 025 10

2030 015 5 024 8 037 12

2040 022 7 034 10 052 15

2050 029 7 045 11 07 18

2060 036 7 057 12 088 18

2070 045 9 07 13 108 20

2080 053 8 084 14 129 21

2090 062 9 097 13 15 21

2100 07 8 11 13 17 20





Results























Elevation change






































each curve


















Location Distance

downstream of DC

(km)

Salinity regime

(~2010)aChannel

width (m)


estimate m2)

x-section area

(m2) 07 m SLR

x-section area

(m2) 11 m SLR

x-section area

(m2) 17 m SLR

US Hwy 301

Bridge

98 5ndash15 psu 3580 19000b - - -

Maryland

Point

75 05ndash5 psu 2190 14000 b - - -











Acknowledgments




Data curation SG DC

Formal analysisDC



MethodologyDCAE SG


ResourcesGS AE KE

Software SG DC

SupervisionAE KE

Validation DCKE

VisualizationDC SG






20 doi 101126science1185782 PMID 20558707




37 doi 1023071942263




1998 21(3)407ndash15 doi 1023071352839



1007s100219900055









pnas1218327110 PMID 23401529



(3)1065ndash80 doi 104319lo20145931065















S69ndashS87 doi 101016S0959-3780(99)00019-9





107



(2)73ndash8 doi 101890070219








18900012-9658(2000)081[2331LMOCHC]20CO2





S0921-8009(98)00019-6










1038ngeo2251






doi 101007s12237-013-9694-0















face 2011 116(F4)na-na doi 1010292011JF002093






(1)132ndash48 doi 101002lno10202



doi 101016jecss201305022




ogy 2012 18(12)3615ndash23 doi 101111gcb12009





1002jgrc20091



PMID 26160951



1038nature16147 PMID 26580020








(1)269ndash81 doi 101007s10584-015-1520-1









1010292010GL045489



(5)507ndash10 doi 101130g317891





2016GL068507








1219600110 PMID 23513219



(6)1037ndash61 doi 101007s13157-013-0461-6




C2011 p 80



doi 101007s10533-010-9427-4




















co2

















NPS)







Swamp Species




gt21 HTU none





flooded forests


days




variable mostly

19ndash21 HTU

rarely large storms

or floods


stands


year




days


days


mud flat



inundated








2s2n thorn Sl

1


thorn Su1


eth1THORN





runs














σn 055 kl 65 ku 20

Sn 0008 Sl 00045 Su -00035

mn -15 ml -08 mu 023










2010 006 01 015

2020 01 4 016 6 025 10

2030 015 5 024 8 037 12

2040 022 7 034 10 052 15

2050 029 7 045 11 07 18

2060 036 7 057 12 088 18

2070 045 9 07 13 108 20

2080 053 8 084 14 129 21

2090 062 9 097 13 15 21

2100 07 8 11 13 17 20





Results























Elevation change






































each curve


















Location Distance

downstream of DC

(km)

Salinity regime

(~2010)aChannel

width (m)


estimate m2)

x-section area

(m2) 07 m SLR

x-section area

(m2) 11 m SLR

x-section area

(m2) 17 m SLR

US Hwy 301

Bridge

98 5ndash15 psu 3580 19000b - - -

Maryland

Point

75 05ndash5 psu 2190 14000 b - - -











Acknowledgments




Data curation SG DC

Formal analysisDC



MethodologyDCAE SG


ResourcesGS AE KE

Software SG DC

SupervisionAE KE

Validation DCKE

VisualizationDC SG






20 doi 101126science1185782 PMID 20558707




37 doi 1023071942263




1998 21(3)407ndash15 doi 1023071352839



1007s100219900055









pnas1218327110 PMID 23401529



(3)1065ndash80 doi 104319lo20145931065















S69ndashS87 doi 101016S0959-3780(99)00019-9





107



(2)73ndash8 doi 101890070219








18900012-9658(2000)081[2331LMOCHC]20CO2





S0921-8009(98)00019-6










1038ngeo2251






doi 101007s12237-013-9694-0















face 2011 116(F4)na-na doi 1010292011JF002093






(1)132ndash48 doi 101002lno10202



doi 101016jecss201305022




ogy 2012 18(12)3615ndash23 doi 101111gcb12009





1002jgrc20091



PMID 26160951



1038nature16147 PMID 26580020








(1)269ndash81 doi 101007s10584-015-1520-1









1010292010GL045489



(5)507ndash10 doi 101130g317891





2016GL068507








1219600110 PMID 23513219



(6)1037ndash61 doi 101007s13157-013-0461-6




C2011 p 80



doi 101007s10533-010-9427-4




















co2





2s2n thorn Sl

1


thorn Su1


eth1THORN





runs














σn 055 kl 65 ku 20

Sn 0008 Sl 00045 Su -00035

mn -15 ml -08 mu 023










2010 006 01 015

2020 01 4 016 6 025 10

2030 015 5 024 8 037 12

2040 022 7 034 10 052 15

2050 029 7 045 11 07 18

2060 036 7 057 12 088 18

2070 045 9 07 13 108 20

2080 053 8 084 14 129 21

2090 062 9 097 13 15 21

2100 07 8 11 13 17 20





Results























Elevation change






































each curve


















Location Distance

downstream of DC

(km)

Salinity regime

(~2010)aChannel

width (m)


estimate m2)

x-section area

(m2) 07 m SLR

x-section area

(m2) 11 m SLR

x-section area

(m2) 17 m SLR

US Hwy 301

Bridge

98 5ndash15 psu 3580 19000b - - -

Maryland

Point

75 05ndash5 psu 2190 14000 b - - -











Acknowledgments




Data curation SG DC

Formal analysisDC



MethodologyDCAE SG


ResourcesGS AE KE

Software SG DC

SupervisionAE KE

Validation DCKE

VisualizationDC SG






20 doi 101126science1185782 PMID 20558707




37 doi 1023071942263




1998 21(3)407ndash15 doi 1023071352839



1007s100219900055









pnas1218327110 PMID 23401529



(3)1065ndash80 doi 104319lo20145931065















S69ndashS87 doi 101016S0959-3780(99)00019-9





107



(2)73ndash8 doi 101890070219








18900012-9658(2000)081[2331LMOCHC]20CO2





S0921-8009(98)00019-6










1038ngeo2251






doi 101007s12237-013-9694-0















face 2011 116(F4)na-na doi 1010292011JF002093






(1)132ndash48 doi 101002lno10202



doi 101016jecss201305022




ogy 2012 18(12)3615ndash23 doi 101111gcb12009





1002jgrc20091



PMID 26160951



1038nature16147 PMID 26580020








(1)269ndash81 doi 101007s10584-015-1520-1









1010292010GL045489



(5)507ndash10 doi 101130g317891





2016GL068507








1219600110 PMID 23513219



(6)1037ndash61 doi 101007s13157-013-0461-6




C2011 p 80



doi 101007s10533-010-9427-4




















co2













σn 055 kl 65 ku 20

Sn 0008 Sl 00045 Su -00035

mn -15 ml -08 mu 023










2010 006 01 015

2020 01 4 016 6 025 10

2030 015 5 024 8 037 12

2040 022 7 034 10 052 15

2050 029 7 045 11 07 18

2060 036 7 057 12 088 18

2070 045 9 07 13 108 20

2080 053 8 084 14 129 21

2090 062 9 097 13 15 21

2100 07 8 11 13 17 20





Results























Elevation change






































each curve


















Location Distance

downstream of DC

(km)

Salinity regime

(~2010)aChannel

width (m)


estimate m2)

x-section area

(m2) 07 m SLR

x-section area

(m2) 11 m SLR

x-section area

(m2) 17 m SLR

US Hwy 301

Bridge

98 5ndash15 psu 3580 19000b - - -

Maryland

Point

75 05ndash5 psu 2190 14000 b - - -











Acknowledgments




Data curation SG DC

Formal analysisDC



MethodologyDCAE SG


ResourcesGS AE KE

Software SG DC

SupervisionAE KE

Validation DCKE

VisualizationDC SG






20 doi 101126science1185782 PMID 20558707




37 doi 1023071942263




1998 21(3)407ndash15 doi 1023071352839



1007s100219900055









pnas1218327110 PMID 23401529



(3)1065ndash80 doi 104319lo20145931065















S69ndashS87 doi 101016S0959-3780(99)00019-9





107



(2)73ndash8 doi 101890070219








18900012-9658(2000)081[2331LMOCHC]20CO2





S0921-8009(98)00019-6










1038ngeo2251






doi 101007s12237-013-9694-0















face 2011 116(F4)na-na doi 1010292011JF002093






(1)132ndash48 doi 101002lno10202



doi 101016jecss201305022




ogy 2012 18(12)3615ndash23 doi 101111gcb12009





1002jgrc20091



PMID 26160951



1038nature16147 PMID 26580020








(1)269ndash81 doi 101007s10584-015-1520-1









1010292010GL045489



(5)507ndash10 doi 101130g317891





2016GL068507








1219600110 PMID 23513219



(6)1037ndash61 doi 101007s13157-013-0461-6




C2011 p 80



doi 101007s10533-010-9427-4




















co2









2010 006 01 015

2020 01 4 016 6 025 10

2030 015 5 024 8 037 12

2040 022 7 034 10 052 15

2050 029 7 045 11 07 18

2060 036 7 057 12 088 18

2070 045 9 07 13 108 20

2080 053 8 084 14 129 21

2090 062 9 097 13 15 21

2100 07 8 11 13 17 20





Results























Elevation change






































each curve


















Location Distance

downstream of DC

(km)

Salinity regime

(~2010)aChannel

width (m)


estimate m2)

x-section area

(m2) 07 m SLR

x-section area

(m2) 11 m SLR

x-section area

(m2) 17 m SLR

US Hwy 301

Bridge

98 5ndash15 psu 3580 19000b - - -

Maryland

Point

75 05ndash5 psu 2190 14000 b - - -











Acknowledgments




Data curation SG DC

Formal analysisDC



MethodologyDCAE SG


ResourcesGS AE KE

Software SG DC

SupervisionAE KE

Validation DCKE

VisualizationDC SG






20 doi 101126science1185782 PMID 20558707




37 doi 1023071942263




1998 21(3)407ndash15 doi 1023071352839



1007s100219900055









pnas1218327110 PMID 23401529



(3)1065ndash80 doi 104319lo20145931065















S69ndashS87 doi 101016S0959-3780(99)00019-9





107



(2)73ndash8 doi 101890070219








18900012-9658(2000)081[2331LMOCHC]20CO2





S0921-8009(98)00019-6










1038ngeo2251






doi 101007s12237-013-9694-0















face 2011 116(F4)na-na doi 1010292011JF002093






(1)132ndash48 doi 101002lno10202



doi 101016jecss201305022




ogy 2012 18(12)3615ndash23 doi 101111gcb12009





1002jgrc20091



PMID 26160951



1038nature16147 PMID 26580020








(1)269ndash81 doi 101007s10584-015-1520-1









1010292010GL045489



(5)507ndash10 doi 101130g317891





2016GL068507








1219600110 PMID 23513219



(6)1037ndash61 doi 101007s13157-013-0461-6




C2011 p 80



doi 101007s10533-010-9427-4




















co2




Results























Elevation change






































each curve


















Location Distance

downstream of DC

(km)

Salinity regime

(~2010)aChannel

width (m)


estimate m2)

x-section area

(m2) 07 m SLR

x-section area

(m2) 11 m SLR

x-section area

(m2) 17 m SLR

US Hwy 301

Bridge

98 5ndash15 psu 3580 19000b - - -

Maryland

Point

75 05ndash5 psu 2190 14000 b - - -











Acknowledgments




Data curation SG DC

Formal analysisDC



MethodologyDCAE SG


ResourcesGS AE KE

Software SG DC

SupervisionAE KE

Validation DCKE

VisualizationDC SG






20 doi 101126science1185782 PMID 20558707




37 doi 1023071942263




1998 21(3)407ndash15 doi 1023071352839



1007s100219900055









pnas1218327110 PMID 23401529



(3)1065ndash80 doi 104319lo20145931065















S69ndashS87 doi 101016S0959-3780(99)00019-9





107



(2)73ndash8 doi 101890070219








18900012-9658(2000)081[2331LMOCHC]20CO2





S0921-8009(98)00019-6










1038ngeo2251






doi 101007s12237-013-9694-0















face 2011 116(F4)na-na doi 1010292011JF002093






(1)132ndash48 doi 101002lno10202



doi 101016jecss201305022




ogy 2012 18(12)3615ndash23 doi 101111gcb12009





1002jgrc20091



PMID 26160951



1038nature16147 PMID 26580020








(1)269ndash81 doi 101007s10584-015-1520-1









1010292010GL045489



(5)507ndash10 doi 101130g317891





2016GL068507








1219600110 PMID 23513219



(6)1037ndash61 doi 101007s13157-013-0461-6




C2011 p 80



doi 101007s10533-010-9427-4




















co2



















Elevation change






































each curve


















Location Distance

downstream of DC

(km)

Salinity regime

(~2010)aChannel

width (m)


estimate m2)

x-section area

(m2) 07 m SLR

x-section area

(m2) 11 m SLR

x-section area

(m2) 17 m SLR

US Hwy 301

Bridge

98 5ndash15 psu 3580 19000b - - -

Maryland

Point

75 05ndash5 psu 2190 14000 b - - -











Acknowledgments




Data curation SG DC

Formal analysisDC



MethodologyDCAE SG


ResourcesGS AE KE

Software SG DC

SupervisionAE KE

Validation DCKE

VisualizationDC SG






20 doi 101126science1185782 PMID 20558707




37 doi 1023071942263




1998 21(3)407ndash15 doi 1023071352839



1007s100219900055









pnas1218327110 PMID 23401529



(3)1065ndash80 doi 104319lo20145931065















S69ndashS87 doi 101016S0959-3780(99)00019-9





107



(2)73ndash8 doi 101890070219








18900012-9658(2000)081[2331LMOCHC]20CO2





S0921-8009(98)00019-6










1038ngeo2251






doi 101007s12237-013-9694-0















face 2011 116(F4)na-na doi 1010292011JF002093






(1)132ndash48 doi 101002lno10202



doi 101016jecss201305022




ogy 2012 18(12)3615ndash23 doi 101111gcb12009





1002jgrc20091



PMID 26160951



1038nature16147 PMID 26580020








(1)269ndash81 doi 101007s10584-015-1520-1









1010292010GL045489



(5)507ndash10 doi 101130g317891





2016GL068507








1219600110 PMID 23513219



(6)1037ndash61 doi 101007s13157-013-0461-6




C2011 p 80



doi 101007s10533-010-9427-4




















co2





































each curve


















Location Distance

downstream of DC

(km)

Salinity regime

(~2010)aChannel

width (m)


estimate m2)

x-section area

(m2) 07 m SLR

x-section area

(m2) 11 m SLR

x-section area

(m2) 17 m SLR

US Hwy 301

Bridge

98 5ndash15 psu 3580 19000b - - -

Maryland

Point

75 05ndash5 psu 2190 14000 b - - -











Acknowledgments




Data curation SG DC

Formal analysisDC



MethodologyDCAE SG


ResourcesGS AE KE

Software SG DC

SupervisionAE KE

Validation DCKE

VisualizationDC SG






20 doi 101126science1185782 PMID 20558707




37 doi 1023071942263




1998 21(3)407ndash15 doi 1023071352839



1007s100219900055









pnas1218327110 PMID 23401529



(3)1065ndash80 doi 104319lo20145931065















S69ndashS87 doi 101016S0959-3780(99)00019-9





107



(2)73ndash8 doi 101890070219








18900012-9658(2000)081[2331LMOCHC]20CO2





S0921-8009(98)00019-6










1038ngeo2251






doi 101007s12237-013-9694-0















face 2011 116(F4)na-na doi 1010292011JF002093






(1)132ndash48 doi 101002lno10202



doi 101016jecss201305022




ogy 2012 18(12)3615ndash23 doi 101111gcb12009





1002jgrc20091



PMID 26160951



1038nature16147 PMID 26580020








(1)269ndash81 doi 101007s10584-015-1520-1









1010292010GL045489



(5)507ndash10 doi 101130g317891





2016GL068507








1219600110 PMID 23513219



(6)1037ndash61 doi 101007s13157-013-0461-6




C2011 p 80



doi 101007s10533-010-9427-4




















co2

















Location Distance

downstream of DC

(km)

Salinity regime

(~2010)aChannel

width (m)


estimate m2)

x-section area

(m2) 07 m SLR

x-section area

(m2) 11 m SLR

x-section area

(m2) 17 m SLR

US Hwy 301

Bridge

98 5ndash15 psu 3580 19000b - - -

Maryland

Point

75 05ndash5 psu 2190 14000 b - - -











Acknowledgments




Data curation SG DC

Formal analysisDC



MethodologyDCAE SG


ResourcesGS AE KE

Software SG DC

SupervisionAE KE

Validation DCKE

VisualizationDC SG






20 doi 101126science1185782 PMID 20558707




37 doi 1023071942263




1998 21(3)407ndash15 doi 1023071352839



1007s100219900055









pnas1218327110 PMID 23401529



(3)1065ndash80 doi 104319lo20145931065















S69ndashS87 doi 101016S0959-3780(99)00019-9





107



(2)73ndash8 doi 101890070219








18900012-9658(2000)081[2331LMOCHC]20CO2





S0921-8009(98)00019-6










1038ngeo2251






doi 101007s12237-013-9694-0















face 2011 116(F4)na-na doi 1010292011JF002093






(1)132ndash48 doi 101002lno10202



doi 101016jecss201305022




ogy 2012 18(12)3615ndash23 doi 101111gcb12009





1002jgrc20091



PMID 26160951



1038nature16147 PMID 26580020








(1)269ndash81 doi 101007s10584-015-1520-1









1010292010GL045489



(5)507ndash10 doi 101130g317891





2016GL068507








1219600110 PMID 23513219



(6)1037ndash61 doi 101007s13157-013-0461-6




C2011 p 80



doi 101007s10533-010-9427-4




















co2






Acknowledgments




Data curation SG DC

Formal analysisDC



MethodologyDCAE SG


ResourcesGS AE KE

Software SG DC

SupervisionAE KE

Validation DCKE

VisualizationDC SG






20 doi 101126science1185782 PMID 20558707




37 doi 1023071942263




1998 21(3)407ndash15 doi 1023071352839



1007s100219900055









pnas1218327110 PMID 23401529



(3)1065ndash80 doi 104319lo20145931065















S69ndashS87 doi 101016S0959-3780(99)00019-9





107



(2)73ndash8 doi 101890070219








18900012-9658(2000)081[2331LMOCHC]20CO2





S0921-8009(98)00019-6










1038ngeo2251






doi 101007s12237-013-9694-0















face 2011 116(F4)na-na doi 1010292011JF002093






(1)132ndash48 doi 101002lno10202



doi 101016jecss201305022




ogy 2012 18(12)3615ndash23 doi 101111gcb12009





1002jgrc20091



PMID 26160951



1038nature16147 PMID 26580020








(1)269ndash81 doi 101007s10584-015-1520-1









1010292010GL045489



(5)507ndash10 doi 101130g317891





2016GL068507








1219600110 PMID 23513219



(6)1037ndash61 doi 101007s13157-013-0461-6




C2011 p 80



doi 101007s10533-010-9427-4




















co2




1998 21(3)407ndash15 doi 1023071352839



1007s100219900055









pnas1218327110 PMID 23401529



(3)1065ndash80 doi 104319lo20145931065















S69ndashS87 doi 101016S0959-3780(99)00019-9





107



(2)73ndash8 doi 101890070219








18900012-9658(2000)081[2331LMOCHC]20CO2





S0921-8009(98)00019-6










1038ngeo2251






doi 101007s12237-013-9694-0















face 2011 116(F4)na-na doi 1010292011JF002093






(1)132ndash48 doi 101002lno10202



doi 101016jecss201305022




ogy 2012 18(12)3615ndash23 doi 101111gcb12009





1002jgrc20091



PMID 26160951



1038nature16147 PMID 26580020








(1)269ndash81 doi 101007s10584-015-1520-1









1010292010GL045489



(5)507ndash10 doi 101130g317891





2016GL068507








1219600110 PMID 23513219



(6)1037ndash61 doi 101007s13157-013-0461-6




C2011 p 80



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