Lifespan of marsh units in Assateague Island National Seashore and Chincoteague Bay, Maryland and Virginia

This process step and subsequent process steps were performed by the same person, Zafer Defne, in ArcGIS Pro (ver.2.8.2) unless otherwise stated. For complex operations, names of specific tools used are given in CAPITAL letters (any critical parameters used are given in parentheses, separated by a semicolon, immediately after the tool name). The input and output file names are provided in [square brackets] when necessary. Units for length and area calculations are meters (m) and square meters (m2) unless otherwise stated.
a) Download elevation and unvegetated to vegetated marsh ratio (UVVR) datasets for Assateague Island National Seashore and Chincoteague Bay, Maryland and Virginia from the USGS ScienceBase (https://doi.org/10.5066/P9HCTQ66 and https://doi.org/10.5066/P9AY52YJ, respectively).
b) Convert the coordinate system to the North American Datum of 1983 (NAD 1983) geographic coordinate system and add the coordinates for polygon centroids to the data table using ADD_GEOMETRY_ATTRIBUTES tool.
c) Export following variables to a comma separated values file [ASIS_life.csv]: X_POINT, Y_POINT, FID_CMU, UVVR, ATOT_M2, AVEG_M2, mu_ELEV, vg_ELEV.

The following processing stepswereperformed by Neil Ganju using VDatum online (VDatum ver.3.9) and Matlab (ver.2018a).
a) Convert elevations from the North American Vertical Datum of 1988 (NAVD88)to Mean Tide Level (MTL) referenced elevations.
Upload the ASCII file of latitude and longitude coordinates and elevation to VDatum online , and transform vertical datum from the NAVD88 to MTL. Do this for the marsh unit elevation and elevation of the vegetated part of the marsh unit to calculate mu_ELEV_MTL and vg_ELEV_MTL, respectively. Use value from the nearest Vdatum point for any point where VDatum has no data.
b) Calculate sediment budget from UVVR based on Ganju and others (2020) with SB= -0.416*log(UVVR)-1.0749, where SB is sediment budget in kilograms per square meter per year, and log() indicates natural logarithm function.
c) Calculate total sediment flux with SF= SB*ATOT_M2, where SF is sediment flux in kilograms per year and ATOT_M2 is total surface area of marsh unit in square meters.
d) Calculate sediment flux for the three scenarios considered: Global mean sea level rise of 0.3 meters, 0.5 meters and 1.0 meters by year 2100.
Sea-level rise (SLR) reduces vegetated marsh area, therefore, causes reduction in sediment flux. The sediment flux under SLR is calculated with SF_SLR= (SB-(GMSLR-BGRND_RSLR)*RHO_F)*ATOT_M2, where SF_SLR is sediment flux under SLR, RHO_F is dry bulk density of future deposited sediment, GMSLR is the local SLR in meters for a specific global mean sea level rise scenario, and BGRND_RSLR is the non-climatic background relative sea level rise in meters. RHO_F was assigned 159 kilograms per cubic meters from Morris and others (2016). For sea level rise projections, the averages of values from two stations (LEWES and KIPTOPEKE) from Sweet and others (2017) were used: 0.00175 meters for BGRND_RSLR, 0.0051, 0.0066, and 0.0132 meters for GMSLR of 0.3, 05. and 1.0 meters by 2100.
e) Total sediment mass in the vegetated plain above MTL is calculated with TS= vg_ELEV_MTL*AVEG_M2*RHO_E, where TS is total sediment mass, AVEG_M2 is the surface area of the vegetated part of the marsh unit and RHO_E is the dry bulk density of existing marsh substrate sediment. RHO_E was assigned 373 kilograms per cubic meters from Morris and others (2016).
f) Calculate lifespan (in years) for the background relative SLR with the equation BGRND= -TS/SF. Calculate lifespan (in years) under the global mean sea level rise by 0.3 meters, 0.5 meters and 1.0 meters by year 2100 scenarios with the equation GMSL= -TS/SF_SLR for each scenario (GMSL03, GMSL05, GMSL10, respectively).
g) Save results as a Matlab data file [ASIS_life.mat].
References:
Morris, J.T., Barber, D.C., Callaway, J.C., Chambers, R., Hagen, S.C., Hopkinson, C.S., Johnson, B.J., Megonigal, P., Neubauer, S.C., Troxler, T., and Wigand, C., 2016. Contributions of organic and inorganic matter to sediment volume and accretion in tidal wetlands at steady state. Earth's future, 4(4), 110–121. https://doi.org/10.1002/2015EF000334
Sweet, W.V., Kopp, R.E., Weaver, C.P., Obeysekera, J., Horton, R.M., Thieler, E.R., and Zervas, C., 2017, Global and regional sea level rise scenarios for the United States (Tech. Rep. NOS CO-OPS 083). Silver Spring, MD: National Oceanic and Atmospheric Administration. https://doi.org/10.7289/v5/tr-nos-coops-083.

The rest of the processing steps were done by Zafer Defne, in ArcGIS Pro (ver.2.8.2) and Matlab (ver.2011b) unless otherwise stated.
a) Output Matlab data as a comma separated text file and merge with the marsh units features using ADD_JOIN and join filed as FID_CMU.
b) For marshes that are more stable, lifespan calculation sometimes results in large numbers that are far beyond the lifespan horizon of interest. Also, positive sediment budget implies unlimited lifespan. For these units, set the maximum lifespan value to 10,000 years.
c) Set lifespan values for flagged units (FLG less than 0) to -9999 so that they can be labeled as not available or excluded while plotting the data.
d) Set label for lifespan values less than 0 as imminent.
e) Rearrange field names and change the projection for better performance of web services with online base maps. PROJECT(Input coordinate system= NAD 1983 UTM Zone 18N; Output coordinate system=WGS 1984 Web Mercator Auxiliary Sphere; Geographic transformation= WGS 1984 (ITRF00) to NAD 1983) the feature dataset to obtain the lifespan values [mu_lifespan_ASISp.shp].