A barrier island habitat prediction model was used to forecast barrier island habitats (for example, beach, dune, intertidal marsh, and woody vegetation) for Dauphin Island, Alabama, based on potential island configurations associated with a variety of restoration measures and varying future conditions of storminess and sea-levels. In this study, we loosely coupled a habitat model framework with decadal hydrodynamic geomorphic model outputs to forecast habitats for 2 potential future conditions related to storminess (that is, “medium” storminess and “high” storminess based on storm climatology data) and 4 sea-level scenarios (that is, a “low” increase in sea level 0.3 m by around 2030 and 2050 and 1.0 m by around 2070 and 2128). Here, storminess refers to decadal-scale variation in the frequency and magnitude of storms. These sea-level rise (SLR) scenarios followed two SLR curves the U.S. Army Corps of Engineers intermediate SLR curve (0.7 m by 2100) and high SLR curve (1.7 m by 2100). The hydrodynamic geomorphic modeling was quasi-static, using an elevated offshore water level to capture impacts of future sea-level increases, and as such did not account for the dynamic effects of rising sea levels. However, for intertidal marshes, it was important to factor in the timing of the SLR since the SLR rate is important for the ability of an intertidal marsh to keep pace with SLR. Thus, we used literature-based assumptions related to the rate of SLR to account for potential vertical accretion in intertidal marshes. This USGS data release contains comma separated values (CSV) files for predictor variables by tidal zone and spatially explicit raster-based habitat prediction results for the various island configurations assessed for this modeling effort. For more information on the habitat model methodology and results, see the publication listed in the larger work section of this metadata (Enwright and others, 2020).

A barrier island habitat prediction model was used to forecast barrier island habitats (for example, beach, dune, intertidal marsh, and woody vegetation) for Dauphin Island, Alabama, based on potential island configurations associated with a variety of restoration measures and varying future conditions of storminess and sea level (Enwright and others, 2020). This USGS data release contains five habitat model predictions from the aforementioned modeling effort. These include: (1) the contemporary period (that is, 2015); (2) with action Year 0 (that is, hypothetically, predicted habitat coverage in 2128 based on our sea-level change rate); (3) with action Year 10 (that is, predicted habitat coverage after ten years of morphodynamic modeling with simulated storms); (4) without action Year 0; and (5) without action Year 10. Additionally, this data release includes change maps that highlight changes over the decadal simulation (that is, Year 0 to Year 10) with and without action, respectively, along with the difference between Year 10 for the with and without the action simulation. For more information on the habitat model methodology and results, see the publication listed in the larger work section of this metadata (Enwright and others, 2020) and Enwright and others (in review).

A barrier island habitat prediction model was used to forecast barrier island habitats (for example, beach, dune, intertidal marsh, and woody vegetation) for Dauphin Island, Alabama, based on potential island configurations associated with a variety of restoration measures and varying future conditions of storminess and sea level (Enwright and others, 2020). This USGS data release contains five habitat model predictions from the aforementioned modeling effort. These include: (1) the contemporary period (that is, 2015); (2) with action Year 0 (that is, hypothetically, predicted habitat coverage in 2128 based on our sea-level change rate); (3) with action Year 10 (that is, predicted habitat coverage after ten years of morphodynamic modeling with simulated storms); (4) without action Year 0; and (5) without action Year 10. Additionally, this data release includes change maps that highlight changes over the decadal simulation (that is, Year 0 to Year 10) with and without action, respectively, along with the difference between Year 10 for the with and without the action simulation. For more information on the habitat model methodology and results, see the publication listed in the larger work section of this metadata (Enwright and others, 2020) and Enwright and others (in review).

Barrier islands provide important ecosystem services, including storm protection and erosion control to the mainland, habitat for fish and wildlife, and tourism (Barbier and others, 2011; Feagin and others, 2010). These islands tend to be dynamic due to their location along the estuarine-marine interface. Besides gradual changes caused by constant forces, such as currents and tides, barrier islands face numerous threats including hurricanes, accelerated sea-level rise, oil spills, and anthropogenic impacts (Pilkey and Cooper, 2014). These threats are likely to influence the future of barrier islands in the latter part of the 21st century, especially as climate-related threats to coastal areas are expected to increase in the future (Knutson and others, 2010; Hansen and others, 2016). As a result, natural resource managers are concerned with monitoring changes to these islands and modeling future states of these environments. Geomorphology regulates many abiotic factors that influence the performance of foundation plant species, including wave energy, salinity, inundation frequency, sea spray, Aeolian transport, and nutrient availability (Young and others, 2011). Researchers have established linkages between barrier island habitats and specific landscape position variables, such as distance from shoreline (Young and others, 2011) and elevation (Anderson and others, 2016; Foster and others, 2017; Halls and others, 2018; Young and others, 2011). Here, we built upon recent barrier island habitat model efforts by Foster and others (2017) and Halls and others (2018) to develop a machine learning-based habitat model for Dauphin Island, Alabama, USA. Our model incorporated elevation uncertainty for elevation-dependent habitat extraction and yields spatially explicit predictions of general barrier island habitats based on landscape position information, such as elevation, distance from shoreline, and relative topography. The habitats that were predicted in this model included: 1) Barrier flat; 2) Beach; 3) Dune; 4) Intertidal beach; 5) Intertidal flat; 6) Intertidal marsh; 7) Water-estuarine; 8) Water-fresh; 9) Water-marine; 10) Woody vegetation; and 11) Woody wetland. Models were developed for three tidal zones: 1) subtidal; 2) intertidal; and 3) supratidal/upland. Deterministic accuracy, fuzzy accuracy, and hindcasting were used for validation. This data release contains data used to develop and validate the machine learning-based habitat model including: 1) final contemporary habitat model results; 2) contemporary habitat model training data per tidal zone; 3) contemporary habitat model predictor variables per tidal zone; 4) contemporary habitat model validation data; 5) final hindcast habitat model results; 6) hindcast habitat predictor variables per tidal zone; and 7) hindcast habitat validation data. For more information, see Enwright and others (2019).

Barrier islands provide important ecosystem services, including storm protection and erosion control to the mainland, habitat for fish and wildlife, and tourism (Barbier and others, 2011; Feagin and others, 2010). These islands tend to be dynamic due to their location along the estuarine-marine interface. Besides gradual changes caused by constant forces, such as currents and tides, barrier islands face numerous threats including hurricanes, accelerated sea-level rise, oil spills, and anthropogenic impacts (Pilkey and Cooper, 2014). These threats are likely to influence the future of barrier islands in the latter part of the 21st century, especially as climate-related threats to coastal areas are expected to increase in the future (Knutson and others, 2010; Hansen and others, 2016). As a result, natural resource managers are concerned with monitoring changes to these islands and modeling future states of these environments. Geomorphology regulates many abiotic factors that influence the performance of foundation plant species, including wave energy, salinity, inundation frequency, sea spray, Aeolian transport, and nutrient availability (Young and others, 2011). Researchers have established linkages between barrier island habitats and specific landscape position variables, such as distance from shoreline (Young and others, 2011) and elevation (Anderson and others, 2016; Foster and others, 2017; Halls and others, 2018; Young and others, 2011). Here, we built upon recent barrier island habitat model efforts by Foster and others (2017) and Halls and others (2018) to develop a machine learning-based habitat model for Dauphin Island, Alabama, USA. Our model incorporated elevation uncertainty for elevation-dependent habitat extraction and yields spatially explicit predictions of general barrier island habitats based on landscape position information, such as elevation, distance from shoreline, and relative topography. The habitats that were predicted in this model included: 1) Barrier flat; 2) Beach; 3) Dune; 4) Intertidal beach; 5) Intertidal flat; 6) Intertidal marsh; 7) Water-estuarine; 8) Water-fresh; 9) Water-marine; 10) Woody vegetation; and 11) Woody wetland. Models were developed for three tidal zones: 1) subtidal; 2) intertidal; and 3) supratidal/upland. Deterministic accuracy, fuzzy accuracy, and hindcasting were used for validation. This data release contains data used to develop and validate the machine learning-based habitat model including: 1) final contemporary habitat model results; 2) contemporary habitat model training data per tidal zone; 3) contemporary habitat model predictor variables per tidal zone; 4) contemporary habitat model validation data; 5) final hindcast habitat model results; 6) hindcast habitat predictor variables per tidal zone; and 7) hindcast habitat validation data. For more information, see Enwright and others (2019).

A barrier island seagrass habitat suitability index (HSI) model was developed for the Alabama barrier island restoration assessment at Dauphin Island. Shoal grass (Halodule wrightii) was selected as the representative species for seagrass community near Dauphin Island waters since H. wrightii is the dominant species (>62%) of seagrass communities in this area due to its rapid growth and tolerance to a wide range of salinity. Five water quality and morphological variables were selected and their relationships with habitat suitability were developed and incorporated into the seagrass HSI model for Dauphin Island restoration assessment: 1) mean salinity during the summer growing season, 2) mean temperature during the growing season, 3) annual mean water depth, 4) mean total suspended solid/turbidity during the growing season, and 5) relative wave exposure index (REI). The final HSI score was calculated using the weighted geometric mean of the suitability scores of these individual variables. The seagrass HSI model was calibrated and validated using field data from National Park Service (NPS) Gulf Coast Inventory and Monitoring Network (GULN). Then, the seagrass HSI model was used to assess seagrass habitat suitability changes with and without restoration under future storminess and sea level (SL) conditions. The barrier island restoration actions being assessed include beach and dune restoration, marsh restoration, and placement of sand in the littoral zone. The storminess bins included realizations with a “medium” storminess, which included 1 to 3 storms over a 10-year period (that is, ST2) and a “high” storminess, which included 4 to 5 storms over an equal period (that is, ST3). The two future sea levels included a SL of 0.3 m (that is, SL1) and a SL of 1.0 m(that is, SL3) above the contemporary SL. Specifically, the medium storminess was paired with the 0.3 m above the contemporary SL (that is, ST2SL1) and the “high” storminess bin was paired with the 1.0 m above the contemporary SL (that is, ST3SL3). To account for intertidal marsh vertical accretion as a component of marsh morphology evolution, two scenarios were included in modeling: the U.S. Army Corps of Engineers (USACE) high and intermediate SLR curves in which marsh kept pace with SLR through accretion (1 cm/yr) through 2022 under high SLR curve whereas marsh kept pace with SLR by accretion for the entirety of the USACE intermediate curve. Inputs of water quality conditions under future storminess and sea level conditions were provided by the CE-QUAL-ICM model that was coupled with a geomorphology model and a hydrodynamic model. The relative wave exposure index (REI) for each scenario was estimated from wind climatology data and fetch and USGS Coastal National Elevation Database (CoNED) topography and bathymetry digital elevation model (TBDEM) that was updated by the landscape-position habitat model. This data release includes simulation results and metadata of seagrass habitat suitability scores at each spatial unit (grid cell) across the study domain: estuarine waters near Dauphin Island.

This dataset includes barrier island land cover types collected from mid-November 2015 to mid-December 2015 along randomly placed transects at seven sites throughout the east end of Dauphin Island. Specifically, this data collection included characterizing land cover types and measuring horizontal position and elevation. We characterized plant community composition and structure for a subset of these points (see Vegetation Survey Data Table). This work was conducted through a joint effort by the State of Alabama, the U.S. Geological Survey, and the U.S. Army Corps of Engineers to evaluate the feasibility of various restoration alternatives and how specific alternatives might increase the resiliency and sustainability of Dauphin Island. The overarching goal of the aforementioned effort is to preserve and enhance the ecological functions and values of the island. This product provides a powerful tool for tracking changes to barrier island habitats over time. This data release includes the following three components, which are included in the attached ZIP file: 1) Dauphin Island Habitat Map (Raster data) 2) Land Cover and Vegetation Field Data Points (Vector data) 3) Vegetation Survey Data (Tabular data)

Vegetation and elevation survey data were collected in 4-square-meter quadrats via Real-Time Kinematic GPS from September 9, 2018 to April 17, 2019 on Dauphin Island, AL. Vegetation data included total percent herbaceous cover, percent cover by plant species, and mean height of vegetation within the quadrat. The percent cover by species was used to determine the dominant species for the plot.

This U.S. Geological Survey data release includes bare-earth digital elevation models (DEMs) that were produced by removing elevation bias in vegetated areas from structure-from-motion (SfM) data products for two sites on Dauphin Island, Alabama. These data were collected in the late fall of 2018 and spring of 2019. In addition to the bare-earth DEMs, this data release also includes vegetation masks, examples of model uncertainty, training data, prediction data, and validation data associated with this effort.

This U.S. Geological Survey data release includes bare-earth digital elevation models (DEMs) that were produced by removing elevation bias in vegetated areas from structure-from-motion (SfM) data products for two sites on Dauphin Island, Alabama. These data were collected in the late fall of 2018 and spring of 2019. In addition to the bare-earth DEMs, this data release also includes vegetation masks, examples of model uncertainty, training data, prediction data, and validation data associated with this effort.