Physical Sciences and Mathematics Commons^™

Dataset: Marsh Migration Methodology Development For Wetland Restoration Targeting, Molly Mitchell, Karinna Nunez, Christine Tombleson, Julie Herman

Data

Coastal marsh loss is a significant issue globally, due in part to rising sea levels and high levels of coastal human activity. Marshes have natural mechanisms to allow them to adapt to rising sea levels, however, migration across the landscape is one of those mechanisms and is frequently in conflict with human use of the shoreline. Ensuring the persistence of marshes into the future requires an understanding of where marshes are likely to migrate under sea level rise and targeting those areas for conservation and preservation activities. The goal of this project was to 1) compile existing datasets and information …

Synthesis Of Shoreline, Sea Level Rise, And Marsh Migration Data For Wetland Restoration Targeting Final Report, Molly Mitchell, Karinna Nunez, Christine Tombleson, Julie Herman

Data

Coastal marsh loss is a significant issue globally, due in part to rising sea levels and high levels of coastal human activity. Marshes have natural mechanisms to allow them to adapt to rising sea levels, however, migration across the landscape is one of those mechanisms and is frequently in conflict with human use of the shoreline. Ensuring the persistence of marshes into the future requires an understanding of where marshes are likely to migrate under sea level rise and targeting those areas for conservation and preservation activities. The goal of this project was to 1) compile existing datasets and information …

Gis Data: Prince George’S County, Maryland – Shoreline Inventory Data 2023, Karinna Nunez, Tamia Rudnicky, Sharon Killeen, Jessica Hendricks, Catherine R. Duning, Evan Hill

Data

The shoreline inventory files have been generated to support the application of the Maryland Shoreline Stabilization Model (SSM), developed by the Center for Coastal Resources Management (CCRM), Virginia Institute of Marine Science (VIMS), to enhance and streamline regulatory decision making in Maryland. This shoreline inventory includes the features needed as inputs to run the SSM.

The data developed for the Shoreline Inventory is based on a three-tiered shoreline assessment approach. This assessment characterizes conditions by using observations made remotely at the desktop using high resolution imagery. The three-tiered shoreline assessment approach divides the shore zone into three regions:

1) the …

Gis Data: Harford County, Maryland – Shoreline Inventory Data 2023, Karinna Nunez, Tamia Rudnicky, Sharon Killeen, Jessica Hendricks, Catherine R. Duning, Evan Hill

Data

The shoreline inventory files have been generated to support the application of the Maryland Shoreline Stabilization Model (SSM), developed by the Center for Coastal Resources Management (CCRM), Virginia Institute of Marine Science (VIMS), to enhance and streamline regulatory decision making in Maryland. This shoreline inventory includes the features needed as inputs to run the SSM.

The data developed for the Shoreline Inventory is based on a three-tiered shoreline assessment approach. This assessment characterizes conditions by using observations made remotely at the desktop using high resolution imagery. The three-tiered shoreline assessment approach divides the shore zone into three regions:

1) the …

Gis Data: Cecil County, Maryland – Shoreline Inventory Data 2023, Karinna Nunez, Tamia Rudnicky, Sharon Killeen, Jessica Hendricks, Catherine R. Duning, Evan Hill

Data

The shoreline inventory files have been generated to support the application of the Maryland Shoreline Stabilization Model (SSM), developed by the Center for Coastal Resources Management (CCRM), Virginia Institute of Marine Science (VIMS), to enhance and streamline regulatory decision making in Maryland. This shoreline inventory includes the features needed as inputs to run the SSM.

The data developed for the Shoreline Inventory is based on a three-tiered shoreline assessment approach. This assessment characterizes conditions by using observations made remotely at the desktop using high resolution imagery. The three-tiered shoreline assessment approach divides the shore zone into three regions:

1) the …

Gis Data: Caroline County, Maryland – Shoreline Inventory Data 2023, Karinna Nunez, Tamia Rudnicky, Sharon Killeen, Jessica Hendricks, Catherine R. Duning, Evan Hill

Data

The shoreline inventory files have been generated to support the application of the Maryland Shoreline Stabilization Model (SSM), developed by the Center for Coastal Resources Management (CCRM), Virginia Institute of Marine Science (VIMS), to enhance and streamline regulatory decision making in Maryland. This shoreline inventory includes the features needed as inputs to run the SSM.

The data developed for the Shoreline Inventory is based on a three-tiered shoreline assessment approach. This assessment characterizes conditions by using observations made remotely at the desktop using high resolution imagery. The three-tiered shoreline assessment approach divides the shore zone into three regions:

1) the …

Elizabeth River Basin Environmental Justice Indicators, Molly Mitchell, Andrew M. Scheld, Sarah Stafford, Tamia Rudnicky, Joseph Snitzer

Data

This data is a portion of the data included in the Elizabeth River Environmental Justice Tool (https://cmap22.vims.edu/EREJTool/) The Elizabeth River Environmental Justice map viewer contains a variety of layers that will help planners target vulnerable locations and populations within the Elizabeth River Watershed. This data was developed specifically to support the Elizabeth River Project’s decision making in this region.

Road Accessibility From County Seat Under Flooding: Middle Peninsula, Northern Neck, Southside, Molly Mitchell, Jessica Hendricks, Daniel Schatt, Marcia Berman

Data

The impacts of recurrent flooding on roadways present challenging social and economic considerations for all coastal jurisdictions. Maintenance, public and private accessibility, evacuation routes, and emergency services are just a few of the common themes local governments are beginning to address for low-lying roadways currently known to flood. The project implements a protocol developed by CCRM to analyze the level at which road flooding may impact communities and their ability to reach key locations at periodic intervals; through the year 2100 in coastal Virginia. Using a network analysis, road accessibility is evaluated at different levels of flooding (at 0.1 meter …

Bringing Stakeholders Along For The Ride: Towards Supporting Intentional Decisions In Software Evolution: Supplemental Material, Alicia M. Grubb, Paola Spoletini

Data

Supplemental material for the research paper entitled, "Bringing Stakeholders Along for the Ride: Towards Supporting Intentional Decisions in Software Evolution". This paper presents an initial literature review to define intentionality, disambiguate it from its use in literature, and position it in relation to similar concepts. This supplement contains the literature review data file.

Dataset: A Numerical Simulation Of The Ocean, Sea Ice And Ice Shelves In The Amundsen Sea (Antarctica) Over The Period 2006-2022 And Its Associated Code And Input Files, Pierre St-Laurent

Data

A three-dimensional numerical model of the Amundsen Sea (Antarctica) was used to simulate the period Jan.2006-Mar.2022 under consistent atmospheric/oceanic forcings, bathymetry/ice shelf topography, and model equations/parameters. The model is an implementation of the Regional Ocean Modeling System (ROMS, https://www.myroms.org/) with extensions for sea ice (Budgell 2005) and ice shelves (Dinniman et al. 2011). It simulates the ocean hydrography and circulation, sea ice thermodynamics and dynamics, and the basal melt of the ice shelves, with a uniform horizontal mesh of 1.5km and 20 topography-following vertical levels. Forcings include the ERA5 reanalysis (3-hourly), 10 tidal constituents from CATS 2008, and ocean/sea ice …

Dataset: Global Seamless Tidal Simulation Using A 3d Unstructured-Grid Model, Yinglong J. Zhang, Tomas Fernandez-Montblanc, William Pringle, Hao-Cheng Yu, Linlin Cui, Saeed Moghimi

Data

Dataset:

We present a new 3D unstructured-grid global ocean model to study both tidal and non-tidal processes, with a focus on the total water elevation. Unlike existing global ocean models, the new model resolves estuaries and rivers down to ~8m without the need for grid nesting. The model is validated with both satellite and in-situ observations for elevation, temperature and salinity. Tidal elevation solutions have a mean complex RMSE of 4.2 cm for M2 and 5.4 cm for all 5 major constituents in the deep ocean (the RMSEs for the other 4 constituents (S2, N2, K1, O1) are respectively: 2.05cm, …

Dataset: Baywide Distribution Of Benthic Ecological Functions In The Past Decades In The Chesapeake Bay, Philip Ignatoff, Xun Cai, Kara Gadeken

Data

We undertook the collection and analysis of long-term benthos data from the Chesapeake Bay Benthic Monitoring Plan. Multiple ecological function traits related to feeding and disturbance were assigned to each observed benthic species based on a thorough literature review. The spatial distributions of the ecological function groups will be utilized in a 3D hydrodynamic biogeochemistry model simulation. This approach aids in estimating the contributions of benthos to estuarine hypoxia and nutrient dynamics. Furthermore, it fosters a connection between ecologists and modelers, promoting collaborative efforts in understanding and modeling the ecosystem.

Gis Data: Charles County, Maryland – Shoreline Inventory Data 2021, Karinna Nunez, Tamia Rudnicky, Sharon Killeen, Jessica Hendricks, Catherine R. Duning, Evan Hill

Data

The shoreline inventory files have been generated to support the application of the Maryland Shoreline Stabilization Model (SSM), developed by the Center for Coastal Resources Management (CCRM), Virginia Institute of Marine Science (VIMS), to enhance and streamline regulatory decision making in Maryland. This shoreline inventory includes the features needed as inputs to run the SSM.

The data developed for the Shoreline Inventory is based on a three-tiered shoreline assessment approach. This assessment characterizes conditions by using observations made remotely at the desktop using high resolution imagery. The three-tiered shoreline assessment approach divides the shore zone into three regions:

1) the …

Gis Data: Worcester County, Maryland – Shoreline Inventory Data 2021, Karinna Nunez, Tamia Rudnicky, Sharon Killeen, Jessica Hendricks, Catherine R. Duning, Evan Hill

Data

The shoreline inventory files have been generated to support the application of the Maryland Shoreline Stabilization Model (SSM), developed by the Center for Coastal Resources Management (CCRM), Virginia Institute of Marine Science (VIMS), to enhance and streamline regulatory decision making in Maryland. This shoreline inventory includes the features needed as inputs to run the SSM.

The data developed for the Shoreline Inventory is based on a three-tiered shoreline assessment approach. This assessment characterizes conditions by using observations made remotely at the desktop using high resolution imagery. The three-tiered shoreline assessment approach divides the shore zone into three regions: 1) the …

Gis Data: St Mary’S County, Maryland – Shoreline Inventory Data 2021, Karinna Nunez, Tamia Rudnicky, Sharon Killeen, Jessica Hendricks, Catherine R. Duning, Evan Hill

Data

The shoreline inventory files have been generated to support the application of the Maryland Shoreline Stabilization Model (SSM), developed by the Center for Coastal Resources Management (CCRM), Virginia Institute of Marine Science (VIMS), to enhance and streamline regulatory decision making in Maryland. This shoreline inventory includes the features needed as inputs to run the SSM.

The data developed for the Shoreline Inventory is based on a three-tiered shoreline assessment approach. This assessment characterizes conditions by using observations made remotely at the desktop using high resolution imagery. The three-tiered shoreline assessment approach divides the shore zone into three regions:

1) the …

Gis Data: Somerset County, Maryland – Shoreline Inventory Data 2021, Karinna Nunez, Tamia Rudnicky, Sharon Killeen, Jessica Hendricks, Catherine R. Duning, Evan Hill

Data

The shoreline inventory files have been generated to support the application of the Maryland Shoreline Stabilization Model (SSM), developed by the Center for Coastal Resources Management (CCRM), Virginia Institute of Marine Science (VIMS), to enhance and streamline regulatory decision making in Maryland. This shoreline inventory includes the features needed as inputs to run the SSM.

The data developed for the Shoreline Inventory is based on a three-tiered shoreline assessment approach. This assessment characterizes conditions by using observations made remotely at the desktop using high resolution imagery. The three-tiered shoreline assessment approach divides the shore zone into three regions: 1) the …

Gis Data: Kent County, Maryland – Shoreline Inventory Data 2022, Karinna Nunez, Tamia Rudnicky, Sharon Killeen, Jessica Hendricks, Catherine R. Duning, Evan Hill

Data

The shoreline inventory files have been generated to support the application of the Maryland Shoreline Stabilization Model (SSM), developed by the Center for Coastal Resources Management (CCRM), Virginia Institute of Marine Science (VIMS), to enhance and streamline regulatory decision making in Maryland. This shoreline inventory includes the features needed as inputs to run the SSM.

The data developed for the Shoreline Inventory is based on a three-tiered shoreline assessment approach. This assessment characterizes conditions by using observations made remotely at the desktop using high resolution imagery. The three-tiered shoreline assessment approach divides the shore zone into three regions:

1) the …

Gis Data: Baltimore County, Maryland – Shoreline Inventory Data 2022, Karinna Nunez, Tamia Rudnicky, Sharon Killeen, Jessica Hendricks, Catherine R. Duning, Evan Hill

Data

The shoreline inventory files have been generated to support the application of the Maryland Shoreline Stabilization Model (SSM), developed by the Center for Coastal Resources Management (CCRM), Virginia Institute of Marine Science (VIMS), to enhance and streamline regulatory decision making in Maryland. This shoreline inventory includes the features needed as inputs to run the SSM.

The data developed for the Shoreline Inventory is based on a three-tiered shoreline assessment approach. This assessment characterizes conditions by using observations made remotely at the desktop using high resolution imagery. The three-tiered shoreline assessment approach divides the shore zone into three regions:

1) the …

Gis Data: Queen Anne’S County, Maryland – Shoreline Inventory Data 2022, Karinna Nunez, Tamia Rudnicky, Sharon Killeen, Jessica Hendricks, Catherine R. Duning, Evan Hill

Data

The shoreline inventory files have been generated to support the application of the Maryland Shoreline Stabilization Model (SSM), developed by the Center for Coastal Resources Management (CCRM), Virginia Institute of Marine Science (VIMS), to enhance and streamline regulatory decision making in Maryland. This shoreline inventory includes the features needed as inputs to run the SSM.

The data developed for the Shoreline Inventory is based on a three-tiered shoreline assessment approach. This assessment characterizes conditions by using observations made remotely at the desktop using high resolution imagery. The three-tiered shoreline assessment approach divides the shore zone into three regions:

1) the …

Gis Data: Baltimore City, Maryland – Shoreline Inventory Data 2022, Karinna Nunez, Tamia Rudnicky, Sharon Killeen, Jessica Hendricks, Catherine R. Duning, Evan Hill

Data

The shoreline inventory files have been generated to support the application of the Maryland Shoreline Stabilization Model (SSM), developed by the Center for Coastal Resources Management (CCRM), Virginia Institute of Marine Science (VIMS), to enhance and streamline regulatory decision making in Maryland. This shoreline inventory includes the features needed as inputs to run the SSM.

The data developed for the Shoreline Inventory is based on a three-tiered shoreline assessment approach. This assessment characterizes conditions by using observations made remotely at the desktop using high resolution imagery. The three-tiered shoreline assessment approach divides the shore zone into three regions:

1) the …

Gis Data: Wicomico County, Maryland – Shoreline Inventory Data 2022, Karinna Nunez, Tamia Rudnicky, Sharon Killeen, Jessica Hendricks, Catherine R. Duning, Evan Hill

Data

The shoreline inventory files have been generated to support the application of the Maryland Shoreline Stabilization Model (SSM), developed by the Center for Coastal Resources Management (CCRM), Virginia Institute of Marine Science (VIMS), to enhance and streamline regulatory decision making in Maryland. This shoreline inventory includes the features needed as inputs to run the SSM.

The data developed for the Shoreline Inventory is based on a three-tiered shoreline assessment approach. This assessment characterizes conditions by using observations made remotely at the desktop using high resolution imagery. The three-tiered shoreline assessment approach divides the shore zone into three regions:

1) the …

Sediment Characteristics Of The Chesapeake Bay And Its Tributaries, Virginia Province: Data Files, Gary F. Anderson

Data

During the 1990’s, Dr. Maynard Nichols and colleagues at the Virginia Institute of Marine Science compiled digital databases of sediment observations in the Chesapeake Bay and other coastal bays and rivers. These projects were performed under several cooperative agreements with NOAA, EPA and USGS. This particular dataset covers the Chesapeake Bay for bulk properties and contaminants. Additional references are provided below. The original files and filenames are provided without edit. See the readme.txt file for overall explanation of the datasets and individual .DOC files for the data dictionary and further data processing information for each waterbody.

Marsh Vulnerability Index And Index Applied To Coastal Shorelines, Molly Mitchell, Donna Marie Bilkovic, Julie Herman, Jessica Hendricks, Evan Hill

Data

The Marsh Vulnerability Index (MVI) is a spatially-resolved assessment of Virginia tidal marsh vulnerabilities from important climate-change drivers – erosion vulnerability, inundation from sea level rise, and salinity intrusion from sea level rise – that can support management decisions. Effects were evaluated for two time-steps (near and longer-term planning horizons): 2050 and 2100.

The Marsh Vulnerability Index Applied to Coastal Shorelines layer extends the MVI evaluation for use in evaluating living shoreline (i.e., created or enhanced shoreline marshes) vulnerability and applies it to tidal shorelines in coastal Virginia. Outputs from this analysis were intended to evaluate the vulnerability of areas …

Sediment Survey: Yr060823, Station 3917, Clay Bank, York River Virginia, Grace M. Massey, Patrick J. Dickhudt, Carl T. Friedrichs

Data

This dataset consists of sediment properties including grain size distribution, percent moisture, percent organic matter, sediment bed erodibility, as well as (in most cases) x-ray images of the sediment structure. Most samples were taken in support of an Acoustic Doppler Velocimeter (ADV) tripod deployed in nearby location.

Sediment Survey: Yr070111, Station 3926, Clay Bank, York River Virginia, Grace M. Massey, Patrick J. Dickhudt, Carl T. Friedrichs

Data

This dataset consists of sediment properties including grain size distribution, percent moisture, percent organic matter, sediment bed erodibility, as well as (in most cases) x-ray images of the sediment structure. Most samples were taken in support of an Acoustic Doppler Velocimeter (ADV) tripod deployed in nearby location.

Sediment Survey: Yr120712, Station S5022-S5025, Clay Bank, York River Virginia, Grace M. Massey, Kelsey A. Fall, Carl T. Friedrichs

Data

This dataset consists of sediment properties including grain size distribution, percent moisture, percent organic matter, sediment bed erodibility, as well as (in most cases) x-ray images of the sediment structure. Most samples were taken in support of an Acoustic Doppler Velocimeter (ADV) tripod deployed in nearby location.

Sediment Survey: Yr061116, Station 3922, Clay Bank, York River Virginia, Grace M. Massey, Patrick J. Dickhudt, Carl T. Friedrichs

Data

This dataset consists of sediment properties including grain size distribution, percent moisture, percent organic matter, sediment bed erodibility, as well as (in most cases) x-ray images of the sediment structure. Most samples were taken in support of an Acoustic Doppler Velocimeter (ADV) tripod deployed in nearby location.

Sediment Survey: Yr120425, Station S4993-S1995, Clay Bank, York River Virginia, Grace M. Massey, Kelsey A. Fall, Carl T. Friedrichs

Data

This dataset consists of sediment properties including grain size distribution, percent moisture, percent organic matter, sediment bed erodibility, as well as (in most cases) x-ray images of the sediment structure. Most samples were taken in support of an Acoustic Doppler Velocimeter (ADV) tripod deployed in nearby location.

Sediment Survey: Yr061120, Station 3924, Gloucester Point, York River Virginia, Grace M. Massey, Patrick J. Dickhudt, Carl T. Friedrichs

Data

This dataset consists of sediment properties including grain size distribution, percent moisture, percent organic matter, sediment bed erodibility, as well as (in most cases) x-ray images of the sediment structure. Most samples were taken in support of an Acoustic Doppler Velocimeter (ADV) tripod deployed in nearby location.

Sediment Survey: Yr070418, Station 3931, Gloucester Point, York River Virginia, Grace M. Massey, Patrick J. Dickhudt, Carl T. Friedrichs

Data

This dataset consists of sediment properties including grain size distribution, percent moisture, percent organic matter, sediment bed erodibility, as well as (in most cases) x-ray images of the sediment structure. Most samples were taken in support of an Acoustic Doppler Velocimeter (ADV) tripod deployed in nearby location.