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Full-Text Articles in Physical Sciences and Mathematics

Simulating Storm Surge And Compound Flooding Events With A Creek-To-Ocean Model: Importance Of Baroclinic Effects : Model Files, Fei Ye, Yinglong J. Zhang, Haocheng Yu, Weiling Sun, Saeed Moghimi, Edward Myers, Karinna Nunez, Ruoyin Zhang, Harry V. Wang, Aron Roland, Kevin Martins, Xavier Bertin, Jiabi Du, Zhou Liu Jan 2019

Simulating Storm Surge And Compound Flooding Events With A Creek-To-Ocean Model: Importance Of Baroclinic Effects : Model Files, Fei Ye, Yinglong J. Zhang, Haocheng Yu, Weiling Sun, Saeed Moghimi, Edward Myers, Karinna Nunez, Ruoyin Zhang, Harry V. Wang, Aron Roland, Kevin Martins, Xavier Bertin, Jiabi Du, Zhou Liu

Data

The supplemental material contains the input files for setting up a 3D baroclinic model based on the Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM), supplementing the description of model setup in Ye et al. (2019; associated publication).

The SCHISM version used for the simulation was r5082 in the SCHISM svn repository. A compressed file (setup.tar.gz) is provided, which can be extracted with common zip/unzip software on Unix/Windows/Mac (such as gzip, winzip, 7-zip, etc.). Since the dataset is intended for conducting a SCHISM simulation, readers/users should familiarize themselves with the SCHISM model system first.

The ...


Catch The King Tide 2018: All King Tide Data, Jon Derek Loftis Dec 2018

Catch The King Tide 2018: All King Tide Data, Jon Derek Loftis

Data

"Catch the King" is a citizen-science GPS data collection effort centered in Hampton Roads, VA, that seeks to interactively map the King Tide's maximum inundation extents. The goal is to validate and improving predictive model accuracy for future forecasting of increasingly pervasive "nuisance" flooding.


A Model Archive For Sediment Transport Model Including Short-Lived Radioisotopes: Model Description And Idealized Test Cases, Justin J. Birchler, Courtney K. Harris, Tara A. Kniskern Oct 2018

A Model Archive For Sediment Transport Model Including Short-Lived Radioisotopes: Model Description And Idealized Test Cases, Justin J. Birchler, Courtney K. Harris, Tara A. Kniskern

Data

This dataset includes model input, code, and output used in the publication Birchler et al. (2018, Journal of Marine Science and Engineering), which used a coupled hydrodynamic-sediment transport-biogeochemical model to investigate the roles of resuspension, deposition, on biodiffusion on the behavior of short-lived radioisotopes in an idealized one-dimensional model setting. Model development for this project focused on incorporating radioisotope tracers into the sediment transport module in the Regional Ocean Modeling System (ROMS). As described in Birchler et al. (2018, Journal of Marine Science and Engineering), the model can account for supply and sorption of radioisotope tracers in the water column ...


A Climatological Dataset Of Nutrient, Chlorophyll, And Particulate Matter Distributions On The Ross Sea Continental Shelf Derived From Cruise-Based Measurements Spanning 1967 To 2016, Walker O. Smith Jr., Daniel E. Kaufman Oct 2018

A Climatological Dataset Of Nutrient, Chlorophyll, And Particulate Matter Distributions On The Ross Sea Continental Shelf Derived From Cruise-Based Measurements Spanning 1967 To 2016, Walker O. Smith Jr., Daniel E. Kaufman

Data

This dataset includes data used in the publication Smith and Kaufman (2018), Progress in Oceanography, which examines the temporal and spatial distributions of nutrients and particulate matter in the Ross Sea continental Shelf using cruise-based observations, and compares the resulting annual productivity estimates with previously reported satellite-based estimates. Specifically, these data represent distributions of nutrients, chlorophyll, particulate organic carbon, particulate organic nitrogen, and biogenic silica that were compiled from 42 cruises (from 1967 - 2016) to the Ross Sea continental shelf to generate a comprehensive climatological dataset for November, December, January, and February. This climatology provides a novel look at broad ...


Section: 01 Line Frame: 01, 18 October 2017: Aerial Imagery Acquired To Monitor The Distribution And Abundance Of Submerged Aquatic Vegetation In Chesapeake Bay And Coastal Bays, Robert J. Orth, David J. Wilcox, Jennifer R. Whiting, Anna K. Kenne, Erica R. Smith Jun 2018

Section: 01 Line Frame: 01, 18 October 2017: Aerial Imagery Acquired To Monitor The Distribution And Abundance Of Submerged Aquatic Vegetation In Chesapeake Bay And Coastal Bays, Robert J. Orth, David J. Wilcox, Jennifer R. Whiting, Anna K. Kenne, Erica R. Smith

Data

Multispectral aerial imagery acquired in 2017 to monitor the distribution and abundance of submerged aquatic vegetation in Chesapeake Bay and coastal bays


Section: 01 Line Frame: 06, 27 August 2017: Aerial Imagery Acquired To Monitor The Distribution And Abundance Of Submerged Aquatic Vegetation In Chesapeake Bay And Coastal Bays, Robert J. Orth, David J. Wilcox, Jennifer R. Whiting, Anna K. Kenne, Erica R. Smith Jun 2018

Section: 01 Line Frame: 06, 27 August 2017: Aerial Imagery Acquired To Monitor The Distribution And Abundance Of Submerged Aquatic Vegetation In Chesapeake Bay And Coastal Bays, Robert J. Orth, David J. Wilcox, Jennifer R. Whiting, Anna K. Kenne, Erica R. Smith

Data

Multispectral aerial imagery acquired in 2017 to monitor the distribution and abundance of submerged aquatic vegetation in Chesapeake Bay and coastal bays


A Model Archive For A Numerical Model Of Geochronological Tracers For Sediment Deposition And Reworking Applied To The Mississippi Subaqueous Delta, Justin J. Birchler, Courtney Harris, Tara A. Kniskern Jan 2018

A Model Archive For A Numerical Model Of Geochronological Tracers For Sediment Deposition And Reworking Applied To The Mississippi Subaqueous Delta, Justin J. Birchler, Courtney Harris, Tara A. Kniskern

Data

This dataset includes model input, code, and output used in the publication Birchler et al. (2018, Journal of Coastal Research), which used a coupled hydrodynamic-sediment transport-geochemical model to investigate the roles of resuspension, deposition, on biodiffusion on the behavior of short-lived radioisotopes on the Mississippi sub-aqueous delta, USA. Model development for this project focused on incorporating radioisotope tracers into the sediment transport module in the Regional Ocean Modeling System (ROMS). As described in Birchler et al. (2018, Journal of Coastal Research), the model can account for supply and sorption of radioisotope tracers in the water column; biodiffusion of radioisotope tracers ...


Gis Data: New Kent County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner Jan 2018

Gis Data: New Kent County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner

Data

The Shoreline Management Model is a GIS spatial model that determines appropriate shoreline best management practices using available spatial data and decision tree logic. Available shoreline conditions used in the model include the presence or absence of tidal marshes, beaches, and forested riparian buffers, bank vegetation cover, bank height, wave exposure (fetch), nearshore water depth, and proximity of coastal development to the shoreline. The model output for shoreline best management practices is displayed in the locality Comprehensive Map Viewer. One GIS shapefile is developed that describes two arcs or lines representing practices in the upland area and practices at the ...


Gis Data: Richmond County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Kory Angstadt, Carl Hershner Jan 2018

Gis Data: Richmond County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Kory Angstadt, Carl Hershner

Data

The Shoreline Management Model is a GIS spatial model that determines appropriate shoreline best management practices using available spatial data and decision tree logic. Available shoreline conditions used in the model include the presence or absence of tidal marshes, beaches, and forested riparian buffers, bank vegetation cover, bank height, wave exposure (fetch), nearshore water depth, and proximity of coastal development to the shoreline. The model output for shoreline best management practices is displayed in the locality Comprehensive Map Viewer. One GIS shapefile is developed that describes two arcs or lines representing practices in the upland area and practices at the ...


Section: 01 Line Frame: 01 Aug27-17: Aerial Imagery Acquired To Monitor The Distribution And Abundance Of Submerged Aquatic Vegetation In Chesapeake Bay And Coastal Bays, R. J. Orth, David J. Wilcox, Jennifer R. Whiting, Anna K. Kenne, Erica R. Smith Jan 2018

Section: 01 Line Frame: 01 Aug27-17: Aerial Imagery Acquired To Monitor The Distribution And Abundance Of Submerged Aquatic Vegetation In Chesapeake Bay And Coastal Bays, R. J. Orth, David J. Wilcox, Jennifer R. Whiting, Anna K. Kenne, Erica R. Smith

Data

Multispectral aerial imagery acquired in 2017 to monitor the distribution and abundance of submerged aquatic vegetation in Chesapeake Bay and coastal bays.


Gis Data: Caroline County, Virginia Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon A. Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl H. Hershner Jan 2018

Gis Data: Caroline County, Virginia Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon A. Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl H. Hershner

Data

No abstract provided.


Gis Data: Caroline County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner Jan 2018

Gis Data: Caroline County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner

Data

The Shoreline Management Model is a GIS spatial model that determines appropriate shoreline best management practices using available spatial data and decision tree logic. Available shoreline conditions used in the model include the presence or absence of tidal marshes, beaches, and forested riparian buffers, bank vegetation cover, bank height, wave exposure (fetch), nearshore water depth, and proximity of coastal development to the shoreline. The model output for shoreline best management practices is displayed in the locality Comprehensive Map Viewer. One GIS shapefile is developed that describes two arcs or lines representing practices in the upland area and practices at the ...


Gis Data: Caroline County, Virginia Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner Jan 2018

Gis Data: Caroline County, Virginia Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner

Data

No abstract provided.


Gis Data: Richmond County, Virginia Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Kory Angstadt, Carl Hershner Jan 2018

Gis Data: Richmond County, Virginia Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Kory Angstadt, Carl Hershner

Data

No abstract provided.


Gis Data: Richmond County, Virginia Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Kory Angstadt, Carl Hershner Jan 2018

Gis Data: Richmond County, Virginia Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Kory Angstadt, Carl Hershner

Data

No abstract provided.


Gis Data: Essex County, Virginia Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, David Stanhope, Carl Hershner Jan 2018

Gis Data: Essex County, Virginia Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, David Stanhope, Carl Hershner

Data

No abstract provided.


Gis Data: Essex County, Virginia Shoreline Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, David Stanhope, Carl Hershner Jan 2018

Gis Data: Essex County, Virginia Shoreline Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, David Stanhope, Carl Hershner

Data

No abstract provided.


Gis Data: Essex County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, David Stanhope, Carl Hershner Jan 2018

Gis Data: Essex County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, David Stanhope, Carl Hershner

Data

The Shoreline Management Model is a GIS spatial model that determines appropriate shoreline best management practices using available spatial data and decision tree logic. Available shoreline conditions used in the model include the presence or absence of tidal marshes, beaches, and forested riparian buffers, bank vegetation cover, bank height, wave exposure (fetch), nearshore water depth, and proximity of coastal development to the shoreline. The model output for shoreline best management practices is displayed in the locality Comprehensive Map Viewer. One GIS shapefile is developed that describes two arcs or lines representing practices in the upland area and practices at the ...


Gis Data: New Kent County, Virginia Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner Jan 2018

Gis Data: New Kent County, Virginia Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner

Data

The 2018 Inventory for New Kent County was generated using on-screen, digitizing techniques in ArcGIS® -ArcMap v10.4.1while viewing conditions observed in Bing high resolution oblique imagery, Google Earth, and2017imagery from the Virginia Base Mapping Program (VBMP).Four GIS shapefiles are developed. The first describes land use and bank conditions (New_Kent_lubc_2018). The second portrays the presence of beaches (New_Kent_beaches_2018). The third reports shoreline structures that are described as arcs or lines(e.g. riprap)(New_Kent_sstru_2018). The final shapefile includes all structures that are represented as points(e.g. piers)(New_Kent_astru_2018).The metadata file accompanies the shapefiles and defines attribute ...


Gis Data: New Kent County, Virginia Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner Jan 2018

Gis Data: New Kent County, Virginia Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner

Data

No abstract provided.


Gis Data:: Arlington County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie Bradshaw, Carl Herschner Jan 2018

Gis Data:: Arlington County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie Bradshaw, Carl Herschner

Data

No abstract provided.


Gis Data: Arlington County, Virginia Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie Bradshaw, Carl Herschner Jan 2018

Gis Data: Arlington County, Virginia Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie Bradshaw, Carl Herschner

Data

No abstract provided.


Matlab Processing Scripts To Accompany Spatially Resolved Measurements Of Crosslinking In Uv-Curable Coatings Using Single-Sided Nmr, Madeline Brass, Frances Jude Morin, Tyler Meldrum Dec 2017

Matlab Processing Scripts To Accompany Spatially Resolved Measurements Of Crosslinking In Uv-Curable Coatings Using Single-Sided Nmr, Madeline Brass, Frances Jude Morin, Tyler Meldrum

Data

These Matlab scripts are used to import CPMG data collected using a Kea spectrometer (through the program Prospa), and to process each echo using the Fourier transformation. This provides spatially resolved NMR relaxation data that can be fitted or subjected to inverse Laplace transformation (not provided) to characterize relaxation at different positions within a sample.


Gis Data: 2016 Chesapeake Bay Sav Coverage, Virginia Institute Of Marine Science, Sav Data Administrator Dec 2017

Gis Data: 2016 Chesapeake Bay Sav Coverage, Virginia Institute Of Marine Science, Sav Data Administrator

Data

Abstract: The 2015 Chesapeake Bay SAV Coverage was mapped from digital multispectral imagery with a 25cm GSD to assess water quality in the Bay. Each area of SAV was interpreted from the rectified imagry and classified into one of four density classes by the percentage of cover. The SAV beds were entered into an SDE GIS fetaure class using the quality control procedures documented below. The dataset contains all SAV areas that were identified from the areas flown. Some areas that are presumed to contain no SAV were not flown. Some small beds, particularly along narrow tributaries may not have ...


Understanding Patterns In Chesapeake Bay Water Clarity: The Importance Of Measurement, Location, And Physical Versus Biological Controls, Carl Friedrichs Dec 2017

Understanding Patterns In Chesapeake Bay Water Clarity: The Importance Of Measurement, Location, And Physical Versus Biological Controls, Carl Friedrichs

Presentations

No abstract provided.


Catch The King Tide 2017 Data: Hampton, Virginia, Jon Derek Loftis Dec 2017

Catch The King Tide 2017 Data: Hampton, Virginia, Jon Derek Loftis

Data

"Catch the King" was a citizen science GPS data collection effort centered in Hampton Roads, VA, that sought to map the King Tide's maximum inundation extents with the goal of validating and improving predictive models for future forecasting of increasingly pervasive "nuisance" flooding. GPS data points were collected by volunteers to effectively breadcrumb/trace the high water line by pressing the 'Save Data' button in the Sea Level Rise App every few steps along the water's edge during the high tide on the morning of Nov. 5th, 2017. Response from the event's dedicated volunteers, fueled by the ...


Catch The King Tide 2017 Data: Portsmouth, Virginia, Jon Derek Loftis Dec 2017

Catch The King Tide 2017 Data: Portsmouth, Virginia, Jon Derek Loftis

Data

"Catch the King" was a citizen science GPS data collection effort centered in Hampton Roads, VA, that sought to map the King Tide's maximum inundation extents with the goal of validating and improving predictive models for future forecasting of increasingly pervasive "nuisance" flooding. GPS data points were collected by volunteers to effectively breadcrumb/trace the high water line by pressing the 'Save Data' button in the Sea Level Rise App every few steps along the water's edge during the high tide on the morning of Nov. 5th, 2017. Response from the event's dedicated volunteers, fueled by the ...


Catch The King Tide 2017 Data: Suffolk, Virginia, Jon Derek Loftis Dec 2017

Catch The King Tide 2017 Data: Suffolk, Virginia, Jon Derek Loftis

Data

"Catch the King" was a citizen science GPS data collection effort centered in Hampton Roads, VA, that sought to map the King Tide's maximum inundation extents with the goal of validating and improving predictive models for future forecasting of increasingly pervasive "nuisance" flooding. GPS data points were collected by volunteers to effectively breadcrumb/trace the high water line by pressing the 'Save Data' button in the Sea Level Rise App every few steps along the water's edge during the high tide on the morning of Nov. 5th, 2017. Response from the event's dedicated volunteers, fueled by the ...


Catch The King Tide 2017: All King Tide Data, Jon Derek Loftis Dec 2017

Catch The King Tide 2017: All King Tide Data, Jon Derek Loftis

Data

"Catch the King" was a citizen science GPS data collection effort centered in Hampton Roads, VA, that sought to map the King Tide's maximum inundation extents with the goal of validating and improving predictive models for future forecasting of increasingly pervasive "nuisance" flooding. GPS data points were collected by volunteers to effectively breadcrumb/trace the high water line by pressing the 'Save Data' button in the Sea Level Rise App every few steps along the water's edge during the high tide on the morning of Nov. 5th, 2017. Response from the event's dedicated volunteers, fueled by the ...


Gis Data: Chesterfield County And Cities Of Colonial Heights, Petersburg, And Richmond Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie Bradshaw, Karen Duhring, Kallie Brown, Jessica Hendricks, David Weiss, Carl Hershner Dec 2017

Gis Data: Chesterfield County And Cities Of Colonial Heights, Petersburg, And Richmond Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie Bradshaw, Karen Duhring, Kallie Brown, Jessica Hendricks, David Weiss, Carl Hershner

Data

The 2017 Inventory for Chesterfield County and the Cities of Colonial Heights, Petersburg, and Richmond was generated using on-screen, digitizing techniques in ArcGIS® -ArcMap v10.4.1while viewing conditions observed in Bing high resolution oblique imagery, Google Earth, and2013imagery from the Virginia Base Mapping Program (VBMP).Four GIS shapefiles are developed. The first describes land use and bank conditions (Chesterfield_lubc_2017). The second portrays the presence of beaches (Chesterfield_beaches_2017). The third reports shoreline structures that are described as arcs or lines(e.g. riprap)(Chesterfield_sstru_2017). The final shapefile includes all structures that are represented as points (e.g. piers)(Chesterfield_astru_2017).The ...