Department of Environmental Sciences

Featured Dataverses

In order to use this feature you must have at least one published or linked dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

721 to 730 of 766 Results

FLOCK.SWCF.monthly.nc Jun 7, 2019 - Replication Data for: Investigating the influence of cloud radiative effects on the extratropical storm tracks Network Common Data Form - 63.3 MB - MD5: e9b5915351642ef4167cb8b403e32aae
FLOCK.TS.monthly.nc Jun 7, 2019 - Replication Data for: Investigating the influence of cloud radiative effects on the extratropical storm tracks Network Common Data Form - 63.3 MB - MD5: 7f65eda3d1a08eeb2d0858f74d6979be
FLOCK.U200.climo.nc Jun 7, 2019 - Replication Data for: Investigating the influence of cloud radiative effects on the extratropical storm tracks Network Common Data Form - 249.4 KB - MD5: 1bb5a1d95d1b8f4d0a9f52b01fc2279b
FLOCK.U850.climo.nc Jun 7, 2019 - Replication Data for: Investigating the influence of cloud radiative effects on the extratropical storm tracks Network Common Data Form - 249.4 KB - MD5: b60cbe666a0d6320a16d2549c7abc13b
ReadMe_GriseGRL2019_20190607.txt Jun 7, 2019 - Replication Data for: Investigating the influence of cloud radiative effects on the extratropical storm tracks Plain Text - 4.5 KB - MD5: 617cfd4f5ab7d16919323e20acbe6ebc README file for data set
Climate change to offset improvements in watershed acid-base status provided by Clean Air Act and Amendments: A model application in Shenandoah National Park, Virginia Sep 5, 2018 Scanlon, Todd; Robison, Andrew, 2018, "Climate change to offset improvements in watershed acid-base status provided by Clean Air Act and Amendments: A model application in Shenandoah National Park, Virginia", https://doi.org/10.18130/V3/R9DDOR, University of Virginia Dataverse, V1 This dataset contains parameter files, input files, and output files for application of the PnET-BGC model to the White Oak Run (WOR1) watershed in Shenandoah National Park, Virginia. This model application is described in the manuscript : Robison, A. L., & Scanlon, T. M. (2018). Climate change to offset improvements in watershed acid-base status p...
PnET Files.zip Sep 5, 2018 - Climate change to offset improvements in watershed acid-base status provided by Clean Air Act and Amendments: A model application in Shenandoah National Park, Virginia ZIP Archive - 176.5 MB - MD5: c2fd3487f46342d3a1d81523cb2c69fb Zipped file retains directory structure of PnET-BGC model inputs and results
Data from: Multi-temporal analysis reveals that predictors of mountain pine beetle infestation change during outbreak cycles Aug 7, 2017 Walter, Jonathan, 2017, "Data from: Multi-temporal analysis reveals that predictors of mountain pine beetle infestation change during outbreak cycles", https://doi.org/10.18130/V3/HDBXVY, University of Virginia Dataverse, V1, UNF:6:CwpNPAsvEUXM4vXAcZW9ug== [fileUNF] Over the past two decades, severe mounta in pine beetle (MPB) outbreaks have affected several million hectares of forest in western North America. The extensive ecological and economic damage caused by widespread insect infestations make understanding the development and spread of MPB outbreaks critical. This study uses a time series of Landsat5 TM...
MPB_map_2003.tfw Aug 7, 2017 - Data from: Multi-temporal analysis reveals that predictors of mountain pine beetle infestation change during outbreak cycles Unknown - 222 B - MD5: d027e73527f2c47ac510692871ed5a6f
MPB_map_2003.tif Aug 7, 2017 - Data from: Multi-temporal analysis reveals that predictors of mountain pine beetle infestation change during outbreak cycles TIFF Image - 16.9 MB - MD5: 8e7ada55c91707dc736c95c0b6ec5b0d

FLOCK.SWCF.monthly.nc

Jun 7, 2019 - Replication Data for: Investigating the influence of cloud radiative effects on the extratropical storm tracks

Network Common Data Form - 63.3 MB -

FLOCK.TS.monthly.nc

Jun 7, 2019 - Replication Data for: Investigating the influence of cloud radiative effects on the extratropical storm tracks

Network Common Data Form - 63.3 MB -

FLOCK.U200.climo.nc

Jun 7, 2019 - Replication Data for: Investigating the influence of cloud radiative effects on the extratropical storm tracks

Network Common Data Form - 249.4 KB -

FLOCK.U850.climo.nc

Jun 7, 2019 - Replication Data for: Investigating the influence of cloud radiative effects on the extratropical storm tracks

Network Common Data Form - 249.4 KB -

ReadMe_GriseGRL2019_20190607.txt

Jun 7, 2019 - Replication Data for: Investigating the influence of cloud radiative effects on the extratropical storm tracks

Plain Text - 4.5 KB -

README file for data set

Climate change to offset improvements in watershed acid-base status provided by Clean Air Act and Amendments: A model application in Shenandoah National Park, Virginia

Sep 5, 2018

Scanlon, Todd; Robison, Andrew, 2018, "Climate change to offset improvements in watershed acid-base status provided by Clean Air Act and Amendments: A model application in Shenandoah National Park, Virginia", https://doi.org/10.18130/V3/R9DDOR, University of Virginia Dataverse, V1

This dataset contains parameter files, input files, and output files for application of the PnET-BGC model to the White Oak Run (WOR1) watershed in Shenandoah National Park, Virginia. This model application is described in the manuscript : Robison, A. L., & Scanlon, T. M. (2018). Climate change to offset improvements in watershed acid-base status p...

PnET Files.zip

Sep 5, 2018 - Climate change to offset improvements in watershed acid-base status provided by Clean Air Act and Amendments: A model application in Shenandoah National Park, Virginia

ZIP Archive - 176.5 MB -

Zipped file retains directory structure of PnET-BGC model inputs and results

Data from: Multi-temporal analysis reveals that predictors of mountain pine beetle infestation change during outbreak cycles

Aug 7, 2017

Walter, Jonathan, 2017, "Data from: Multi-temporal analysis reveals that predictors of mountain pine beetle infestation change during outbreak cycles", https://doi.org/10.18130/V3/HDBXVY, University of Virginia Dataverse, V1, UNF:6:CwpNPAsvEUXM4vXAcZW9ug== [fileUNF]

Over the past two decades, severe mounta in pine beetle (MPB) outbreaks have affected several million hectares of forest in western North America. The extensive ecological and economic damage caused by widespread insect infestations make understanding the development and spread of MPB outbreaks critical. This study uses a time series of Landsat5 TM...

MPB_map_2003.tfw

Aug 7, 2017 - Data from: Multi-temporal analysis reveals that predictors of mountain pine beetle infestation change during outbreak cycles

Unknown - 222 B -

MPB_map_2003.tif

Aug 7, 2017 - Data from: Multi-temporal analysis reveals that predictors of mountain pine beetle infestation change during outbreak cycles

TIFF Image - 16.9 MB -

Add Data

Share Dataverse

Link Dataverse

Reset Modifications