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Resources

Resources

A repository of data, publications, tools, and other products from project teams, Science Collaborative program, and partners.

Displaying 81 - 90 of 175
Journal Article |

This article, submitted for publication to Earth Surface Processes and Landforms in 2020, describes findings from the Dams and Sediment in the Hudson (DaSH) project related to tidal wetland growth in the Hudson River estuary as a result of human activities. It presents sediment accumulation rates in marshes along the Hudson and reveals the rapid growth of marshes associated with anthropogenic structures.

Factsheet |

This factsheet summarizes findings from the Dams and Sediment in the Hudson (DaSH) collaborative research project.

Factsheet |

This factsheet summarizes findings from the Dams and Sediment in the Hudson (DaSH) collaborative research project related to sediment trapped behind dams and sediment supply in the Hudson River estuary

Factsheet |

This factsheet summarizes findings from the Dams and Sediment in the Hudson collaborative research project related to tidal wetlands in the Hudson River estuary.

Journal Article |

This article uses a hydrodynamic model of the Coos estuary in southwestern Orgeon to examine seasonal variability of salinity dynamics and estuarine exchange flow.

Journal Article |

This article discusses changes to the Coos estuary over the past 150 years, and their present and future impacts.

Tool |

This stakeholder engagement plan outlines an approach to strengthen stakeholder networks and advance blue carbon conversations in the Kenai Lowlands, Alaska.

Tool |

This focus group template is part of a training and facilitation toolkit, developed to help coastal leaders use the Alaska Fisheries Resilience Index to promote coastal resilience.

Journal Article |

This paper, published in Remote Sensing in 2020, describes a new satellite-based habitat mapping technique that was tested at Rookery Bay NERR in southwest Florida.

Tool |

A 2018 catalyst project developed tools for working with SET data including a series of computer codes - R scripts - for processing, quality checking, analyzing and visualizing these complex datasets. The statistical codes re available through GitHub and are explained in a Guide to the SETr Workflow.