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.
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Resources
A repository of data, publications, tools, and other products from project teams, Science Collaborative program, and partners.
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This factsheet summarizes findings from the Dams and Sediment in the Hudson (DaSH) collaborative research project.
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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
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This factsheet summarizes findings from the Dams and Sediment in the Hudson collaborative research project related to tidal wetlands in the Hudson River estuary.
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This 2018 catalyst project streamlined and enhanced mapping and decision support tools to help New Jersey coastal communities prepare for sea level rise and extreme storms.
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This needs assessment of conservation policy stakeholders in the Pacific Northwest identified data needs and barriers for potential blue carbon project partners.
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These multimedia outreach and communication tools were developed by the Kachemak Bay National Estuarine Research Reserve and its partners in support of a project to advance peatland blue carbon projects in the Kenai Lowlands, Alaska.
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This stakeholder engagement plan outlines an approach to strengthen stakeholder networks and advance blue carbon conversations in the Kenai Lowlands, Alaska.
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This national synthesis report analyzes SET data from 15 National Estuarine Research Reserves across the continental United States, summarizing wetland water level trends over a 19-year period.
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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.