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.

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

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

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

This project overview describes a 2018 Catalyst project that created an Olympia oyster restoration network to enhance the success of West Coast restoration efforts.

This project overview describes a 2017 science transfer project that developed a risk communication training for reserves to build risk communication capacity in four coastal communities.

This project overview describes a 2018 catalyst project led by the San Francisco Bay Reserve that brought together key stakeholders and decision makers to advance adaptation planning for a low-lying coastal road at China Camp State Park.

This report provides foundational science and social context to inform the development of adaptation options for a low-lying road in China Camp State Park, along San Francisco Bay, CA.

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.

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.