This dam sediment estimation tool, developed through the Dams and Sediment in the Hudson (DaSH) project, supports dam removal planning for the Lower Hudson River valley.

This open-access article, published Geophysical Research Letters in 2020, uses turbidity observations to characterize estuary response following extreme discharge such as from storm-related flooding, which can be a proxy for sediment release from dam removals.

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 needs assessment of conservation policy stakeholders in the Pacific Northwest identified data needs and barriers for potential blue carbon project partners.

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.