This project overview describes a 2018 Catalyst project that created the web-based toolkit Resilience Metrics to share lessons learned on successful climate adaptation planning within the National Estuarine Research Reserve System.

This project overview describes a 2017 Science Transfer project in which the southeastern National Estuarine Research Reserves created a region-wide, student-driven program for teachers to further understanding of estuary restoration.

These sediment and hydrodynamic data were collected as part of the 2016-2020 collaborative research project Improved Understanding of Sediment Dynamics for the Coos Estuary that produced a new bathymetric dataset for Coos Bay and a hydrodynamic model characterizing sediment distribution and circulation in the estuary.

This dataset compiles salt marsh monitoring from four New England NERRs from 2010 to 2018, as part of a catalyst project to sythesize and identify regional trends in salt marsh data in the reserve system.

These datasets contain sediment core samples from dam impoundments on tributaries to the Hudson River and tidal wetland complexes in the Hudson River estuary, collected as part of the 2016-2020 collaborative research project Dams and Sediment on the Hudson (DaSH).

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 multi-year collaborative research project that analyzed a suite of living shoreline possibilities for South Carolina to help the state develop a living shoreline policy.

This project overview describes a 2017 Collaborative Research project where Kachemak Bay Reserve staff and local partners are developing a conceptual model and geospatial layer that can be used to predict specific locations where groundwater discharge and recharge occur.