This 2022 paper which appeared in Nature discusses a modeling approach to examine the marsh ’s buffering capacity in a changing climate (from 2020 to 2100), considering a potential marsh restoration plan (from 2020 to 2025) and potential marsh loss due to sea-level rise.

This 2021 paper from the University of South Florida discusses how machine learning was used to map aquifers throughout the Kenai Lowlands to locate groundwater discharge, providing a framework to extend this method of modeling groundwater to other reserves.

This 2021 article which appeared in Geophysical Research Letters describes a study that took a novel approach to characterize soil organic carbon accumulation supporting marsh elevation maintenance as part of a 2017-2020 collaborative research project.

This open access article describes an assessment of the storm buffering services provided by Piermont Marsh, New York.

This 2020 article which appeared in Estuaries and Coasts describes a study that evaluated rates of gross oxygen production over different time scales in a shallow temperate salt marsh pond as part of a 2017-2020 collaborative research project.

This 2020 article which appeared in Geomorphology describes a model to predict marsh pond dynamics in New England salt marshes that was developed as part of a 2017-2020 collaborative research project .

This 2020 article which appeared in the Journal of Geophysical Research: Biogeosciences describes a study that examined pond development and properties in salt marshes in order to better characterize them under different management and sea level rise scenarios as part of a 2017-2020 collaborative research project.

This 2019 article, which appeared in Nature Geoscience, suggests the importance of the integration of decomposition mechanisms into blue carbon models for predicting soil organic carbon stores. These findings were generated as part of a 2017-2020 collaborative research project.

This decision support tool, developed as part of a 2017 collaborative research project, allows users to select different combinations of tidal range, suspended sediment, ditch density, and sea-level rise variables and visualize predicted outcomes over different time frames.

This article, published in Scientific Reports in 2021, describes work done as part of a 2016-2020 collaborative research project conducted at Hudson River Reserve in New York. The article describes a regression model that can be used for wetland restoration planning to help reduce storm-related structural damage.