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Abstract Marsh accretion models predict the resiliency of coastal wetlands and their ability to store carbon in the face of accelerating sea level rise. Most existing marsh accretion models are derived from two parent models: the Marsh Equilibrium Model, which formalizes the biophysical relationships between sea level rise, dominant macrophyte growth, and elevation change; and the Cohort Theory Model, which formalizes how carbon mass pools belowground contribute to soil volume expansion over time. While there are several existing marsh accretion models, the application of these models by a broader base of researchers and practitioners is hindered because of (a) limited descriptions of how empirically derived ecological mechanism informed the development of these models, (b) limitations in the ability to apply models to geographies with variable tidal regimes, and (c) a lack of open‐source code to apply models. Here, we provide for the first time an explicit description of a mathematical version of the Cohort Theory Model and a numerical version of a combined model: the Cohort Marsh Equilibrium Model (CMEM) with an accompanying open‐sourceRpackage,rCMEM. We show that, through this “depth‐aware” model, we can capture how tidal variation impacts broad patterns of marsh accretion and carbon sequestration across the United States. The application of this model will likely be imperative in predicting the fate and state of coastal wetlands and the ecosystem services they provide in an era of rapid environmental change. 

Abstract Tidal wetlands provide valuable ecosystem services, including storing large amounts of carbon. However, the net exchanges of carbon dioxide (CO₂) and methane (CH₄) in tidal wetlands are highly uncertain. While several biogeochemical models can operate in tidal wetlands, they have yet to be parameterized and validated against high‐frequency, ecosystem‐scale CO₂and CH₄flux measurements across diverse sites. We paired the Cohort Marsh Equilibrium Model (CMEM) with a version of the PEPRMT model called PEPRMT‐Tidal, which considers the effects of water table height, sulfate, and nitrate availability on CO₂and CH₄emissions. Using a model‐data fusion approach, we parameterized the model with three sites and validated it with two independent sites, with representation from the three marine coasts of North America. Gross primary productivity (GPP) and ecosystem respiration (R_eco) modules explained, on average, 73% of the variation in CO₂exchange with low model error (normalized root mean square error (nRMSE) <1). The CH₄module also explained the majority of variance in CH₄emissions in validation sites (R² = 0.54; nRMSE = 1.15). The PEPRMT‐Tidal‐CMEM model coupling is a key advance toward constraining estimates of greenhouse gas emissions across diverse North American tidal wetlands. Further analyses of model error and case studies during changing salinity conditions guide future modeling efforts regarding four main processes: (a) the influence of salinity and nitrate on GPP, (b) the influence of laterally transported dissolved inorganic C on R_eco, (c) heterogeneous sulfate availability and methylotrophic methanogenesis impacts on surface CH₄emissions, and (d) CH₄responses to non‐periodic changes in salinity. 

Accounting for the rapid evolution of belowground plant traits alters forecasts of coastal wetland structure and function.