Between the land and ocean, diverse coastal ecosystems transform, store, and transport material. Across these interfaces, the dynamic exchange of energy and matter is driven by hydrological and hydrodynamic processes such as river and groundwater discharge, tides, waves, and storms. These dynamics regulate ecosystem functions and Earth’s climate, yet global models lack representation of coastal processes and related feedbacks, impeding their predictions of coastal and global responses to change. Here, we assess existing coastal monitoring networks and regional models, existing challenges in these efforts, and recommend a path towards development of global models that more robustly reflect the coastal interface.

Tidal wetlands produce long-term soil organic carbon (C) stocks. Thus for carbon accounting purposes, we need accurate and precise information on the magnitude and spatial distribution of those stocks. We assembled and analyzed an unprecedented soil core dataset, and tested three strategies for mapping carbon stocks: applying the average value from the synthesis to mapped tidal wetlands, applying models fit using empirical data and applied using soil, vegetation and salinity maps, and relying on independently generated soil carbon maps. Soil carbon stocks were far lower on average and varied less spatially and with depth than stocks calculated from available soils maps. Further, variation in carbon density was not well-predicted based on climate, salinity, vegetation, or soil classes. Instead, the assembled dataset showed that carbon density across the conterminous united states (CONUS) was normally distributed, with a predictable range of observations. We identified the simplest strategy, applying mean carbon density (27.0 kg C m⁻³), as the best performing strategy, and conservatively estimated that the top meter of CONUS tidal wetland soil contains 0.72 petagrams C. This strategy could provide standardization in CONUS tidal carbon accounting until such a time as modeling and mapping advancements can quantitatively improve accuracy and precision.

Wetlands are responsible for 20%–31% of global methane (CH₄) emissions and account for a large source of uncertainty in the global CH₄ budget. Data‐driven upscaling of CH₄ fluxes from eddy covariance measurements can provide new and independent bottom‐up estimates of wetland CH₄ emissions. Here, we develop a six‐predictor random forest upscaling model (UpCH4), trained on 119 site‐years of eddy covariance CH₄ flux data from 43 freshwater wetland sites in the FLUXNET‐CH4 Community Product. Network patterns in site‐level annual means and mean seasonal cycles of CH₄ fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash‐Sutcliffe Efficiency ∼0.52–0.63 and 0.53). UpCH4 estimated annual global wetland CH₄ emissions of 146 ± 43 TgCH₄ y⁻¹ for 2001–2018 which agrees closely with current bottom‐up land surface models (102–181 TgCH₄ y⁻¹) and overlaps with top‐down atmospheric inversion models (155–200 TgCH₄ y⁻¹). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH₄ fluxes has the potential to produce realistic extra‐tropical wetland CH₄ emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid‐to‐arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25° from UpCH4 are available via ORNL DAAC (https://doi.org/10.3334/ORNLDAAC/2253).

Coastal wetlands are among the most productive habitats on Earth and sequester globally significant amounts of atmospheric carbon (C). Extreme rates of soil C accumulation are widely assumed to reflect efficient C storage. Yet the fraction of wetland C lost via hydrologic export has not been directly quantified, since comprehensive budgets including direct estimates of lateral C loss are lacking. We present a complete net ecosystem C budget (NECB), demonstrating that lateral losses of C are a major component of the NECB for the largest stable brackish tidal marsh on the U.S. Pacific coast. Mean annual net ecosystem exchange of CO₂ with the atmosphere (NEE = −185 g C m² year⁻¹, negative NEE denoting ecosystem uptake) was compared to long‐term soil C burial (87–110 g C m² year⁻¹), suggesting only 47–59% of fixed atmospheric C accumulates in soils. Consistently, direct monitoring in 2017–2018 showed NEE of −255 g C m⁻² year⁻¹, and hydrologic export of 105 g C m⁻² year⁻¹ (59% of NEE remaining on site). Despite their high C sequestration capacity, lateral losses from coastal wetlands are typically a larger fraction of the NECB when compared to other terrestrial ecosystems. Loss of inorganic C (the least measured NECB term) was 91% of hydrologic export and may be the most important term limiting C sequestration. The high productivity of coastal wetlands thus serves a dual function of C burial and estuarine export, and the multiple fates of fixed C must be considered when evaluating wetland capacity for C sequestration.

Wetland methane (CH₄) emissions ( $${F}_{{{CH}}_{4}}$$F C H 4 ) are important in global carbon budgets and climate change assessments. Currently, $${F}_{{{CH}}_{4}}$$F C H 4 projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent $${F}_{{{CH}}_{4}}$$F C H 4 temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that $${F}_{{{CH}}_{4}}$$F C H 4 are often controlled by factors beyond temperature. Here, we evaluate the relationship between $${F}_{{{CH}}_{4}}$$F C H 4 and temperature using observations from the FLUXNET-CH₄ database. Measurements collected across the globe show substantial seasonal hysteresis between $${F}_{{{CH}}_{4}}$$F C H 4 and temperature, suggesting larger $${F}_{{{CH}}_{4}}$$F C H 4 sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH₄ production are thus needed to improve global CH₄ budget assessments.