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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 Methane (CH₄) is a potent greenhouse gas (GHG) with atmospheric concentrations that have nearly tripled since pre‐industrial times. Wetlands account for a large share of global CH₄emissions, yet the magnitude and factors controlling CH₄fluxes in tidal wetlands remain uncertain. We synthesized CH₄flux data from 100 chamber and 9 eddy covariance (EC) sites across tidal marshes in the conterminous United States to assess controlling factors and improve predictions of CH₄emissions. This effort included creating an open‐source database of chamber‐based GHG fluxes (https://doi.org/10.25573/serc.14227085). Annual fluxes across chamber and EC sites averaged 26 ± 53 g CH₄m⁻² year⁻¹, with a median of 3.9 g CH₄m⁻² year⁻¹, and only 25% of sites exceeding 18 g CH₄m⁻² year⁻¹. The highest fluxes were observed at fresh‐oligohaline sites with daily maximum temperature normals (MATmax) above 25.6°C. These were followed by frequently inundated low and mid‐fresh‐oligohaline marshes with MATmax ≤25.6°C, and mesohaline sites with MATmax >19°C. Quantile regressions of paired chamber CH₄flux and porewater biogeochemistry revealed that the 90th percentile of fluxes fell below 5 ± 3 nmol m⁻² s⁻¹at sulfate concentrations >4.7 ± 0.6 mM, porewater salinity >21 ± 2 psu, or surface water salinity >15 ± 3 psu. Across sites, salinity was the dominant predictor of annual CH₄fluxes, while within sites, temperature, gross primary productivity (GPP), and tidal height controlled variability at diel and seasonal scales. At the diel scale, GPP preceded temperature in importance for predicting CH₄flux changes, while the opposite was observed at the seasonal scale. Water levels influenced the timing and pathway of diel CH₄fluxes, with pulsed releases of stored CH₄at low to rising tide. This study provides data and methods to improve tidal marsh CH₄emission estimates, support blue carbon assessments, and refine national and global GHG inventories. 

Abstract Quantifying carbon fluxes into and out of coastal soils is critical to meeting greenhouse gas reduction and coastal resiliency goals. Numerous ‘blue carbon’ studies have generated, or benefitted from, synthetic datasets. However, the community those efforts inspired does not have a centralized, standardized database of disaggregated data used to estimate carbon stocks and fluxes. In this paper, we describe a data structure designed to standardize data reporting, maximize reuse, and maintain a chain of credit from synthesis to original source. We introduce version 1.0.0. of the Coastal Carbon Library, a global database of 6723 soil profiles representing blue carbon‐storing systems including marshes, mangroves, tidal freshwater forests, and seagrasses. We also present the Coastal Carbon Atlas, an R‐shiny application that can be used to visualize, query, and download portions of the Coastal Carbon Library. The majority (4815) of entries in the database can be used for carbon stock assessments without the need for interpolating missing soil variables, 533 are available for estimating carbon burial rate, and 326 are useful for fitting dynamic soil formation models. Organic matter density significantly varied by habitat with tidal freshwater forests having the highest density, and seagrasses having the lowest. Future work could involve expansion of the synthesis to include more deep stock assessments, increasing the representation of data outside of the U.S., and increasing the amount of data available for mangroves and seagrasses, especially carbon burial rate data. We present proposed best practices for blue carbon data including an emphasis on disaggregation, data publication, dataset documentation, and use of standardized vocabulary and templates whenever appropriate. To conclude, the Coastal Carbon Library and Atlas serve as a general example of a grassroots F.A.I.R. (Findable, Accessible, Interoperable, and Reusable) data effort demonstrating how data producers can coordinate to develop tools relevant to policy and decision‐making. 

Abstract Coastal wetlands have two dimensions of vulnerability to sea‐level rise (SLR), a vertical one, in cases where SLR outpaces their capacity to vertically accrete, and a lateral one, in cases where they are restricted from migrating inland by topography and land use. We conducted a meta‐analysis of accretion rates, standardized our analysis by using only¹³⁷Cs based estimates, and used model intercomparison to generate a vertical resilience index, a function of local SLR, tidal range, and tidal elevation category for the tidal wetlands of the contiguous US. We paired the vertical resilience index with a lateral resilience index made up of elevation, water level, and land cover maps, then projected them both into the future using localized SLR scenarios. At the regional scale, the vertical resilience index predicts changes from marsh aggradation to submergence for the coastal US Mid‐Atlantic, Southeast, and portions of the Northeast by 2100. At the sub‐regional scale, there is a geographic tradeoff between vertical and lateral resilience with more northerly wetlands vulnerable to the lack of suitable proportional area to migrate into, and more southerly wetlands vulnerable to accretion deficit. We estimate between 43% and 48% of the existing contiguous US wetland area, almost entirely located in watersheds along the Gulf of Mexico and Mid‐Atlantic coasts, is subject to both vertical and lateral limitations. These vertical and lateral resilience indices could help direct future research, planning, and mitigation efforts at a national scale, as well as supplement more processed informed approaches by local planners and practitioners. 

Abstract Tidal wetlands provide myriad ecosystem services across local to global scales. With their uncertain vulnerability or resilience to rising sea levels, there is a need for mapping flooding drivers and vulnerability proxies for these ecosystems at a national scale. However, tidal wetlands in the conterminous USA are diverse with differing elevation gradients, and tidal amplitudes, making broad geographic comparisons difficult. To address this, a national-scale map of relative tidal elevation ( Z * MHW ), a physical metric that normalizes elevation to tidal amplitude at mean high water (MHW), was constructed for the first time at 30 × 30-m resolution spanning the conterminous USA. Contrary to two study hypotheses, watershed-level median Z * MHW and its variability generally increased from north to south as a function of tidal amplitude and relative sea-level rise. These trends were also observed in a reanalysis of ground elevation data from the Pacific Coast by Janousek et al. (Estuaries and Coasts 42 (1): 85–98, 2019). Supporting a third hypothesis, propagated uncertainty in Z * MHW increased from north to south as light detection and ranging (LiDAR) errors had an outsized effect under narrowing tidal amplitudes. The drivers of Z * MHW and its variability are difficult to determine because several potential causal variables are correlated with latitude, but future studies could investigate highest astronomical tide and diurnal high tide inequality as drivers of median Z * MHW and Z * MHW variability, respectively. Watersheds of the Gulf Coast often had propagated Z * MHW uncertainty greater than the tidal amplitude itself emphasizing the diminished practicality of applying Z * MHW as a flooding proxy to microtidal wetlands. Future studies could focus on validating and improving these physical map products and using them for synoptic modeling of tidal wetland carbon dynamics and sea-level rise vulnerability analyses. 

Abstract. In the age of big data, soil data are more available and richer than ever, but – outside of a few large soil survey resources – they remain largely unusable for informing soil management and understanding Earth system processes beyond the original study.Data science has promised a fully reusable research pipeline where data from past studies are used to contextualize new findings and reanalyzed for new insight.Yet synthesis projects encounter challenges at all steps of the data reuse pipeline, including unavailable data, labor-intensive transcription of datasets, incomplete metadata, and a lack of communication between collaborators.Here, using insights from a diversity of soil, data, and climate scientists, we summarize current practices in soil data synthesis across all stages of database creation: availability, input, harmonization, curation, and publication.We then suggest new soil-focused semantic tools to improve existing data pipelines, such as ontologies, vocabulary lists, and community practices.Our goal is to provide the soil data community with an overview of current practices in soil data and where we need to go to fully leverage big data to solve soil problems in the next century. 

Elevation is a major driver of plant ecology and sediment dynamics in tidal wetlands, so accurate and precise spatial data are essential for assessing wetland vulnerability to sea-level rise and making forecasts. We performed survey-grade elevation and vegetation surveys of the Global Change Research Wetland, a brackish microtidal wetland in the Chesapeake Bay estuary, Maryland (USA), to both intercompare unbiased digital elevation model (DEM) creation techniques and to describe niche partitioning of several common tidal wetland plant species. We identified a tradeoff between scalability and performance in creating unbiased DEMs, with more data intensive methods such as kriging performing better than 3 more scalable methods involving postprocessing of light detection and ranging (LiDAR)-based DEMs. The LiDAR Elevation Correction with Normalized Difference Vegetation Index (LEAN) method provided a compromise between scalability and performance, although it underpredicted variability in elevation. In areas where native plants dominated, the sedge Schoenoplectus americanus occupied more frequently flooded areas (median: 0.22, 95% range: 0.09 to 0.31 m relative to North America Vertical Datum of 1988 [NAVD88]) and the grass Spartina patens, less frequently flooded (0.27, 0.1 to 0.35 m NAVD88). Non-native Phragmites australis dominated at lower elevations more than the native graminoids, but had a wide flooding tolerance, encompassing both their ranges (0.19, −0.05 to 0.36 m NAVD88). The native shrub Iva frutescens also dominated at lower elevations (0.20, 0.04 to 0.30 m NAVD88), despite being previously described as a high marsh species. These analyses not only provide valuable context for the temporally rich but spatially restricted data collected at a single well-studied site, but also provide broad insight into mapping techniques and species zonation. 

Soil carbon has been measured for over a century in applications ranging from understanding biogeochemical processes in natural ecosystems to quantifying the productivity and health of managed systems. Consolidating diverse soil carbon datasets is increasingly important to maximize their value, particularly with growing anthropogenic and climate change pressures. In this progress report, we describe recent advances in soil carbon data led by the International Soil Carbon Network and other networks. We highlight priority areas of research requiring soil carbon data, including (a) quantifying boreal, arctic and wetland carbon stocks, (b) understanding the timescales of soil carbon persistence using radiocarbon and chronosequence studies, (c) synthesizing long-term and experimental data to inform carbon stock vulnerability to global change, (d) quantifying root influences on soil carbon and (e) identifying gaps in model–data integration. We also describe the landscape of soil datasets currently available, highlighting their strengths, weaknesses and synergies. Now more than ever, integrated soil data are needed to inform climate mitigation, land management and agricultural practices. This report will aid new data users in navigating various soil databases and encourage scientists to make their measurements publicly available and to join forces to find soil-related solutions.