The Coastal Carbon Library and Atlas: Open source soil data and tools supporting blue carbon research and policy.

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.

Increasing Salt Marsh Elevation Using Sediment Augmentation: Critical Insights from Surface Sediments and Sediment Cores.

Sea-level rise is particularly concerning for tidal wetlands that reside within an area with steep topography or are constrained by human development and alteration of sedimentation. Sediment augmentation to increase wetland elevations has been considered as a potential strategy for such areas to prevent wetland loss over the coming decades. However, there is little information on the best approaches and whether adaptive management actions can mimic natural processes to build sea-level rise resilience. In addition, the lack of information on long-term marsh characteristics, processes, and variability can hamper development of effective augmentation strategies. Here, we assess a case study in a southern California marsh to determine the nature of the pre-existing sediments and variability of the site in relation to sediments applied during an augmentation experiment. Although sediment cores revealed natural variations in the grain size and organic content of sediments deposited at the site over the past 1500 years, the applied sediments were markedly coarser in grain size than prehistoric sediments at the site (100% maximum sand versus 76% maximum sand). The rate of the experimental sediment application (25.1 ± 1.09 cm in ~2 months) was also much more rapid than natural accretion rates measured for the site historically. In contrast, post-augmentation sediment accretion rates on the augmentation site have been markedly slower than pre-augmentation rates or current rates on a nearby control site. The mismatch between the characteristics of the applied sediment and thickness of application and the historic conditions are likely strong contributors to the slow initial recovery of vegetation. Sediment augmentation has been shown to be a useful strategy in some marshes, but this case study illustrates that vegetation recovery may be slow if applied sediments are not similar or at a thickness similar to historic conditions. However, testing adaptation strategies to build wetland elevations is important given the long-term risk of habitat loss with sea-level rise. Lessons learned in the case study could be applied elsewhere.

Author Correction: Accuracy and Precision of Tidal Wetland Soil Carbon Mapping in the Conterminous United States.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

Accuracy and Precision of Tidal Wetland Soil Carbon Mapping in the Conterminous United States.

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.