Wetland soils are a key global sink for organic carbon (C) and a focal point for C management and accounting efforts. The ongoing push for wetland restoration presents an opportunity for climate mitigation, but C storage expectations are poorly defined due to a lack of reference information and an incomplete understanding of what drives natural variability among wetlands. We sought to address these shortcomings by (1) quantifying the range of variability in wetland soil organic C (SOC) stocks on a depressional landscape (Delmarva Peninsula, USA) and (2) investigating the role of hydrology and relative topography in explaining variability among wetlands. We found a high degree of variability within individual wetlands and among wetlands with similar vegetation and hydrogeomorphic characteristics. This suggests that uncertainty should be presented explicitly when inferring ecosystem processes from wetland types or land cover classes. Differences in hydrologic regimes, particularly the rate of water level recession, explained some of the variability among wetlands, but relationships between SOC stocks and some hydrologic metrics were eclipsed by factors associated with separate study sites. Relative topography accounted for a similar portion of SOC stock variability as hydrology, indicating that it could be an effective substitute in large-scale analyses. As wetlands worldwide are restored and focus increases on quantifying C benefits, the importance of appropriately defining and assessing reference systems is paramount. Our results highlight the current uncertainty in this process, but suggest that incorporating landscape heterogeneity and drivers of natural variability into reference information may improve how wetland restoration is implemented and evaluated.
Tidal wetlands, widely considered the most extensive reservoir of soil organic carbon (SOC), can benefit from remote sensing studies enabling spatiotemporal estimation and mapping of SOC stock. We found that a majority of the remote-sensing-based SOC mapping efforts have been focused on upland ecosystems, not on tidal wetlands. We present a comprehensive review detailing the types of remote sensing models and methods used, standard input variables, results, and limitations for the handful of studies on tidal wetland SOC. Based on that synthesis, we pose several unexplored research questions and methods that are critical for moving tidal wetland SOC science forward. Among these, the applicability of machine learning and deep learning models for predicting surface SOC and the modeling requirements for SOC in subsurface soils (soils without a remote sensing signal, i.e., a soil depth greater than 5 cm) are the most important. We did not find any remote sensing study aimed at modeling subsurface SOC in tidal wetlands. Since tidal wetlands store a significant amount of SOC at greater depths, we hypothesized that surface SOC could be an important covariable along with other biophysical and climate variables for predicting subsurface SOC. Preliminary results using field data from tidal wetlands in the southeastern United States and machine learning model output from mangrove ecosystems in India revealed a strong nonlinear but significant relationship (r2 = 0.68 and 0.20, respectively, p < 2.2 × 10−16 for both) between surface and subsurface SOC at different depths. We investigated the applicability of the Soil Survey Geographic Database (SSURGO) for tidal wetlands by comparing the data with SOC data from the Smithsonian’s Coastal Blue Carbon Network collected during the same decade and found that the SSURGO data consistently over-reported SOC stock in tidal wetlands. We concluded that a novel machine learning framework that utilizes remote sensing data and derived products, the standard covariables reported in the limited literature, and more importantly, other new and potentially informative covariables specific to tidal wetlands such as tidal inundation frequency and height, vegetation species, and soil algal biomass could improve remote-sensing-based tidal wetland SOC studies.
more » « less- Award ID(s):
- 1832178
- PAR ID:
- 10483489
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Remote Sensing
- Volume:
- 14
- Issue:
- 12
- ISSN:
- 2072-4292
- Page Range / eLocation ID:
- 2940
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Tidal marshes store large amounts of organic carbon in their soils. Field data quantifying soil organic carbon (SOC) stocks provide an important resource for researchers, natural resource managers, and policy-makers working towards the protection, restoration, and valuation of these ecosystems. We collated a global dataset of tidal marsh soil organic carbon (MarSOC) from 99 studies that includes location, soil depth, site name, dry bulk density, SOC, and/or soil organic matter (SOM). The MarSOC dataset includes 17,454 data points from 2,329 unique locations, and 29 countries. We generated a general transfer function for the conversion of SOM to SOC. Using this data we estimated a median (± median absolute deviation) value of 79.2 ± 38.1 Mg SOC ha−1in the top 30 cm and 231 ± 134 Mg SOC ha−1in the top 1 m of tidal marsh soils globally. This data can serve as a basis for future work, and may contribute to incorporation of tidal marsh ecosystems into climate change mitigation and adaptation strategies and policies.
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Coastal wetlands display ecohydrological zonation such that vertical differences of plant zones are driven by varying groundwater levels over tidal cycles. It is unclear how variable levels of tidal drainage directly impact biotic and abiotic factors in coastal wetland ecosystems. To determine the impacts of drainage levels, simulated tides in mesocosms with varying degrees of drainage were created with Spartina alterniflora, the salt marsh coastal ecosystem dominant species on the United States Atlantic Coast, and Salicornia pacifica, the Pacific Coast dominant. We measured biomass production and photosynthesis as indicators of plant health, and we also measured soil and porewater characteristics to help interpret patterns of productivity. These measures included above and belowground biomass, porewater pH, salinity, ammonium concentration, sulfide concentration, soil redox potential, net ecosystem exchange, photosynthesis rate, respiration rate, and methane flux. We found the greatest plant production in soils with intermediate drainage levels, with production values that were 13.7% higher for S. alterniflora and 57.7% higher for S. pacifica in the intermediate flooding levels than found in more inundated and more drained conditions. Understanding how drainage impacts plant species is important for predicting wetland resilience to sea level rise, as increasing water levels alter ecohydrological zonation.more » « less
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Abstract Hydrologic controls on carbon processing and export are a critical feature of wetland ecosystems. Hydrologic response to climate variability has important implications for carbon‐climate feedbacks, aquatic metabolism, and water quality. Little is known about how hydrologic processes along the terrestrial‐aquatic interface in low‐relief, depressional wetland catchments influence carbon dynamics, particularly regarding soil‐derived dissolved organic matter (DOM) transport and transformation. To understand the role of different soil horizons as potential sources of DOM to wetland systems, we measured water‐soluble organic matter (WSOM) concentration and composition in soils collected from upland to wetland transects at four Delmarva Bay wetlands in the eastern United States. Spectral metrics indicated that WSOM in shallow organic horizons had increased aromaticity, higher molecular weight, and plant‐like signatures. In contrast, WSOM from deeper, mineral horizons had lower aromaticity, lower molecular weights, and microbial‐like signatures. Organic soil horizons had the highest concentrations of WSOM, and WSOM decreased with increasing soil depth. WSOM concentrations also decreased from the upland to the wetland, suggesting that continuous soil saturation reduces WSOM concentrations. Despite wetland soils having lower WSOM, these horizons are thicker and continuously hydrologically connected to wetland surface and groundwater, leading to wetland soils representing the largest potential source of soil‐derived DOM to the Delmarva Bay wetland system. Knowledge of which soil horizons are most biogeochemically significant for DOM transport in wetland ecosystems will become increasingly important as climate change is expected to alter hydrologic regimes of wetland soils and their resulting carbon contributions from the landscape.
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Abstract Redox processes, aqueous and solid‐phase chemistry, and pH dynamics are key drivers of subsurface biogeochemical cycling and methanogenesis in terrestrial and wetland ecosystems but are typically not included in terrestrial carbon cycle models. These omissions may introduce errors when simulating systems where redox interactions and pH fluctuations are important, such as wetlands where saturation of soils can produce anoxic conditions and coastal systems where sulfate inputs from seawater can influence biogeochemistry. Integrating cycling of redox‐sensitive elements could therefore allow models to better represent key elements of carbon cycling and greenhouse gas production. We describe a model framework that couples the Energy Exascale Earth System Model (E3SM) Land Model (ELM) with PFLOTRAN biogeochemistry, allowing geochemical processes and redox interactions to be integrated with land surface model simulations. We implemented a reaction network including aerobic decomposition, fermentation, sulfate reduction, sulfide oxidation, methanogenesis, and methanotrophy as well as pH dynamics along with iron oxide and iron sulfide mineral precipitation and dissolution. We simulated biogeochemical cycling in tidal wetlands subject to either saltwater or freshwater inputs driven by tidal hydrological dynamics. In simulations with saltwater tidal inputs, sulfate reduction led to accumulation of sulfide, higher dissolved inorganic carbon concentrations, lower dissolved organic carbon concentrations, and lower methane emissions than simulations with freshwater tidal inputs. Model simulations compared well with measured porewater concentrations and surface gas emissions from coastal wetlands in the Northeastern United States. These results demonstrate how simulating geochemical reaction networks can improve land surface model simulations of subsurface biogeochemistry and carbon cycling.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/rs14122940
