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Title: A Continental‐Scale Estimate of Soil Organic Carbon Change at NEON Sites and Their Environmental and Edaphic Controls
Key Points The NEON sites were estimated to have large soil organic carbon (SOC) loss in both topsoil and subsoil during 1984–2014 The carbon sequestration potential is limited in well‐developed and near carbon‐saturated soils in managed ecosystems Runoff/erosion and leaching, vertical translocation, and mineral sorption are dominant factors affecting SOC variation at National Ecological Observatory Network sites  more » « less
Award ID(s):
2226568 1638720
NSF-PAR ID:
10466432
Author(s) / Creator(s):
; ; ; ; ; ; ;
Date Published:
Journal Name:
Journal of Geophysical Research: Biogeosciences
Volume:
128
Issue:
5
ISSN:
2169-8953
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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  1. Abstract

    Soil organic carbon (SOC) is going through rapid reorganization due to anthropogenic influences. Understanding how biogeochemical transformation and erosion‐induced SOC redistribution influence SOC profiles and stocks is critical to our food security and adaptation to climate change. The important roles of erosion and deposition on SOC dynamics have drawn increasing attention in the past decades, but quantifying such dynamics is still challenging. Here we develop a process‐based quasi 3‐D model that couples surface runoff, soil moisture dynamics, biogeochemical transformation, and landscape evolution. We apply this model to a subcatchment in Iowa to understand how natural forcing and farming practices affect the SOC dynamics in the critical zone. The net soil thickness and SOC stock change rates are −0.336 (mm/yr) and −1.9 (g C/m2/year), respectively. Our model shows that in a fast transport landscape, SOC transport is the dominant control on SOC dynamics compared to biogeochemical transformation. The SOC profiles have “noses” below the surface at depositional sites, which are consistent with cores sampled at the same site. Generally, erosional sites are local net atmospheric carbon sinks and vice versa for depositional sites, but exceptions exist as seen in the simulation results. Furthermore, the mechanical soil mixing arising from tillage enhances SOC stock at erosional sites and reduces it at depositional ones. This study not only helps us understand the evolution of SOC stock and profiles in a watershed but can also serve as an instrument to develop practical means for protecting carbon loss due to human activities.

     
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  2. Abstract

    The magnitude of future emissions of greenhouse gases from the northern permafrost region depends crucially on the mineralization of soil organic carbon (SOC) that has accumulated over millennia in these perennially frozen soils. Many recent studies have used radiocarbon (14C) to quantify the release of this “old” SOC as CO2or CH4to the atmosphere or as dissolved and particulate organic carbon (DOC and POC) to surface waters. We compiled ~1,90014C measurements from 51 sites in the northern permafrost region to assess the vulnerability of thawing SOC in tundra, forest, peatland, lake, and river ecosystems. We found that growing season soil14C‐CO2emissions generally had a modern (post‐1950s) signature, but that well‐drained, oxic soils had increased CO2emissions derived from older sources following recent thaw. The age of CO2and CH4emitted from lakes depended primarily on the age and quantity of SOC in sediments and on the mode of emission, and indicated substantial losses of previously frozen SOC from actively expanding thermokarst lakes. Increased fluvial export of aged DOC and POC occurred from sites where permafrost thaw caused soil thermal erosion. There was limited evidence supporting release of previously frozen SOC as CO2, CH4, and DOC from thawing peatlands with anoxic soils. This synthesis thus suggests widespread but not universal release of permafrost SOC following thaw. We show that different definitions of “old” sources among studies hamper the comparison of vulnerability of permafrost SOC across ecosystems and disturbances. We also highlight opportunities for future14C studies in the permafrost region.

     
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  3. Data Description:

    To improve SOC estimation in the United States, we upscaled site-based SOC measurements to the continental scale using multivariate geographic clustering (MGC) approach coupled with machine learning models. First, we used the MGC approach to segment the United States at 30 arc second resolution based on principal component information from environmental covariates (gNATSGO soil properties, WorldClim bioclimatic variables, MODIS biological variables, and physiographic variables) to 20 SOC regions. We then trained separate random forest model ensembles for each of the SOC regions identified using environmental covariates and soil profile measurements from the International Soil Carbon Network (ISCN) and an Alaska soil profile data. We estimated United States SOC for 0-30 cm and 0-100 cm depths were 52.6 + 3.2 and 108.3 + 8.2 Pg C, respectively.

    Files in collection (32):

    Collection contains 22 soil properties geospatial rasters, 4 soil SOC geospatial rasters, 2 ISCN site SOC observations csv files, and 4 R scripts

    gNATSGO TIF files:

    ├── available_water_storage_30arc_30cm_us.tif                   [30 cm depth soil available water storage]
    ├── available_water_storage_30arc_100cm_us.tif                 [100 cm depth soil available water storage]
    ├── caco3_30arc_30cm_us.tif                                                 [30 cm depth soil CaCO3 content]
    ├── caco3_30arc_100cm_us.tif                                               [100 cm depth soil CaCO3 content]
    ├── cec_30arc_30cm_us.tif                                                     [30 cm depth soil cation exchange capacity]
    ├── cec_30arc_100cm_us.tif                                                   [100 cm depth soil cation exchange capacity]
    ├── clay_30arc_30cm_us.tif                                                     [30 cm depth soil clay content]
    ├── clay_30arc_100cm_us.tif                                                   [100 cm depth soil clay content]
    ├── depthWT_30arc_us.tif                                                        [depth to water table]
    ├── kfactor_30arc_30cm_us.tif                                                 [30 cm depth soil erosion factor]
    ├── kfactor_30arc_100cm_us.tif                                               [100 cm depth soil erosion factor]
    ├── ph_30arc_100cm_us.tif                                                      [100 cm depth soil pH]
    ├── ph_30arc_100cm_us.tif                                                      [30 cm depth soil pH]
    ├── pondingFre_30arc_us.tif                                                     [ponding frequency]
    ├── sand_30arc_30cm_us.tif                                                    [30 cm depth soil sand content]
    ├── sand_30arc_100cm_us.tif                                                  [100 cm depth soil sand content]
    ├── silt_30arc_30cm_us.tif                                                        [30 cm depth soil silt content]
    ├── silt_30arc_100cm_us.tif                                                      [100 cm depth soil silt content]
    ├── water_content_30arc_30cm_us.tif                                      [30 cm depth soil water content]
    └── water_content_30arc_100cm_us.tif                                   [100 cm depth soil water content]

    SOC TIF files:

    ├──30cm SOC mean.tif                             [30 cm depth soil SOC]
    ├──100cm SOC mean.tif                           [100 cm depth soil SOC]
    ├──30cm SOC CV.tif                                 [30 cm depth soil SOC coefficient of variation]
    └──100cm SOC CV.tif                              [100 cm depth soil SOC coefficient of variation]

    site observations csv files:

    ISCN_rmNRCS_addNCSS_30cm.csv       30cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data

    ISCN_rmNRCS_addNCSS_100cm.csv       100cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data


    Data format:

    Geospatial files are provided in Geotiff format in Lat/Lon WGS84 EPSG: 4326 projection at 30 arc second resolution.

    Geospatial projection

    GEOGCS["GCS_WGS_1984", DATUM["D_WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["Degree",0.017453292519943295]] (base) [jbk@theseus ltar_regionalization]$ g.proj -w GEOGCS["wgs84", DATUM["WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["degree",0.0174532925199433]]

     

     
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  4. null (Ed.)
    A major limitation to building credible soil carbon sequestration programs is the cost of measuring soil carbon change. Diffuse reflectance spectroscopy (DRS) is considered a viable low-cost alternative to traditional laboratory analysis of soil organic carbon (SOC). While numerous studies have shown that DRS can produce accurate and precise estimates of SOC across landscapes, whether DRS can detect subtle management induced changes in SOC at a given site has not been resolved. Here, we leverage archived soil samples from seven long-term research trials in the U.S. to test this question using mid infrared (MIR) spectroscopy coupled with the USDA-NRCS Kellogg Soil Survey Laboratory MIR spectral library. Overall, MIR-based estimates of SOC%, with samples scanned on a secondary instrument, were excellent with the root mean square error ranging from 0.10 to 0.33% across the seven sites. In all but two instances, the same statistically significant (p < 0.10) management effect was found using both the lab-based SOC% and MIR estimated SOC% data. Despite some additional uncertainty, primarily in the form of bias, these results suggest that large existing MIR spectral libraries can be operationalized in other laboratories for successful carbon monitoring. 
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  5. null (Ed.)
    Abstract The rarity of rapid campaigns to characterize soils across scales limits opportunities to investigate variation in soil carbon stocks (SOC) storage simultaneously at large and small scales, with and without site-level replication. We used data from two complementary campaigns at 40 sites in the United States across the National Ecological Observatory Network (NEON), in which one campaign sampled profiles from closely co-located intensive plots and physically composited similar horizons, and the other sampled dozens of pedons across the landscape at each site. We demonstrate some consistencies between these distinct designs, while also revealing that within-site replication reveals patterns and predictors of SOC stocks not detectable with non-replicated designs. Both designs demonstrate that SOC stocks of whole soil profiles vary across continental-scale climate gradients. However, broad climate patterns may mask the importance of localized variation in soil physicochemical properties, as captured by within-site sampling, especially for SOC stocks of discrete genetic horizons. Within-site replication also reveals examples in which expectations based on readily explained continental-scale patterns do not hold. For example, even wide-ranging drainage class sequences within landscapes do not duplicate the clear differences in profile SOC stocks across drainage classes at the continental scale, and physicochemical factors associated with increasing B horizon SOC stocks at continental scales frequently do not follow the same patterns within landscapes. Because inferences from SOC studies are a product of their context (where, when, how), this study provides context—in terms of SOC stocks and the factors that influence them—for others assessing soils and the C cycle at NEON sites. 
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