Dataset Information

Optimizing process-based models to predict current and future soil organic carbon stocks at high-resolution.

ABSTRACT: From hillslope to small catchment scales (< 50 km²), soil carbon management and mitigation policies rely on estimates and projections of soil organic carbon (SOC) stocks. Here we apply a process-based modeling approach that parameterizes the MIcrobial-MIneral Carbon Stabilization (MIMICS) model with SOC measurements and remotely sensed environmental data from the Reynolds Creek Experimental Watershed in SW Idaho, USA. Calibrating model parameters reduced error between simulated and observed SOC stocks by 25%, relative to the initial parameter estimates and better captured local gradients in climate and productivity. The calibrated parameter ensemble was used to produce spatially continuous, high-resolution (10 m²) estimates of stocks and associated uncertainties of litter, microbial biomass, particulate, and protected SOC pools across the complex landscape. Subsequent projections of SOC response to idealized environmental disturbances illustrate the spatial complexity of potential SOC vulnerabilities across the watershed. Parametric uncertainty generated physicochemically protected soil C stocks that varied by a mean factor of 4.4 × across individual locations in the watershed and a - 14.9 to + 20.4% range in potential SOC stock response to idealized disturbances, illustrating the need for additional measurements of soil carbon fractions and their turnover time to improve confidence in the MIMICS simulations of SOC dynamics.

SUBMITTER: Pierson D

PROVIDER: S-EPMC9233666 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Optimizing process-based models to predict current and future soil organic carbon stocks at high-resolution.

Pierson Derek D Lohse Kathleen A KA Wieder William R WR Patton Nicholas R NR Facer Jeremy J de Graaff Marie-Anne MA Georgiou Katerina K Seyfried Mark S MS Flerchinger Gerald G Will Ryan R

Scientific reports 20220625 1

From hillslope to small catchment scales (< 50 km<sup>2</sup>), soil carbon management and mitigation policies rely on estimates and projections of soil organic carbon (SOC) stocks. Here we apply a process-based modeling approach that parameterizes the MIcrobial-MIneral Carbon Stabilization (MIMICS) model with SOC measurements and remotely sensed environmental data from the Reynolds Creek Experimental Watershed in SW Idaho, USA. Calibrating model parameters reduced error between simulated and ob ...[more]

PMID: 35752734

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Project description:Understanding the influence of environmental factors on soil organic carbon (SOC) is critical for quantifying and reducing the uncertainty in carbon climate feedback projections under changing environmental conditions. We explored the effect of climatic variables, land cover types, topographic attributes, soil types and bedrock geology on SOC stocks of top 1 m depth across conterminous United States (US) ecoregions. Using 4559 soil profile observations and high-resolution data of environmental factors, we identified dominant environmental controllers of SOC stocks in 21 US ecoregions using geographically weighted regression. We used projected climatic data of SSP126 and SSP585 scenarios from GFDL-ESM 4 Earth System Model of Coupled Model Intercomparison Project phase 6 to predict SOC stock changes across continental US between 2030 and 2100. Both baseline and predicted changes in SOC stocks were compared with SOC stocks represented in GFDL-ESM4 projections. Among 56 environmental predictors, we found 12 as dominant controllers across all ecoregions. The adjusted geospatial model with the 12 environmental controllers showed an R2 of 0.48 in testing dataset. Higher precipitation and lower temperatures were associated with higher levels of SOC stocks in majority of ecoregions. Changes in land cover types (vegetation properties) was important in drier ecosystem as North American deserts, whereas soil types and topography were more important in American prairies. Wetlands of the Everglades was highly sensitive to projected temperature changes. The SOC stocks did not change under SSP126 until 2100, however SOC stocks decreased up to 21% under SSP585. Our results, based on environmental controllers of SOC stocks, help to predict impacts of changing environmental conditions on SOC stocks more reliably and may reduce uncertainties found in both, geospatial and Earth System Models. In addition, the description of different environmental controllers for US ecoregions can help to describe the scope and importance of global and local models.

Project description:Wetland soil stocks are important global repositories of carbon (C) but are difficult to quantify and model due to varying sampling protocols, and geomorphic/spatio-temporal discontinuity. Merging scales of soil-survey spatial extents with wetland-specific point-based data offers an explicit, empirical and updatable improvement for regional and continental scale soil C stock assessments. Agency-collected and community-contributed soil datasets were compared for representativeness and bias, with the goal of producing a harmonized national map of wetland soil C stocks with error quantification for wetland areas of the conterminous United States (CONUS) identified by the USGS National Landcover Change Dataset. This allowed an empirical predictive model of SOC density to be applied across the entire CONUS using relational %OC distribution alone. A broken-stick quantile-regression model identified %OC with its relatively high analytical confidence as a key predictor of SOC density in soil segments; soils less than 6% OC (hereafter, mineral wetland soils, 85% of the dataset) had a strong linear relationship of %OC to SOC density (RMSE = 0.0059, ~4% mean RMSE) and soils greater than 6% OC (organic wetland soils, 15% of the dataset) had virtually no predictive relationship of %OC to SOC density (RMSE = 0.0348 g C cm-3, ~56% mean RMSE). Disaggregation by vegetation type, or region did not alter the breakpoint significantly (6% OC) nor improve model accuracies for inland and tidal wetlands. Similarly, SOC stocks in tidal wetlands were related to %OC, but without a mappable product for disaggregation to improve accuracy by soil class, region or depth. Our layered, harmonized CONUS wetland soil maps revised wetland SOC stock estimates downward by 24% (9.5 vs. 12.5Pg C) with the overestimation being entirely an issue of inland, organic wetland soils, (35% lower than SSURGO-derived SOC stocks). Further, SSURGO underestimated soil carbon stocks at depth, as modeled wetland SOC stocks for organic-rich soils showed significant preservation downcore in the NWCA dataset (<3% loss between 0-30 cm and 30-100 cm depths) in contrast to mineral-rich soils (37% downcore stock loss). Future CONUS wetland soil C assessments will benefit from focused attention on improved organic wetland soil measurements, land history, and spatial representativeness.

Project description:BackgroundThe restoration of conventional tea plantations and the adoption of organic farming practices could impact soil organic carbon (SOC) and nitrogen (N) stocks. This study investigated the soil properties, SOC and N contents and stocks, and their vertical distributions of a secondary forest restored from an abandoned conventional tea plantation and a converted organic tea plantation. An adjacent conventional tea plantation employing similar intermediate farming served as a comparison.ResultsWithin a 50-cm depth, the secondary forest exhibited a higher SOC stock of 115.53 ± 7.23 Mg C ha- 1 compared to 92.1 ± 8.54 Mg C ha- 1 for the conventional tea plantation. No significant differences in N stocks were seen between the two land uses. Significantly high SOC and N contents and stocks were found in the 0-10 cm layer of the secondary forest compared to the conventional tea plantation. No significant disparities in SOC and N stocks were found between the conventional and organic tea plantations within the 50 cm depth (92.1 ± 8.54 Mg C ha- 1 and 10.06 ± 1.01 Mg N ha- 1 vs. 97.47 ± 1.53 Mg C ha- 1 and 9.70 ± 0.10 Mg N ha- 1). However, higher levels of SOC and N contents and stocks were observed at a depth of 10 cm in the conventional tea plantation and below 10 cm in the organic tea plantation.ConclusionsThe C and N inputs derived from high litter production at the top soil strongly contributed to higher SOC and N contents and stocks in the secondary forest. The application of soybean amendments in the conventional tea plantation and the longer tea plantation age of the organic tea plantation influenced their distribution of SOC and N contents and stocks, respectively. Reverting a conventional tea plantation into a secondary forest contributed to C recovery and reaccumulation. The conventional tea plantation, employing similar intermediate farming practices, increased SOC and N contents and stocks in the surface soil compared to the organic tea plantation. However, adopting organic farming did not significantly increase SOC stocks compared to the conventional tea plantation.

Dataset Information

Optimizing process-based models to predict current and future soil organic carbon stocks at high-resolution.

Publications

Optimizing process-based models to predict current and future soil organic carbon stocks at high-resolution.

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