Abstract
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.
Highlights
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
The geographically weighted regression (GWR) approach consistently underestimated the SOC stocks, bias is higher in the ecoregion with higher observed SOC stocks (Table 2)
The high values of SOC stocks in coastal environments showed the high capacity of wetlands like the Everglades to store SOC
Summary
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. Understanding the relationship between SOC and its environmental controllers is key for accurately predicting climate and land use change impacts on SOC and reducing uncertainties in large scale carbon climate feedback projections. Earth System Models (ESMs)[3] are used to predict the global carbon climate feedbacks and simulate the future state of soils and ecology Despite their key roles in determining the spatial heterogeneity of SOC and regulating the rate of SOC decomposition, many environmental factors that regulate soil formation are not adequately represented in current land surface m odels[4]. To reduce the uncertainty in future carbon climate feedback projections, it is critical to appropriately represent environmental controllers and the spatial heterogeneity of SOC in land surface models. The same way Weintraub, et al.[12] highlighted the potential of soil observation networks to accelerate process representation in SOC models and improve our current capacity to make predictions
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