|MISHRA, UMAKANT - Argonne National Laboratory|
|LIBOHOVA, ZAMIR - Natural Resources Conservation Service (NRCS, USDA)|
|WILLS, SKYE - Natural Resources Conservation Service (NRCS, USDA)|
|RILEY, WILLIAM - Lawrence Berkeley National Laboratory|
|HOFFMAN, FORREST - Oak Ridge National Laboratory|
Submitted to: Geoderma
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/21/2020
Publication Date: 6/23/2020
Citation: Adhikari, K., Mishra, U., Owens, P.R., Libohova, Z., Wills, S.A., Riley, W.J., Hoffman, F.M., Smith, D.R. 2020. Importance and strength of environmental controllers of soil organic carbon changes with scale. Geoderma. 375:114472. Available: https://doi.org/10.1016/j.geoderma.2020.114472.
Interpretive Summary: Carbon stored in soils (soil organic carbon) has multiple benefits. It maintains soil fertility and promote soil quality and soil health. Spatial distribution of soil carbon is primarily controlled by landuse and land cover, climate and topography, and it changes over time. The strength and importance of these controller’s change with the spatial scale, however, scale dependency of soil carbon on its controllers is very limited in the literature. We studied the scaling property of soil carbon using over 6200 soil samples from across the conterminous USA and a wide range of environmental variables (land use and land cover, elevation, precipitation, temperature, soil types, etc.) as controllers of soil carbon. The study was conducted at 9 different scales ranging from 100 m to 50 km. We found that changes in the scale of prediction significantly influenced the importance and strength of the controllers. In general, topography was important at smaller scales, whereas climate was important at coarser scales. The contribution of precipitation was important at almost all scales. We believe that results from this study could benefit soil and earth system modeling communities by incorporating scale-specific soil carbon properties for its proper spatial representation.
Technical Abstract: The spatial heterogeneity of the land surface regulates land-atmosphere exchanges of energy, moisture, and greenhouse gases. However, representing the spatial heterogeneity of terrestrial processes in Earth System Models (ESMs) remains a critical scientific challenge. We used a large dataset of environmental factors (n = 31) representing soil-forming factors, field observations of soil organic carbon (SOC) stocks (n = 6213), and a machine learning algorithm (Cubist) to analyze the scaling behavior of SOC stocks across the conterminous USA. We found different environmental factors as significant predictors of SOC stocks at different spatial scales. Out of 31 environmental factors we investigated, only 13 were significant predictors of SOC stocks at spatial scales ranging from 100 m to 50 km. Overall, topographic variables had higher influence at finer scales, whereas climatic variables were more important at coarser scales. The model performance decreased with increasing scale of prediction (R2 = 0.38 - 0.65). The strength of environmental controls (median regression coefficient) on SOC stocks weakened with scale and were represented using mathematical functions (R2 = 0.38 - 0.98). Both the mean and variance of SOC stocks decreased linearly with increasing the scale in soils of the conterminous USA. Fitted linear functions accounted for 83% and 81% of the variability in the mean and variance of SOC stocks, respectively. We also found linear relationships among mean and high-order moments of SOC stocks (R2 = 0.51 - 0.97). We conclude that improved understanding of the scaling behavior of SOC stocks and their environmental controllers can improve ESMs land model benchmarking and may eventually improve representation of spatial heterogeneity of land surface biogeochemistry.