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Soil respiration that releases CO 2 into the atmosphere roughly balances the net primary productivity and varies widely in space and time. However, predicting its spatial variability, particularly in intensively managed landscapes, is challenging due to a lack of understanding of the roles of soil organic carbon (SOC) redistribution resulting from accelerated soil erosion. Here we simulate the heterotrophic carbon loss (HCL)—defined as microbial decomposition of SOC—with soil transport, SOC surface redistribution, and biogeochemical transformation in an agricultural field. The results show that accelerated soil erosion extends the spatial variation of the HCL, and the mechanical-mixing due to tillage further accentuates the contrast. The peak values of HCL occur in areas where soil transport rates are relatively small. Moreover, HCL has a strong correlation with the SOC redistribution rate rather than the soil transport rate. This work characterizes the roles of soil and SOC transport in restructuring the spatial variability of HCL at high spatio-temporal resolution.
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A Landscape Evolution Modeling Approach for Predicting Three‐Dimensional Soil Organic Carbon Redistribution in Agricultural Landscapes
Abstract Soil erosion diminishes agricultural productivity by driving the loss of soil organic carbon (SOC). The ability to predict SOC redistribution is important for guiding sustainable agricultural practices and determining the influence of soil erosion on the carbon cycle. Here, we develop a landscape evolution model that couples soil mixing and transport to predict soil loss and SOC patterns within agricultural fields. Our reduced complexity numerical model requires the specification of only two physical parameters: a plow mixing depth,Lp, and a hillslope diffusion coefficient,D. Using topography as an input, the model predicts spatial patterns of surficial SOC concentrations and complex 3D SOC pedostratigraphy. We use soil cores from native prairies to determine initial SOC‐depth relations and the spatial pattern of remote sensing‐derived SOC in adjacent agricultural fields to evaluate the model predictions. The model reproduces spatial patterns of soil loss comparable to those observed in satellite images. Our results indicate that the distribution of soil erosion and SOC in agricultural fields can be predicted using a simple geomorphic model where hillslope diffusion plays a dominant role. Such predictions can aid estimates of carbon burial and evaluate the potential for future soil loss in agricultural landscapes.
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- Award ID(s):
- 1653191
- PAR ID:
- 10375659
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Biogeosciences
- Volume:
- 127
- Issue:
- 2
- ISSN:
- 2169-8953
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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