Replicating measured site-scale soil organic carbon dynamics in the U.S. corn belt using the SWAT-C model

Authors: LIANG, KANG - University Of Maryland; QI, JUNYU - University Of Maryland; Zhang, Xuesong

Submitted to: Environmental Modelling & Software
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/3/2022
Publication Date: 10/21/2022
Citation: Liang, K., Qi, J., Zhang, X. 2022. Replicating measured site-scale soil organic carbon dynamics in the U.S. corn belt using the SWAT-C model. Environmental Modelling & Software. 158. Article 105553. https://doi.org/10.1016/j.envsoft.2022.105553.
DOI: https://doi.org/10.1016/j.envsoft.2022.105553

Interpretive Summary: Agriculture is a major source of global greenhouse gas emissions into the atmosphere. Numerical models are a powerful tool to explore opportunities in the agricultural sector to sequester and store carbon in soils. Here, we revised and improved the Soil and Water Assessment Tool – Carbon (SWAT-C) model to better represent the control of soil temperature, moisture, and tillage on soil organic carbon decomposition. We evaluated the performance of the SWAT-C model against a total 1219 field measurements of soil organic carbon across seven sites in the U.S. Corn Belt and found that the improvements significantly enhanced the model’s ability to capture soil organic carbon (SOC) dynamics at different soil depths and under varied tillage intensities. The SWAT-C model will be shared as an open-source tool to support robust decision making to promote climate smart agriculture.

Technical Abstract: Accurate quantification of soil organic carbon (SOC) change is essential for designing effective agricultural practices to maximize agronomic, climatic, and environmental benefits. Here, we modified the SOC algorithms within the Soil and Water Assessment Tool – Carbon (SWAT-C) model and applied it to simulate SOC dynamics across seven sites in the U.S. Corn Belt. We examined multiple methods for estimating the effects of soil temperature (3 options), soil water (2 options), and tillage (4 options) on SOC decomposition. We found that model simulation results are sensitive to the choice of methods and identified the best performing combination of methods. Further analyses show that the model captured well the SOC dynamics at different sites and soil depths and under different tillage intensities. SWAT-C, as an open-source model, is shared to contribute to future carbon assessment and management in agroecosystems and support robust decision making to promote climate smart agriculture.