Replicating measured site-scale soil organic carbon dynamics in the U.S. Corn Belt using the SWAT-C model. (December 2022)

• Record Type: Journal Article
• Authors: Liang, Kang; Qi, Junyu; Zhang, Xuesong; Deng, Jia
• Is Part Of: Environmental modelling & software. Volume 158(2022)
• Publication Date: 2022-12
• Subjects: Agroecosystem -- Soil organic carbon -- Carbon dynamics -- Physical-based modeling; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105553
• Languages: English
• ISSNs: 1364-8152

Abstract

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 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. Graphical abstract: Image 1 Highlights: We modified the environmental control algorithms within the SWAT-C model to better simulate soil organic carbon dynamics. We compiled soil organic carbon data form multiple sources and across multiple sites in the U.S. Corn Belt. Model simulations are sensitive to the choice of environmental control algorithms. SWAT-C captured well the SOC dynamics at different sites and soil depths and under different tillage intensities. We discussed limitations and future directions to further improve the model performance.