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Modifying the Soil and Water Assessment Tool to simulate cropland carbon flux: model development and initial
Xuesong Zhang1, R César Izaurralde, Jeffrey G Arnold
1Joint Global Change Research Institute, Pacific Northwest National Laboratory and University of Maryland, College Park, MD 20740, USA.
This study enhances the Soil and Water Assessment Tool (SWAT) with a new carbon algorithm (SWAT-C) to better simulate greenhouse gas emissions and land-atmosphere carbon exchange, crucial for climate change research.
Area of Science:
- Environmental Science
- Climate Modeling
- Agricultural Science
Background:
- Climate change necessitates accurate modeling of greenhouse gas emissions.
- Existing Soil and Water Assessment Tool (SWAT) carbon algorithms have limitations in assessing human impacts on CO2 emissions.
- Soil organic matter (SOM) dynamics and land-atmosphere carbon exchange are critical components of climate systems.
Purpose of the Study:
- To incorporate a revised CENTURY carbon model into SWAT, creating SWAT-C.
- To improve the simulation of soil organic matter dynamics and land-atmosphere carbon exchange.
- To assess the performance of the new SWAT-C model using eddy covariance data.
Main Methods:
- Integrated a revised CENTURY carbon model into the SWAT model.
- Tested the SWAT-C model against daily eddy covariance (EC) observations of net ecosystem exchange (NEE) and evapotranspiration (ET).
- Evaluated model performance across six U.S. Midwest sites with varying agricultural management practices.
Main Results:
- SWAT-C accurately simulates multi-year average NEE and ET across diverse sites.
- The model captures significant temporal variations in NEE and ET at a daily scale.
- Model performance is influenced by factors including crop management, data quality, and site-specific conditions.
Conclusions:
- The enhanced SWAT-C model demonstrates robust performance in simulating land-atmosphere carbon exchange in agricultural settings.
- SWAT-C is a valuable tool for incorporating carbon flux into sustainable watershed management under climate change.
- Further validation with extensive field data is recommended for refining the model and understanding regional uncertainties.
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