Skip to main content
ARS Home » Midwest Area » St. Paul, Minnesota » Soil and Water Management Research » Research » Publications at this Location » Publication #342730

Research Project: Developing Agricultural Practices to Protect Water Quality and Conserve Water and Soil Resources in the Upper Midwest United States

Location: Soil and Water Management Research

Title: Comparing crop growth and carbon budgets simulated across AmeriFlux agricultural sites using the community land model (CLM)

Author
item Chen, Ming - University Of Minnesota
item Griffis, Timothy - University Of Minnesota
item Baker, John
item Wood, Jeffrey - University Of Missouri
item Meyers, Tilden - National Oceanic & Atmospheric Administration (NOAA)
item Suyker, Andrew - University Of Nebraska

Submitted to: Agricultural and Forest Meteorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/16/2018
Publication Date: 4/5/2018
Citation: Chen, M., Griffis, T.J., Baker, J.M., Wood, J.M., Meyers, T., Suyker, A. 2018. Comparing crop growth and carbon budgets simulated across AmeriFlux agricultural sites using the community land model (CLM). Agricultural and Forest Meteorology. 256-257:315-333. https://doi.org/10.1016/j.agrformet.2018.03.012.
DOI: https://doi.org/10.1016/j.agrformet.2018.03.012

Interpretive Summary: Prediction of the impact of changes in climate on productivity and prediction of the impact of land use change on climate require accurate simulation of both atmopsheric processes and land surface processes. The scale at which atmospheric processes are modeled is much larger (tens of kilometers) than the scale at which land cover varies, particularly agricultural regions. Consequently, most early methods to combine the two relied on land surface parameterizations more appropriate for forests and grasslands. This has resulted in rather poor simulations of vegetative processes in agricultural regions, which degrades the overall performance of the models. Recently, efforts have been made to improve the representation of agricultural crops in the Community Land Model, which is the primary land surface scheme in use. We tested this modification, as well as a similar version for which we optimized several key variables, against long-term measurements of energy balance, carbon dioxide exchange and crop yield in corn/soybean systems at 3 sites within the US Corn Belt. We found that both models performed better on corn than on soybeans, primarily because they over-predicted gross primary production in soybeans. We also found that the modifications that we made siginificantly improved overall predictions of photosynthesis, respiration, and carbon balance at both short (hourly) and long(annual) time scales. These results will be helpful in improving the accuracy of coupled climate and land surface models.

Technical Abstract: Improving process-based crop models is needed to achieve high fidelity forecasts of regional energy, water, and carbon exchange. However, most state-of-the-art Land Surface Models (LSMs) assessed in the fifth phase of the Coupled Model Inter-comparison project (CMIP5) simulated crops as simple C3 or C4 grasses. This study evaluated the crop-enabled version of one of the most widely used LSMs, the Community Land Model (CLM4-Crop), for simulating corn and soybean agro-ecosystems at relatively long time scales (up to 10 years) using 54 site-years of data. The results indicated that CLM4-Crop had a biased phenology during the early growing season and underestimated carbon emissions from corn and soybean sites. Since the model uses universal physiological parameters such as specific leaf area (SLA), leaf nitrogen content, and vcmax25 for all the crops, model performance over different crop types varies. Overall, the energy and carbon exchange of corn systems were better simulated than soybean systems. Long-term simulations at multiple sites showed that gross primary production (GPP) was consistently over-estimated at soybean sites leading to very large short-term and long-term biases. A modified model, CLM4-CropM, with optimized phenology and crop physiological parameters was shown to provide a significantly better simulation of gross primary production (GPP), ecosystem respiration (ER) and leaf area index (LAI) at both short (hourly) and long-term (inter-annual to decadal) timescales for both soybean and corn