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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 8, issue 4 | Copyright
Geosci. Model Dev., 8, 1071-1083, 2015
https://doi.org/10.5194/gmd-8-1071-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Development and technical paper 15 Apr 2015

Development and technical paper | 15 Apr 2015

Crop physiology calibration in the CLM

I. Bilionis1, B. A. Drewniak2, and E. M. Constantinescu1 I. Bilionis et al.
  • 1Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, USA
  • 2Environmental Science Division, Argonne National Laboratory, Argonne, IL, USA

Abstract. Farming is using more of the land surface, as population increases and agriculture is increasingly applied for non-nutritional purposes such as biofuel production. This agricultural expansion exerts an increasing impact on the terrestrial carbon cycle. In order to understand the impact of such processes, the Community Land Model (CLM) has been augmented with a CLM-Crop extension that simulates the development of three crop types: maize, soybean, and spring wheat. The CLM-Crop model is a complex system that relies on a suite of parametric inputs that govern plant growth under a given atmospheric forcing and available resources. CLM-Crop development used measurements of gross primary productivity (GPP) and net ecosystem exchange (NEE) from AmeriFlux sites to choose parameter values that optimize crop productivity in the model. In this paper, we calibrate these parameters for one crop type, soybean, in order to provide a faithful projection in terms of both plant development and net carbon exchange. Calibration is performed in a Bayesian framework by developing a scalable and adaptive scheme based on sequential Monte Carlo (SMC). The model showed significant improvement of crop productivity with the new calibrated parameters. We demonstrate that the calibrated parameters are applicable across alternative years and different sites.

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Farming is using more of the land surface terrestrial ground and this expansion exerts an increasing impact on the terrestrial carbon cycle. In order to understand the impact of such processes, we calibrate the parametric models within CLM-Crop (part of the Community Land Model (CLM)). The agreement between AmeriFlux observations and model projections is greatly improved for soybean, which is the focus of this study.
Farming is using more of the land surface terrestrial ground and this expansion exerts an...
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