Representing winter wheat in the Community Land Model (version 4.5)
Abstract
Winter wheat is a staple crop for global food security, and is the dominant vegetation cover for a significant fraction of Earth's croplands. As such, it plays an important role in carbon cycling and land–atmosphere interactions in these key regions. Accurate simulation of winter wheat growth is not only crucial for future yield prediction under a changing climate, but also for accurately predicting the energy and water cycles for winter wheat dominated regions. We modified the winter wheat model in the Community Land Model (CLM) to better simulate winter wheat leaf area index, latent heat flux, net ecosystem exchange of CO2, and grain yield. These included schemes to represent vernalization as well as frost tolerance and damage. We calibrated three key parameters (minimum planting temperature, maximum crop growth days, and initial value of leaf carbon allocation coefficient) and modified the grain carbon allocation algorithm for simulations at the US Southern Great Plains ARM site (US-ARM), and validated the model performance at eight additional sites across North America. We found that the new winter wheat model improved the prediction of monthly variation in leaf area index, reduced latent heat flux, and net ecosystem exchange root mean square error (RMSE) by 41more »
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1355654
- Alternate Identifier(s):
- OSTI ID: 1379839
- Grant/Contract Number:
- AC02-05CH11231
- Resource Type:
- Published Article
- Journal Name:
- Geoscientific Model Development (Online)
- Additional Journal Information:
- Journal Name: Geoscientific Model Development (Online) Journal Volume: 10 Journal Issue: 5; Journal ID: ISSN 1991-9603
- Publisher:
- Copernicus Publications, EGU
- Country of Publication:
- Germany
- Language:
- English
- Subject:
- 58 GEOSCIENCES
Citation Formats
Lu, Yaqiong, Williams, Ian N., Bagley, Justin E., Torn, Margaret S., and Kueppers, Lara M. Representing winter wheat in the Community Land Model (version 4.5). Germany: N. p., 2017.
Web. doi:10.5194/gmd-10-1873-2017.
Lu, Yaqiong, Williams, Ian N., Bagley, Justin E., Torn, Margaret S., & Kueppers, Lara M. Representing winter wheat in the Community Land Model (version 4.5). Germany. https://doi.org/10.5194/gmd-10-1873-2017
Lu, Yaqiong, Williams, Ian N., Bagley, Justin E., Torn, Margaret S., and Kueppers, Lara M. Fri .
"Representing winter wheat in the Community Land Model (version 4.5)". Germany. https://doi.org/10.5194/gmd-10-1873-2017.
@article{osti_1355654,
title = {Representing winter wheat in the Community Land Model (version 4.5)},
author = {Lu, Yaqiong and Williams, Ian N. and Bagley, Justin E. and Torn, Margaret S. and Kueppers, Lara M.},
abstractNote = {Winter wheat is a staple crop for global food security, and is the dominant vegetation cover for a significant fraction of Earth's croplands. As such, it plays an important role in carbon cycling and land–atmosphere interactions in these key regions. Accurate simulation of winter wheat growth is not only crucial for future yield prediction under a changing climate, but also for accurately predicting the energy and water cycles for winter wheat dominated regions. We modified the winter wheat model in the Community Land Model (CLM) to better simulate winter wheat leaf area index, latent heat flux, net ecosystem exchange of CO2, and grain yield. These included schemes to represent vernalization as well as frost tolerance and damage. We calibrated three key parameters (minimum planting temperature, maximum crop growth days, and initial value of leaf carbon allocation coefficient) and modified the grain carbon allocation algorithm for simulations at the US Southern Great Plains ARM site (US-ARM), and validated the model performance at eight additional sites across North America. We found that the new winter wheat model improved the prediction of monthly variation in leaf area index, reduced latent heat flux, and net ecosystem exchange root mean square error (RMSE) by 41 and 35 % during the spring growing season. The model accurately simulated the interannual variation in yield at the US-ARM site, but underestimated yield at sites and in regions (northwestern and southeastern US) with historically greater yields by 35 %.},
doi = {10.5194/gmd-10-1873-2017},
journal = {Geoscientific Model Development (Online)},
number = 5,
volume = 10,
place = {Germany},
year = {Fri May 05 00:00:00 EDT 2017},
month = {Fri May 05 00:00:00 EDT 2017}
}
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https://doi.org/10.5194/gmd-10-1873-2017
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