Cargando…
Global Validation of a Process-Based Model on Vegetation Gross Primary Production Using Eddy Covariance Observations
Gross Primary Production (GPP) is the largest flux in the global carbon cycle. However, large uncertainties in current global estimations persist. In this study, we examined the performance of a process-based model (Integrated BIosphere Simulator, IBIS) at 62 eddy covariance sites around the world....
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4222824/ https://www.ncbi.nlm.nih.gov/pubmed/25375227 http://dx.doi.org/10.1371/journal.pone.0110407 |
_version_ | 1782343113387278336 |
---|---|
author | Liu, Dan Cai, Wenwen Xia, Jiangzhou Dong, Wenjie Zhou, Guangsheng Chen, Yang Zhang, Haicheng Yuan, Wenping |
author_facet | Liu, Dan Cai, Wenwen Xia, Jiangzhou Dong, Wenjie Zhou, Guangsheng Chen, Yang Zhang, Haicheng Yuan, Wenping |
author_sort | Liu, Dan |
collection | PubMed |
description | Gross Primary Production (GPP) is the largest flux in the global carbon cycle. However, large uncertainties in current global estimations persist. In this study, we examined the performance of a process-based model (Integrated BIosphere Simulator, IBIS) at 62 eddy covariance sites around the world. Our results indicated that the IBIS model explained 60% of the observed variation in daily GPP at all validation sites. Comparison with a satellite-based vegetation model (Eddy Covariance-Light Use Efficiency, EC-LUE) revealed that the IBIS simulations yielded comparable GPP results as the EC-LUE model. Global mean GPP estimated by the IBIS model was 107.50±1.37 Pg C year(−1) (mean value ± standard deviation) across the vegetated area for the period 2000–2006, consistent with the results of the EC-LUE model (109.39±1.48 Pg C year(−1)). To evaluate the uncertainty introduced by the parameter V(cmax), which represents the maximum photosynthetic capacity, we inversed V(cmax) using Markov Chain-Monte Carlo (MCMC) procedures. Using the inversed V(cmax) values, the simulated global GPP increased by 16.5 Pg C year(−1), indicating that IBIS model is sensitive to V(cmax), and large uncertainty exists in model parameterization. |
format | Online Article Text |
id | pubmed-4222824 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-42228242014-11-13 Global Validation of a Process-Based Model on Vegetation Gross Primary Production Using Eddy Covariance Observations Liu, Dan Cai, Wenwen Xia, Jiangzhou Dong, Wenjie Zhou, Guangsheng Chen, Yang Zhang, Haicheng Yuan, Wenping PLoS One Research Article Gross Primary Production (GPP) is the largest flux in the global carbon cycle. However, large uncertainties in current global estimations persist. In this study, we examined the performance of a process-based model (Integrated BIosphere Simulator, IBIS) at 62 eddy covariance sites around the world. Our results indicated that the IBIS model explained 60% of the observed variation in daily GPP at all validation sites. Comparison with a satellite-based vegetation model (Eddy Covariance-Light Use Efficiency, EC-LUE) revealed that the IBIS simulations yielded comparable GPP results as the EC-LUE model. Global mean GPP estimated by the IBIS model was 107.50±1.37 Pg C year(−1) (mean value ± standard deviation) across the vegetated area for the period 2000–2006, consistent with the results of the EC-LUE model (109.39±1.48 Pg C year(−1)). To evaluate the uncertainty introduced by the parameter V(cmax), which represents the maximum photosynthetic capacity, we inversed V(cmax) using Markov Chain-Monte Carlo (MCMC) procedures. Using the inversed V(cmax) values, the simulated global GPP increased by 16.5 Pg C year(−1), indicating that IBIS model is sensitive to V(cmax), and large uncertainty exists in model parameterization. Public Library of Science 2014-11-06 /pmc/articles/PMC4222824/ /pubmed/25375227 http://dx.doi.org/10.1371/journal.pone.0110407 Text en © 2014 Liu et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Liu, Dan Cai, Wenwen Xia, Jiangzhou Dong, Wenjie Zhou, Guangsheng Chen, Yang Zhang, Haicheng Yuan, Wenping Global Validation of a Process-Based Model on Vegetation Gross Primary Production Using Eddy Covariance Observations |
title | Global Validation of a Process-Based Model on Vegetation Gross Primary Production Using Eddy Covariance Observations |
title_full | Global Validation of a Process-Based Model on Vegetation Gross Primary Production Using Eddy Covariance Observations |
title_fullStr | Global Validation of a Process-Based Model on Vegetation Gross Primary Production Using Eddy Covariance Observations |
title_full_unstemmed | Global Validation of a Process-Based Model on Vegetation Gross Primary Production Using Eddy Covariance Observations |
title_short | Global Validation of a Process-Based Model on Vegetation Gross Primary Production Using Eddy Covariance Observations |
title_sort | global validation of a process-based model on vegetation gross primary production using eddy covariance observations |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4222824/ https://www.ncbi.nlm.nih.gov/pubmed/25375227 http://dx.doi.org/10.1371/journal.pone.0110407 |
work_keys_str_mv | AT liudan globalvalidationofaprocessbasedmodelonvegetationgrossprimaryproductionusingeddycovarianceobservations AT caiwenwen globalvalidationofaprocessbasedmodelonvegetationgrossprimaryproductionusingeddycovarianceobservations AT xiajiangzhou globalvalidationofaprocessbasedmodelonvegetationgrossprimaryproductionusingeddycovarianceobservations AT dongwenjie globalvalidationofaprocessbasedmodelonvegetationgrossprimaryproductionusingeddycovarianceobservations AT zhouguangsheng globalvalidationofaprocessbasedmodelonvegetationgrossprimaryproductionusingeddycovarianceobservations AT chenyang globalvalidationofaprocessbasedmodelonvegetationgrossprimaryproductionusingeddycovarianceobservations AT zhanghaicheng globalvalidationofaprocessbasedmodelonvegetationgrossprimaryproductionusingeddycovarianceobservations AT yuanwenping globalvalidationofaprocessbasedmodelonvegetationgrossprimaryproductionusingeddycovarianceobservations |