TY - JOUR
T1 - Uncertainty in model parameters and regional carbon fluxes
T2 - A model-data fusion approach
AU - Xiao, Jingfeng
AU - Davis, Kenneth J.
AU - Urban, Nathan M.
AU - Keller, Klaus
N1 - Funding Information:
This study was supported by the National Aeronautics and Space Administration (NASA) Terrestrial Ecology Program, the National Science Foundation (NSF) through the MacroSystems Biology program (award number: 1065777 ), and the Department of Energy's Office of Biological and Environmental Research, Terrestrial Carbon Program and National Institute for Climatic Change Research (NICCR) . We thank K. Cherrey, A. Desai, P. Curtis, J. Chen, A. Noormets, N. Saliendra, and other research personnel for contributions to the flux observations and site biomass data used in this study. We thank the anonymous reviewers for their constructive comments on the manuscript.
PY - 2014/6/1
Y1 - 2014/6/1
N2 - Models have been widely used to estimate carbon fluxes at regional scales, and the uncertainty of modeled fluxes, however, has rarely been quantified and remains a challenge. A quantitative uncertainty assessment of regional flux estimates is essential for better understanding of terrestrial carbon dynamics and informing carbon and climate decision-making. We use a simple ecosystem model, eddy covariance (EC) flux observations, and a model-data fusion approach to assess the uncertainty of regional carbon flux estimates for the Upper Midwest region of northern Wisconsin and Michigan, USA. We combine net ecosystem exchange (NEE) observations and an adaptive Markov chain Monte Carlo (MCMC) approach to quantify the parameter uncertainty of the Diagnostic Carbon Flux Model (DCFM). Our MCMC approach eliminates the need for an initial equilibration or "burn-in" phase of the random walk, and also improves the performance of the algorithm for parameter optimization. For each plant functional type (PFT), we use NEE observations from multiple EC sites to estimate parameters, and the resulting parameter estimates are more representative of the PFT than estimates based on observations from a single site. A probability density function (PDF) is generated for each parameter, and the spread of the PDF provides an estimate of parameter uncertainty. We then apply the model with parameter PDFs to estimate NEE for each grid cell across our study region, and propagate the parameter uncertainty through simulations to produce probabilistic flux estimates. Over the period from 2001 to 2007, the mean annual NEE of the region was estimated to be -30.0TgCyr-1, and the associated uncertainty as measured by standard deviation was±7.6TgCyr-1. Uncertainty in parameters can lead to a large uncertainty to estimates of regional carbon fluxes, and our model-data approach can provide uncertainty bounds to regional carbon fluxes. Future research is needed to apply our approach to more complex ecosystem models, assess the usefulness, validity, and alternatives of the PFT and vegetation type concepts, and to fully quantify the uncertainty of regional carbon fluxes by incorporating other sources of uncertainty.
AB - Models have been widely used to estimate carbon fluxes at regional scales, and the uncertainty of modeled fluxes, however, has rarely been quantified and remains a challenge. A quantitative uncertainty assessment of regional flux estimates is essential for better understanding of terrestrial carbon dynamics and informing carbon and climate decision-making. We use a simple ecosystem model, eddy covariance (EC) flux observations, and a model-data fusion approach to assess the uncertainty of regional carbon flux estimates for the Upper Midwest region of northern Wisconsin and Michigan, USA. We combine net ecosystem exchange (NEE) observations and an adaptive Markov chain Monte Carlo (MCMC) approach to quantify the parameter uncertainty of the Diagnostic Carbon Flux Model (DCFM). Our MCMC approach eliminates the need for an initial equilibration or "burn-in" phase of the random walk, and also improves the performance of the algorithm for parameter optimization. For each plant functional type (PFT), we use NEE observations from multiple EC sites to estimate parameters, and the resulting parameter estimates are more representative of the PFT than estimates based on observations from a single site. A probability density function (PDF) is generated for each parameter, and the spread of the PDF provides an estimate of parameter uncertainty. We then apply the model with parameter PDFs to estimate NEE for each grid cell across our study region, and propagate the parameter uncertainty through simulations to produce probabilistic flux estimates. Over the period from 2001 to 2007, the mean annual NEE of the region was estimated to be -30.0TgCyr-1, and the associated uncertainty as measured by standard deviation was±7.6TgCyr-1. Uncertainty in parameters can lead to a large uncertainty to estimates of regional carbon fluxes, and our model-data approach can provide uncertainty bounds to regional carbon fluxes. Future research is needed to apply our approach to more complex ecosystem models, assess the usefulness, validity, and alternatives of the PFT and vegetation type concepts, and to fully quantify the uncertainty of regional carbon fluxes by incorporating other sources of uncertainty.
UR - http://www.scopus.com/inward/record.url?scp=84894253759&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84894253759&partnerID=8YFLogxK
U2 - 10.1016/j.agrformet.2014.01.022
DO - 10.1016/j.agrformet.2014.01.022
M3 - Article
AN - SCOPUS:84894253759
SN - 0168-1923
VL - 189-190
SP - 175
EP - 186
JO - Agricultural and Forest Meteorology
JF - Agricultural and Forest Meteorology
ER -
