TY - JOUR
T1 - Comparing simple respiration models for eddy flux and dynamic chamber data
AU - Richardson, Andrew D.
AU - Braswell, Bobby H.
AU - Hollinger, David Y.
AU - Burman, Prabir
AU - Davidson, Eric A.
AU - Evans, Robert S.
AU - Flanagan, Lawrence B.
AU - Munger, J. William
AU - Savage, Kathleen
AU - Urbanski, Shawn P.
AU - Wofsy, Steven C.
N1 - Funding Information:
This project was supported by the Office of Science (BER), U.S. Department of Energy, through the National Institute for Global Environmental Change and TCP programs, the USDA Forest Service Northern Global Change Program, and the Natural Sciences and Engineering Research Council of Canada (NSERC). Research at Howland was supported by the Department of Energy through Agreement Nos. DE-AI02-00ER63028, DE-FC03-90ER61010, DE-FC02-03ER63613 and DE-FG02-00ER63002, with additional support from USDA Award No. 04-DG-11242343-016. Flux data from the tower sites are available at http://public.ornl.gov/ameriflux/ subject to AmeriFlux “Fair-use” rules.
PY - 2006/12/20
Y1 - 2006/12/20
N2 - Selection of an appropriate model for respiration (R) is important for accurate gap-filling of CO2 flux data, and for partitioning measurements of net ecosystem exchange (NEE) to respiration and gross ecosystem exchange (GEE). Using cross-validation methods and a version of Akaike's Information Criterion (AIC), we evaluate a wide range of simple respiration models with the objective of quantifying the implications of selecting a particular model. We fit the models to eddy covariance measurements of whole-ecosystem respiration (Reco) from three different ecosystem types (a coniferous forest, a deciduous forest, and a grassland), as well as soil respiration data from one of these sites. The well-known Q10 model, whether driven by air or soil temperature, performed poorly compared to other models, as did the Lloyd and Taylor model when used with two of the parameters constrained to previously published values and only the scale parameter being fit. The continued use of these models is discouraged. However, a variant of the Q10 model, in which the temperature sensitivity of respiration varied seasonally, performed reasonably well, as did the unconstrained three-parameter Lloyd and Taylor model. Highly parameterized neural network models, using additional covariates, generally provided the best fits to the data, but appeared not to perform well when making predictions outside the domain used for parameterization, and should thus be avoided when large gaps must be filled. For each data set, the annual sum of modeled respiration (annual ΣR) was positively correlated with model goodness-of-fit, implying that poor model selection may inject a systematic bias into gap-filled estimates of annual ΣR.
AB - Selection of an appropriate model for respiration (R) is important for accurate gap-filling of CO2 flux data, and for partitioning measurements of net ecosystem exchange (NEE) to respiration and gross ecosystem exchange (GEE). Using cross-validation methods and a version of Akaike's Information Criterion (AIC), we evaluate a wide range of simple respiration models with the objective of quantifying the implications of selecting a particular model. We fit the models to eddy covariance measurements of whole-ecosystem respiration (Reco) from three different ecosystem types (a coniferous forest, a deciduous forest, and a grassland), as well as soil respiration data from one of these sites. The well-known Q10 model, whether driven by air or soil temperature, performed poorly compared to other models, as did the Lloyd and Taylor model when used with two of the parameters constrained to previously published values and only the scale parameter being fit. The continued use of these models is discouraged. However, a variant of the Q10 model, in which the temperature sensitivity of respiration varied seasonally, performed reasonably well, as did the unconstrained three-parameter Lloyd and Taylor model. Highly parameterized neural network models, using additional covariates, generally provided the best fits to the data, but appeared not to perform well when making predictions outside the domain used for parameterization, and should thus be avoided when large gaps must be filled. For each data set, the annual sum of modeled respiration (annual ΣR) was positively correlated with model goodness-of-fit, implying that poor model selection may inject a systematic bias into gap-filled estimates of annual ΣR.
KW - Absolute deviations regression
KW - Akaike's information criterion (AIC)
KW - AmeriFlux
KW - Cross-validation
KW - Eddy covariance
KW - Maximum likelihood
KW - Model selection criteria
KW - Respiration
KW - Uncertainty
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U2 - 10.1016/j.agrformet.2006.10.010
DO - 10.1016/j.agrformet.2006.10.010
M3 - Article
AN - SCOPUS:33845336460
SN - 0168-1923
VL - 141
SP - 219
EP - 234
JO - Agricultural and Forest Meteorology
JF - Agricultural and Forest Meteorology
IS - 2-4
ER -