Comparing simple respiration models for eddy flux and dynamic chamber data

Authors

Andrew D. Richardson, University of New HampshireFollow
Rob Braswell, University of New Hampshire - Main CampusFollow
David Y. Hollinger, USDA Forest Service
Prabir Burman, University of California, Davis
Eric A. Davidson, Woods Hole Research Center, Woods Hole, MA
R L. Evans, Woods Hole Oceanographic Institution
Lawrence B. Flanagan, University of Lethbridge
J W. Munger, Harvard University
Kathleen Savage, Woods Hole Oceanographic Institution
Shawn Urbanski, Harvard University
Steven C. Wofsy, Harvard University

Abstract

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 (R_eco) 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.

Department

Earth Sciences, Earth Systems Research Center

Publication Date

12-20-2006

Journal Title

Agricultural and Forest Meteorology

Publisher

Elsevier

Digital Object Identifier (DOI)

10.1016/j.agrformet.2006.10.010

Document Type

Article

Recommended Citation

ndrew D. Richardson, Bobby H. Braswell, David Y. Hollinger, Prabir Burman, Eric A. Davidson, Robert S. Evans, Lawrence B. Flanagan, J. William Munger, Kathleen Savage, Shawn P. Urbanski, Steven C. Wofsy, Comparing simple respiration models for eddy flux and dynamic chamber data, Agricultural and Forest Meteorology, Volume 141, Issues 2–4, 20 December 2006, Pages 219-234, ISSN 0168-1923, http://dx.doi.org/10.1016/j.agrformet.2006.10.010.

Rights

Copyright © 2006 Elsevier B.V. All rights reserved.