© 2005 Heron Publishing—Victoria, Canada
On the validation of models of forest CO2 exchange using eddy covariance data: some perils and pitfalls
Belinda E. Medlyn (1, 2), Andrew P. Robinson (3), Robert Clement (4) and Ross E. McMurtrie (1)
1. School of Biological, Earth and Environmental Sciences, University of NSW, UNSW Sydney 2052, Australia / 2. Corresponding author (b.medlyn@unsw.edu.au) / 3. Department of Forest Resources, University of Idaho, Moscow, ID 83843, USA / 4. School of GeoSciences, University of Edinburgh, King’s Buildings, Mayfield Road, Edinburgh, EH9 3JG, U.K. / Received August 6, 2004; accepted November 5, 2004; published online May 2, 2005
Summary
With the widespread application of eddy covariance technology, long-term records of hourly ecosystem mass and energy exchange
are becoming available for forests around the world. These data sets hold great promise for testing and validation of models
of forest function. However, model validation is not a straightforward task. The goals of this paper were to: (1) review some
of the problems inherent in model validation; and (2) survey the tools available to modelers to improve validation procedures,
with particular reference to eddy covariance data. A simple set of models applied to a data set of ecosystem CO2 exchange is used to illustrate our points.
The major problems discussed are equifinality, insensitivity and uncertainty. Equifinality is the problem that different models,
or different parameterizations of the same model, may yield similar results, making it difficult to distinguish which is correct.
Insensitivity arises because the major sources of variation in eddy covariance data are the annual and diurnal cycles, which
are represented by even the most basic models, and the size of the response to these cycles can mask effects of other driving
variables. Uncertainty arises from three main sources: parameters, model structure and data, each of which is discussed in
turn. Uncertainty is a particular issue with eddy covariance data because of the lack of replicated measurements and the potential
for unquantified systematic errors such as flux loss due to advection.
We surveyed several tools that improve model validation, including sensitivity analysis, uncertainty analysis, residual analysis
and model comparison. Illustrative examples are used to demonstrate the use of each tool. We show that simplistic comparisons
of model outputs with eddy covariance data are problematic, but use of these tools can greatly improve our confidence in model
predictions.
Keywords:
eddy flux, identifiability, model comparison, model validation, residual analysis, sensitivity, uncertainty.
