On the information content of forest transpiration measurements for identifying canopy conductance model parameters

Abstract

AbstractGenerally, forest transpiration models contain model parameters that cannot be measured independently and therefore are tuned to fit the model results to measurements. Only unique parameter estimates with high accuracy can be used for extrapolation in time or space. However, parameter identification problems may occur as a result of the properties of the data set. Time‐series of environmental conditions, which control the forest transpiration, may contain periods with redundant or coupled information, so called collinearity, and other combinations of conditions may be measured only with difficulty or incompletely. The aim of this study is to select environmental conditions that yield a unique parameter set of a canopy conductance model. The parameter identification method based on localization of information (PIMLI) was used to calculate the information content of every individual artificial transpiration measurement. It is concluded that every measurement has its own information with respect to a parameter. Independent criteria were assessed to localize the environmental conditions, which contain measurements with most information. These measurements were used in separate subdata sets to identify the parameters. The selected measurements do not overlap and the accuracies of the parameter estimates are maximized. Measurements that were not selected do not contain additional information that can be used to further maximize the parameter accuracy. Thereupon, the independent criteria were used to select eddy correlation measurements and parameters were identified with only the selected measurements. It is concluded that, for this forest and data set, PIMLI identifies a unique parameter set with high accuracy, whereas conventional calibrations on subdata sets give non‐unique parameter estimates. Copyright © 2001 John Wiley & Sons, Ltd.