iForest - Biogeosciences and Forestry


Incorporating management history into forest growth modelling

CS Eastaugh   , H Hasenauer

iForest - Biogeosciences and Forestry, Volume 4, Issue 5, Pages 212-217 (2011)
doi: https://doi.org/10.3832/ifor0597-004
Published: Nov 03, 2011 - Copyright © 2011 SISEF

Research Articles

Collection/Special Issue: COST Action FP0903 (2010) - Rome (Italy)
Research, monitoring and modelling in the study of climate change and air pollution impacts on forest ecosystems
Guest Editors: E Paoletti, J-P Tuovinen, N Clarke, G Matteucci, R Matyssek, G Wieser, R Fischer, P Cudlin, N Potocic

Mechanistic modelling is an important tool for understanding the impacts of climate change and pollutants on forest growth. One of the common practical limitations of these models is a lack of specific information regarding management activities such as thinning or harvesting, which can have a very strong influence on the accuracy of results. The use of inventory data for model parameterization and calibration is also problematic, as inventories are designed to have large volumes of data amalgamated to give accurate mean results across large areas. The precision of single point estimates is often quite low.This study uses BIOME-BGC to model forest growth on 1133 sites of the Austrian National Forest Inventory, and develops a method to estimate timber removal patterns prior to the commencement of record keeping on the sites. Recognizing the poor precision of individual point estimates in the data, we do not seek to precisely calibrate the model to the data on each point. Rather, we assume that the point-wise inventory estimates will be normally distributed around the true values. We then model each site assuming no management interventions, and compare this with inventory results. Plotting the “error” between model results and NFI data shows a strong right-skew, reflecting the modelled lack of timber removals. A Box-Cox transformation of the error plot, centred on zero, would represent an unbiased model estimate of the data, thus we can determine the historic timber removals as the difference between the original error curve and its Box-Cox transformation. Calibrating the model with this information allow us to represent forest volume with greater accuracy than would otherwise be possible.


BIOME-BGC, Inventory, Uncertainty, Thinning, Model initialisation

Authors’ address

CS Eastaugh
H Hasenauer
Institute of Silviculture, University of Natural Resources and Life Sciences (BOKU), Vienna (Austria).

Corresponding author



Eastaugh CS, Hasenauer H (2011). Incorporating management history into forest growth modelling. iForest 4: 212-217. - doi: 10.3832/ifor0597-004

Paper history

Received: Nov 30, 2010
Accepted: Aug 22, 2011

First online: Nov 03, 2011
Publication Date: Nov 03, 2011
Publication Time: 2.43 months

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