A thinning routine for large-scale biogeochemical mechanistic ecosystem models

• A stand-level thinning model has been developed.
• It is intended to be used within large-scale biogeochemical modelling applications.
• It is based on the Austrian forest inventory data.
• The model is based on logistic regression and empirical probability density maps.
• Validation shows that the model is able to correctly mimic the thinning intensity.

Biogeochemical mechanistic models (BGC models) are used to model the carbon balance of forest ecosystems. Since European forests are managed intensively, a crucial part of carbon modelling is integrating management and thinning routines in the modelling process. In this study, forest inventory data are used to derive information concerning forest management practice. Based on this, a harvesting model is calibrated for simulating the ‘business as usual’ management that can be used in large-scale BGC models. Our approach is based on data from the Austrian National Forest Inventory. The model comprises two sub-models: (1) a logistic model to assess the probability of an inventory point to be thinned and (2) a non-parametric model based on empirical probability density maps to assess the thinning intensity. Since BGC models operate on the stand level, only stand level parameters are integrated in the model such as standing timber carbon, site quality, cover type, elevation and age. A comparison of the predicted and observed proportion of thinned points and the thinning intensity suggests that the model is able to correctly mimic the management regime derived from the inventory data. No systematic trends in the results are evident. Using this thinning model in combination with a mechanistic model will enable assessment of the overall carbon stored in managed forest ecosystems, especially in large-scale modelling applications.