Extending a physiological forest growth model by an observation-based tree competition module improves spatial representation of diameter growth

One of the pivotal objectives in forestry research is to estimate the response of silvicultural target variables to climate change scenarios at high temporal resolution in order to consider within-year feedbacks between growth and environmental conditions. To meet this challenge, models are needed which support and complement the widely used observation-based decision systems in forest management and consulting. Physiological models in particular provide the fundamental prerequisites to reflect the impact of various simultaneously changing environmental conditions. However, a physiological representation at the individual tree level is computationally very expensive and sensitive to uncertain initializations. We thus propose an approach that combines a modern representative of the physiological cohort model type, MoBiLE-PSIM, with the individual tree competition concept of a distance-dependent empirical growth simulator (SILVA). The resulting hybrid provides a key feature for the consideration of forest management in long-term simulations at high computational efficiency. The extended model was evaluated with growth-diameter distributions obtained from core-boring at two beech (Fagus sylvatica L.) forest sites in south-west Germany that differ in exposure and soil conditions. The mean bias of annual stand-scale growth from 2001 to 2007 decreased from -0.59 to -0.41 mm at one evaluation plot and from -0.55 to -0.24 mm at the other when the competition module was coupled in. Inclusion of the SILVA-based individual tree module into MoBiLE-PSIM improved the size-dependent representation of competition and growth on five-year and even annual timescale. This was particularly the case where the spatial distribution of dominant trees was clustered.