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

Abstract

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 standscale 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.

Assessing the effects of management on forest growth across France: insights from a new functional–structural model

“…Lastly, CASTANEA -SSM will be used to investigate the impact of fundamental competition rules (such as self-thinning; Caspersen et al, 2011) on long-term forest stand functioning, along with the size dependency of physiological processes (e.g. the effect of hydraulic conductance on stomatal control and growth), which has been reported as a major key challenge for physiological modelling (Poschenrieder et al, 2013). In the following we discuss: (1) the rules of tree growth competition used in the SSM calibration; (2) the functional implications of thinning in terms of stand -atmosphere C fluxes and within-stand C allocation; and (3) the potential of management to affect forest functioning at the national scale.…”

“…We consequently highlighted a monotonous change with stand age of the CBH threshold for significant growth, which is not influenced by the environmental factors causing growth to vary and indicates a conservative size hierarchy in beech and oak stands. Further, these results indicate that tree growth competition in deciduous mature temperate forest can be represented without the spatially explicit framework that strongly limits the potential applications of most tree-level growth models (Poschenrieder et al, 2013).…”

“…Wang et al (2011) simulated the impact of thinning on a coniferous forest within the TRIPLEX model using empirical prediction of the tree size distribution characteristics. In another approach, PBMs were coupled with empirical tree growth models to simulate the size trajectories of all the trees of a forest stand (Bellassen et al, 2010;Mäkelä et al, 2013;Poschenrieder et al, 2013). In a national-scale study, Bellassen et al (2010) incorporated the fundamental rules of tree growth competition exposed in Dhôte (1999) into the global-scale PBM ORCHIDEE (Krinner et al 2005).…”

Forest biomass carbon pool dynamics in Tibet Autonomous Region of China: Inventory data 1999-2019

“…The second utilizes non-destructive methods to estimate forest biomass on a large scale, using models, parameters, and remote sensing techniques [16,17]. However, owing to the complexity of forest ecosystem structures, the non-destructive methods have a low degree of accuracy and a high level of uncertainty, and most methods fail to achieve continuous monitoring [18,19]. In recent years, the guidelines of the Intergovernmental Panel on Climate Change (IPCC) have been widely applied.…”