Given the multiple abiotic and biotic stressors resulting from global changes, management systems and practices must be adapted in order to maintain and reinforce the resilience of forests. Among others, the transformation of monocultures into uneven-aged and mixed stands is an avenue to improve forest resilience. To explore the forest response to these new silvicultural practices under a changing environment, one needs models combining a processbased approach with a detailed spatial representation, which is quite rare.We therefore decided to develop our own model (HET-EROFOR for HETEROgeneous FORest) according to a spatially explicit approach, describing individual tree growth based on resource sharing (light, water and nutrients). HETEROFOR was progressively elaborated within Capsis (Computer-Aided Projection for Strategies in Silviculture), a collaborative modelling platform devoted to tree growth and stand dynamics.This paper describes the carbon-related processes of HET-EROFOR (photosynthesis, respiration, carbon allocation and tree dimensional growth) and evaluates the model performances for three broadleaved stands with different species compositions (Wallonia, Belgium). This first evaluation showed that HETEROFOR predicts well individual radial growth (Pearson's correlation of 0.83 and 0.63 for the European beech and sessile oak, respectively) and is able to repro-duce size-growth relationships. We also noticed that the net to gross primary production (npp to gpp) ratio option for describing maintenance respiration provides better results than the temperature-dependent routine, while the process-based (Farquhar model) and empirical (radiation use efficiency) approaches perform similarly for photosynthesis. To illustrate how the model can be used to predict climate change impacts on forest ecosystems, we simulated the growth dynamics of the mixed stand driven by three IPCC climate scenarios. According to these simulations, the tree growth trends will be governed by the CO 2 fertilization effect, with the increase in vegetation period length and the increase in water stress also playing a role but offsetting each other.

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Abstract. Climate change affects forest growth in numerous and sometimes opposite ways, and the resulting trend is often difficult to predict for a given site. Integrating and structuring the knowledge gained from the monitoring and experimental studies into process-based models is an interesting approach to predict the response of forest ecosystems to climate change. While the first generation of models operates at stand level, one now needs spatially explicit individual-based approaches in order to account for individual variability, local environment modification and tree adaptive behaviour in mixed and uneven-aged forests that are supposed to be more resilient under stressful conditions. The local environment of a tree is strongly influenced by the neighbouring trees, which modify the resource level through positive and negative interactions with the target tree. Among other things, drought stress and vegetation period length vary with tree size and crown position within the canopy. In this paper, we describe the phenology and water balance modules integrated in the tree growth model HETEROFOR (HETEROgenous FORest) and evaluate them on six heterogeneous sessile oak and European beech stands with different levels of mixing and development stages and installed on various soil types. More precisely, we assess the ability of the model to reproduce key phenological processes (budburst, leaf development, yellowing and fall) as well as water fluxes. Two two-phase models differing regarding their response function to temperature during the chilling period (optimum and sigmoid functions) and a simplified one-phase model are used to predict budburst date. The two-phase model with the optimum function is the least biased (overestimation of 2.46 d), while the one-phase model best accounts for the interannual variability (Pearson's r=0.68). For the leaf development, yellowing and fall, predictions and observations are in accordance. Regarding the water balance module, the predicted throughfall is also in close agreement with the measurements (Pearson's r=0.856; bias =-1.3 %), and the soil water dynamics across the year are well reproduced for all the study sites (Pearson's r was between 0.893 and 0.950, and bias was between −1.81 and −9.33 %). The model also reproduced well the individual transpiration for sessile oak and European beech, with similar performances at the tree and stand scale (Pearson's r of 0.84–0.85 for sessile oak and 0.88–0.89 for European beech). The good results of the model assessment will allow us to use it reliably in projection studies to evaluate the impact of climate change on tree growth in structurally complex stands and test various management strategies to improve forest resilience.

Abstract. Climate change affects forest growth in numerous and sometimes opposite ways and the resulting trend is often difficult to predict for a given site. Integrating and structuring the knowledge gained from the monitoring and experimental studies into process-based models is an interesting approach to predict the response of forest ecosystems to climate change. While the first generation of such models operates at stand level, we need now individual-based and spatially-explicit approaches in order to account for structurally complex stands whose importance is increasingly recognized in the changing environment context. Among the climate-sensitive drivers of forest growth, phenology and water availability are often cited as crucial elements. They influence, for example, the length of the vegetation period during which photosynthesis takes place and the stomata opening, which determines the photosynthesis rate. In this paper, we describe the phenology and water balance modules integrated in the tree growth model HETEROFOR and evaluate them on six Belgian sites. More precisely, we assess the ability of the model to reproduce key phenological processes (budburst, leaf development, yellowing and fall) as well as water fluxes. Three variants are used to predict budburst (Uniforc, Unichill and Sequential), which differ regarding the inclusion of chilling and/or forcing periods and the calculation of the coldness or heat accumulation. Among the three, the Sequential approach is the least biased (overestimation of 2.46 days) while Uniforc (chilling not considered) best accounts for the interannual variability (Pearson’s R = 0.68). For the leaf development, yellowing and fall, predictions and observation are in accordance. Regarding the water balance module, the predicted throughfall is also in close agreement with the measurements (Pearson’s R = 0.856, bias = −1.3 %) and the soil water dynamics across the year is well-reproduced for all the study sites (Pearson’s R comprised between 0.893 and 0.950, and bias between −1.81 and −9.33 %). The positive results from the model assessment will allow us to use it reliably in projection studies to evaluate the impact of climate change on tree growth and test how diverse forestry practices can adapt forests to these changes.