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.