HETEROFOR 1.0: a spatially explicit model for exploring the response of structurally complex forests to uncertain future conditions. II. Phenology and water cycle

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.