Assessing the response of forest productivity to climate extremes in Switzerland using model–data fusion
Abstract
The response of forest productivity to climate extremes strongly depends on ambient environmental and site conditions. To better understand these relationships at a regional scale, we used nearly 800 observation years from 271 permanent long‐term forest monitoring plots across Switzerland, obtained between 1980 and 2017. We assimilated these data into the 3‐PG forest ecosystem model using Bayesian inference, reducing the bias of model predictions from 14% to 5% for forest stem carbon stocks and from 45% to 9% for stem carbon stock changes. We then estimated the productivity of forests dominated by Picea abies and Fagus sylvatica for the period of 1960–2018, and tested for productivity shifts in response to climate along elevational gradient and in extreme years. Simulated net primary productivity (NPP) decreased with elevation (2.86 ± 0.006 Mg C ha⁻¹ year⁻¹ km⁻¹ for P. abies and 0.93 ± 0.010 Mg C ha⁻¹ year⁻¹ km⁻¹ for F. sylvatica). During warm–dry extremes, simulated NPP for both species increased at higher and decreased at lower elevations, with reductions in NPP of more than 25% for up to 21% of the potential species distribution range in Switzerland. Reduced plant water availability had a stronger effect on NPP than temperature during warm‐dry extremes. Importantly, cold–dry extremes had negative impacts on regional forest NPP comparable to warm–dry extremes. Overall, our calibrated model suggests that the response of forest productivity to climate extremes is more complex than simple shift toward higher elevation. Such robust estimates of NPP are key for increasing our understanding of forests ecosystems carbon dynamics under climate extremes.
What the paper shows and why it matters
(AI-generated)
Forests in cold, high-elevation places are limited by low temperature and may gain from warming, whereas forests at lower elevations may increasingly suffer from a lack of soil water. Research on extremes has concentrated on warm and dry years, cold and wet extremes have received less attention, and high-resolution estimates of how forest productivity responds to extremes are still rare. Such estimates can come from dynamic vegetation models (process-based computer simulations of vegetation) constrained by data, yet the paper notes that only few studies have fed extensive monitoring data into such a model at regional scale. The paper addresses this gap: whether such a model can show how productivity responds to extremes across environmental gradients, and whether the response amounts to more than a simple shift towards higher elevation.
The study finds that a forest model fitted to monitoring data indicates a response of forest productivity to climate extremes that is more complex than a shift towards higher elevation, with a cold–dry extreme year (1984) reducing the simulated productivity of Norway spruce and European beech stands in Switzerland about as much as warm–dry years. It combined almost 800 observation years from 271 plots with the 3-PG forest model, using Bayesian inference to estimate parameters and their uncertainty. Calibration brought prediction bias below 10% for all variables. The study simulated net primary productivity (NPP, the carbon plants take up net of their own respiration) of hypothetical stands of both species. During warm–dry extremes, simulated NPP of both species fell at lower elevations, and that of Norway spruce also rose at higher elevations in 2003, with reductions above 25% over up to 21% of a species’ potential range. The cold–dry year 1984 reduced NPP comparably to the warm–dry year 2018.
The paper reads these results as a case for including cold extremes alongside warm and dry ones and for spatially resolved analyses, because NPP changes vary non-linearly along elevation. It also presents them as showing that monitoring data from several networks can be integrated into a process-based model. It notes that 3-PG and similar models have often overestimated forest sensitivity to drought. A later paper on statistical inference for complex computer models uses the study as an example of such a model (Oberpriller et al. 2021).
References
- Oberpriller et al. (2021). Towards robust statistical inference for complex computer models. Ecology Letters 24, 1251–1261. doi:10.1111/ele.13728