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.

Physiological response of Swiss ecosystems to 2018 drought across plant types and elevation

“…were positively affected. There, both grasslands and forests profited, because precipitation in normal years tends to be in surplus of plant and atmospheric demand, and temperatures are below the plant physiological growth optimum, so that a drought period similar to 2018 brings high elevation sites closer towards optimum growth conditions [72,73]. By contrast, conditions at lower elevation sites shifted away from such an optimum, towards too warm and too dry [73].…”

“…There, both grasslands and forests precipitation in normal years tends to be in surplus of plant and atmospheric 290 demand, and temperatures are below the plant physiological growth optimum, so that a drought 291 period similar to 2018 brings high elevation sites closer towards optimum growth conditions 292[63,64]. In contrast, conditions at lower elevation sites shifted away from such an optimum, 293 towards too warm and too dry[64].294Mountain ranges in general are orographic barriers that tend to lift the advected air, which leads 295 to orographic precipitation[65,66] that is still available during drought years, although less296 abundant than in normal years. During summer, thunderstorms and shower cells tend to travel 297 along orographic structures, in Switzerland along the Alps [67].…”

Tree growth in Switzerland is increasingly constrained by rising evaporative demand

“…increasing vapour pressure deficit, VPD) and low soil water availability (Allen et al, 2015;Gharun et al, 2020;Morales-Castilla et al, 2020;Pachauri et al, 2014;Williams et al, 2013). These alterations in environmental conditions have led to important changes in forest dynamics and distribution, including a vegetation shift to higher elevation and/or latitudes (Lamprecht et al, 2018;Rees et al, 2020), largescale die-off events at the dry edge of species' distribution ranges (Hartmann et al, 2015), reduced primary productivity (Trotsiuk, Hartig, Cailleret, et al, 2020), and a general shift towards younger stands with a faster generation turnover (McDowell et al, 2020).…”

r3PG – Anrpackage for simulating forest growth using the 3‐PG process‐based model

“…Several recent studies that calibrated 3-PG or made larger spatial simulations (e.g. Augustynczik et al, 2017;Thomas et al, 2017;Trotsiuk et al, 2020) therefore used Fortran re-implementations of 3-PG. These implementations, however, were not designed for 'end users' with appropriate help and instruction files, were not made available on the comprehensive r archive network (CRAN) and did not include the above-mentioned extensions of the model for deciduous, uneven-aged or mixed-species forests.…”