Assessing the response of forest productivity to climate extremes in Switzerland using model–data fusion

Volodymyr Trotsiuk, Florian Hartig, Maxime Cailleret, Flurin Babst, David I. Forrester, Andri Baltensweiler, Nina Buchmann, Harald Bugmann, Arthur Gessler, Mana Gharun, Francesco Minunno, Andreas Rigling, Brigitte Rohner, Jonas Stillhard, Esther Thürig, Peter Waldner, Marco Ferretti, Werner Eugster, Marcus Schaub

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)2463-2476
Number of pages14
JournalGlobal change biology
Volume26
Issue number4
DOIs
StatePublished - Apr 1 2020
Externally publishedYes

Keywords

  • Bayesian inference
  • Fagus sylvatica
  • Picea abies
  • carbon cycling
  • data assimilation
  • drought
  • ecosystem productivity
  • extreme events
  • inverse modeling
  • model calibration

ASJC Scopus subject areas

  • Global and Planetary Change
  • Environmental Chemistry
  • Ecology
  • Environmental Science(all)

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