@article{cbc94286de2846328c2025be674cb106,
title = "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.",
keywords = "Bayesian inference, Fagus sylvatica, Picea abies, carbon cycling, data assimilation, drought, ecosystem productivity, extreme events, inverse modeling, model calibration",
author = "Volodymyr Trotsiuk and Florian Hartig and Maxime Cailleret and Flurin Babst and Forrester, {David I.} and Andri Baltensweiler and Nina Buchmann and Harald Bugmann and Arthur Gessler and Mana Gharun and Francesco Minunno and Andreas Rigling and Brigitte Rohner and Jonas Stillhard and Esther Th{\"u}rig and Peter Waldner and Marco Ferretti and Werner Eugster and Marcus Schaub",
note = "Funding Information: This study was funded by the SwissForestLab based on a proposal by W.E. and co‐authors. V.T., W.E., M.S., M.F., and N.B. designed the research. V.T. and F.H. performed the analysis. V.T., F.H., M.C., F.B., D.F., and M.S. wrote the paper with substantial inputs from all co‐authors. We are thankful to Dirk Schmatz for providing the gridded DAYMET data, and acknowledge MeteoSwiss for providing meteorological station data for the spatial interpolation. We acknowledge WSL and ETH and its scientists, field staff, laboratory personal, and database managers who designed, carried out, and maintained the measurements on the permanent monitoring plots used in this study. We also are grateful to Samuel Abiven, Susanne Burri, Frank Hagedorn, Heike Lischke, Ansgar Kahmen, Nele Rogiers, Kerstin Treydte, Lorenz Walthert, and Roman Zweifel for their contributions to the project. We acknowledge Michel Piot for statistical support in initial phase of the project and Simpal Kumar for support in data extraction. We further thank the reviewer and editor for very constructive feedback. Funding Information: This study was funded by the SwissForestLab based on a proposal by W.E. and co-authors. V.T., W.E., M.S., M.F., and N.B. designed the research. V.T. and F.H. performed the analysis. V.T., F.H., M.C., F.B., D.F., and M.S. wrote the paper with substantial inputs from all co-authors. We are thankful to Dirk Schmatz for providing the gridded DAYMET data, and acknowledge MeteoSwiss for providing meteorological station data for the spatial interpolation. We acknowledge WSL and ETH and its scientists, field staff, laboratory personal, and database managers who designed, carried out, and maintained the measurements on the permanent monitoring plots used in this study. We also are grateful to Samuel Abiven, Susanne Burri, Frank Hagedorn, Heike Lischke, Ansgar Kahmen, Nele Rogiers, Kerstin Treydte, Lorenz Walthert, and Roman Zweifel for their contributions to the project. We acknowledge Michel Piot for statistical support in initial phase of the project and Simpal Kumar for support in data extraction. We further thank the reviewer and editor for very constructive feedback. Evaluations were based on data from the: (a) Swiss National Forest Inventory (https://www.lfi.ch); (b) Experimental Forest Management (EFM) (https://www.wsl.ch/en/forest/forest-development-and-monitoring/growth-and-yield.html); (c) Swiss Long-term Forest Ecosystem Research LWF (www.lwf.ch), which is part of the UNECE Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests ICP Forests (www.icp-forests.net); (d) Swiss FluxNet (http://www.swissfluxnet.ch). The Swiss Federal Office for the Environment (FOEN) supported N.B. and W.E. during the preparatory phase of this project via grant 16.0074.PJ/Q351-0167. F.H. acknowledges funding from the Bavarian Climate Research Network (bayklif) via the research network BLIZ. F.B. acknowledges statutory funds from the W. Szafer Institute of Botany PAS, as well as support from the project ?Inside out? (#POIR.04.04.00-00-5F85/18-00) funded by the HOMING programme of the Foundation for Polish Science co-financed by the European Union under the European Regional Development Fund. MG acknowledges funding by Swiss National Science Foundation project ICOS-CH Phase 2 20FI20_173691. The data that support the findings of this study are available on request from the Swiss NFI (https://www.lfi.ch), Swiss Long-term Forest Ecosystem Research LWF (www.lwf.ch), and Swiss FluxNet (http://www.swissfluxnet.ch). For our simulations, we used a reimplementation of the 3-PG model programmed in Fortran 90 (Minunno, Hartig, & Trotsiuk,). Publisher Copyright: {\textcopyright} 2020 The Authors. Global Change Biology published by John Wiley & Sons Ltd",
year = "2020",
month = apr,
day = "1",
doi = "10.1111/gcb.15011",
language = "English (US)",
volume = "26",
pages = "2463--2476",
journal = "Global Change Biology",
issn = "1354-1013",
publisher = "Wiley-Blackwell",
number = "4",
}