ORCHIDEE-PEAT (revision 4596), a model for northern peatland CO2, water, and energy fluxes on daily to annual scales

Research output: Contribution to journalArticle


Peatlands store substantial amounts of carbon and are vulnerable to climate change. We present a modified version of the Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) land surface model for simulating the hydrology, surface energy, and CO2 fluxes of peatlands on daily to annual timescales. The model includes a separate soil tile in each 0.5° grid cell, defined from a global peatland map and identified with peat-specific soil hydraulic properties. Runoff from non-peat vegetation within a grid cell containing a fraction of peat is routed to this peat soil tile, which maintains shallow water tables. The water table position separates oxic from anoxic decomposition. The model was evaluated against eddy-covariance (EC) observations from 30 northern peatland sites, with the maximum rate of carboxylation (Vcmax) being optimized at each site. Regarding short-term day-to-day variations, the model performance was good for gross primary production (GPP) (r2 Combining double low line 0.76; Nash-Sutcliffe modeling efficiency, MEF Combining double low line 0.76) and ecosystem respiration (ER, r2 Combining double low line 0.78, MEF Combining double low line 0.75), with lesser accuracy for latent heat fluxes (LE, r2 Combining double low line 0.42, MEF Combining double low line 0.14) and and net ecosystem CO2 exchange (NEE, r2 Combining double low line 0.38, MEF Combining double low line 0.26). Seasonal variations in GPP, ER, NEE, and energy fluxes on monthly scales showed moderate to high r2 values (0.57-0.86). For spatial across-site gradients of annual mean GPP, ER, NEE, and LE, r2 values of 0.93, 0.89, 0.27, and 0.71 were achieved, respectively. Water table (WT) variation was not well predicted (r2<0.1), likely due to the uncertain water input to the peat from surrounding areas. However, the poor performance of WT simulation did not greatly affect predictions of ER and NEE. We found a significant relationship between optimized Vcmax and latitude (temperature), which better reflects the spatial gradients of annual NEE than using an average Vcmax value.


  • Chunjing Qiu
  • Dan Zhu
  • Philippe Ciais
  • Bertrand Guenet
  • Gerhard Krinner
  • Shushi Peng
  • Mika Aurela
  • Christian Bernhofer
  • Christian Brümmer
  • Syndonia Bret-Harte
  • Housen Chu
  • Jiquan Chen
  • Ankur R. Desai
  • Jǐrí Dušek
  • Eugénie S. Euskirchen
  • Krzysztof Fortuniak
  • Lawrence B. Flanagan
  • Thomas Friborg
  • Mateusz Grygoruk
  • Sébastien Gogo
  • Thomas Grünwald
  • Birger U. Hansen
  • David Holl
  • Elyn Humphreys
  • Miriam Hurkuck
  • Gerard Kiely
  • Janina Klatt
  • Lars Kutzbach
  • Chloé Largeron
  • Fatima Laggoun-Défarge
  • Magnus Lund
  • Peter M. Lafleur
  • Xuefei Li
  • Ivan Mammarella
  • Lutz Merbold
  • Mats B. Nilsson
  • Janusz Olejnik
  • Mikaell Ottosson-Löfvenius
  • Walter Oechel
  • Matthias Peichl
  • Norbert Pirk
  • Olli Peltola
  • Włodzimierz Pawlak
  • Daniel Rasse
  • Gaius Shaver
  • Hans Peter Schmid
  • Matteo Sottocornola
  • Rainer Steinbrecher
  • Torsten Sachs
  • Marek Urbaniak
  • Donatella Zona
  • Klaudia Ziemblinska
External organisations
  • University Grenoble Alpes
  • Peking University
  • Finnish Meteorological Institute
  • Dresden University of Technology
  • University of Alaska Fairbanks
  • University of California, Berkeley
  • Michigan State University
  • University of Wisconsin-Madison
  • University of Lodz
  • University of Lethbridge
  • University of Copenhagen
  • Warsaw University of Life Sciences
  • University of Orléans
  • University of Hamburg
  • Carleton University
  • Wilfrid Laurier University
  • University Of Quebec In Montreal
  • University College Cork
  • Karlsruhe Institute of Technology
  • Aarhus University
  • Trent University
  • University of Helsinki
  • International Livestock Research Institute Nairobi
  • Swedish University of Agricultural Sciences
  • San Diego State University
  • University of Oslo
  • UiT The Arctic University of Norway, Tromsø
  • Waterford Institute of Technology
  • University of Sheffield
  • Laboratoire des Sciences du Climat et de l'Environnement
  • Johann Heinrich von Thünen Institute
  • Global Change Research Centre of the Czech Academy of Sciences
  • Institut des Sciences de la Terre d'Orléans (ISTO)
  • Poznań University of Life Sciences
  • Norwegian Institute of Bioeconomy Research (Nibio)
  • Marine Biological Laboratory
  • GFZ German Research Centre for Geosciences
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Climate Research
Original languageEnglish
Pages (from-to)497-519
Number of pages23
JournalGeoscientific Model Development
Issue number2
Publication statusPublished - 2018 Feb 5
Publication categoryResearch