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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 11, issue 2
Geosci. Model Dev., 11, 497-519, 2018
https://doi.org/10.5194/gmd-11-497-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 11, 497-519, 2018
https://doi.org/10.5194/gmd-11-497-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Model description paper 05 Feb 2018

Model description paper | 05 Feb 2018

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

Chunjing Qiu1, Dan Zhu1, Philippe Ciais1, Bertrand Guenet1, Gerhard Krinner2, Shushi Peng3, Mika Aurela4, Christian Bernhofer5, Christian Brümmer6, Syndonia Bret-Harte7, Housen Chu8, Jiquan Chen9, Ankur R. Desai10, Jiří Dušek11, Eugénie S. Euskirchen7, Krzysztof Fortuniak12, Lawrence B. Flanagan13, Thomas Friborg14, Mateusz Grygoruk15, Sébastien Gogo16,17,18, Thomas Grünwald5, Birger U. Hansen14, David Holl19, Elyn Humphreys20, Miriam Hurkuck20,21,22, Gerard Kiely23, Janina Klatt24, Lars Kutzbach19, Chloé Largeron1,2, Fatima Laggoun-Défarge16,17,18, Magnus Lund25, Peter M. Lafleur26, Xuefei Li27, Ivan Mammarella27, Lutz Merbold28, Mats B. Nilsson29, Janusz Olejnik30,31, Mikaell Ottosson-Löfvenius29, Walter Oechel32, Frans-Jan W. Parmentier33,34, Matthias Peichl29, Norbert Pirk35, Olli Peltola27, Włodzimierz Pawlak12, Daniel Rasse36, Janne Rinne35, Gaius Shaver37, Hans Peter Schmid24, Matteo Sottocornola38, Rainer Steinbrecher24, Torsten Sachs39, Marek Urbaniak30, Donatella Zona31,40, and Klaudia Ziemblinska30 Chunjing Qiu et al.
  • 1Laboratoire des Sciences du Climat et de l'Environnement, UMR8212, CEA-CNRS-UVSQ, Gif-sur-Yvette, France
  • 2CNRS, Université Grenoble Alpes, Institut de Géosciences de l'Environnement (IGE), Grenoble, France
  • 3Department of Ecology, College of Urban and Environmental Sciences, Peking University, Beijing, China
  • 4Finnish Meteorological Institute, Climate Change Research, Helsinki, Finland
  • 5Technische Universität (TU) Dresden, Institute of Hydrology and Meteorology, Chair of Meteorology, Dresden, Germany
  • 6Thünen Institute of Climate-Smart Agriculture, Bundesallee 50, Braunschweig, Germany
  • 7Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, AK, USA
  • 8Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, USA
  • 9Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI, USA
  • 10Department of Atmospheric and Oceanic Sciences, University of Wisconsin–Madison, Madison, WI, USA
  • 11Department of Matters and Energy Fluxes, Global Change Research Institute, Czech Academy of Sciences, Brno, Czech Republic
  • 12Department of Meteorology and Climatology, University of Łódź, Narutowicza 88, Łódź, Poland
  • 13Department of Biological Sciences, University of Lethbridge, Lethbridge, Alberta, Canada
  • 14Department of Geosciences and Natural Resource Management, University of Copenhagen, Oester Voldgade 10, Copenhagen, Denmark
  • 15Department of Hydraulic Engineering, Warsaw University of Life Sciences–SGGW, Nowoursynowska 159, Warsaw, Poland
  • 16Université d'Orléans, ISTO, UMR7327, 45071 Orléans, France
  • 17CNRS, ISTO, UMR7327, Orléans, France
  • 18BRGM, ISTO, UMR7327, BP36009, Orléans, France
  • 19Institute of Soil Science, Center for Earth System Research and Sustainability (CEN), Universität Hamburg, Hamburg, Germany
  • 20Department of Geography and Environmental Studies, Carleton University, Ottawa, Canada
  • 21Department of Geography and Environmental Studies, Wilfrid Laurier University, Waterloo, Canada
  • 22Départment de Géographie, Université de Montréal, Montréal, Canada
  • 23Department of Civil and Environmental Engineering, University College Cork, Cork, Ireland
  • 24Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK–IFU), Garmisch-Partenkirchen, Germany
  • 25Department of Bioscience, Arctic Research Centre, Aarhus University, Roskilde, Denmark
  • 26School of the Environment – Geography, Trent University, Peterborough, Ontario, Canada
  • 27Department of Physics, University of Helsinki, Helsinki, Finland
  • 28Mazingira Centre, International Livestock Research Institute (ILRI), Nairobi, Kenya
  • 29Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, Sweden
  • 30Department of Meteorology, Poznań University of Life Sciences, Poznań, Poland
  • 31Department of Matter and Energy Fluxes, Global Change Research Center, AS CR, v.v.i. Belidla 986/4a, Brno, Czech Republic
  • 32Department of Biology, San Diego State University, San Diego, CA, USA
  • 33The Arctic University of Norway, Institute for Arctic and Marine Biology, Postboks 6050 Langnes, Tromsø, Norway
  • 34Department of Geosciences, University of Oslo, Postboks 1022 Blindern, Oslo, Norway
  • 35Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden
  • 36Norwegian Institute of Bioeconomy Research, Oslo, Akershus, Norway
  • 37Marine Biological Laboratory, The Ecosystems Center, Woods Hole, MA, USA
  • 38Department of Science, Waterford Institute of Technology, Waterford, Ireland
  • 39Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Potsdam, Germany
  • 40Department of Animal and Plant Sciences, University of Sheffield, Western Bank, Sheffield, UK

Abstract. 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 = 0.76; Nash–Sutcliffe modeling efficiency, MEF = 0.76) and ecosystem respiration (ER, r2 = 0.78, MEF = 0.75), with lesser accuracy for latent heat fluxes (LE, r2 = 0.42, MEF = 0.14) and and net ecosystem CO2 exchange (NEE, r2 = 0.38, MEF = 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.

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Northern peatlands store large amount of soil carbon and are vulnerable to climate change. We implemented peatland hydrological and carbon accumulation processes into the ORCHIDEE land surface model. The model was evaluated against EC measurements from 30 northern peatland sites. The model generally well reproduced the spatial gradient and temporal variations in GPP and NEE at these sites. Water table depth was not well predicted but had only small influence on simulated NEE.
Northern peatlands store large amount of soil carbon and are vulnerable to climate change. We...
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