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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
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 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.

Citation: Qiu, C., Zhu, D., Ciais, P., Guenet, B., Krinner, G., Peng, S., Aurela, M., Bernhofer, C., Brümmer, C., Bret-Harte, S., Chu, H., Chen, J., Desai, A. R., Dušek, J., Euskirchen, E. S., Fortuniak, K., Flanagan, L. B., Friborg, T., Grygoruk, M., Gogo, S., Grünwald, T., Hansen, B. U., Holl, D., Humphreys, E., Hurkuck, M., Kiely, G., Klatt, J., Kutzbach, L., Largeron, C., Laggoun-Défarge, F., Lund, M., Lafleur, P. M., Li, X., Mammarella, I., Merbold, L., Nilsson, M. B., Olejnik, J., Ottosson-Löfvenius, M., Oechel, W., Parmentier, F.-J. W., Peichl, M., Pirk, N., Peltola, O., Pawlak, W., Rasse, D., Rinne, J., Shaver, G., Schmid, H. P., Sottocornola, M., Steinbrecher, R., Sachs, T., Urbaniak, M., Zona, D., and Ziemblinska, K.: ORCHIDEE-PEAT (revision 4596), a model for northern peatland CO2, water, and energy fluxes on daily to annual scales, Geosci. Model Dev., 11, 497-519, https://doi.org/10.5194/gmd-11-497-2018, 2018.
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Short summary
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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