ORCHIDEE-PEAT (revision 4596), a model for northern peatland CO$_2$ water, and energy fluxes on daily to annual scales

Chunjing Qiu (1) , Dan Zhu (1) , Philippe Ciais (1, 2) , Bertrand Guenet (1) , Gerhard Krinner (3) , Shushi Peng (4) , Mika Aurela (5) , Christian Bernhofer (6) , Christian Brümmer (7) , Syndonia Bret-Harte (8) , Housen Chu (9) , Jiquan Chen (10) , Ankur R Desai (11) , Jiří Dušek (12) , Eugénie S Euskirchen (8) , Krzysztof Fortuniak (13) , Lawrence B Flanagan (14) , Thomas Friborg (15) , Mateusz Grygoruk (16) , Sébastien Gogo (17, 18) , Thomas Grünwald (19) , Birger U Hansen (15) , David Holl (20) , Elyn Humphreys (21) , Miriam Hurkuck (21) , Gerard Kiely (22) , Janina Klatt (23) , Lars Kutzbach (20) , Chloé Largeron (1) , Fatima Laggoun-Défarge (17, 18) , Magnus Lund (24) , Peter M Lafleur (25) , Xuefei Li (26) , Ivan Mammarella (26) , Lutz Merbold (27) , Mats B Nilsson (28) , Janusz Olejnik (29, 12) , Mikaell Ottosson-Löfvenius (28) , Walter Oechel , Frans-Jan W Parmentier , Matthias Peichl (28) , Norbert Pirk , Olli Peltola (26) , Włodzimierz Pawlak , Daniel Rasse , Janne Rinne , Gaius Shaver , Hans Peter Schmid (30) , Matteo Sottocornola , Rainer Steinbrecher , Torsten Sachs (31) , Marek Urbaniak (29) , Donatella Zona , Klaudia Ziemblinska (29)

1 LSCE - Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette]
2 ICOS-ATC - ICOS-ATC
3 IGE - Institut des Géosciences de l’Environnement
4 Department of Ecology, College of Urban and Environmental Sciences
5 Climate and Global Change Research [Helsinki]
6 Institute of Hydrology and Meteorology [Dresden]
7 Thünen Institute of Climate-Smart Agriculture
8 Institute of Arctic Biology
9 ESPM - Department of Environmental Science, Policy, and Management [Berkeley]
10 Center for Global Change and Earth Observations
11 Department of Atmospheric and Oceanic Sciences [Madison]
12 Department of Matters and Energy Fluxes
13 Department of Meteorology and Climatology [Łódz ́]
14 University of Lethbridge
15 IGN - Department of Geosciences and Natural Resource Management [Copenhagen]
16 SGGW - Warsaw University of Life Sciences
17 ISTO - Institut des Sciences de la Terre d'Orléans - UMR7327
18 Biogéosystèmes Continentaux - UMR7327
19 TU Dresden - Technische Universität Dresden = Dresden University of Technology
20 University of Hamburg
21 Carleton University
22 Department of Civil and Environmental Engineering [Cork]
23 KIT - Karlsruhe Institute of Technology
24 Department of Bioscience and Arctic Research Center
25 School of the Environment – Geography
26 Department of Physics [Helsinki]
27 Institute of Agricultural Sciences
28 Department of Forest Ecology and Management
29 Department of Meteorology
30 IMK-IFU - Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung
31 GFZ - German Research Centre for Geosciences - Helmholtz-Centre Potsdam

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 CO$_2$ 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 car-boxylation (V$_{cmax}$) being optimized at each site. Regarding short-term day-today variations, the model performance was good for gross primary production (GPP) ($r^2$ = 0.76; Nash– Sutcliffe modeling efficiency, MEF = 0.76) and ecosystem respiration (ER, $r^2$ = 0.78, MEF = 0.75), with lesser accuracy for latent heat fluxes (LE, $r^2$ = 0.42, MEF = 0.14) and and net ecosystem CO$_2$ exchange (NEE, $r^2$ = 0.38, MEF = 0.26). Seasonal variations in GPP, ER, NEE, and energy fluxes on monthly scales showed moderate to high $r^2$ values (0.57–0.86). For spatial across-site gradients of annual mean GPP, ER, NEE, and LE, $r^2$ values of 0.93, 0.89, 0.27, and 0.71 were achieved, respectively. Water table (WT) variation was not well predicted ($r^2$ < 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 V$_{cmax}$ and latitude (temperature), which better reflects the spatial gradients of annual NEE than using an average V$_{cmax}$ value.

Domains: Sciences of the Universe [physics]; Sciences of the Universe [physics]; Continental interfaces, environment

Chunjing Qiu, Dan Zhu, Philippe Ciais, Bertrand Guenet, Gerhard Krinner, et al.. ORCHIDEE-PEAT (revision 4596), a model for northern peatland CO$_2$ water, and energy fluxes on daily to annual scales. Geoscientific Model Development, 2018, 11, pp.497 - 519. ⟨10.5194/gmd-11-497-2018⟩. ⟨insu-01719357⟩