Incorporating grassland management in ORCHIDEE: Model description and evaluation at 11 eddy-covariance sites in Europe

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Abstract

This study describes how management of grasslands is included in the Organizing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) process-based ecosystem model designed for large-scale applications, and how management affects modeled grassland-atmosphere CO2 fluxes. The new model, ORCHIDEE-GM (grassland management) is enabled with a management module inspired from a grassland model (PaSim, version 5.0), with two grassland management practices being considered, cutting and grazing. The evaluation of the results from ORCHIDEE compared with those of ORCHIDEE-GM at 11 European sites, equipped with eddy covariance and biometric measurements, shows that ORCHIDEE-GM can realistically capture the cut-induced seasonal variation in biometric variables (LAI: leaf area index; AGB: aboveground biomass) and in CO2 fluxes (GPP: gross primary productivity; TER: total ecosystem respiration; and NEE: net ecosystem exchange). However, improvements at grazing sites are only marginal in ORCHIDEE-GM due to the difficulty in accounting for continuous grazing disturbance and its induced complex animal-vegetation interactions. Both NEE and GPP on monthly to annual timescales can be better simulated in ORCHIDEE-GM than in ORCHIDEE without management. For annual CO2 fluxes, the NEE bias and RMSE (root mean square error) in ORCHIDEE-GM are reduced by 53% and 20%, respectively, compared to ORCHIDEE. ORCHIDEE-GM is capable of modeling the net carbon balance (NBP) of managed temperate grasslands (37 ±30 gC m-2 yr-1 (P < 0.01) over the 11 sites) because the management module contains provisions to simulate the carbon fluxes of forage yield, herbage consumption, animal respiration and methane emissions.

Figures

  • Fig. 1. Schematic of ORCHIDEE-GM.
  • Fig. 2. Distribution of the 11 European grassland sites in this study.
  • Table 1. Location, climate and management for the 11 managed grassland sites in Europe (from the FLUXNET program, http://www.fluxnet. ornl.gov; Baldocchi et al., 2001).
  • Table 2. Limits of the two frequency binning schemes.
  • Figure 3 1028 Fig. 3. Age-related SLA and its impact on LAI. Results are simulated by ORCHIDEE-GM with fixed SLA and age-related SLA respectively on a mowed grassland (CH-Oe1) and a grazed grassland (FR-Lq1) for the year 2007.
  • Fig. 4. Comparison of simulated/observed biometric variables and carbon fluxes for the cut grassland of Oensingen, CH-Oe1. LAI, leaf area index; AGB, aboveground biomass (dry matter); GPP, gross primary production; TER, terrestrial ecosystem respiration; NEE, net ecosystem exchange. GPP, TER and NEE are presented as 15 day running means to smooth out very high frequencies.
  • Figure 5 1032 Fig. 5. Comparison of simulated/observed biometric variables and carbon fluxes for the grazed grassland of Laqueuille, FR-Lq1.
  • Fig. 6. Model–data comparison on multiple timescales. Observed and modeled time series are decomposed into subsignals corresponding to characteristic frequency bins. Qualitative or quantitative model–data comparisons can be carried out on the corresponding pairs of subsignals. Figure 6 exemplifies the model–data comparison with two models simulations of GPP and corresponding observations at the CH-Oe1.

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CITATION STYLE

APA

Chang, J. F., Viovy, N., Vuichard, N., Ciais, P., Wang, T., Cozic, A., … Soussana, J. F. (2013). Incorporating grassland management in ORCHIDEE: Model description and evaluation at 11 eddy-covariance sites in Europe. Geoscientific Model Development, 6(6), 2165–2181. https://doi.org/10.5194/gmd-6-2165-2013

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