wiki:GroupActivities/CodeAvalaibilityPublication/ORCHIDEE-GMv3.2

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ORCHIDEE-GMv3.2

This version of ORCHIDEE has been used in a publication under review. << Reference to be added as soon as the manuscript is open for review >>.

Abstract

ORCHIDEE is a process-based ecosystem model developed for simulating carbon, water and energy fluxes in ecosystems, from site level to global scale1-3. ORCHIDEE-GM4 is a version specifically developed to integrate the management of grassland4. The equations describing management in ORCHIDEE are derived from PaSim5-8. Accounting for the management practices such as mowing, livestock grazing and fertilizer application on a daily basis, ORCHIDEE-GM proves capable of simulating the dynamics of leaf area index, biomass, and carbon fluxes of managed grasslands. ORCHIDEE-GM v1 was evaluated and some of its parameters calibrated at 11 European grassland sites representative of a range of management practices, with eddy-covariance net ecosystem exchange and biomass measurements. The model successfully simulates the net carbon budget of these managed grasslands4. Ref9 then added a parameterization of adaptive management through which farmers react to a climate-driven change of previous-year productivity. Though a full nitrogen cycle is not included in ORCHIDEE-GM, the positive effect of nitrogen fertilizers on grass photosynthesis rates, and thus on subsequent ecosystem productivity and carbon storage, is parameterized with an empirical function calibrated from literature estimates (v2.19). ORCHIDEE-GM v2.1 was applied over Europe to calculate the spatial pattern, inter-annual variability, and the trends of potential productivity, i.e. the productivity that maximizes simulated livestock densities assuming an optimal management system in each grid cell9. This version was further used to simulate the net carbon budget, budget trends, and the GHG balance of European grasslands during the last five decades at a spatial resolution of 25 km10. Ref11 recently updated the model to version 2.2 with the general parameterizations from ORCHIDEE Trunk.rev3623 (​https://forge.ipsl.jussieu.fr/orchidee/browser/trunk#ORCHIDEE), and a new parameterization limiting grazing practices under specific conditions such as frost, snow cover and wet soil. ORCHIDEE-GM v2.2 was driven by projected future climate change to provide a European‐wide assessment of the future changes in productivity and phenology of grassland, and their consequences for the management intensity and the carbon balance.

At global scale, ORCHIDEE-GM v3.1 is a development of v2.1, includes a parameter adjustment for the C4 grassland biome and implemented a specific strategy for wild herbivores 12. Combining livestock production information, ORCHIDEE-GM v3.1 was applied to reconstruct a series of global gridded maps containing a time-dependent history of grassland management intensity. These maps are model-dependent, and provide a unique opportunity for models with explicit representation of grassland management to make a more accurate estimate of global carbon and GHG budgets of grassland 12 – as we have here. In this study, ORCHIDEE-GM v3.1 has been updated with the parameterizations of v2.2 to a new version named ORCHIDEE-GM v3.2.

Reference: 1 Ciais, P. et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437, 529-533, doi:10.1038/nature03972 (2005).

2 Krinner, G. et al. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Global Biogeochemical Cycles 19, Gb1015. doi:10.1029/2003gb002199 (2005).

3 Piao, S. et al. Changes in climate and land use have a larger direct impact than rising CO2 on global river runoff trends. Proceedings of the National Academy of Sciences of the United States of America 104, 15242-15247, doi:10.1073/pnas.0707213104 (2007).

4 Chang, J. F. et al. Incorporating grassland management in ORCHIDEE: model description and evaluation at 11 eddy-covariance sites in Europe. Geoscientific Model Development 6, 2165-2181, doi:10.5194/gmd-6-2165-2013 (2013).

5 Graux, A. I. et al. Development of the Pasture Simulation Model for assessing livestock production under climate change. Agriculture Ecosystems & Environment 144, 69-91, doi:10.1016/j.agee.2011.07.001 (2011).

6 Riedo, M., Grub, A., Rosset, M. & Fuhrer, J. A pasture simulation model for dry matter production, and fluxes of carbon, nitrogen, water and energy. Ecological Modelling 105, 141-183, doi:10.1016/s0304-3800(97)00110-5 (1998).

7 Vuichard, N., Ciais, P., Viovy, N., Calanca, P. & Soussana, J.-F. Estimating the greenhouse gas fluxes of European grasslands with a process-based model: 2. Simulations at the continental level. Global Biogeochemical Cycles 21, Gb1005. doi:10.1029/2005gb002612 (2007).

8 Vuichard, N. et al. Estimating the greenhouse gas fluxes of European grasslands with a process-based model: 1. Model evaluation from in situ measurements. Global Biogeochemical Cycles 21, Gb1004. doi:10.1029/2005gb002611 (2007).

9 Chang, J. et al. Modeled Changes in Potential Grassland Productivity and in Grass-Fed Ruminant Livestock Density in Europe over 1961–2010. (2015).

10 Chang, J. et al. The greenhouse gas balance of European grasslands. Global change biology 21, 3748-3761 (2015).

11 Chang, J. et al. Future productivity and phenology changes in European grasslands for different warming levels: implications for grassland management and carbon balance. Carbon balance and management 12, 11 (2017).

12 Chang, J. et al. Combining livestock production information in a process-based vegetation model to reconstruct the history of grassland management. Biogeosciences 13, 3757-3776 (2016).

Code access

Metadata

DOI TBD
Creator Jinfeng Chang
Affiliation LSCE, CNRS
Title TBD
Publisher Institut Pierre Simon Laplace (IPSL)
PublicationYear 2019
ResourceType Software
Rights This software is distributed under the CeCILL license
rightsURI ​http://www.cecill.info/
Subject Land surface model, pixel-to-point comparison, biomass
DataManager Karim Ramage (IPSL)
DataCurator Josefine Ghattas (IPSL)
ContactPerson Philippe Ciais (LSCE/CNRS)
FundingReference TBD