Residual correlation and ensemble modelling to improve crop and grassland models
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Author
Sándor, Renáta
Ehrhardt, Fiona
Grace, Peter
Recous, Sylvie
Smith, Pete
Snow, Val
Soussana, Jean-François
Basso, Bruno
Bhatia, Arti
Brilli, Lorenzo
Dorich, Christopher D.
Doro, Luca
Fitton, Nuala
Grant, Brian
Harrison, Matthew Tom
Skiba, Ute
Kirschbaum, Miko U.F.
Klumpp, Katja
Laville, Patricia
Léonard, Joel
Martin, Raphaël
Massad, Raia Silvia
Moore, Andrew
Myrgiotis, Vasileios
Pattey, Elizabeth
Rolinski, Susanne
Sharp, Joanna
Smith, Ward
Wu, Lianhai
Zhang, Qing
Bellocchi, Gianni
Publication date
2023-01-13ISSN
1364-8152
Abstract
Multi-model ensembles are becoming increasingly accepted for the estimation of agricultural carbon-nitrogen fluxes, productivity and sustainability. There is mounting evidence that with some site-specific observations available for model calibration (with vegetation data as a minimum requirement), median outputs assimilated from biogeochemical models (multi-model medians) provide more accurate simulations than individual models. Here, we evaluate potential deficiencies in how model ensembles represent (in relation to climatic factors) the processes underlying biogeochemical outputs in complex agricultural systems such as grassland and crop rotations including fallow periods. We do that by exploring the correlation of model residuals. We restricted the distinction between partial and full calibration to the two most relevant calibration stages, i.e. with plant data only (partial) and with a combination of plant, soil physical and biogeochemical data (full). It introduces and evaluates the trade-off between (1) what is practical to apply for model users and beneficiaries, and (2) what constitutes best modelling practice. The lower correlations obtained overall with fully calibrated models highlight the centrality of the full calibration scenario for identifying areas of model structures that require further development.
Document Type
Article
Document version
Accepted version
Language
English
Subject (CDU)
633 - Field crops and their production
Pages
37
Publisher
Elsevier
Is part of
Enviromental Modelling and Software
Citation
Sándor, Renáta, Fiona Ehrhardt, Peter Grace, Sylvie Recous, Pete Smith, Val Snow, and Jean-François Soussana et al. 2023. "Residual Correlation And Ensemble Modelling To Improve Crop And Grassland Models". Environmental Modelling &Amp; Software 161: 105625. doi:10.1016/j.envsoft.2023.105625.
Grant agreement number
EC/FP5/EVK2-CT-2001-00105/EU/Sources and sinks of greenhouse gases from managed european grasslands and mitigation scenarios/GREENGRASS
EC/FP6/505572/EU/ASSESSMENT OF THE EUROPEAN TERRESTRIAL CARBON BALANCE/CARBOEUROPE-IP
EC/FP6/17841/EU/The nitrogen cycle and its influence on the European greenhouse gas balance/NITROEUROPE IP
Program
Cultius Extensius Sostenibles
This item appears in the following Collection(s)
- ARTICLES CIENTÍFICS [2850]
Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by-nc-nd/4.0/