Residual correlation and ensemble modelling to improve crop and grassland models. (March 2023)

• Record Type: Journal Article
• Authors: Sándor, Renáta; Ehrhardt, Fiona; Grace, Peter; Recous, Sylvie; Smith, Pete; Snow, Val; Soussana, Jean-François; Basso, Bruno; Bhatia, Arti; Brilli, Lorenzo; Doltra, Jordi; 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 D.; Myrgiotis, Vasileios; Pattey, Elizabeth; Rolinski, Susanne; Sharp, Joanna; Smith, Ward; Wu, Lianhai; Zhang, Qing; Bellocchi, Gianni;
• Is Part Of: Environmental modelling & software. Volume 161(2023)
• Publication Date: 2023-03
• Subjects: Biogeochemical models -- Correlation matrices -- Ensemble modelling -- Model calibration -- Residual plot analysis; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2023.105625
• Languages: English
• ISSNs: 1364-8152

Abstract

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. Graphical abstract: Image 1 Highlights: We investigate multi-model performance in simulating C and N fluxes in agriculture. Correlated model residuals hinder reliable C–N flux estimates. Residual correlation analysis is applied to ensemble crop and grassland models. Partially calibrated models can be practical for implementing model ensembles. Fully calibrated models are key to model development.