Residual correlation and ensemble modelling to improve crop and grassland models

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 ORCID: https://orcid.org/0000-0001-8659-6092; 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. 2023 Residual correlation and ensemble modelling to improve crop and grassland models. Environmental Modelling & Software, 161, 105625. 12, pp. https://doi.org/10.1016/j.envsoft.2023.105625

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Official URL: http://dx.doi.org/10.1016/j.envsoft.2023.105625

Abstract/Summary

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.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1016/j.envsoft.2023.105625
UKCEH and CEH Sections/Science Areas:	Atmospheric Chemistry and Effects (Science Area 2017-)
ISSN:	1364-8152
Additional Keywords:	biogeochemical models, correlation matrices, ensemble modelling, model calibration, residual plot analysis
NORA Subject Terms:	Agriculture and Soil Science Meteorology and Climatology Data and Information
Date made live:	02 Nov 2023 11:18 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/535342

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1016/j.envsoft.2023.105625

UKCEH and CEH Sections/Science Areas:

Atmospheric Chemistry and Effects (Science Area 2017-)

ISSN:

1364-8152

Additional Keywords:

biogeochemical models, correlation matrices, ensemble modelling, model calibration, residual plot analysis

NORA Subject Terms:

Agriculture and Soil Science
Meteorology and Climatology
Data and Information