Residual correlation and ensemble modelling to improve crop and grassland models

A - Papers appearing in refereed journals

Sandor, R., Ehrhardt, F., Grace, P., Recous, S., Smith, P., Snow, V., Soussana, J., Basso, B., Bhatia, A., Brilli, L., Doltra, J., Dorich, C. D., Doro, L., Fitton, N., Grant, B., Harrison, M. T., Skiba, U., Kirschbaum, M. U., Klumpp, K., Laville, P., Leonard, J., Martin, R., Massad, R. S., Moore, A. D., Myrgiotis, V., Pattey, E., Rolinski, S., Sharp, J., Smith, W., Wu, L., Zhang, Q. and Bellocchi, G. 2023. Residual correlation and ensemble modelling to improve crop and grassland models. Environmental Modelling and Software. 161, p. 105625. https://doi.org/10.1016/j.envsoft.2023.105625

AuthorsSandor, R., Ehrhardt, F., Grace, P., Recous, S., Smith, P., Snow, V., Soussana, J., Basso, B., Bhatia, A., Brilli, L., Doltra, J., Dorich, C. D., Doro, L., Fitton, N., Grant, B., Harrison, M. T., Skiba, U., Kirschbaum, M. U., Klumpp, K., Laville, P., Leonard, J., Martin, R., Massad, R. S., Moore, A. D., Myrgiotis, V., Pattey, E., Rolinski, S., Sharp, J., Smith, W., Wu, L., Zhang, Q. and Bellocchi, G.
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.

KeywordsBiogeochemical models; Correlation matrices; Ensemble modelling
Year of Publication2023
JournalEnvironmental Modelling and Software
Journal citation161, p. 105625
Digital Object Identifier (DOI)https://doi.org/10.1016/j.envsoft.2023.105625
Open accessPublished as green open access
Funder FACCE MACSUR
Accepted author manuscript
Copyright license
CC BY
Output statusPublished
Publication dates
Online13 Jan 2023
Publication process dates
Accepted10 Jan 2023
PublisherElsevier
ISSN1364-8152

Permalink - https://repository.rothamsted.ac.uk/item/98v9q/residual-correlation-and-ensemble-modelling-to-improve-crop-and-grassland-models

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