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Residual correlation and ensemble modelling to improve crop and grassland models

Urheber*innen

Sándor,  Renáta
External Organizations;

Ehrhardt,  Fiona
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Grace,  Peter
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Recous,  Sylvie
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Smith,  Pete
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Snow,  Val
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Soussana,  Jean-François
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Basso,  Bruno
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Bhatia,  Arti
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Brilli,  Lorenzo
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Doltra,  Jordi
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Dorich,  Christopher D.
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Doro,  Luca
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Fitton,  Nuala
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Grant,  Brian
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Harrison,  Matthew Tom
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Skiba,  Ute
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Kirschbaum,  Miko U.F.
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Klumpp,  Katja
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Laville,  Patricia
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Léonard,  Joel
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Martin,  Raphaël
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Massad,  Raia Silvia
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Moore,  Andrew D.
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Myrgiotis,  Vasileios
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Pattey,  Elizabeth
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/persons/resource/Rolinski

Rolinski,  Susanne
Potsdam Institute for Climate Impact Research;

Sharp,  Joanna
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Smith,  Ward
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Wu,  Lianhai
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Zhang,  Qing
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Bellocchi,  Gianni
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Zitation

Sándor, R., Ehrhardt, F., Grace, P., Recous, S., Smith, P., Snow, V., Soussana, J.-F., 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., Léonard, J., Martin, R., Massad, R. S., Moore, A. D., Myrgiotis, V., Pattey, E., Rolinski, S., Sharp, J., Smith, W., Wu, L., Zhang, Q., Bellocchi, G. (2023): Residual correlation and ensemble modelling to improve crop and grassland models. - Environmental Modelling and Software, 161, 105625.
https://doi.org/10.1016/j.envsoft.2023.105625


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_28890
Zusammenfassung
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.