English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsRelease HistoryDetailsSummary

Released
Journal Article

Residual correlation and ensemble modelling to improve crop and grassland models

Authors

Sándor,  Renáta
External Organizations;

Ehrhardt,  Fiona
External Organizations;

Grace,  Peter
External Organizations;

Recous,  Sylvie
External Organizations;

Smith,  Pete
External Organizations;

Snow,  Val
External Organizations;

Soussana,  Jean-François
External Organizations;

Basso,  Bruno
External Organizations;

Bhatia,  Arti
External Organizations;

Brilli,  Lorenzo
External Organizations;

Doltra,  Jordi
External Organizations;

Dorich,  Christopher D.
External Organizations;

Doro,  Luca
External Organizations;

Fitton,  Nuala
External Organizations;

Grant,  Brian
External Organizations;

Harrison,  Matthew Tom
External Organizations;

Skiba,  Ute
External Organizations;

Kirschbaum,  Miko U.F.
External Organizations;

Klumpp,  Katja
External Organizations;

Laville,  Patricia
External Organizations;

Léonard,  Joel
External Organizations;

Martin,  Raphaël
External Organizations;

Massad,  Raia Silvia
External Organizations;

Moore,  Andrew D.
External Organizations;

Myrgiotis,  Vasileios
External Organizations;

Pattey,  Elizabeth
External Organizations;

/persons/resource/Rolinski

Rolinski,  Susanne
Potsdam Institute for Climate Impact Research;

Sharp,  Joanna
External Organizations;

Smith,  Ward
External Organizations;

Wu,  Lianhai
External Organizations;

Zhang,  Qing
External Organizations;

Bellocchi,  Gianni
External Organizations;

External Ressource
No external resources are shared
Fulltext (public)
There are no public fulltexts stored in PIKpublic
Supplementary Material (public)
There is no public supplementary material available
Citation

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


Cite as: https://publications.pik-potsdam.de/pubman/item/item_28890
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.