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  Assessing the impact on crop modelling of multi- and uni-variate climate model bias adjustments

Galmarini, S., Solazzo, E., Ferrise, R., Kumar Srivastava, A., Ahmed, M., Asseng, S., Cannon, A. J., Dentener, F., De Sanctis, G., Gaiser, T., Gao, Y., Gayler, S., Gutierrez, J. M., Hoogenboom, G., Iturbide, M., Jury, M., Lange, S., Loukos, H., Maraun, D., Moriondo, M., McGinnis, S., Nendel, C., Padovan, G., Riccio, A., Ripoche, D., Stockle, C. O., Supit, I., Thao, S., Trombi, G., Vrac, M., Weber, T. K. D., Zhao, C. (2024): Assessing the impact on crop modelling of multi- and uni-variate climate model bias adjustments. - Agricultural Systems, 215, 103846.
https://doi.org/10.1016/j.agsy.2023.103846

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 Creators:
Galmarini, S.1, Author
Solazzo, E.1, Author
Ferrise, R.1, Author
Kumar Srivastava, A.1, Author
Ahmed, M.1, Author
Asseng, S.1, Author
Cannon, A. J.1, Author
Dentener, F.1, Author
De Sanctis, G.1, Author
Gaiser, T.1, Author
Gao, Y.1, Author
Gayler, S.1, Author
Gutierrez, J. M.1, Author
Hoogenboom, G.1, Author
Iturbide, M.1, Author
Jury, M.1, Author
Lange, Stefan2, Author              
Loukos, H.1, Author
Maraun, D.1, Author
Moriondo, M.1, Author
McGinnis, S.1, AuthorNendel, C.1, AuthorPadovan, G.1, AuthorRiccio, A.1, AuthorRipoche, D.1, AuthorStockle, C. O.1, AuthorSupit, I.1, AuthorThao, S.1, AuthorTrombi, G.1, AuthorVrac, M.1, AuthorWeber, T. K. D.1, AuthorZhao, C.1, Author more..
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Crop models are essential tools for assessing the impact of climate change on national or regional agricultural production. Starting from meteorology, soil and crop management, fertilization and irrigation practices, they predict the yield of specific crop varieties. For long term assessments, climate models are the source of primary information. To make climate model results usable in a specific time frame context, bias adjustment (BA) is required. In fact, climate models tend to deviate from day-to-day values of the physical parameters while conserving the climate variability signal. BA brings the climatic signal to the actual values observed in a specific location and period, and to be representative of a specific period in absolute terms. BA techniques come in different flavours. The broadest categorization is univariate and multivariate methods. Multivariate methods adjust the variables considering possible cross-correlations while univariate methods treat the variables one by one without accounting for possible dependence on one another.

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Language(s): eng - English
 Dates: 2023-04-182023-12-182024-01-292024-03-01
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.agsy.2023.103846
Organisational keyword: RD3 - Transformation Pathways
PIKDOMAIN: RD3 - Transformation Pathways
Working Group: Data-Centric Modeling of Cross-Sectoral Impacts
Regional keyword: Europe
Regional keyword: Africa
Model / method: Model Intercomparison
Research topic keyword: Food & Agriculture
Research topic keyword: Land use
MDB-ID: No data to archive
 Degree: -

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Title: Agricultural Systems
Source Genre: Journal, SCI, Scopus
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Publ. Info: -
Pages: - Volume / Issue: 215 Sequence Number: 103846 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/agricultural-systems
Publisher: Elsevier