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

S. Galmarini, E. Solazzo, R. Ferrise, A. Kumar Srivastava, M. Ahmed, S. Asseng, A.J. Cannon, F. Dentener, G. De Sanctis, T. Gaiser, Y. Gao, S. Gayler, J.M. Gutierrez, G. Hoogenboom, M. Iturbide, M. Jury, S. Lange, H. Loukos, D. Maraun, M. Moriondo, S. McGinnis, C. Nendel, G. Padovan, A. Riccio, D. Ripoche, C.O. Stockle, I. Supit, S. Thao, G. Trombi, M. Vrac, T.K.D. Weber and C. Zhao

Agricultural Systems, 2024, vol. 215, issue C

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.

Keywords: Bias-adjustment; Crop modelling; Climate models; Impact models; Multivariate and univariate methods; BADJAM (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:agisys:v:215:y:2024:i:c:s0308521x23002512

DOI: 10.1016/j.agsy.2023.103846

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