气候变化领域集成服务门户(CCPortal): Assessing the impact on crop modelling of multi- and uni-variate climate model bias adjustments

CCPortal

DOI	10.1016/j.agsy.2023.103846
	Assessing the impact on crop modelling of multi- and uni-variate climate model bias adjustments
	Galmarini, S.; Solazzo, E.; Ferrise, R.; Srivastava, A. Kumar; 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.; Langes, S.; Loukos, H.; Maraun, D.; Moriondo, M.; McGinnis, S.; Nendel, C.; Padovan, G.; Riccio, A.; Ripoche, D.; Stocklem, C. O.; Supit, I.; Thao, S.; Trombi, G.; Vrac, M.; Webern, T. K. D.; Zhaoj, C.
发表日期	2024
ISSN	0308-521X
EISSN	1873-2267
起始页码	215
卷号	215
英文摘要	CONTEXT: 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. OBJECTIVE: The hypothesis tested in this paper is that since crop models require as input climate variables that are in most of the cases cross -correlated, the multi-variate bias adjustment of the latter is likely to improve performance compared to univariate bias adjusted climate model results. METHODS: To verify this hypothesis, 14 BA methods were applied to 9 variables from 8 climate models at 21 locations across Europe and Northern Africa for a period of 5 years. Twelve crop models, from the AgMIP Wheat community, were run using the climate model results. All crop models, except one, were restarted at every growing season. The crop models were also run using the AgMERRA re -analysis. The latter were used as reference to compare the results when using the other climate models treated with the various sets of biasadjustment methods. RESULTS AND CONCLUSIONS: The results show that multivariate BA treatment should be preferred to univariate ones. The error obtained by comparing crop simulation obtained with AgMERRA with those obtained with multivariate bias -adjusted climate prediction is systematically lower. The error reduction varies as a function of the variable, the location, the crop model, and the climate model though the tendency is for smaller errors when multivariate methods are used to treat the latter. The results are attributed to the nature of crop models and the fact that multivariate methods consider more adequately the correlation existing between the meteorological variables. SIGNIFICANCE: The study shows the importance of considering the nature of a model and the selection of input data that best suited to the former. In this case the improvements produced when using multivariate data appears to be significant especially in the light of the variety of crop models used and the similar response obtained and it is therefore recommended.
英文关键词	Bias-adjustment; Crop modelling; Climate models; Impact models; Multivariate and univariate methods; BADJAM
语种	英语
WOS研究方向	Agriculture
WOS类目	Agriculture, Multidisciplinary
WOS记录号	WOS:001170816200001
来源期刊	AGRICULTURAL SYSTEMS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/306501
作者单位	European Commission Joint Research Centre; EC JRC ISPRA Site; University of Florence; Technical University of Munich; State University System of Florida; University of Florida; University Hohenheim; University of Bonn; European Food Safety Authority; INRAE; China Agricultural University; Arid Agriculture University; Washington State University; Universitat Kassel; Wageningen University & Research; Environment & Climate Change Canada; Consejo Superior de Investigaciones Cientificas (CSIC); Universidad de Cantabria; CSIC - Instituto de Fisica de Cantabria (IFCA); University of Graz; Potsdam Institut fur Klimafolgenforschung; National Center Atmospheric Research (NCAR) - USA; Parthenope University Naples; CEA; Centre National de la Recherche Scientifique (CNRS); Universite Paris Saclay; Leibniz Zentrum fur Agrarlandschaftsforschung (ZALF); University of Potsdam; Universitat Politecnica de Catalunya; Barcelona Supercomputer Center (BSC-CNS); European Commission Joint Research Centre; EC JRC ISPRA Site; Europe...
推荐引用方式 GB/T 7714	Galmarini, S.,Solazzo, E.,Ferrise, R.,et al. Assessing the impact on crop modelling of multi- and uni-variate climate model bias adjustments[J],2024,215.
APA	Galmarini, S..,Solazzo, E..,Ferrise, R..,Srivastava, A. Kumar.,Ahmed, M..,...&Zhaoj, C..(2024).Assessing the impact on crop modelling of multi- and uni-variate climate model bias adjustments.AGRICULTURAL SYSTEMS,215.
MLA	Galmarini, S.,et al."Assessing the impact on crop modelling of multi- and uni-variate climate model bias adjustments".AGRICULTURAL SYSTEMS 215(2024).