Climatic Change

, Volume 119, Issue 3–4, pp 825–839 | Cite as

Projected shifts of wine regions in response to climate change

  • M. Moriondo
  • G. V. Jones
  • B. Bois
  • C. Dibari
  • R. Ferrise
  • G. Trombi
  • M. Bindi
Article

Abstract

This research simulates the impact of climate change on the distribution of the most important European wine regions using a comprehensive suite of spatially informative layers, including bioclimatic indices and water deficit, as predictor variables. More specifically, a machine learning approach (Random Forest, RF) was first calibrated for the present period and applied to future climate conditions as simulated by HadCM3 General Circulation Model (GCM) to predict the possible spatial expansion and/or shift in potential grapevine cultivated area in 2020 and 2050 under A2 and B2 SRES scenarios. Projected changes in climate depicted by the GCM and SRES scenarios results in a progressive warming in all bioclimatic indices as well as increasing water deficit over the European domain, altering the climatic profile of each of the grapevine cultivated areas. The two main responses to these warmer and drier conditions are 1) progressive shifts of existing grapevine cultivated area to the north–northwest of their original ranges, and 2) expansion or contraction of the wine regions due to changes in within region suitability for grapevine cultivation. Wine regions with climatic conditions from the Mediterranean basin today (e.g., the Languedoc, Provence, Côtes Rhône Méridionales, etc.) were shown to potentially shift the most over time. Overall the results show the potential for a dramatic change in the landscape for winegrape production in Europe due to changes in climate.

Supplementary material

10584_2013_739_MOESM1_ESM.doc (58 kb)
ESM 1Table describing climatic indices calculation (DOC 57.5 kb)
10584_2013_739_MOESM2_ESM.doc (61 kb)
ESM 2Table describing the spatial datasets used in this work (DOC 61 kb)
10584_2013_739_MOESM3_ESM.doc (52 kb)
ESM 3Description of the model of water balance (DOC 52 kb)
10584_2013_739_MOESM4_ESM.doc (3.6 mb)
ESM 4Description of Random Forest calibration strategy and relevant byproducts (Partial Plots) (DOC 3.59 MB)
10584_2013_739_MOESM5_ESM.doc (324 kb)
ESM 5Description of climatic structure of wine regions (DOC 323 kb)
10584_2013_739_MOESM6_ESM.doc (162 kb)
ESM 6Figures depicting the Random Forest accuracy in predicting grapevine cultivated area (Fig. 1, 2) and trend in wine regions shift in 2020 and 2050 (Fig. 3) (DOC 162 kb)
10584_2013_739_MOESM7_ESM.doc (112 kb)
ESM 7Tables showing Huglin index, Water deficit, minimum temperature of the coldest and maximum temperatures of warmest months of wine regions projected in 2020 and 2050 (A2 and B2 scenarios) (DOC 112 kb)

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • M. Moriondo
    • 1
  • G. V. Jones
    • 2
  • B. Bois
    • 3
  • C. Dibari
    • 4
  • R. Ferrise
    • 4
  • G. Trombi
    • 4
  • M. Bindi
    • 4
  1. 1.CNR-IBIMETFlorenceItaly
  2. 2.Department of Environmental StudiesSouthern Oregon UniversityAshlandUSA
  3. 3.Institute of the Vine and Wine “J. Guyot”University of BurgundyDijon CedexFrance
  4. 4.Department of Agri-food Production and Environmental Sciences, University of FlorenceFlorenceItaly

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