SPATIAL-TEMPORAL ANALYSIS OF CLIMATE CHANGE AND INFLUENCE OF MEDITERRANEAN SEA ON SITE VALENCIA DO

Speaker: Igor Sirnik Supervisors: Hervé Quénol (Université Rennes 2, France), Miguel Ángel Jiménez-Bello (Universidad Politécnica de Valencia) and Juan Manzano (Universidad Politécnica de Valencia) Valencia, 24.10.2016 The facts France: during the last 25 years, the harvest time moved from late October to early September. By the 2050 by the worst case scenario, the 85% decrease in wine production in Bordeaux, Rhone, and Toscany region. South Africa – by the 2050, expected 55% descrease in wine production. English wine is back! 400 commercial wineayards, sparkling wines are beating the French rivals. Source: Theguardian Context

The climate change has an impact on viticulture in function of:

Evolution of vine growing and its characteristics.

 Evolution differs according to a different location of regions in accordance with local topographical characteristics.

Necessity of adaptation.

Methodology

Analyse of historical meteorological data retrieved from weather stations.

Data retrieved: daily temperature (min, max, mean) and daily precipitation.

Bioclimatic indexes analysis.

Confrontation of historical data to modelized data in function of climate change (RCP scenarios) for historical and future period.

Uncertainty, data analysis, critical approach to data analysis;

Valencia DO

Source: IVIA, 2016 Teruel Location of weather stations on study site Valencia DO, Spain with available data

Castellon

Llíria Study site Valencia DO Cerrito Valencia Station Daily Elevation Distence name temperature from sea Cheste airport data Valencia 1965-2014 69m 14km Benifaio airport Albacete 1914-2013 686m 140km Castellon 1937-2013 30m 9km Benifaio 1999-2014 35m 12km Teruel 1986-2013 915m 104km Albacete Cerrito 2000-2014 810m 57km Lliria 2000 -2014 172m 28km Cheste 1999-2014 110m 31km Topographic profiles: meteorological stations toward Mediterranean sea

Valencia airport

Valencia airport

Albacete Albacete 25.0 Progress of minimum and Tmax_VLC_AIR Tmax_Albacete 24.0 maximum temperature +1.6°C 23.0

22.0 +1.9°C 21.0 Tmin_VLC_AIR Tmin_Albacete 14.0 20.0 13.0 +1.6°C 12.0 19.0 11.0 18.0 10.0

9.0 +2.5°C 8.0 7.0 6.0 5.0 Meteorological station Increase of mean Progress of mean temperature temeprature [°C] for study period 1965-2013 VLC_air 1.6 Albacete 2.2

19.0

18.0 +1.6°C

17.0

16.0

15.0 +2.2°C

14.0

13.0

12.0 HUGLIN index

k= 1.02 Huglin Index points Wine varieties Region Index points Wine varieties class Muller-Thurgau, , , , , , Very cold ≤ 5 Gewurztraminer Region I 850-1389 gewurztraminer, pinot grigio sauvignon Cold Riesling, Pinot noir, Chardonnay, , blanc 1500-1800

Cool 1800-2100 Cabernet-Sauvignon, Ugni Blanc, , chardonnay, merlot, Region II 1389-1667 semillon, syrah , Mourvèdre, Warm 2100-2400

Potential which exceeds the heliothermal Region III 1671-1950 grenache, barbera, tempranillo, syrah, needs to ripen the varieties, even the late 2400-3000 ones (with some associated risks of stress) Hot carignan, cinsault, mourvedre, Region IV 1951-2220 tempranillo There is no heliothermal constraint for the

>3000 grapes to ripen Region V >2221 priitivo, ero d’avola, palomino, fiano Very hot 3000 Progress of bioclimatic 2900 +388.08 2800 indexes 1965-2013 2700 +470.55 2600 2500 2400 2300 2200 Winkler progress 2100 2000 2800

Huglin progress 2600 +452.35 2400

2200 +531.36 2000 1800 1600 1400 Analysis of average temperature and average bioclimatic indexes increase during 1965-2013

Valencia DO [°C]

Temperature mean 1,9 Temperature minimum 2,0 Temperature maximum 1,8 Valencia DO [bioclimatic points] HUGLIN 429,3 WINKLER 491,9 1200 Precipitation in seasons – Valencia airport 1000

800

600 Valencia_airport 400

200

0 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

SPRING (1) SOMMER (2) FALL (3) WINTER (4)

300 350 700 300

300 600 250 250

250 500 200 200 200 400 150 150 150 300

100 100 100 200

50 50 50 100

0 0 0 0 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 1966 1970 1974 1978 1982 1986 1990 1994 1998 2002 2006 2010 The rise of temperature trend on all weather stations in the study period Valencia airport The highest increase (2.17°C) was detected on weather stations Albacete weather station Albacete The average rise of temperature at Valencia site: +1.88°C

The increase of Huglin and Winkler index at all weather stations

Precipitation decrease was noticed especially in fall season

Temperature analysis in Valencia study site: observed and modeled data  Used four stations for analysis

 Historical data: 1963-2013

 Euro-Cordex data CMIP5, rcp8.5: 1985-2100

 Resolution: 0,11°

Temperature progress in Valencia DO: [°C] observation and simulation (rcp8.5) 26 +4.9°C

24 +1.7°C 22

20 Tmin_observation Tmin_simulation 18 Tmax_observation Tmax_simulation 16

14 +4.5°C

12 +2.0°C

10

8 1965 1969 1973 1977 1981 1985 1989 1993 1997 2001 2005 2009 2013 2017 2021 2025 2029 2033 2037 2041 2045 2049 2053 2057 2061 2065 2069 2073 2077 2081 2085 2089 2093 2097 [°C] Detail of maximum temperature progress Minimum temperature 23.5 Observed rcp85 progress – observations and 23 simulation (rcp8.5) detail in 22.5 22 Valencia DO (1985 – 2013) 21.5 21 20.5 Average difference: 1,1°C 20 [°C] Detail of minimum temperature progress 12.5 12 Observed rcp85 11.5 11 10.5 10 9.5 Average difference: 1,5°C 9 Valencia DO average temperature progress

21

Valencia_observations 20 Valencia_rcp85 +4.7°C 19

18

+1.8°C 17

16

15

14 1963 1983 2003 2023 2043 2063 2083 2100 Limitations of comparison between observed and modelized data

Uncertainties linked to the climate models = it is impossible to validate future of climate data.

Climate model is a simplified representation of a climate system.

Spatial resolution of the climate models is far higher in comparison to observed data on meteorological stations (point Vs. 0,11 degree net).

Conclusion

Temperature and bioclimatic indexes increase in the last five decades, decrease of precipitation. Simulated rcp8.5 scenarios – significant increase of temperatures. Future steps: Precipitation analysis simulation 1985 – 2100, rcp4.5 scenario; Usage of all available weather stations for further climate change analysis. The outcome of this research will be beneficiary for understanding local climate conditions. Possible adaptation scenarios: other varieties, changing fertilizers, changing location, irrigation,.. Comparative analysis with a second viticulture site: Brda wine region in Slovenia.

Thank you all for your attention

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