Methodologies for climate variability analysis of winegrowing regions.

A case study of Bordeaux winegrowing area.

Benjamin BOISCentre de Recherches de Climatologie

Université de Bourgogne

Spatial analysis of climatefor viticulture

• Risk management– Pest management (models)– Frost, hail, wind, excessive drought risks

• Terroir comprehension and characterization– Vineyard management– Site selection

Source : RSVAH (2007), Sandoz

Bindi & Maselli (2001)

1 cm 10 cm 1 m 10 m 100 m 1 km 10 km 100 km 1000 km

Micro-scale

Macro-scale

Leaf Canopy Plot Vineyard

Climate differences > Uncertainty From Oke (1978)

Climate differences Uncertainty ?

Local scale

Meso-scale

Spatial scale in climatology

Region Country

Climate spatialization

11,5°C

12°C

14°C13°C

?

Interpolation

Meteorological models

Remotesensing

Bordeaux winegrowing region

Normals (1976-2005)Villenave d’Ornon

0

5

10

15

20

25

30

Jan.

Mar

.

Mai

Jul.

Sep

t.

Nov

.

Tem

pera

ture

(°C

)0

20

40

60

80

100

120

Rai

nfal

l (m

m)

RRTminTmaxTmoy

Rainfall

ET0

Rs

MODELS ETAGRO-

CLIMATICINDICES

at daily time step

Soil water balance

Degree-days

Stat

ions

TminTmax

Rad

arC

OR

INE

DEM

Sat Zoning

Zoning relevance

• Is the spatialization uncertainty sufficiently low to draw reliable analysis of the spatial structure of climate ?

Spatialization accuracy

Variable Spatialization technique

Available period

Evaluation period

RMSE (Apr.-Sept.)

Rainfall Ordinary kriging 1994-2005 1994-2005 5.6 mm/month(11%)

Tmin

Multiple regression +

kriging2001-2005 2001-2005 0.86°C

Tmax

Multiple regression +

kriging2001-2005 2001-2005 0.6°C

RsSatellite sensing

+ DEM 1985-2005 2001-2005 3.3 MJ/m²/day(17%)

ET0 Turc method 2001-2005 2001-2005 0.57 mm/day (16%)

Spatialization accuracy

TEMPERATUREHalf-véraison

(DD prediction vs. field observations)

Predicted values vs. grapevine phenology

Spatialization residuals (errors) vs. Measured spatial variability

RAINFALLComparison of several interpolation

methods

Zoning relevance

• Is the spatialization uncertainty sufficiently low to draw reliable analysis of the spatial structure of climate ?

• Is the spatial structure redundant ?

Rainfall year-to-year variability

20041994

64%21%

12%1%1%

1%

Degree.days Zoning2001-2005 period

Cool zones ( +5 to +15 days): Early cultivars

(Merlot, white cultivars,…)

Warm zones ( -5 to -15 days): Late cultivars

(Cabernet-Sauvignon, Petit Verdot)Climate change consideration

Interclass interval : 42 to 79 DD 5 to 11 days

Conclusions

• New technologies (remote sensing, GIS, increasing computing potentialities)More accurate interpolations, reduction of the

“cost / scale dilemma”

Conclusions

• The need for climate spatial analysis recommendations Numerous methods and data

Ergonomic ≠ simple !

Thanks !

…Work in progress…

TERVICLIM(Very large scale climate analysis and modelling)

Large scale climate analysis(Jones et al.)

World classification of winegrowing

regions

Bois (unpublished)

Résultats

RMSE = 0.86°C RMSE = 0.59°C

Temperature2001-2005 period Strong inter-annual variations

ST Jan.-Sept. 2002 (DD) ST Jan.-Sept. 2003 (DD)

0.5

0.6

0.7

0.8

0.9

1

Year Apr. -Sept.

RM

SE (°

C) OK

IDWA

MRK

Tmin

Daily temperature spatialization

0.5

0.6

0.7

0.8

0.9

1

Year Apr. -Sept.

RM

SE (°

C)

Tmax

Minimal temperature vs. distance to Gironde estuary

0.00

0.20

0.40

0.60

0.80

1.00

1.20

Année Avr. -Sept.

HYDRAM

Ordinary Kriging

Inverse distance

Multiple regression

0.00

0.20

0.40

0.60

0.80

1.00

1.20

Année Avr. -Sept.

HYDRAM

Ordinary Kriging

Inverse distance

Multiple regression

Rayonnement global• RMSE annuelle de 2,7

MJ.m-2 par jour (19,8%)• Sous-estimation : biais de

-11,1%• Climatologie de la

Gironde– Gradient Ouest-Est de

temps clair décroissant.– Jusqu’à 20% d’écart max.

en hiver, 16% en été (8% en Gironde viticole)

– Effet notable du relief

N

R2 = 0.9289

0

5

10

15

20

25

30

35

0 5 10 15 20 25 30 35

Rs H

elio

Clim

-1 (M

J.m

-2)

Rs pyranometer (MJ.m-2)

Solar radiation spatilization

2001-2005 (1826 days)

Annual RMSE = 2.7 MJ.m-2 (19.8%)RMSE Aug.-Sept. =2.9 MJ.m-2 (16.6%)

10 to 6%6 to 1%3 to -3%-3 to -8%-8 to -13%Urban areas

Saint-Émilion village

ET0

Rs

TminTmax

Potential Evapotranspiration : Turc Method with local re-adjustement

Annual RMSE = 0.51 (21%)Apr.-Sept. RMSE= 0.57 (15.9%)

Saint-emilion – “zoom” – moy. Ver mat 20 ans

N

ETP

RMSE = 0.25 mm (11%) RMSE = 0.41 mm (17%)

ETP (prop. Interpolation T)

• Données de validation croisée du processus d’interpolation des températures (MRK)

• Faible propagation des erreurs d’interpolation de la température quotidienne

RMSE ~ 0.05 mm

Radar (Hydram Method)RMSE = 4.4 mm

Ordinary krigingRMSE = 2.5 mm

RADAR

Rainfall spatialization

• Numerous artifacts with radar rainfall estimates

Ordinary kriging at different time steps

114 days – 51 raingauges location