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![Page 1: Methodologies for climate variability analysis of winegrowing regions. A case study of Bordeaux winegrowing area. Benjamin BOIS Centre de Recherches de.](https://reader036.fdocuments.net/reader036/viewer/2022070306/551626b055034694308b5c08/html5/thumbnails/1.jpg)
Methodologies for climate variability analysis of winegrowing regions.
A case study of Bordeaux winegrowing area.
Benjamin BOISCentre de Recherches de Climatologie
Université de Bourgogne
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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)
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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
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Climate spatialization
11,5°C
12°C
14°C
13°C
?
Interpolation
Meteorological models
Remotesensing
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Bordeaux winegrowing region
Normals (1976-2005)Villenave d’Ornon
0
5
10
15
20
25
30
Jan.
Mar
.
Mai
Jul.
Sep
t.
Nov
.
Tem
per
atur
e (°
C)
0
20
40
60
80
100
120
Rai
nfal
l (m
m)
RR
Tmin
Tmax
Tmoy
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Rainfall
ET0
Rs
MODELS ETAGRO-
CLIMATICINDICES
at daily time step
Soil water balance
Degree-days
Sta
tion
s
TminTmax
Rad
arC
OR
INE
DE
MS
at Zoning
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Zoning relevance
• Is the spatialization uncertainty sufficiently low to draw reliable analysis of the spatial structure of climate ?
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Spatialization accuracy
VariableSpatialization
techniqueAvailable
periodEvaluation
periodRMSE
(Apr.-Sept.)
Rainfall Ordinary kriging 1994-2005 1994-20055.6 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
+ DEM1985-2005 2001-2005
3.3 MJ/m²/day(17%)
ET0 Turc method 2001-2005 2001-2005 0.57 mm/day
(16%)
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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
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Zoning relevance
• Is the spatialization uncertainty sufficiently low to draw reliable analysis of the spatial structure of climate ?
• Is the spatial structure redundant ?
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Rainfall year-to-year variability
20041994
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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
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Conclusions
• New technologies (remote sensing, GIS, increasing computing potentialities)More accurate interpolations, reduction of the
“cost / scale dilemma”
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Conclusions
• The need for climate spatial analysis recommendations Numerous methods and data
Ergonomic ≠ simple !
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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)
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Résultats
RMSE = 0.86°C RMSE = 0.59°C
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Temperature2001-2005 period Strong inter-annual variations
ST Jan.-Sept. 2002 (DD) ST Jan.-Sept. 2003 (DD)
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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
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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
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R2 = 0.9289
0
5
10
15
20
25
30
35
0 5 10 15 20 25 30 35
Rs
Hel
ioC
lim
-1 (
MJ.
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
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ET0
Rs
TminTmax
Potential Evapotranspiration : Turc Method with local re-adjustement
Annual RMSE = 0.51 (21%)Apr.-Sept. RMSE= 0.57 (15.9%)
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Saint-emilion – “zoom” – moy. Ver mat 20 ans
N
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ETP
RMSE = 0.25 mm (11%) RMSE = 0.41 mm (17%)
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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
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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
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