11th ems / 10th ecam - berlin 2011 / 181 micrometeorological models applied to growth and production...

Micrometeorological models applied to Micrometeorological models applied to growth and production ofgrowth and production of

GrapevineGrapevine

Claudio CassardoClaudio Cassardo11, Caterina Francone, Caterina Francone11, , Tiziana La IaconaTiziana La Iacona22, Federico Spanna, Federico Spanna22, , Matteo MonchieroMatteo Monchiero33, Irene Vercellino, Irene Vercellino33

1 Department of Physics, University of Torino, Italy2 Phytosanitary Service, Regione Piemonte, Torino, Italy

3 Centre of Competence for the Agroenvironmental Innovation (AGROINNOVA), University of Torino, Italy

11th EMS / 10th ECAM - Berlin 2011 / 182

MASGRAPE adoption of a Multidisciplinary Approach to Study the

GRAPEvine agro-ecosystem: analysis of biotic and abiotic factors able to influence yield and quality of wine

Development of instruments for vineyard management and wine quality improvement

• Phytopathologists

• Physiologists

• Entomologists

• Agronomists

• Physicists

• Chemicals

• Wine companies

Project Phases

Experimental activity

Numericalmodeling

The project


Growing seasons 2008-2009-2010Experimental measurements carried out

in three vineyardsVarieties: Nebbiolo and BarberaRegions: Monferrato and Langhe

Directly measured variables:

CONTINUOUSLY• Air temperature and humidity• PAR (Photosynthetically active

radiation)• Wind velocity and direction• Soil temperature and humidity• Net radiation

BIMONTHLY• Physiological variables (dry matter)• Point gaseous exchanges

The experimental activity

Cocconato

Fubine

CastiglionFalletto


LAIk

inc

eR

R 1int

LAI2000 Li-Cor

Experimental setup Multi PAR probe Tecno-El

Lcpro+ LiCor ECH2O EC-TM Decagon

Photosintetic Light Temp Smart SensorPro RH and TempHobo

Data from:Agrometeorological network, Regione Piemonte

Meteohydrographic network, ARPA Piemonte


''

'

2

spa

sss

dds

TwcH

TTT

R

CT

''

''

''

qwL

wcH

wu

p

Use of ultrasonic anemometersCorrections for:

- Too high temperatures- Tilted positionment with respect to the active surface

Need to evaluate the fluxes with respect to the streamline plane

Execution of a planar fit: mean values over a “long” (30 min) period in order to avoid short term variations

-z axis is fixed (perpendicular to the plane)-x-y axes vary in the time: u wind speed is aligned along the mean wind speed (// to the plane)

Turbulence measurements

Solent R2 Gill Ins.KH2O Campbell


Meteorological behavior 2008-2009

Precipitation

Temperature

Relative humidity


Sensible heat fluxesin the three sites (2009)

------ Cocconato-- -- Castiglion Falletto- - - Fubine


Vegetation parameters 2008 - 2009

Vegetation height

Vegetation cover

LAI


University of TOrino model of land Process Interaction with

AtmosphereNew version (2010) of LSPM (Land Surface Process Model)

(Cassardo et al., 1992)1-D diagnostic model

Vegetation – 1 layer ( “BIG LEAF”)

Vegetation classes characterized by:Vegetation classes characterized by:

• Minimum stomatal resistance• Leaf diameter• Root depth• Albedo • Emissivity • Height • Cover • Leaf Area Index (LAI)

The UTOPIA model


Turbulent fluxes, flux-gradient law by analogy with Ohm's Law

'' wcH p

Heat flux physics in UTOPIA

H = Hf +Hg faffbpaf TTscH

fafdpag TTscH 11

Sensible heat flux for a vegetated surface Latent heat flux for a vegetated surface

HfHg

H


Calculation of energy budget components: using traditional initialization of vegetation parameters

using observations for initializing vegetation parameters

Comparison with observations Net radiation

Sensible heat flux

Latent heat flux

Sensitivity analysis on vegetation parameters in order to investigate the most critical ones

The experiments with UTOPIA


UTOPIA simulations: energy balance 2008 - 2009

Latent HFSensible HF

Soil HF

Net radiation

2008 2009


UTOPIA simulations: vegetation parameters 2008 - 2009

Vegetation height

LAI

Vegetation cover

Value of the vegetation class vineyards

Measured value


UTOPIA: comparison with different initializationstranspiration 2008 - 2009

20092008

Model using a specific vegetation classModel using measured data for some variables


Comparison between UTOPIA and measurements – 2009

r2 0.91

r2 0.86

Sensible HF

Net radiation

Francone et al., 2010. Preliminary Results on the Evaluation of Factors Influencing Evapotranspiration Processes in Vineyards. Water, 2, 916-937


Sensitivity analysys on parameters used by UTOPIAFor describing vegetation processes

e)

0

4

8

12

16

20

24

0 4 8 12 16 20 24

Morris m*M

orri

s

MaxVegCov

LAImax

0.E+00

5.E-01

1.E+00

0.E+00 5.E-01 1.E+00

a)

0

10

20

30

40

50

0 10 20 30 40 50

Morris m*

Mor

ris

MaxVegCovLAImax

VegAlbe

0

1

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0 1 2

b)

0

6

12

18

24

30

36

0 6 12 18 24 30 36

Morris m*

Mor

ris

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c)

0

2

4

6

8

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0 2 4 6 8 10 12

Morris m*

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d)

0

10

20

30

40

50

0 10 20 30 40 50

Morris m*)

Mor

ris

LAImax

MaxVegCov

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2

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a) Net Radiation (W m-2)b) Sensible Heat Flux (Wm-2)c) Latent Heat Flux (W m-2) d) Soil-Veg Heat Flux (W m-2)e) Transpiration (W m-2)

Francone et. al, 2011. Sensitivity analysis based investigation of the behaviour of the UTOPIA land surface process model. A case study for vineyards in northern Italy. Submitted.


Infections in the three sites during 2009

• At Fubine and Cocconato, the intensity and spread of downy mildew attacks was evaluated. • The first symptoms occurred around June 20th, following the rains in the first half of June. On June 30th,

at Fubine more than two third of leaves (corresponding to more than 10% of total leaf area) were affected for Barbera, and sligthly less for Nebbiolo. At Cocconato, the percentages were sligthly lower

• Due to the several rainfall events occurred in July, at Fubine almost every night the leaves were wet for few hours. Thus the disease spread on about 90% of leaves for Barbera and three quarters for Nebbiolo

• At Cocconato, the observed nocturnal leaf wetness was lower or absent, causing the intensity of the attack to decrease to three fifties for Barbera and one quarter for Nebbiolo

• These preliminary results show that microclimatic factors can consistently affect the quality of the wine, altering the conditions in which the fruits maturate

• Further deeper analyses are required to get more quantitative results, however these results evidence the impact of microclimate and, considering the effects of climate change, pose several threatens for the possible impacts on the wine quality

DATE 17/JUL/2009 30/JUL/2009 27/AUG/2009

% LEAF

SURFACE % LEAVES

ATTACKED % LEAF

SURFACE % LEAVES

ATTACKED % LEAF

SURFACE % LEAVES

ATTACKED

“BARBERA” FUBINE 1,6 21,3 2,3 26,5 8,1 46,5

“NEBBIOLO” FUBINE 2,7 27,5 3,0 29,8 17,3 78,0

“BARBERA” COCCONATO 2,8 22,3 1,7 22,0 0,1 3,0

“NEBBIOLO” COCCONATO 1,8 19,3 1,3 16,0 0,1 2,0

Table 1 - Downy mildew infection trend detected on Fubine and Cocconato vineyards (2009)


Conclusions• Meteorological behaviors are similar in the three sites, with interannual

variations larger than site-to-site variations:2009 warmer and drier than 20082009 heat wave one month earlier than in 2008

• Micrometeorological measurements allow detecting some local differences:Cocconato is the driest siteIn Cocconato soil and air are warmer starting from mid July ( importance of

soil texture and site orientation) effects on sensible heat flux• The application of UTOPIA model at local scale can provide a wide range of

variables difficult to measure extensively in the wine regionsThese parameters can be linked with wine qualityNeed to check UTOPIA to be confident on these values – critical points are:

Influence of tilting on solar radiationInitial/boundary conditions of vegetation parameters (LAI, cover, height, …)Accurate description of soil texture

• The micrometeorological measurements, as well as model output, can be used to understand the vineyard microclimate and to assess some factors relevant for determining the must quality

11th ems / 10th ecam - berlin 2011 / 181 micrometeorological models applied to growth and production...

Documents

hf hg sensible heat

fluxgradient law

ohms lawheat flux physics

fubinevegetation parameters

mean wind speed

u wind speed

directionsoil temperature

agrometeorological network