Federico Spanna: Regione Piemonte - Agrometeorological Service federico.spanna@regione.piemonte.itAlberto Rainero: S.I.T. – Alessandria County Councilalbertorainero@libero.it

Workshop on climatic analysis and mapping for agriculture (14-17 june 2005, Bologna, Italy)

ContentsContents

• Context, aim, method • Multivariate analysis

• Spatial representation

Territorial Territorial representationrepresentation

Contoured map showing elevation

50 % mountainous30 % plain20 % hill

Distribution of meteorological Distribution of meteorological stationsstations

150 agrometeorological stations (RAM)300 hydrographic station

AimAim

Georepresentation of agrometeorological variables

as influenced by land morphology

MethodologyMethodology

• Analysis and selection of main morphological informations

• Individuation of homogeneous agrometeorological areas (multivariate analysis)• Spatial representation (statistical multiregressive analysis)

ContentsContents

• Context, aim, method

• Multivariate analysis • Spatial representation

Morphological features:Morphological features:1- agrarian landscape 1- agrarian landscape

mapmap

3 perceptive levelsScale 1:100.000CultivationAgrariantrend

Morphological features: Morphological features: 2 – soil yield 2 – soil yield

Scale 1:100.0009 classes

Potential soil use for crops

Morphological features: Morphological features: 3 – 3 – Corine coverageCorine coverage

Actual soil useScale 1:100.00044 classes

Morphological features: Morphological features: 4 - morphology4 - morphology

HeightSlopeExposureDistance from valley bottom

Piedmont Digital ElevationModel (DEM)Scale 1:100.000

Territorial information Territorial information foundfound

Multivariate analysis

Homogeneous areas features

SlopeExposureHeightYield soil useCorine coverage

Description of morphological and topological parameters

Categorical qualitative table

Aggregation classesAggregation classes

92 stations

91 typologies

8 cluster(homogeneous areas)

Objective functionObjective function

8 areas

ContentsContents

• Context, aim, method• Multivariate analysis • Spatial representation

Homogeneous areas Homogeneous areas representationrepresentation

Watershed

Borough boundaries

Spatial interpolation Spatial interpolation AlgorithmAlgorithm

Station cluster Influence territorial area

Meteo information M

Morphological parameters xi

Morphological parameters

Meteo information synthesis

M=F(xi) ?

Multiregressive Multiregressive analysisanalysis

M = F(xi)

M = kp*H + kd*S + ke*E + kq*D

Meteo information: dependent variable M

Morphological variables: independentH, S , E , D

Multiple regression

Height, Slope, Exposure, River bed distance

+H *kh+S *ks

Substrata Substrata superpositionsuperposition

M

+E *ke+D *kd

Coefficient explorationCoefficient exploration

Sample Dependentvariable

Performance(R2 )Period

All (92) station

Mean of T min 2003 0,139

Area 1Stations

Mean of T min february 0,791

Area 1Stations

Mean ofT max autumn 0,784

Traditional Traditional representationrepresentation

Field ofTemperature

range

AstiAstiArea 1Area 1

Mean of T min - 2003

““Barolo” AreaBarolo” Area Mean of T min – 02/03

““Barolo” Barolo” AreaArea

Asti andAsti andCuneo ProvinceCuneo ProvinceArea 2Area 2

Mean of T mean - Spring 02/03

ASTI andASTI andCuneo ProvinceCuneo ProvinceArea 2Area 2

ConclusionsConclusions

Innovative and significant methodology for a “young”agrometeorological regionMap developing of the most important climatic indexes (ex. Winkler, Huglin, Thermal excursions etc.)

Production of useful supports for local advisors and farmers

BackupBackup

Area Area del Barolodel Barolo

Aree Aree dell’Astigiano dell’Astigiano e del Cuneesee del Cuneese

Area Area dell’Astigianodell’Astigiano