Spatial Climate Assessments: Data, Methods, and Applications to Vitcultural Zoning. Gregory V. Jones Department of Environmental Studies

Transcription

1 Spatial Climate Assessments: Data, Methods, and Applications to Vitcultural Zoning Gregory V. Jones Department of Environmental Studies

2 Outline of Talk Overview of the Development of Spatial Climate Products Overview of the General Use of Spatial Climate Data in Viticulture Zoning Application of Spatial Climate Data to Portugal and the Douro Valley Summary/Conclusions

3 Overview of the Development of Spatial Climate Products

4 Spatial Climate Data Overview Understanding spatial variations in climatic conditions is key to many agricultural and natural resource management activities However, the most common source of climatic data is meteorological stations, which are most often not spatially representative of the activity of interest Increasing demand for better spatial coverage of climate data has driven the need for faster and better interpolation methods of meteorological station data over regions (other environmental data too)

5 Spatial Climate Data Overview The development of spatial climate data has been driven by the need to: Better understand climate structure within regions Develop regional to local suitability/zoning studies Facilitate short-term forecasting Enhance climate change research Ideally spatial climate data interpolation should account for variations in climate due to elevation, topographic aspect, rain shadows, coastal effects, temperature inversions, and more

6 Climate Mapping The process of interpolating climate statistics observed at irregularly-spaced station locations to a regular grid Geospatial Climatology The study of the spatial and temporal patterns of climate on the earth s surface and their causes

7 Generating Temperature Maps from Climate Stations Necessary Steps: Overlay stations with DEM Calculate regressions Interpolate regionally adjusted intercepts (+/- residuals) Use fine scale DEM, intercept and lapse rate to derive new climate map

8 Spatial Climate Data Sources Spatial climate data developments have followed site or region specific primary data (individual research projects) versus secondary data developed as continental to national scale grids Numerous public domain spatial climate data sets have been developed: Global FAO, WorldClim, GHCN Europe E-OBS, CRU, ENSEMBLES North America PRISM, DayMet Australia, New Zealand BOM, NIWA South America PACS, INMET, AGRIMED

9 Spatial Climate Data Methods Three Main Categories of Interpolation Methods: Deterministic (NN, IDW, RBF/Spline, TSA) Probabilistic (numerous forms of Kriging) Composite (combination of the first two) Types of Interpolators: Global/Local Interpolators Exact/Approximate Interpolators Gradual/Abrupt Interpolators Temporal Scale Influences Interpolation Strategy: Absolute values, anomalies or normalized values Validation Methods: Data Splitting, Cross Validation, Kriging Variance

10 Overview of the General Use of Spatial Climate Data in Viticulture Zoning

11 Spatial Climate Data Use Using spatial climate data requires: Knowledge of the temporal scale of the input data (e.g., hourly, daily, monthly) Comparable time periods (e.g., 30 year climate normal periods, etc.) Understanding the limitation of the interpolation method used (e.g., IDW, kriging, etc.) A resolution that is appropriate for the region of interest or context of research (i.e., grid/cell size) Validation assessment in the region of interest

12 Global Assessments of Spatial Climate Characteristics April- September September- April

13 Country Scale Assessments of Spatial Climate Characteristics

14 Continental Scale Assessments of Temporal Variability in Climate Difference in the medians of the HI between and Santos et al. (2012)

15 Dominant Modes of Spatial Variability Patterns of the four significant PCs (82%) for the Huglin Index during PC1 48%; overall HI warming PC2 19%; east-west HI contrast PC3 9%; N/NW and SE HI contrast PC4 6%; C/Iberia/Turkey HI contrast Santos et al. (2012)

16 Country Scale Assessments of Spatial Climate Characteristics and Climate Index Usefulness Hall and Jones (2010)

17 Characterisation of within region Spatial Variability in Temperature Climatology Hall and Jones (2010)

18 Assessment of the Suitability of Different Temperature Based Climate Indices GST is simple to calculate and functional identical to GDD Growing season temperature (GST) and growing degree days (GDD) most sensitive to increasing average temperatures, Huglin Index to increasing maximum temperatures Anderson et al. (2012)

19 Regional Assessments of Homoclimes Source: Fernando Santibanez, Univ de Chile

20 Regional Assessments of Suitability in New Regions Jones and Duff (2011)

21 Application of Spatial Climate Data to Portugal and the Douro Valley Gregory V. Jones Department of Environmental Studies Fernando Alves Association for Viticultural Development in the Douro Valley (ADVID) IXe Congrès International des Terroirs vitivinicoles 2012 / IXe International Terroirs Congress 2012

22 Spatial Analysis in Winegrape Growing Regions in Portugal Digitized DO wine regions and sub-regions (50 regions) Produced complete wine region area, elevation, and climate index summaries (1 km resolution) Examined the utility of different climate indices in Portugal Similar work is done or in process for other wine regions worldwide

23 Spatial Analysis in Winegrape Growing Regions in Portugal WorldClim Database, Time Period, 1 km resolution (Hijmans et al. 2005)

24 Huglin Index ( ) General DO and sub-region statistics: Overall median HI = 2113 Lowest median HI = 1612 Highest median HI = 2693 Median class structure over all regions: Class Count % < > Spatial distribution within AVAs depends on elevation differences, and proximity and orientation to coast

25 Mean 2113 Stdev 273 Max 2693 Min 1612 Range 1082 Map # Wine Region Min 25% Median 75% Max 39 Vinho Verde-Melgaço Monção Lourinhã Vinho Verde-Lima Colares Vinho Verde-Cávado Vinho Verde-Baião Torres Vedras Óbidos Vinho Verde-Ave Lafões Vinho Verde-Basto Vinho Verde-Vale do Sousa Távora Varosa Trás os Montes-Planalto Mirandês Encostas d Aire Vinho Verde-Paiva Alenquer Arruda Carcavelos Beira Interor-Castelo Rodrigo Bucelas Trás os Montes-Chaves Vinho Verde-Amarante Bairrada Beira Interior-Pinhel Douro-Baixo Corgo Tejo-Santarém Dão Trás os Montes-Valpaços Douro-Cima Corgo Lagos Tejo-Cartaxo Douro-Douro Superior Tejo-Tomar Tejo-Almeirim Beira Interior-Cova da Beira Lagoa Portimão Tejo-Chamusca Tavira Tejo-Coruche Palmela-Setúbal Alentejo-Évora Alentejo-Portalegre Alentejo-Borba Alentejo-Redondo Alentejo-Vidigueira Alentejo-Moura Alentejo-Reguengos Alentejo-Granja Amareleja

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27 With gridded climate data one can assess not only the visual spatial characteristics of climate parameters, but also summarize the statistical properties both within and between regions Annual Precipitation (mm) Statistics Min 25% Median 75% Max Baixo Corgo Cima Corgo Douro Superior

28 Again, with gridded climate data one can assess not only the visual spatial characteristics of climate parameters, but also summarize the area of each region that falls within certain categories of climate suitability

29 Summary/Conclusions

30 Summary/Conclusions Viticulture and wine production are an extremely environmentally sensitive endeavor To understand terroir component influences on viticulture and wine production requires spatially appropriate data Understanding the climate structure in wine producing regions helps define/understand cultivar suitability, along with wine style, production and quality potential As such, a better depiction and understanding of the spatial climate structure in wine regions is critical for better regional assessments that can enhance long term sustainability

31 Summary/Conclusions Spatial climate data is being developed for specific research projects and as national or regional grids The most used and promising interpolation techniques are universal kriging and regression models in combination with kriging Numerous software tools have been developed to interpolate station data GMT, R/GSTAT, ArcMap, MISH cost, ease of use, and familiarity issues Strong validation needed to understand usefulness and limitations (cross validation or kriging variance) For most viticulture applications a cell size of < 1 km is necessary to capture vineyard-scale patterns

32 Summary/Conclusions A global gridded spatial climate product (WorldClim) provides the first complete representation of DO region and sub-region climate structure in Portugal Portugal is shown to have a wide range of climate suitability for viticulture, cool to very warm on the HI, however some regions can have 3-4 HI classes alone At 1 km resolution the WorldClim data give a sound representation of the Douro Wine Region climate structure, better than typical station comparisons Additional work has used gridded climate data to examine projected changes in the region s climate structure for future time periods

33 Thank You! Gregory V. Jones Dept of Environmental Studies