Spatial Climate Assessments: Data, Methods, and Applications to Vitcultural Zoning Gregory V. Jones Department of Environmental Studies
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
Overview of the Development of Spatial Climate Products
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)
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
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
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
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
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
Overview of the General Use of Spatial Climate Data in Viticulture Zoning
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
Global Assessments of Spatial Climate Characteristics April- September September- April
Country Scale Assessments of Spatial Climate Characteristics
Continental Scale Assessments of Temporal Variability in Climate Difference in the medians of the HI between 1980-2009 and 1950-1979 Santos et al. (2012)
Dominant Modes of Spatial Variability Patterns of the four significant PCs (82%) for the Huglin Index during 1950-2009 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)
Country Scale Assessments of Spatial Climate Characteristics and Climate Index Usefulness Hall and Jones (2010)
Characterisation of within region Spatial Variability in Temperature Climatology Hall and Jones (2010)
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)
Regional Assessments of Homoclimes Source: Fernando Santibanez, Univ de Chile
Regional Assessments of Suitability in New Regions Jones and Duff (2011)
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
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
Spatial Analysis in Winegrape Growing Regions in Portugal WorldClim Database, 1950-2000 Time Period, 1 km resolution (Hijmans et al. 2005)
Huglin Index (1950-2000) 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 % 0 0 6 22 14 < 1200 1200-1500 1500-1800 1800-2100 2100-2400 0.0 0.0 12.0 44.0 28.0 2400-2700 8 16.0 2700-3000 0 0.0 > 3000 0 0.0 Spatial distribution within AVAs depends on elevation differences, and proximity and orientation to coast
Mean 2113 Stdev 273 Max 2693 Min 1612 Range 1082 Map # Wine Region Min 25% Median 75% Max 39 Vinho Verde-Melgaço Monção 873 1244 1612 1884 2083 23 Lourinhã 1578 1624 1659 1714 1795 38 Vinho Verde-Lima 813 1567 1725 1824 2081 17 Colares 1514 1728 1741 1771 1870 37 Vinho Verde-Cávado 778 1692 1787 1878 2093 35 Vinho Verde-Baião 958 1483 1793 1988 2364 29 Torres Vedras 1676 1776 1813 1847 1925 24 Óbidos 1526 1793 1827 1858 1936 34 Vinho Verde-Ave 1023 1757 1835 1915 2083 20 Lafões 1154 1687 1885 1999 2214 36 Vinho Verde-Basto 1056 1680 1901 2072 2261 41 Vinho Verde-Vale do Sousa 1624 1832 1910 1971 2139 28 Távora Varosa 1287 1802 1914 2011 2330 31 Trás os Montes-Planalto Mirandês 1686 1878 1939 2037 2490 19 Encostas d Aire 1543 1840 1950 2033 2284 40 Vinho Verde-Paiva 1663 1876 1953 2003 2147 1 Alenquer 1677 1904 1959 2034 2146 10 Arruda 1791 1872 1962 2038 2142 16 Carcavelos 1889 1929 1963 2016 2093 12 Beira Interor-Castelo Rodrigo 1695 1941 1998 2084 2479 15 Bucelas 1797 1948 2005 2057 2132 30 Trás os Montes-Chaves 1305 1875 2019 2097 2199 33 Vinho Verde-Amarante 1040 1907 2030 2107 2249 11 Bairrada 1763 1961 2033 2110 2272 14 Beira Interior-Pinhel 1572 1935 2034 2129 2363 42 Douro-Baixo Corgo 978 1900 2087 2175 2370 49 Tejo-Santarém 1620 2002 2091 2189 2322 18 Dão 626 1912 2096 2197 2399 32 Trás os Montes-Valpaços 1573 1965 2106 2217 2370 43 Douro-Cima Corgo 1537 1968 2118 2263 2515 22 Lagos 1912 2054 2139 2195 2340 46 Tejo-Cartaxo 1917 2090 2158 2232 2292 44 Douro-Douro Superior 1709 2034 2200 2327 2542 50 Tejo-Tomar 1637 2181 2236 2301 2431 45 Tejo-Almeirim 2237 2299 2308 2320 2382 13 Beira Interior-Cova da Beira 685 2126 2317 2474 2767 21 Lagoa 2163 2291 2322 2344 2375 26 Portimão 2279 2316 2325 2338 2362 47 Tejo-Chamusca 2045 2321 2361 2408 2653 27 Tavira 2159 2330 2370 2414 2524 48 Tejo-Coruche 2245 2345 2373 2422 2524 25 Palmela-Setúbal 1913 2327 2380 2401 2453 3 Alentejo-Évora 2237 2352 2404 2459 2555 6 Alentejo-Portalegre 1836 2327 2454 2528 2593 2 Alentejo-Borba 2334 2453 2502 2540 2635 7 Alentejo-Redondo 2229 2520 2544 2560 2646 9 Alentejo-Vidigueira 2414 2531 2559 2608 2723 5 Alentejo-Moura 2576 2615 2627 2645 2733 8 Alentejo-Reguengos 2509 2601 2636 2665 2747 4 Alentejo-Granja Amareleja 2624 2677 2693 2706 2753
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 971 1128 1190 1282 1625 Cima Corgo 778 938 1026 1089 1314 Douro Superior 643 776 832 927 1123
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
Summary/Conclusions
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
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
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
Thank You! Gregory V. Jones Dept of Environmental Studies