IXe Congrès International des Terroirs vitivinicoles 2012 / IXe International Terroirs Congress 2012 RÉFÉRENCES 1. R. MORLAT, 2010. Traité de Viticulture de Terroir. Paris, Lavoisier. 492 p. 2. M. KELLER, 2010. Australian Journal of Grape and Wine Research. Vol. 16, 56-69. 3. C. BONNEFOY, H. QUÉNOL, V. BONNARDOT, G. BARBEAU, M. MADELIN, O. PLANCHON, E.
NEETHLING, 2012. (Accepted in International Journal of Climatology). 4. E. NEETHLING, G. BARBEAU, C. BONNEFOY, H. QUÉNOL, 2012. (Accepted in Climate Research). 5. A.J. WINKLER, J.A. COOK, W.M. KLIEWER, L.A. LIDER, 1974. General Viticulture, 2nd ed. University of California Press, California.
High resolution rainfall variability in the vineyard: first results from a local scale network in Burgundy Basile PAUTHIER1, Alice FAVRE1, Albin ULMANN1, Benjamin BOIS1,2,* 1
Centre de Recherches de Climatologie, UMR 6282 Biogéosciences CNRS Université de Bourgogne, 6 boulevard Gabriel, 21000 Dijon, France 2 Institut Universitaire de la Vigne et du Vin « Jules Guyot », Université de Bourgogne, 1, rue Claude Ladrey, 21000 Dijon, France *Corresp. author: B. Bois, +33.380.393.821, +33.380.395.741, Email :
[email protected] ABSTRACT Rainfall is a major component of Vitivinicultural Terroirs. In many regions, it controls a large part of water intake by vine and it has an important role in diseases occurrence. Winegrowers often record rainfall with only one or a couple of rain gauges. Such a sparse coverage might not be accurate enough to capture efficiently the spatial variability of rainfall, which is a necessary prerequisite for efficient crop management (e.g. for irrigation or spraying decisions, etc.). In order to study high resolution variability of rainfall, we implemented a 40 tipping bucket rain gauges network over an area of 28 km² in the hilly region of Beaune (France). The mesh size of the network varies from 300 meters to 1,000 meters. Between the 19th and the 22nd of January 2012, a rain sequence was recorded in the studied area. Using Convective Available Potential Energy (CAPE) data and analysing atmospheric circulation patterns, this sequence has been classified as a stratiform precipitation event. During this 4-day span, rain accumulations varied from 11 mm to 21 mm. Geostatistical analysis of the rainfall field indicates that variability increases with distance, in an east-to-west pattern mainly according with topography. Such a variation of about 100% on a four day stratiform rain accumulation event was unexpected. These observations, if confirmed by further measurements, might confirm that rain gauges network, as usually developed for rainfall monitoring in winegrowing regions, might not be tight enough to capture a strong local variability. Consequently, water intake and parasites monitoring at the vineyard scale might be strongly biased when assessed by a single rain gauge. Keywords: Rain gauge, spatial variability, local scale, geostatistics, CAPE, Circulation pattern. Mots-clés: Pluviomètres, échelle locale, variabilité spatiale, géostatistiques, CAPE, régime de circulation. 1 INTRODUCTION Rainfall is a critical component in viticulture. It controls grapevine water status and it has an important role in vine diseases occurrence [1]. Among several instruments estimating rainfall accumulation, rain gauges are mainly used within a vineyard of a winegrowing region. One of the major limitations of these devices is that they provide a discontinuous measurement in space. Rainfall has therefore to be estimated by means of interpolation/extrapolation methods. Whereas sophisticated interpolation methods like kriging are relevant to interpolate rainfall fields [2], the common usage is to use the value from the nearest rain gauge (i.e. Theissen Polygon method [3]). Such method might lead to considerable estimates when rainfall presents strong spatial variations. To address this problem, we recently implemented a 40 rain gauges network implemented over a winegrowing
area of about 28 km² close to the city of Beaune (Burgundy, France). This paper presents the first results concerning the precipitation variability at high spatial resolution. 2 MATERIALS AND METHODS 2.1 Rain gauges network The data sample we used in this study was obtained from a dense network of 40 rain gauges deployed over a 28 km² area to the north of Beaune (47.04°N, 4.85°E, Burgundy, France). The study area is a grape growing region. Terrain morphology consists in a plain and hillsides facing southeast (Fig. 1). The establishment of rain gauges was constraint by nearest obstacle. A minimum distance corresponding to four times their height has been respected. Each measuring station is composed of a rain gauge presenting a 20.5 cm diameter collector, with 0.254 mm typing buckets, manufactured by Rainwise 3 - 51
IXe Congrès International des Terroirs vitivinicoles 2012 / IXe International Terroirs Congress 2012 Inc. Rain gauges are connected to an individual eventcounting-data-logger (Hobo Pendant® Event Logger
UA-003-64) which records every event with a time resolution of a second.
Figure 1. Rain gauge network location (left, dark grey: Burgundy region; black point study area) and rain gauge distribution within the study area (right, + indicates the location of each rain gauge).
Because of logger or sensor malfunctioning, data from 8 rain gauges are considered as non available during January and February.
2.3 Rainfall spatial analysis From original rain gauge measurements, rainfall patterns were analyzed by cartography and geostatistics (semi-variograms) at daily time step.
2.2 Characterization of rainy events Circulation atmospheric patterns have been defined from NCEP-NCAR reanalysis daily sea level pressure data and analyzed. Next, Convective Available Potential Energy (CAPE) was used as an estimation of atmospheric instability and therefore indicator of rainfall type (i.e. convective, orographic, stratiform) ([4][5]). CAPE is calculated using 1° (~100 km) spatial resolution data from the Global Forecasting System analysis, a general circulation model used for weather forecasting. A first rainy event has been recorded from the 19th to the 22nd of January 2012 and is classified as a stratiform rain episode.
3 RESULTS AND DISCUSSION From the 19th to the 21st of January, typical winter westerly disturbances were observed over the region (fig. 2). On the 20th showers following a cold front provided clustered rainfall fields over northeast France. On the 21st, stratiform rainfall was associated with the arrival of a warm front. Calculated CAPE remained below 100 J.kg-1, which suggests a very low probability of convective rainfall as specified in previous studies [4, 6].
Figure 2. Mean sea level pressure from 19th to 21st January 2012.
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IXe Congrès International des Terroirs vitivinicoles 2012 / IXe International Terroirs Congress 2012 On the 20th, rainfall collected by gauges varies between 4.3 to 9.15 mm (Fig. 3). Similar amounts were
collected on January 21st.
Figure 3. Daily rainfall in the study area. Rainfall fields exhibit a clear spatial structure. For each event, the pattern is almost similar: rainfall amounts tend to be higher over the hills, whereas they are considerably lower towards the plain. The semi-variogram is a geostatistical indicator which represents the difference (semi-variance) in rainfall rates between pairs of points (i.e. rain gauges) according to the distance between each other. As for every spatially dependent phenomenon, it can be expected that two remote points might have a larger rainfall rates difference than two close points. Indeed, the semi-variogram of two rainy days (Fig. 4) shows that difference in collected rainfall amounts increases until a given distance, , beyond which the semivariance
stops increasing. Such distance, also called the semivariogram range, is 3.5km on the 20th and 4 km on the 21st of January. This suggests that rainfall fields can provide noticeable variability at local scale. Such variability is not totally random (at least for the rainy event presented here) as the difference in rainfall rates increases over the first kilometres before reaching a sill.It seems however that the semi-variance starts increasing again beyond beyond 4 or 5km, suggesting that rainfall fields varies at least at two difference spatial scales : a local scale (from 0 to 4km) and a mesoscale, which range cannot be capture with the rainfall network used in this study.
Figure 4. Semi-variogram of daily precipitation rates recorded by the rain gauges Th semi variance is given in mm². Further rainfall events during February and March provided similar results, suggesting that such spatial differences might be stable. The phenomenon controlling this trend is not already identified. A hypothesis concerning an influence of topography appears like the most consistent. An orographic effect might be at the origin of such contrast [7]. However, systematic rainfall event analyses are necessary to confirm this hypothesis.
4 CONCLUSIONS This analysis, even though based upon a single rainfall event, underlines that a single rain gauge might not capture accurately rainfall rates for a whole vineyard, at least in this particular study area. Fungi disease modelling is a crucial aspect in grapegrowing. Most of these models are sensitive to rainfall data, and 5 mm differences might lead to different decisions concerning treatment. Similar conclusions could be 3 - 53
IXe Congrès International des Terroirs vitivinicoles 2012 / IXe International Terroirs Congress 2012 made concerning irrigation management. Rain quantity may be captured with precision to guarantee relevant input of complementary irrigation water. Grapevine water status strongly affects grape composition. Rainfall spatial variability, providing a recurrent spatial structure might therefore play a complementary role in the local variability of grape and wine characteristics. To our knowledge this is an issue that has not been discussed yet.
3. A.H. THIESSEN, 1911. Precipitation Averages for Large Areas, Monthly Weather Review 39, 1082–1089. 4. M. Hagen, B. Bartenschlager, U. Finke, 2006. Motion characteristics of thunderstorms in southern Germany, Met. App. 6, 227-239. 5. H. HUNTRIESER, H.H. SCHIESSER, W. SCHMID, A. WALDVOGEL, 1997. Comparison of Traditional and Newly Developed Thunderstorm Indices for Switzerland, Wea. Forecasting 12, 108-112. 6. R.A. HOUZE, W. SCHMID, R.G. FOVELL, H.H. SCHIESSER, 1993. Hailstorms in Switzerland: Left Movers, Right Movers, and False Hooks, Mon. Wea. Rev. 121, 3345-3370. 7. J.P. CHABIN, 2004. L’excellence aux limites ? Ou le paradoxe des vignobles septentrionaux français d’après l’exemple côte-d’orien, Revue Géographique de l’Est, 44, 9-16.
REFERENCES 1. B. DUBOS, 2002. Maladies cryptogamiques de la vigne. Féret, 2e Edition, 207 p. 2. P. GOOVAERTS, 2000. Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall, Journal of Hydrology 228, 113-129.
Viticultural climatic zoning in temperate, subtropical and tropical zones, Brazil Bases for Estimating the Impact of Climate Change Marco A.F. CONCEIÇÃO1, Sílvio R.M. EVANGELISTA2, José E.B. de A. MONTEIRO3, Francisco MANDELLI4, Antônio H. de C. TEIXEIRA5, Jorge TONIETTO3* 1
Embrapa Uva e Vinho - EEVT, CP 241, CEP 15700-971, Jales, SP, Brazil; Embrapa Informática Agropecuária, CP 6041, CEP 13083-886, Campinas, SP, Brazil; 3 Embrapa Uva e Vinho, CP 130, CEP 95700-000, Bento Gonçalves, RS, Brazil; 4 Embrapa Uva e Vinho - Retired; 5 Embrapa Semi-Árido, CP 23, CEP 56302-970, Petrolina, PE, Brazil; * Corresp. Author: Telephone (55) 54-3455-8000, e-mail:
[email protected] 2
ABSTRACT Fine wine production in Brazil was historically established in the extreme South of the country, in temperate climate. More recently, new producer regions appeared in South, Southeast and Northeast of the country, including subtropical and tropical type of climate in the production scenario. This work had the goal to characterize the Brazilian climate diversity related to viticulture potential, in all types of climates. The methodology used climatic database for all country - series 1961-1990. Many climatic variables and viticultural climatic indices were mapped in GIS at the whole Brazil: viticultural climatic indices of the Geoviticulture MCC System, Thermal Index of Winkler, Zuluaga Index, among others. The Frost Risk was used to separate zones of the country with viticultural potential to produce only one vegetative cycle/harvest per year, from the others, with potential to have more than one cycle and, in particular cases, more than one harvest per year. In this case, the viticultural indices were calculated for two periods: spring-summer and autumn-winter periods of the year. The results identified the climatic regions of Brazil with no potential for viticulture. Also, the results identified the regions with viticultural potential in different types of climates, with the characterization of the classes of viticultural climate for different indices. This climatic zoning will be used as a baseline for estimating, in a medium and long-term, the impact of different scenarios of Climate Change in the climatic potential of current fine wine producer regions and also to prospect potential climates for quality wine production in the future. Keywords: viticulture, grapevine, wine, Vitis vinifera. 1 INTRODUCTION Brazil has about 100 million hectares with potential for agriculture (1), of which 80,000 hectares are used for grape production (2). Historically, the main grape producing areas for wine-making in Brazil have been located in the extreme South of the country, in temperate climate zone. However, new areas are emerging in South, Southeast and Northeast regions, including subtropical and tropical type of climate (3).
Some of these areas have been developed, with specific viticulture for their local conditions (4, 5). Nevertheless, many other areas of the country could present a grape production potential for wine-making, grape juice and table grape production. Furthermore, the global climate change scenarios can modify regional climatic conditions. This work had the goal to characterize the Brazilian climate diversity related to viticulture potential, in all its types of climates. 3 - 54
