District Heating And Cooling In Bordeaux: An Efficient Approach To Energy Management – Local-Scale Temperature Variability in the Bordeaux Area. Relationships with Environmental Factors and the Impact on the phenology of the vines
Climate is a key factor of the physical environment that influences the expression of terroir in viticulture. The thermal conditions have a strong impact on the development of the vines and the composition of the grapes. Spacing this parameter on a local scale allows a more refined vineyard management. In this study, temperature variability was investigated over an area of 19, 233 hectares within the denominations of Saint-Émilion, Pomerol, and their satellites (Bordeaux, France). A network of 90 temperature sensors was deployed inside the vine canopies of this area and temperatures were measured from 2012 to 2018. To determine the effect of temperature on vine development, the phenological stages (break, flowering, and véraison) were recorded on 60 reference plots. planted with Vitis vinifera L. cv. Merlot located next to the temperature sensors. The results showed a large spatial variability in temperature, especially minimum temperature, with an amplitude of up to 10°C on a given day. The spatial variability of the Winkler index measured in the canopy within a given vintage was about 320 degree-days. This research explores the main factors affecting spatial variability in temperature, such as environmental factors and meteorological conditions. The impact of temperature on the behavior of the vines was also analyzed. The observed phenological dates were compared with those estimated using the Grapevine Flowering Véraison model. Temperature maps and phenological observations were created in this area and provided a useful tool for improved adaptation of plant material and training systems to local temperature variability and change.
District Heating And Cooling In Bordeaux: An Efficient Approach To Energy Management
Climate is a key factor of the physical environment that influences the expression of terroir in viticulture (van Leeuwen et al., 2004; Jones, 2018). The climate, and particularly the temperature, largely determine the growing areas well suited for quality viticulture. Such areas are mainly located between latitudes 30 and 50°N and 30 and 40°S, with an average temperature ranging from 12 to 22°C during the growing season (Gladstones, 1992; Jones et al., 2012). The production of high quality wine grapes requires temperatures that allow ripening in a specific period of the year, ideally in September or early October in the northern hemisphere (van Leeuwen and Seguin, 2006). Extreme temperatures are not beneficial for the development of the vines and the quality of the grapes. High temperatures (> 35 ° C) can induce damage to the leaves or the bunch, reduce photosynthesis, and reduce anthocyanin concentrations (Kriedemann and Smart, 1971; Spayd et al., 2002; Mori et al. , 2007). Extreme negative temperatures (<-15°C) during winter are likely to cause permanent damage to the wood and winter buds, possibly leading to the death of the vine. The impact of negative winter temperatures depend on many parameters such as genotype, environment, cultural practices, duration of exposure to frost, and tissue hydration (Zabadal et al., 2007; Ferguson et al., 2014). Temperatures below -2.2°C after emergence can damage the young shoots and severely reduce production without, however, killing the vines (Poling, 2008; Dami et al., 2012).
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The air temperature has a strong impact on the development of the vines and the timing of the phenological stages (De Cortázar-Atauri et al., 2009; Parker et al., 2011, 2013, 2020; Chuine et al. , 2013) as well as the composition of the grapes (Mira and de Orduña). , 2010). Sugar content and acidity in the harvest are related to temperature (Coombe, 1987). This is also the case for secondary metabolites such as anthocyanins, which increase with increased temperature up to a limit and then decrease (Spayd et al., 2002; Mori et al., 2007; Tarara et al., 2008). Temperature also affects aromas and flavor precursors such as methoxypyrazines (including IBMP; green pepper flavor), which decrease with higher temperature during the growing season (Falcão et al., 2007). Trimethyl dihydronaphthalene (TDN; petroleum notes), massoia lactone (dried fig and coconut flavors), and γ-nonalactone (cooked peach flavor), are higher in wines made from grapes ripened under more hot (Marais et al., 1992; Pons et al., 2017) which is quite a negative effect on the quality of the wine.
Considering that the thermal conditions have a strong impact on the development of the vines and the composition of the grapes, the characterization of this parameter is very important. Various temperature indicators have been developed to characterize wine production areas. The indices of Winkler and Huglin (Winkler, 1974; Huglin and Schneider, 1998) or the Average Temperature of the Growing Season (Jones, 2006) are simple indicators based on the air temperature of the growing season -growth and allow the classification of wine producing areas. Depending on the objectives of climate zoning, it may be appropriate to use a multi-criteria approach (Tonietto and Carbonneau, 2004).
The climate varies temporally and spatially and the annual temporal variations that affect the development of the vines and the potential of the quality of the grapes are considered as part of the vineyard effect (van Leeuwen et al., 2004; Ubalde et al., 2010). Spatial climate variability has an impact on grape variety distribution, vine training system, technical management, and wine styles (Gladstones, 2011). Climate can be reduced to several different scales from macroclimate to microclimate and these scales are interdependent (Hess, 1974; Neethling et al., 2019). The spatial variability of local scale climate can be very important and in some cases even more than the large scale variability, due to the influence of local parameters such as relief, human infrastructure, vegetation, or bodies of water (Quénol, 2014). The high variability of the local temperature also depends on the different energy transfer processes between the atmosphere and the surface, thus characterizing the energy balance. It is the ratio between the energy input and the loss that determines the air temperature. The energy balance is strongly determined by surface characteristics and atmospheric conditions (solar radiation, cloud cover, wind conditions). The spatial variability of temperatures is higher in anticyclonic atmospheric situations (calm and clear skies) than in low pressure situations (cloudy and windy skies). Cloud cover and wind have a homogenizing effect on temperatures, reducing the impact of surface features (eg, topography) on the spatial distribution of temperatures (Guyot, 1997) . For these reasons, it is of interest to characterize the climate on a local scale in the grape growing areas.
Studying climate at local scales requires measurement networks and appropriate climate models. Climate has historically been studied at global and regional scales (continent, country, broad region) using weather station data from national networks or simulated data from climate models. Weather stations only produce point data and the mesh of the network is not fine enough to study local climates. During the last few years, many applied climatology studies have required the installation of measurement networks at local scales and the development of modeling tools adapted to these scales (Joly et al., 2003 ; Stahl et al., 2006; Bonnefoy et al., 2013; Wu and Li, 2013; Quénol et al., 2017). Different types of models exist to represent climate at various scales. On a global scale, general circulation models (GCMs) are mainly used to construct climate change scenarios that estimate trends in climate variables at low spatial resolution. Global climate models (GCMs) have a resolution of several tens to hundreds of kilometers (IPCC, 2013). Obviously, these types of models cannot take into account the influence of local effects related to surface features. Downscaling methods are therefore used to integrate the effects of surface features to increase the spatial resolution of the models (Daniels et al., 2012). Regional climate models (RCMs) are downscaled global climate models that aim to regionalize global model outputs using nesting model grids of increasing resolution (Rhoades et al., 2015 ). In viticulture research, regional climate models (RCMs) have been used to produce climate maps at regional scales in the Marlborough region of New Zealand (Sturman et al., 2017), in the growing area of -Stellenbosch vines in South Africa (Bonnarrot and Cautenet, 2009), and in Burgundy (Xu et al., 2012). Recent technological development, including miniaturization of temperature sensors and shelters, development of weather stations, as well as the use of digital elevation models (DEMs), geographic information systems (GISs), geostatistics, linear and non-linear regression modeling allow the mapping of air temperatures in viticulture areas on an even finer scale. Temperature variability was characterized at the regional scale using weather station networks (Madelin and Beltrando, 2005; Bois, 2007; Cuccia, 2013). More recently, temperature variability has been characterized at a local scale using temperature sensor networks deployed in vineyards (Bonnarrot et al., 2012; Bonnefoy, 2013; Le Roux et al., 2017a) .
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Accurate knowledge of temperature distribution at high spatial resolution allows producers to optimize viticultural practices and the selection of plant material according to local conditions. This issue becomes even more strategic in a context of global warming, where producers need to adapt to spatial temperature variability and evolution over time. There is a broad consensus in the scientific community that the climate is changing (IPCC, 2013), and the recent increase in temperature has already affected the development of vines, in particular advancing the timing of the phenological stages ( Bock et al., 2011; Tomasi et al., 2011; Duchêne et al., 2012; van Leeuwen et al., 2019), and modifies the composition of the grapes resulting in higher levels of potential alcohol and acidity reduced (Duchêne and Schneider, 2005; van Leeuwen et al., 2019) and wine aromas (Pons et al., 2017).
Considering the evolution of the climatic conditions and the objective of preserving the potential of the quality and typicality of the wine, the producers will have to adjust the viticulture techniques such as the ratio of the area of leaves with fruit weight, pruning time, or modify roots, cultivars, or clones (van Leeuwen and Desrac -Irvine, 2017).
In this context, the temperature variability was investigated on
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