A fine-scale approach to map bioclimatic indices using and comparing dynamical and geostatistical methods

Climate, especially temperature, plays a major role in grapevine development. Several bioclimaticindices have been created to relate temperature to grapevine phenology (e.g. Winkler Index, Huglin Index, Grapevine Flowering Véraison model [GFV]). However, temperature variability can be significant at vineyard scale, so knowledge of the various climatic mechanisms leading to this variability is essential in order to improve local management of vineyards in response to climate change. Indeed, current climate change models are not accurate enough to take into account temperature variability at the vineyard scale (Dunn et al, 2015). This study therefore proposes a method for compare regional modelling and fine-scale observations to map temperatures and bioclimatic indices at fine spatial resolution for some recent growing seasons. This study focuses on two vineyard areas, the Saint-Emilion and Pomerol region in France and the Marlborough vineyard region in New Zealand. A regression model using temperature from networks of measurements has been created in order to map temperature and bioclimatic indices at vineyard scale (100 metres for Marlborough and 25 metres for SaintEmilion and Pomerol). To complement the field measurements, the advanced physics-based three-dimensional numerical weather model Weather Research and Forecasting – WRF (http://wrf-model.org/index.php) has been used, providing hourly meteorological parameters over a complete growing season for each site at 1, 3 and 9 and 27 kilometre resolution. The output of the WRF model provides temperature, wind speed and direction, pressure, and solar radiation data at these different resolutions. The application of different scales of modelling allows improvement in understanding the climate component of the specific terroirs of the study areas