Grapevine phenology of the Marlborough region, New Zealand

High resolution temporal and spatial temperature mapping together with phenology models are being used to predict the time and duration of key phenological events of Sauvignon blanc grapevines. The objectives of the research are to: 1) develop models to enable industry to anticipate the consequences of climate variation for phenology within the Marlborough region, and 2) to enable growers to consistently produce fruit of optimum composition for the Marlborough wine style. By combining field measurements of grapevine response, automatic weather station data, improved phenological models, and advanced high-resolution weather prediction models, web-based tools are being developed to help wine-makers adapt to changing conditions so that they can continue to produce high quality wine

Application of high-resolution climate measurement and modelling to the adaptation of New Zealand vineyard regions to climate variability

Initial results are presented of research into the relationship between climate variability and viticulture in New Zealand vineyards. Atmospheric modelling and analytical tools are being developed to improve adaptation of viticultural practices and grape varieties to current and future climate. The research involves application of advanced local and regional scale weather and climate models, and their integration with grapevine phenological and crop models. The key aims are to improve adaptation of grape varieties to fine scale spatial variations of climate, and reduce the impact of climate variation and risk factors such as frost, cool spells and high temperatures. Improved optimization of wine-grape production through better knowledge of climate at high resolution within vineyard regions will contribute to the future sustainability of high quality wine production. An enhanced network of automatic weather stations (AWS) has been installed in New Zealand’s premier vineyard region (Marlborough) and the Weather Research and Forecasting (WRF) model has been set up to run twice daily at 1 km resolution through the growing season. Model performance has been assessed using AWS data and the model output is being used to derive high-resolution maps and graphs of bioclimatic indices for the vineyard region. Initial assessment of model performance suggested that WRF had a cold bias, but this was found to be due to errors in the default surface characteristics. Spatial patterns of predicted air temperature and bioclimatic indices appear to accurately represent the significant spatial variability caused by the complex terrain of the Marlborough region. An automated web page is being developed to provide wine-producers with daily up-dates of observed and modelled information for the vineyard region. Latest results of this research will be provided along with a review of the 2013-14 growing season, using data from both the climate station network and WRF model output

Understanding flowering of Sauvignon blanc in the Marlborough region, New Zealand, using high-resolution weather forecasting and the grapevine flowering véraison model

High-resolution weather forecasting and phenological models can be combined to better understand spatial and temporal variations in the phenology of grapevine varieties. The objective of this study was to compare predictions of the time of flowering of Sauvignon blanc in the Marlborough region, New Zealand, using the Grapevine Flowering Véraison (GFV) model using temperature input data from: 1) traditional Automated Weather Stations (AWS); and 2) the Weather Research and Forecasting (WRF) model. Phenology was monitored at ten sites in 2013-14, and seven of the same sites in 2014-15, where there were corresponding AWS on site. The day of 50% flowering was determined at these sites and compared with the predicted dates simulated using the combination of the GFV model with temperature data from the AWS data and WRF models. For most sites in the central Wairau and Awatere valleys, the GFV predictions based on both temperature data sources were in agreement with observations However, there were some spatial trends in the GFV prediction bias with both temperature data sources (AWS and WRF); for example, in 2013-14 coastal and the most inland sites the predicted flowering dates were earlier than those observed. The WRF model produced differences between observations and predictions of similar magnitude to those of the AWS data and therefore provides suitable temperature input data input for phenological modelling. The agreement between AWS and WRF indicates that the observed biases are likely from the phenological model predictions, not the temperature data sources. The WRF model can therefore be used instead of AWS to generate regional maps of flowering date at 1-km resolution.This combined modelling approach can be used to integrate new phenological models, for other phenological stages, other varieties and existing or new regions, to anticipate sub-regional differences in grapevine development

The application of high-resolution atmospheric modelling to weather and climate variability in vineyard regions

Grapevines are highly sensitive to environmental conditions, with variability in weather and climate (particularly temperature) having a significant influence on wine quality, quantity and style. Improved knowledge of spatial and temporal variations in climate and their impact on grapevine response allows better decisionmaking to help maintain a sustainable wine industry in the context of medium to long term climate change. This paper describes recent research into the application of mesoscale weather and climate models that aims to improve our understanding of climate variability at high spatial (1 km and less) and temporal (hourly) resolution within vineyard regions of varying terrain complexity. The Weather Research and Forecasting (WRF) model has been used to simulate the weather and climate in the complex terrain of the Marlborough region of New Zealand. The performance of the WRF model in reproducing the temperature variability across vineyard regions is assessed through comparison with automatic weather stations. Coupling the atmospheric model with bioclimatic indices and phenological models (e.g. Huglin, cool nights, Grapevine Flowering Véraison model) also provides useful insights into grapevine response to spatial variability of climate during the growing season, as well as assessment of spatial variability in the optimal climate conditions for specific grape varieties

Development of advanced weather and climate modelling tools to help vineyard regions adapt to climate change

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