SEARCH

SEARCH BY CITATION

Keywords:

  • climate change;
  • temperature index;
  • viticulture;
  • wine

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. References

Background and Aims:  This paper describes the changes in temperature-based indices used to classify viticultural climates in Australia for three warming scenarios produced by the Commonwealth Scientific and Industrial Research Organisation: Mk3.0 global climate model for the years 2030, 2050 and 2070.

Methods and Results:  Temperature indices that describe grapevine growing season temperature (GST), ripening period temperature, accumulated biologically effective degree days and growing season length were calculated to produce maps of Australia for each warming scenario. Summary statistics of each index's median and range are presented for each Australian wine region under each warming scenario. The greatest change in GST (above the 1971–2000 mean) was modelled to occur for the Perth Hills region, increasing by 1.0°C by 2030, 1.9°C by 2050 and 2.7°C by 2070. The least change in GST was modelled to occur for the Kangaroo Island region, increasing by 0.5°C by 2030, 0.9°C by 2050 and 1.3°C by 2070.

Conclusion:  Of the 61 recognised wine regions, a median GST of over 21°C (an indicator of the limit of quality wine grape production conditions) was found for three regions for the period 1971–2000, for eight regions for the 2030 scenario, 12 regions for the 2050 scenario and 21 regions for the 2070 scenario.

Significance of the Study:  Without appropriate adaptations, some established viticultural regions of Australia may become less suitable for quality winegrape production, whereas regions that were once considered unsuitable for quality winegrape production may become more suitable.


Abbreviations
BEDD

biologically effective degree days

CSIRO

Commonwealth Scientific and Industrial Research Organisation

DEM

digital elevation model

GCM

global climate model

GDD

growing degree days

GHG

greenhouse gases

GST

growing season temperature

IPCC

Intergovernmental Panel on Climate Change

MTA

mean temperature anomaly

RPT

ripening period temperature

SRES

Special Report on Emissions Scenarios

Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. References

The 2007 IPCC reports contain best estimates for global temperature increases under six different GHG emission scenarios (as well as likely ranges for these estimates) for the period 2090–2099 relative to 1980–1999. The total range for the estimated temperature increase for all scenarios over this period is 0.7 to 10.4°C (IPCC 2007), reflecting the diversity of scenarios and the variability and uncertainty in the forecasting models. Spatial heterogeneity in future warming is expected, and for the area of Australia south of 30°S, the predicted median warming by the year 2100 of the models included by the IPCC is 2.6 (with an inter-quartile range of 2.4 to 2.9°C) and 3.0°C (with an inter-quartile range of 2.8 to 3.5°C) for Australia north of 30°S (Christensen et al. 2007). These projections are similar to earlier studies, and therefore, projections made by CSIRO in 2001 (CSIRO 2001) remain valid (Christensen et al. 2007). The climate warming projections made by CSIRO (2001) are that by 2030, annual average temperatures will have increased by 0.4 to 2.0°C above 1990 temperatures over most of Australia, and by 2070, annual average temperatures will have increased by 1.0 to 6.0°C above 1990 temperatures. Spatial variability in the rate of warming is expected with temperature increases in the lower end of the range for some coastal areas of Australia, particularly in the south (Suppiah et al. 2007).

Temperature is widely accepted as being the primary climatic factor affecting the quality of viticultural production (Winkler et al. 1974, Jackson and Lombard 1993, Gladstones 2004). As a consequence, increases in temperature due to an enhanced greenhouse effect will likely have a significant effect on viticultural production (Bindi et al. 1996, Schultz 2000, Jones et al. 2005). Possible beneficial aspects of climate change include less bud and crop damage from frost events and less extreme winter minimum temperatures that would otherwise damage grapevines (Jones 2005b). A reduction in cold events may lead to a poleward shift in the zones of viable viticulture (Jones 2006), and a move to more beneficial climates for some cool climate regions such as the Okanagan Valley (Caprio and Quamme 2002), the Mosel Valley, Alsace, Champagne and the Rhine Valley (Jones et al. 2005). In Europe, higher average temperatures may allow for grapevine production to become more suitable in the north and east through higher temperature accumulation and longer growing seasons and change the spatial distribution of varieties in already established viticultural regions (Schultz 2000). From 1952 to 1997, Jones and Davis (2000) report that warming in Bordeaux has led to shorter phenological intervals and greater potential wine quality. However, temperature increases in several warm climate viticultural regions (southern California, southern Portugal, Barossa and Hunter Valleys in Australia) may have a detrimental effect on winegrape production, perhaps becoming too warm to produce high-quality wine of any type (Jones et al. 2005). It may be inferred that Australia will also experience significant changes to both varietal suitability in its cooler climate viticultural regions and to the spatial distribution of viable winegrape growing areas (Jones 2005a).

The length of the growing season is considered an important determinant of grape quality and consequent wine value (Jackson and Lombard 1993; Coombe and Iland 2004) because air temperature during ripening affects the composition of harvested grapes (Gladstones 1992, Mullins et al. 1992, Webb et al. 2006, 2007). Therefore, the time at which ripening takes place, whether it be in the heat of midsummer or in cooler autumn months, can determine potential wine quality for a particular vintage. For example, in Alsace (France), a move of the ripening period to warmer conditions resulted in changes to grape composition at harvest (Duchene and Schneider 2005). The temperature of the final ripening month is regarded as a particularly important factor influencing wine styles. Studies under controlled conditions have demonstrated that temperature influences many components of grape development, including the breakdown of acids (Buttrose et al. 1971) and berry colour development (Buttrose et al. 1971, Kliewer 1977). In particular, prolonged periods with temperatures above 30°C can induce heat stress, which may lead to premature veraison, berry abscission, enzyme inactivation and reduced flavour development (Mullins et al. 1992).

Modelling the effect of different warming scenarios on the phenology of grapevines has been completed for Australia (Webb et al. 2007). The major conclusions of this study were that shorter seasons would be experienced, chilling requirements might not be met in all regions and harvest would occur in warmer conditions earlier in the year. The study presented in this paper differs from that of Webb et al. (2007) in terms of (i) the way in which grapevine response to warming scenarios is derived; and (ii) its geographic extent. In comparison with Webb et al. (2007), who use a specially modified version of proprietary software, i.e. Vinelogic (Godwin et al. 2002), to conduct grapevine phenology modelling, this study uses easily repeatable and widely accepted temperature-based approaches to characterise climatic suitability for winegrape growing. In addition, this study ascertains suitability of viticultural production under different warming scenarios for all established viticultural regions of Australia.

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. References

To investigate the effect of potential warming on the geography of Australian winegrape growing conditions, average GST, BEDD, grapevine growing season length and RPT (for a grapevine variety that requires 1300 BEDD) were calculated using maps of average daily temperatures for the period 1971–2000 and for each modelled future time period. It must be noted that this study considers projected rises in temperature in deriving models of future grapevine growing conditions only; other climatic changes associated with an enhanced greenhouse effect are not considered but are likely to have significant effects on viticultural production in the future. For instance, simultaneous rises in atmospheric CO2 concentration will likely have a confounding effect on the response of grapevines to temperature increases (Schultz 2000). Elevated CO2 environments have been shown to stimulate grapevine production with expected rises in CO2 leading to increases in yield. A 40–45% increase in fruit dry weight for atmospheric CO2 concentrations of 550 ppm (cf. the seasonally adjusted CO2 concentration of 383 ppm in August 2007 (Keeling et al. 2001)) has been reported with no apparent loss in grape and wine quality (Bindi et al. 2001). Higher CO2 concentrations may, however, cause vegetative growth that increases canopy shading and potentially decrease fruitfulness (McInnes et al. 2003). In addition, changes to the moisture balance (i.e. the net change in precipitation and evaporation) are not considered in the modelling presented in this paper. A range of climate model simulations all suggested that for Australia, the moisture balance deficit will become larger under enhanced greenhouse conditions (IPCC 2007). Average decreases in the annual water balance in Australia range from about 40 to 120 mm per °C of warming (CSIRO 2001). Possible consequent changes to water allocations to vineyard irrigation may, therefore, have a significant impact on the viability of some viticultural regions notwithstanding the effects of changes in average temperature (Jones 2003).

Projections of future warming used in this study are derived from the CSIRO Mk3.0 GCM (Gordon et al. 2002) accessed via the OzClim web interface (CSIRO 1996). GCMS deliver modelled forecasts of climatic outcomes based on GHG emission scenarios from the IPCC's SRES of which there are 40, each being ‘equally valid with no assigned probabilities of occurrence’ (IPCC 2000). Each SRES scenario encompasses different projections of temporally varying atmospheric GHG concentrations resulting from various probable future demographic, economic and technological developments. The SRES scenario selected for this study is the mid-range A1B case. The A1 ‘family’ of scenarios describes a future with very rapid economic growth, a global population that peaks mid-century and then declines and the rapid introduction of new and more efficient technologies. The appended ‘B’ describes the technological emphasis as balanced across fossil intensive and non-fossil energy sources. In addition to the many different scenarios, each SRES scenario's level of effect on atmospheric temperature depends on the sensitivity of Earth's climate system, for which there is a degree of uncertainty. In response to a doubling of CO2 from 280 to 560 ppm, the commonly accepted range for atmospheric temperature increases is 1.5 to 4.5°C (Houghton et al. 2001). Climate models are run with a set sensitivity, and the IPCC uses 1.7°C for a model that assumes a low level of sensitivity of the climate system in response to a doubling of atmospheric GHG, and 4.2°C for a high level of sensitivity (Houghton et al. 2001). A medium-level sensitivity of 2.6°C was used in this study, common to the medium-level sensitivity used by OzClim (CSIRO 1996). Comparison maps of Australian October to April temperature anomalies, modelled using SRES scenario A1B and the three different climate sensitivities of 1.7, 2.6 and 4.3°C, are illustrated in Figure 1 for the years 2030, 2050 and 2070. The maps feature common attributes in terms of the spatial differences in response to increasing GHG concentrations. The general pattern is greater warming inland and to the north and west, with less warming close to the coasts and in the south, particularly coastal South Australia, south-west Victoria and Tasmania. The mean warming experienced for each combination of climate sensitivity and year is included in the upper left of each panel of Figure 1. The three combinations selected in this study can be compared against other possible combinations using this chart. For example, assuming similarity in the spatial rate of change in temperatures, the models produced using a medium sensitivity for 2050 would be similar to those produced with a low sensitivity for 2070.

image

Figure 1. Projected mean temperature increases from 1971–2000 temperature average using three climate system sensitivity levels for the period 1 October to 30 April in 2030, 2050 and 2070, presented as maps of temperature increases. Mean temperature anomalies (MTA) for each scenario are included at the upper left of each panel. An italicised MTA indicates the climatic sensitivity and year combination was selected for modelling in this paper. Sensitivity levels: warming of 1.7°C for low, 2.6°C for medium and 4.2°C for high in response to a doubling of atmospheric CO2 from 280 to 560 ppm. Global Climate Model: CSIRO Mk3.0. SRES Emission Scenario: A1B.

Download figure to PowerPoint

Average GST (mean average daily temperature from 1 October to 30 April) used in this study is similar to that used by Jones et al. (2005) (which used average temperatures of April to October for Northern Hemisphere studies). How the groupings of Jones et al. (2005) relate to those used in this study is shown in Table 1. Jones (2006) orders grapegrowing climates into cool, intermediate, warm and hot groupings based on average GST, a simpler climate classification method but analogous to that developed by Winkler et al. (1974) based on heat accumulation.

Table 1.  Growing season temperature categories of Jones et al. (2005) and those used in this study.
Growing season temperature categoriesGrowing season temperature category ranges used by (Jones et al. 2005)Growing season temperature category ranges used in this study
Cool13–15°C13–15°C
Intermediate15–17°C15–17°C
Warm17–19°C17–19°C
Hot19–24°C19–21°C
Very HotUnused21–24°C

A common measure of heat accumulation is GDD, which is used to determine the growth rate and phenological development of many crops. The GDD for a single day (GDDi) is calculated using

  • image(1)

where Tmax and Tmin are the daily maximum and minimum recorded air temperatures (in °C) and b is the base temperature, below which there will be no significant growth of a particular plant (10°C is typically used for grapevines). GDD is often used to determine climatic regions for grapevine suitability, following the work of Winkler et al. (1974) who present seasonal summations of GDD to classify five viticultural climatic regions for California.

For grapevines, a linearly increasing phenological response to mean daily temperature between 10 and 19°C can be used to find approximate maturity dates (Gladstones 1992). Below a temperature of 10°C, no growth occurs, and above 19°C, the growth rate flattens out so that no further increase in temperature results in an increase in growth rate (Gladstones 2004). When calculating GDD, setting b to 10°C accounts for the temperatures below which no growth will occur, and restricting the maximum GDDi to 9°Cdays accounts for no further growth above an average temperature of 19°C. This leads to lower heat accumulation units than those produced using a method with no upper limit to GDDi (Winkler et al. 1974). Heat accumulation units calculated with a maximum GDDi cap of 9°Cdays per day are termed biologically effective °Cdays (Gladstones 1992). Cumulative biologically effective °Cdays (BEDD) calculated for this study are the sum of GDDi for a number of days (n) in a particular period restricted to a maximum accumulation of 9°C on any 1 day, i.e.

  • image(2)

Gladstones's (1992)biologically effective °Cdays calculations, like Winkler et al. (1974), uses averaged monthly temperature data (rather than daily average temperature as described in Eqns 1,2), and a 7-month growing period, but instead of using April to October, the months of October to April are used to suit the Southern Hemisphere summer.

The day on which BEDD reaches a target heat accumulation level can be taken as the season-end date (Coops et al. 2001). To enable comparisons of BEDD to be made with accumulated heat unit calculations in previous research and publications, the season start date has been assumed to be 1 October. In phenological terms, 1 October is an arbitrary date for the season start date. Many factors, mainly early spring temperatures, can affect the season start date; it varies from year to year, and for warmer temperatures, the season start date is likely to be earlier. Nevertheless, previous research and publications use data from 1 October, and classification of climates or varietal suitability using accumulated heat units has proceeded based on this assumption. Therefore, in this study, the length of the growing season is considered to be the number of days it takes for accumulated BEDD to reach a target value after 1 October.

RPT was estimated in this study using the estimate of the harvest date and then calculating the average temperature of the preceding 30 days. To determine the harvest date, a target BEDD of 1300°Cdays accumulated since 1 October was chosen, which is the boundary between maturity groups 5 and 6 defined by Gladstones (2004). Group 5 contains Shiraz and group 6 contains Cabernet Sauvignon; these two varieties made up 39% of the Australian winemaking grape crop in 2006 (Australian Bureau of Statistics 2006).

Production of base daily temperature surfaces

Meteorological stations with a continuous record (above 95% complete) of maximum temperature (Tmax) and minimum temperature (Tmin) for the years 1971 to 2000 (the same base period used in the CSIRO Mk3.0 climate model) were identified (238 stations). The geographic distribution of the stations that met the above criteria is illustrated in Figure 2. Although there is not an overall good level of coverage for Australia as a whole, the southern and eastern areas in which Australia's viticultural regions are located are represented well by temporally continuous climate data, suggesting that spatial interpolations of climate for the wine growing regions will be more accurate in the south and east than that for Australia as a whole.

image

Figure 2. Spatial distribution of meteorological stations used to derive the daily mean average temperature maps (Stations Used in Model, inline image) and those used to validate the maps (Stations Used in Validation, inline image).

Download figure to PowerPoint

Daily mean temperatures (T) were calculated from the maximum and minimum daily temperatures for each station, i.e.

  • image(3)

Daily mean temperatures were then altered to take into account the elevation of the meteorological stations, producing sea-level-equivalent temperatures (Tsle). An environmental lapse rate of 6.5°C/km, which is widely accepted as the average rate of change of temperature with elevation (Donn 1975, Sturman and Tapper 1996), was applied to the daily mean temperature data (T), i.e.

  • image(4)

where h is the elevation above sea level of the meteorological station in kilometres.

There are many interpolation functions available for producing continuous maps of point data. In a comparison of different interpolation techniques, kriging was shown to be the most accurate method in interpolating climatic data over the UK (Luo et al. 2008). Therefore, the daily sea-level-equivalent temperature data were interpolated at a spatial resolution of 0.05 decimal degrees for the Australian continent using the ordinary kriging function of ArcGIS 9.2 Spatial Analyst (Environmental Systems Research Institute 2006). A spherical variogram model was employed, with the range, sill and nugget parameters calculated internally by ArcGIS separately for each set of daily temperature data. This resulted in a series of 365 maps of mean daily sea-level-equivalent temperature with a pixel size of 0.05 decimal degrees (approximately 5.6 km latitude by 4.6 km longitude at 35°S). Note that the longitudinal length of the pixels varies with latitude, so that south of 35°S the area covered by a pixel is slightly smaller and north of 35°S the area is slightly larger.

Each mean daily sea-level-equivalent temperature map was then adjusted for elevation using a 3-s (<0.001 decimal degrees) DEM of Australia (Jet Propulsion Laboratory 2004). The DEM was converted to a temperature adjustment map (6.5 hDEM), which was subtracted from the sea-level-equivalent temperature maps, i.e.

  • image(5)

The 0.05-decimal degree pixel size was retained for the resultant maps, which are termed temperature surfaces. Regions of missing data in the DEM, due to areas of shadow or low radar backscatter, where an elevation solution could not be resolved by the remote sensing device (Rosen et al. 2000), resulted in a small number of pixels with no data in the temperature surfaces.

Ten meteorological stations were selected for use in the validation of the interpolated temperature surfaces (Figure 2). These stations were removed from the data set before the interpolation process described in the last section was completed. Data were extracted from the resulting interpolated map files in a 0.08-decimal degree radius around the location of the validation stations (delivering six to eight pixels). The average of the extracted pixels (the modelled temperature) was calculated for each validation station, and compared against the actual recorded temperature by calculating both the mean error (inline image) and the mean absolute error (inline image).

Production of modelled maps of temperature indices

Separate maps for each of the three temperature indices (BEDD, GST and RPT) and season-end date for the four different time periods (1971–2000, 2030, 2050 and 2070) were produced. For the maps that describe the indices for the three future time periods, a map of temperature increases for the corresponding month was added to the daily modelled temperature surfaces before calculating the maps. To produce BEDD maps, each daily modelled temperature surface for the period 1 October to 30 April was converted to maps of GDDi using Equation 1. BEDD was then calculated for each pixel using Equation 2 and was recorded as the value of the co-located pixel in a new map. To produce maps of the season-end date (the day on which 1300 BEDD is reached), maps of GDDi were converted to maps of daily BEDD for each day in the period 1 October to 30 April using Equation 2. Each daily BEDD map was then assessed in sequence. The day on which each pixel's BEDD reached 1300°Cdays was recorded as the value of the co-located pixel in a new map. To produce maps of RPT, for each pixel, the mean of the temperature records between the day on which 1300°Cdays was reached and the day 30 days before the day on which 1300°Cdays was reached was calculated. The value determined for each pixel was assigned to the co-located pixel in a new map.

Unlike the other three maps of temperature indices produced in this study, the daily modelled temperature surfaces were not used in the production of the GST maps. Instead, the daily average temperatures for the period 1 October to 30 April for the years 1971–2000 were averaged to produce point data, which were then interpolated. The interpolation method was the same as that used to produce the daily modelled temperature surfaces, i.e. sea-level-equivalent temperatures were calculated; the data was interpolated using the ordinary kriging function of ArcGIS 9.2 Spatial Analyst (Environmental Systems Research Institute 2006), and then the DEM was used to correct the temperature surfaces for elevation. Separate maps for the different future time periods were produced by adding the temperature anomaly for the SRES scenario A1B for 2030, 2050 and 2070 (with medium climate system sensitivity) to the GST maps calculated for the 1971–2000 average temperatures.

Wine region summaries

Using the modelled maps, summary data were produced to describe each Australian wine region. The wine regions used in this study are those that are officially described by the Australian Wine and Brandy Corporation (2008), called Geographical Indications (GIs), which are the official descriptions of Australian wine zones, regions or sub-regions (Figure 3). In addition, two unofficial regions in northern and southern Tasmania were added to account for the growing industry there, which in combination with the official GIs resulted in 63 wine regions being used (Figure 3). Elevational differences result in many regions containing highland areas with climates that are obviously too cool for winegrape production. Statistics generated using data for the whole of such regions are therefore not representative of the areas in which grapes are produced. To account for this issue, those regions that had mean seasonal BEDD totals of less than 1400 (approximately the mean BEDD of all regions), along with two further regions that have large elevational ranges (Hunter and New England), were processed to remove those pixels that were below the median BEDD for the base period 1971–2000 within each region. Using these criteria, the regions that were processed were Alpine Valleys, Beechworth, Canberra District, Geelong, Grampians, Henty, Hunter Valley, Macedon Ranges, New England, North Tasmania, Orange, Pyrenees, South Tasmania, Southern Highlands, Strathbogie Ranges, Sunbury, Tumbarumba, Upper Goulburn and Yarra Valley. The remaining pixels of the climate index maps, whose centres were within each region, were extracted and summarised by generating the median, maximum, minimum, first quartile and third quartile of the index values of each region for the four time periods.

image

Figure 3. Australian wine regions used in this study. Regions labelled 3–63 are derived from data supplied by the Australian Wine and Brandy Corporation describing official regions (Geographic Indications). Regions labelled 1–2 are regions produced by the authors to include Tasmania.

Download figure to PowerPoint

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. References

Validation of temperature surfaces

The validation process indicated that the modelled temperature surfaces were close to the actual recorded temperatures for the 10 validation stations and varied by amounts less than what would be expected through instrumental error alone. For example, for the station at Meekatharra Airport, which has the greatest inline imageof the validation stations (1.04°C), the 95% confidence interval of the expected cumulative error over 365 days is −1.0 ± 1.2°C. For the 10 stations as a whole, inline imageand inline image, which shows that the model underestimated temperatures for the 10 validation stations by an average of 0.26°C, and the accuracy on any one particular day was on average 0.47°C different from the actual recorded temperature. The absolute mean error is highest for stations at Lismore, Bushy Park, Meekatharra Airport and Norseman, all with inline image. For areas that have few meteorological stations nearby, extrapolated data will be less accurate, thus explaining the high errors for these locations. For the six validation climate stations within the viticultural regions, mean inline image. It may be reasonably assumed therefore that errors in the modelled temperature surface are little more than 0.3°C for those regions of interest to this study.

Modelled temperature indices

Each temperature index map for each time period is presented in Figure 4. The general spatial trends of each of the temperature indices show latitudinal shifts southward and/or upward in elevation. The maps show that for all locations, increasing temperatures lead to warmer GST, more accumulated BEDD, earlier ripening and warmer RPT. The rate of the increases in the temperature indices varies spatially mainly with respect to the degree of continentality; generally, for locations closer to the coast, projected temperature increases are lower than those inland.

image

Figure 4. Winegrape growing conditions for the period 1971–2000 and projected for 2030, 2050 and 2070 described by: (a) mean temperature for the period 1 October to 30 April; (b) total biologically effective degree days for the period 1 October to 30 April; (c) estimated mean ripening period temperature (air temperature of the 30 days preceding day on which target heat unit accumulation reached) experienced for a grapevine cultivar that has a target heat accumulation of 1300 biologically effective °Cdays assuming a start date of 1 October (note areas of no data are present in regions where 1300 biologically effective °Cdays are not reached); and (d) estimated season-end dates based on a start date of 1 October and a target heat accumulation of 1300 biologically effective °Cdays.

Download figure to PowerPoint

A visual comparison of the maps of GST with maps of BEDD and RPT (Figure 4) suggests that the spatial patterns are very similar. However, correlation coefficients describing the spatial correlation between the different temperature index maps suggest that there are some large spatial differences between the indices. For example, the correlation coefficient (r) for BEDD and GST is 0.75 for 1971–2000 decreasing to 0.60 for 2070. This shows that the two indices describe different climatic characteristics, each being affected differently by increasing atmospheric temperatures. RPT is also shown to be a different climatic descriptor to GST, although the correlation coefficient (r = 0.80 for 1971–2000) indicates that these indices are more similar to each other than BEDD is to GST.

Summary data for the baseline period (1971–2000) and change in index values for 2030, 2050 and 2070 are presented for each region in Table 2. For 1971–2000, GST averages 18.0°C across all 63 wine regions but varies from 13.6°C in Southern Tasmania to 23.7°C in South Burnett. BEDD values for the baseline period reveal a wine region average of 1540 with the lowest (756) and highest (1899, the maximum BEDD that can be accumulated for any region) values being seen in Southern Tasmania and South Burnett, respectively. For RPT, the wine region average is 19.1°C with the lowest observed found in Mudgee (12.6°C) and the highest in the Riverina (25.4°C) (note that the wine regions in Tasmania are cooler overall than Mudgee, but as 1300°Cdays was not reached in these regions, the RPT could not be calculated). The estimated season-end date averages 20 March over the wine regions, with the earliest in South Burnett (22 February, the earliest season start date that can be achieved with the indexing method) and the latest occurring after 30 April in many locations (Table 2).

Table 2.  Index summaries (minimum (Min), first quartile (Q1), median, third quartile (Q3) and maximum (Max) values) of the pixel sets inside each Australian wine region for the base period (1971–2000) and the projection years of 2030, 2050 and 2070 (excluding pixels representing high elevation areas as described under Wine region summaries of the Methods section).
 Time periodGrowing season temperatureBiologically effective degree daysRipening period temperatureSeason-end date
MinQ1MedianQ3MaxMinQ1MedianQ3MaxMinQ1MedianQ3MaxMinQ1MedianQ3Max
  • Differences in the index value from the base period are included below the summary data of each region. Regions are listed in order by the greatest change in median GST.

  • † 

    The modelled index value exceeded the limits of the indexing system, resulting in changes that are likely to be underestimated or invalid.

  • ‡ RPT could not be calculated because 1300 BEDD was not reached in that region.

WAPerth HillsBase18.420.020.320.821.81546170217341763182120.022.322.823.324.42-Mar7-Mar10-Mar12-Mar23-Mar
203019.421.021.321.822.81649177417981819185621.523.523.924.425.527-Feb2-Mar4-Mar6-Mar16-Mar
205020.221.922.222.723.71723181918371850187322.524.524.825.326.425-Feb27-Feb1-Mar2-Mar10-Mar
207021.122.723.123.524.61779185118611870188923.425.325.726.127.123-Feb25-Feb26-Feb27-Feb6-Mar
2030-Base1.01.01.01.01.0103726456351.51.21.11.11.1−4−5−6−6−7
2050-Base1.81.91.91.91.917711710387522.52.22.02.02.0−6−9−9−10−13
2070-Base2.72.72.82.72.8233149127107683.43.02.92.82.7−8−11−13−14−17
QLDGranite BeltBase16.617.617.918.319.01387155915911647170916.118.018.419.120.16-Mar10-Mar14-Mar16-Mar5-Apr
203017.618.618.919.320.01551168317061746179018.019.419.720.321.228-Feb3-Mar5-Mar6-Mar16-Mar
205018.419.419.720.220.91658176117771806183819.120.320.621.222.025-Feb27-Feb28-Feb1-Mar8-Mar
207019.320.320.621.021.71742181618271849187320.121.221.522.122.922-Feb24-Feb25-Feb25-Feb2-Mar
2030-Base1.01.01.01.01.016412411599811.91.41.31.21.1−7−7−9−10−20
2050-Base1.81.81.81.91.92712021861591293.02.32.22.11.9−10−12−15−15−28
2070-Base2.72.72.72.72.73552572362021644.03.23.13.02.8−13−15−18−20−34
NSWNew EnglandBase17.017.818.619.421.71467155316291709187317.118.519.620.823.324-Feb6-Mar12-Mar17-Mar26-Mar
203018.018.819.620.422.71595166617251785189718.519.920.921.924.222-Feb28-Feb4-Mar9-Mar14-Mar
205018.819.720.421.323.61681174517891834189919.620.821.822.824.922-Feb25-Feb28-Feb2-Mar7-Mar
207019.620.521.322.124.41749180018361869189920.621.822.623.625.722-Feb23-Feb25-Feb27-Feb1-Mar
2030-Base1.01.01.01.01.01281139676241.41.41.31.10.9−2−7−8−8−12
2050-Base1.81.91.81.91.9214192160125262.52.32.22.01.6−2−10−13−15−19
2070-Base2.62.72.72.72.7282247207160263.53.33.02.82.4−2−12−16−19−25
QLDSouth BurnettBase20.021.622.022.523.71787187518881895189921.022.623.123.524.622-Feb22-Feb22-Feb23-Feb28-Feb
203021.022.623.023.524.71841189418981898189921.923.524.024.425.522-Feb22-Feb22-Feb22-Feb24-Feb
205021.923.523.924.425.51876189818991899189922.824.324.825.226.322-Feb22-Feb22-Feb22-Feb22-Feb
207022.724.324.725.226.31890189918991899189923.625.125.526.027.022-Feb22-Feb22-Feb22-Feb22-Feb
2030-Base1.01.01.01.01.0541910300.90.90.90.90.9000−1−4
2050-Base1.91.91.91.91.8892311401.81.71.71.71.7000−1−6
2070-Base2.72.72.72.72.61032411402.62.52.42.52.4000−1−6
WAPeelBase18.119.019.319.821.41531161116401680180619.520.921.422.024.04-Mar13-Mar16-Mar18-Mar24-Mar
203019.120.020.320.822.31632170317241754184821.122.122.523.124.928-Feb8-Mar10-Mar12-Mar17-Mar
205019.920.821.121.723.21710176317821806186822.123.123.524.025.825-Feb3-Mar5-Mar7-Mar11-Mar
207020.721.621.922.524.01769181118261842188423.024.024.324.926.524-Feb28-Feb2-Mar3-Mar7-Mar
2030-Base1.01.01.01.00.9101928474421.61.21.11.10.9−5−5−6−6−7
2050-Base1.81.81.81.91.8179152142126622.62.22.12.01.8−8−10−11−11−13
2070-Base2.62.62.62.72.6238200186162783.53.12.92.92.5−9−14−14−15−17
NSWMudgeeBase15.217.719.120.121.01104151216501722179812.618.620.521.723.028-Feb6-Mar11-Mar20-Mar30-Apr
203016.118.720.121.122.01287161617371794184914.819.821.823.024.025-Feb28-Feb4-Mar12-Mar30-Apr
205017.019.520.921.922.81417169717951837188116.821.022.923.824.822-Feb25-Feb28-Feb6-Mar28-Mar
207017.720.321.722.723.61522176118351870189318.422.123.724.625.622-Feb23-Feb25-Feb1-Mar17-Mar
2030-Base0.91.01.01.01.01831048772512.21.21.31.31.0−3−7−7−80
2050-Base1.81.81.81.81.8313185145115834.22.42.42.11.8−6−10−12−14−33
2070-Base2.52.62.62.62.6418249185148955.83.53.22.92.6−6−12−15−19−44
NSWHunterBase16.120.520.821.121.61288179218181840186316.921.922.322.623.424-Feb26-Feb27-Feb1-Mar30-Apr
203017.121.421.822.022.61437184418661882189317.222.823.223.624.422-Feb23-Feb24-Feb25-Feb27-Mar
205017.922.322.622.823.41545187918901896189818.623.624.024.425.122-Feb22-Feb22-Feb23-Feb16-Mar
207018.723.023.423.624.21632189318971898189919.824.424.825.125.822-Feb22-Feb22-Feb22-Feb9-Mar
2030-Base1.00.91.00.91.0149524842300.30.90.91.01.0−2−3−3−5−34
2050-Base1.81.81.81.71.8257877256350.71.71.71.81.7−2−4−5−7−45
2070-Base2.62.52.62.52.63441017958361.92.52.52.52.4−2−4−5−8−52
WASwan DistrictBase20.321.221.321.521.71737180618141823183522.823.623.823.924.41-Mar2-Mar3-Mar4-Mar10-Mar
203021.322.122.322.422.71799184518501855186323.924.624.825.025.326-Feb27-Feb27-Feb28-Feb4-Mar
205022.223.023.123.323.61838186718711875188124.825.425.625.826.224-Feb25-Feb25-Feb25-Feb1-Mar
207023.023.823.924.124.41862188418861889189325.526.126.326.526.923-Feb23-Feb23-Feb24-Feb26-Feb
2030-Base1.00.91.00.91.062393632281.11.01.01.10.9−4−4−5−5−6
2050-Base1.91.81.81.81.9101615752462.01.81.81.91.8−6−6−7−8−9
2070-Base2.72.62.62.62.7125787266582.72.52.52.62.5−7−8−9−9−13
NSWOrangeBase16.316.817.217.618.61293136114141464158712.716.017.318.320.015-Mar25-Mar31-Mar8-Apr30-Apr
203017.217.718.118.519.61427148715321573167817.418.519.219.821.29-Mar16-Mar19-Mar22-Mar28-Mar
205018.118.518.919.320.41532158116201655174919.119.820.521.122.43-Mar9-Mar12-Mar14-Mar18-Mar
207018.819.319.720.121.21615165916931728180520.321.021.622.223.527-Feb4-Mar7-Mar9-Mar12-Mar
2030-Base0.90.90.90.91.0134126118109914.72.51.91.51.2−6−9−12−17−33
2050-Base1.81.71.71.71.82392202061911626.43.83.22.82.4−12−16−19−25−43
2070-Base2.52.52.52.52.63222982792642187.65.04.33.93.5−17−21−24−30−49
VICBeechworthBase16.716.717.117.217.71316132213691382145014.214.716.316.718.427-Mar4-Apr6-Apr16-Apr19-Apr
203017.617.618.018.118.61435144014811492155018.018.219.119.320.218-Mar22-Mar23-Mar27-Mar28-Mar
205018.418.418.818.919.41527153215681578162819.819.920.520.621.612-Mar16-Mar16-Mar19-Mar19-Mar
207019.119.219.619.720.21604160816391649169321.121.321.821.922.78-Mar11-Mar11-Mar13-Mar14-Mar
2030-Base0.90.90.90.90.91191181121101003.83.52.82.61.8−9−13−14−20−22
2050-Base1.71.71.71.71.72112101991961785.65.24.23.93.2−15−19−21−28−31
2070-Base2.42.52.52.52.52882862702672436.96.65.55.24.3−19−24−26−34−36
VICRutherglenBase17.418.218.618.819.01404149815401557158017.319.520.220.420.816-Mar18-Mar19-Mar22-Mar1-Apr
203018.319.119.519.719.91511159016271642166219.621.021.822.022.310-Mar12-Mar13-Mar15-Mar21-Mar
205019.119.920.320.520.71594166316941708172620.922.322.923.123.56-Mar7-Mar8-Mar10-Mar15-Mar
207019.820.721.121.221.51663172317501763177922.123.424.024.224.52-Mar3-Mar4-Mar6-Mar10-Mar
2030-Base0.90.90.90.90.9107928785822.31.51.61.61.5−6−6−6−7−11
2050-Base1.71.71.71.71.71901651541511463.62.82.72.72.7−10−11−11−12−17
2070-Base2.42.52.52.42.52592252102061994.83.93.83.83.7−14−15−15−16−22
VICGlenrowanBase17.118.518.718.919.11378154315671587160716.520.020.420.721.214-Mar16-Mar17-Mar19-Mar5-Apr
203018.019.419.719.820.01490163116511668168519.221.621.922.122.58-Mar10-Mar11-Mar12-Mar22-Mar
205018.820.220.420.620.81574169717151729174520.522.623.023.223.64-Mar5-Mar6-Mar8-Mar16-Mar
207019.621.021.221.321.61644175417701782179721.723.724.024.224.51-Mar2-Mar2-Mar4-Mar11-Mar
2030-Base0.90.91.00.90.9112888481782.71.61.51.41.3−6−6−6−7−14
2050-Base1.71.71.71.71.71961541481421384.02.62.62.52.4−10−11−11−11−20
2070-Base2.52.52.52.42.52662112031951905.23.73.63.53.3−13−14−15−15−25
VICGoulburn ValleyBase17.118.418.819.320.01396155815941631168816.719.920.621.422.48-Mar12-Mar15-Mar17-Mar4-Apr
203017.919.319.720.220.91506164516751707175719.121.421.922.723.63-Mar7-Mar9-Mar11-Mar22-Mar
205018.720.120.521.021.71585171017361764180520.322.422.923.724.628-Feb3-Mar5-Mar7-Mar16-Mar
207019.420.821.321.822.51654176517891809183921.523.424.024.625.426-Feb28-Feb1-Mar3-Mar11-Mar
2030-Base0.80.90.90.90.9110878176692.41.51.31.31.2−5−5−6−6−13
2050-Base1.61.71.71.71.71891521421331173.62.52.32.32.2−9−9−10−10−19
2070-Base2.32.42.52.52.52582071951781514.83.53.43.23.0−11−13−14−14−24
NSWPerricootaBase19.219.319.419.519.61616162816391645165921.121.321.421.521.710-Mar11-Mar12-Mar12-Mar13-Mar
203020.120.220.320.420.51693170317131718173022.322.522.622.723.05-Mar6-Mar6-Mar7-Mar8-Mar
205020.921.021.121.221.31752176117691773178423.423.523.723.824.01-Mar2-Mar2-Mar3-Mar3-Mar
207021.621.821.921.922.11801180818131816182424.324.424.624.724.927-Feb27-Feb27-Feb28-Feb28-Feb
2030-Base0.90.90.90.90.977757473711.21.21.21.21.3−5−5−6−5−5
2050-Base1.71.71.71.71.71361331301281252.32.22.32.32.3−9−9−10−9−10
2070-Base2.42.52.52.42.51851801741711653.23.13.23.23.2−12−13−14−13−14
NSWCowraBase18.719.720.020.320.61554165516851705173020.121.622.022.422.86-Mar8-Mar9-Mar11-Mar18-Mar
203019.620.620.921.221.51644173317601776179621.522.923.323.624.01-Mar2-Mar3-Mar5-Mar11-Mar
205020.421.421.722.022.31712178918121824183922.724.024.424.724.926-Feb27-Feb27-Feb1-Mar6-Mar
207021.122.222.522.823.11770182918481858187023.724.925.125.425.623-Feb24-Feb25-Feb26-Feb2-Mar
2030-Base0.90.90.90.90.990787571661.41.31.31.21.2−5−6−6−6−7
2050-Base1.71.71.71.71.71581341271191092.62.42.42.32.1−9−10−11−10−12
2070-Base2.42.52.52.52.52161741631531403.63.33.13.02.8−12−13−13−14−16
NSWRiverinaBase18.120.521.121.622.61486172017591790183619.323.123.824.425.425-Feb1-Mar3-Mar6-Mar23-Mar
203019.121.522.022.523.61579178318131836187320.924.324.925.426.323-Feb26-Feb27-Feb1-Mar16-Mar
205019.822.322.823.424.41651182518491868189022.225.325.726.227.222-Feb23-Feb25-Feb26-Feb10-Mar
207020.623.123.624.225.21715185718771888189523.326.026.527.027.922-Feb22-Feb23-Feb24-Feb6-Mar
2030-Base1.01.00.90.91.093635446371.61.21.11.00.9−2−4−5−5−7
2050-Base1.71.81.71.81.81651059078542.92.21.91.81.8−3−7−7−9−13
2070-Base2.52.62.52.62.622913711898594.02.92.72.62.5−3−8−9−11−17
NSWHastings RiverBase18.720.520.921.021.11699184018601865187119.721.822.222.322.525-Feb25-Feb26-Feb27-Feb8-Mar
203019.721.521.822.022.11784188518931895189620.822.723.123.223.422-Feb22-Feb22-Feb23-Feb2-Mar
205020.522.322.622.822.91839189818991899189921.723.523.824.024.222-Feb22-Feb22-Feb22-Feb26-Feb
207021.323.023.423.523.61874189918991899189922.524.324.624.724.922-Feb22-Feb22-Feb22-Feb23-Feb
2030-Base1.01.00.91.01.085453330251.10.90.90.90.9−3−3−4−4−6
2050-Base1.81.81.71.81.8140583934282.01.71.61.71.7−3−3−4−5−11
2070-Base2.62.52.52.52.5175593934282.82.52.42.42.4−3−3−4−5−14
NSWSouthern HighlandsBase16.416.717.117.719.01340140514771575171715.016.517.418.420.38-Mar18-Mar27-Mar5-Apr16-Apr
203017.217.618.018.619.91482154116011680179517.618.218.919.621.52-Mar10-Mar15-Mar20-Mar25-Mar
205018.018.418.819.420.71588164916891756185118.919.420.020.722.526-Feb4-Mar9-Mar12-Mar15-Mar
207018.819.119.520.121.41672172717601818188319.920.420.921.723.223-Feb27-Feb3-Mar6-Mar9-Mar
2030-Base0.80.90.90.90.9142136124105782.61.71.51.21.2−6−8−12−16−22
2050-Base1.61.71.71.71.72482442121811343.92.92.62.32.2−11−14−18−24−32
2070-Base2.42.42.42.42.43323222832431664.93.93.53.32.9−14−20−24−30−38
NSWCanberra DistrictBase16.817.017.417.719.11361139214221468161416.116.917.718.721.014-Mar24-Mar29-Mar2-Apr7-Apr
203017.717.918.218.620.01475150415301566169418.518.919.520.222.38-Mar16-Mar19-Mar20-Mar23-Mar
205018.418.719.019.420.81564159016101643175619.820.320.721.323.33-Mar11-Mar13-Mar14-Mar16-Mar
207019.219.419.820.221.51637166216821712180520.821.421.822.424.328-Feb6-Mar8-Mar9-Mar11-Mar
2030-Base0.90.90.80.90.911411210898802.42.01.81.51.3−6−8−10−13−15
2050-Base1.61.71.61.71.72031981881751423.73.43.02.62.3−11−13−16−19−22
2070-Base2.42.42.42.52.42762702602441914.74.54.13.73.3−15−18−21−24−27
VICStrathbogie RangesBase16.416.917.317.818.51310137414331498156013.616.117.719.020.217-Mar22-Mar29-Mar6-Apr23-Apr
203017.317.818.218.719.41432148615401594164517.618.919.620.521.711-Mar15-Mar19-Mar22-Mar29-Mar
205018.118.619.019.520.21525157316171665171019.420.221.021.622.67-Mar10-Mar13-Mar16-Mar19-Mar
207018.819.319.720.221.01601164216831723176520.621.421.922.623.73-Mar6-Mar8-Mar11-Mar14-Mar
2030-Base0.90.90.90.90.912211210796854.02.81.91.51.5−6−7−10−15−25
2050-Base1.71.71.71.71.72151991841671505.84.13.32.62.4−10−12−16−21−35
2070-Base2.42.42.42.42.52912682502252057.05.34.23.63.5−14−16−21−26−40
VICAlpine ValleysBase16.317.017.618.219.21275137914501523161413.016.818.219.721.314-Mar20-Mar27-Mar5-Apr30-Apr
203017.217.918.519.120.11400149215541614169217.019.019.921.222.78-Mar13-Mar18-Mar22-Mar1-Apr
205018.018.719.219.920.91497157816321683175219.120.321.322.323.74-Mar8-Mar12-Mar15-Mar21-Mar
207018.719.420.020.621.71578165016981742180320.421.522.323.224.628-Feb4-Mar7-Mar10-Mar15-Mar
2030-Base0.90.90.90.90.912511310491784.02.21.71.51.4−6−7−9−14−29
2050-Base1.71.71.61.71.72221991821601386.13.53.12.62.4−10−12−15−21−40
2070-Base2.42.42.42.42.53032712482191897.44.74.13.53.3−15−16−20−26−46
WABlackwood ValleyBase17.117.718.018.318.81415149415211556165516.718.419.119.820.616-Mar22-Mar24-Mar28-Mar6-Apr
203018.018.618.919.219.71532159616201652173619.220.120.520.921.610-Mar15-Mar17-Mar19-Mar24-Mar
205018.719.319.719.920.41618167616981724178920.420.921.421.822.56-Mar10-Mar12-Mar14-Mar18-Mar
207019.420.020.420.721.21692173917581780182821.121.822.322.723.32-Mar6-Mar7-Mar9-Mar13-Mar
2030-Base0.90.90.90.90.91171029996812.51.71.41.11.0−6−7−7−9−13
2050-Base1.61.61.71.61.62031821771681343.72.52.32.01.9−10−12−12−14−19
2070-Base2.32.32.42.42.42772452372241734.43.43.22.92.7−14−16−17−19−24
NSWTumbarumbaBase17.017.517.918.319.01377144114801541158416.518.118.919.720.816-Mar19-Mar24-Mar27-Mar5-Apr
203017.918.418.819.219.91493154415771634166619.019.920.421.222.210-Mar12-Mar16-Mar18-Mar22-Mar
205018.719.219.520.020.71576161916491703172920.321.121.722.223.25-Mar7-Mar11-Mar13-Mar16-Mar
207019.419.920.320.721.41650168817141763178521.422.122.823.424.41-Mar3-Mar6-Mar8-Mar10-Mar
2030-Base0.90.90.90.90.91161039793822.51.81.51.51.4−6−-7−8−9−14
2050-Base1.71.71.61.71.71991781691621453.83.02.82.52.4−11−12−13−14−20
2070-Base2.42.42.42.42.42732472342222014.94.03.93.73.6−15−16−18−19−26
VICHeathcoteBase16.317.417.818.218.71292144414901528158514.118.118.919.520.316-Mar20-Mar23-Mar28-Mar30-Apr
203017.218.318.719.119.61417154915881618166617.319.720.421.121.810-Mar13-Mar15-Mar18-Mar31-Mar
205017.919.019.519.820.41505162216561685172819.021.121.622.122.85-Mar8-Mar11-Mar13-Mar22-Mar
207018.619.720.220.621.21583168717161741178020.422.022.523.123.82-Mar4-Mar6-Mar8-Mar15-Mar
2030-Base0.90.90.90.90.91251059890813.21.61.51.61.5−6−7−8−10−30
2050-Base1.61.61.71.61.72131781661571434.93.02.72.62.5−11−12−12−15−39
2070-Base2.32.32.42.42.52912432262131956.33.93.63.63.5−14−16−17−20−46
NSWYoungBase17.518.318.618.919.91424152115501577167317.919.720.220.622.210-Mar17-Mar18-Mar20-Mar29-Mar
203018.419.219.519.820.81530161216371662174619.721.121.622.023.45-Mar10-Mar12-Mar13-Mar19-Mar
205019.220.020.320.621.61609168317071728179921.022.322.723.224.41-Mar5-Mar7-Mar8-Mar13-Mar
207019.920.721.021.422.31681174517661783183622.123.323.824.225.326-Feb1-Mar2-Mar4-Mar8-Mar
2030-Base0.90.90.90.90.9106918785731.81.41.41.41.2−5−7−6−7−10
2050-Base1.71.71.71.71.71851621571511263.12.62.52.62.2−9−12−11−12−16
2070-Base2.42.42.42.52.42572242162061634.23.63.63.63.1−13−16−16−16−21
NSWGundagaiBase15.718.419.019.420.01188153515871626167513.120.020.921.722.410-Mar13-Mar16-Mar20-Mar30-Apr
203016.619.319.920.320.81322162216681704174714.621.422.222.923.74-Mar7-Mar10-Mar13-Mar16-Apr
205017.420.120.721.121.61431169217331764179917.922.523.324.024.81-Mar3-Mar5-Mar8-Mar27-Mar
207018.120.921.421.922.41521175117871810183719.423.624.425.025.626-Feb28-Feb1-Mar4-Mar19-Mar
2030-Base0.90.90.90.90.8134878178721.51.41.31.21.3−6−6−6−7−14
2050-Base1.71.71.71.71.62431571461381244.82.52.42.32.4−9−10−11−12−34
2070-Base2.42.52.42.52.43332162001841626.33.63.53.33.2−13−14−15−16−42
NSWShoalhaven CoastBase17.118.919.319.619.91486171817561780180917.520.120.620.921.32-Mar4-Mar6-Mar9-Mar28-Mar
203018.019.820.220.520.81617179518281849186919.021.221.721.922.325-Feb26-Feb28-Feb2-Mar15-Mar
205018.820.621.021.321.61705185318741885189420.022.122.422.723.022-Feb23-Feb24-Feb26-Feb9-Mar
207019.521.321.722.022.31776188518951897189820.922.823.123.423.722-Feb22-Feb22-Feb23-Feb3-Mar
2030-Base0.90.90.90.90.9131777269601.51.11.11.01.0−6−7−7−7−13
2050-Base1.71.71.71.71.7219135118105852.52.01.81.81.7−9−10−11−12−19
2070-Base2.42.42.42.42.4290167139117893.42.72.52.52.4−9−11−13−15−25
VICSwan HillBase19.720.120.320.520.61682171117251736174721.922.422.722.923.13-Mar4-Mar5-Mar6-Mar8-Mar
203020.521.021.221.321.51749177417851794180223.123.623.824.124.228-Feb28-Feb1-Mar2-Mar4-Mar
205021.321.721.922.122.31799181718251832184024.024.524.724.925.025-Feb26-Feb26-Feb27-Feb28-Feb
207022.022.522.722.923.11835185018561861186724.825.225.425.625.823-Feb24-Feb24-Feb25-Feb26-Feb
2030-Base0.80.90.90.80.967636058551.21.21.11.21.1−4−5−4−4−4
2050-Base1.61.61.61.61.711710610096932.12.12.02.01.9−7−7−8−8−9
2070-Base2.32.42.42.42.51531391311251202.92.82.72.72.7−9−9−10−10−11
VICMurray DarlingBase20.420.720.821.022.01733175317631780182222.823.123.323.624.726-Feb1-Mar2-Mar3-Mar4-Mar
203021.321.521.721.922.91791180618141826186023.824.224.424.625.624-Feb26-Feb27-Feb27-Feb28-Feb
205022.022.322.522.723.71829184118481857188324.524.925.125.326.422-Feb24-Feb25-Feb25-Feb26-Feb
207022.723.023.223.424.51859187018761882189325.225.625.826.027.122-Feb22-Feb23-Feb23-Feb24-Feb
2030-Base0.90.80.90.90.958535146381.01.11.11.00.9−2−4−4−5−5
2050-Base1.61.61.71.71.796888577611.71.81.81.71.7−4−6−6−7−7
2070-Base2.32.32.42.42.5126117113102712.42.52.52.42.4−4−8−8−9−9
VICUpper GoulburnBase16.416.717.017.318.01308136013971435152613.415.516.717.619.221-Mar29-Mar3-Apr10-Apr24-Apr
203017.217.617.918.118.91431147615091543161917.518.519.019.520.714-Mar19-Mar21-Mar24-Mar29-Mar
205017.918.318.618.919.61525156315911621168319.119.820.420.821.89-Mar13-Mar15-Mar17-Mar20-Mar
207018.719.119.319.620.31601163716611687174020.421.121.421.822.75-Mar8-Mar10-Mar11-Mar14-Mar
2030-Base0.80.90.90.80.9123116112108934.13.02.31.91.5−7−10−13−17−26
2050-Base1.51.61.61.61.62172031941861575.74.33.73.22.6−12−16−19−24−35
2070-Base2.32.42.32.32.32932772642522147.05.64.74.23.5−16−21−24−30−41
WAManjimupBase17.317.617.817.918.31411148115011521155916.617.918.318.619.422-Mar26-Mar28-Mar30-Mar8-Apr
203018.218.518.718.819.21534158916051621165718.919.719.920.120.515-Mar18-Mar19-Mar20-Mar25-Mar
205018.919.219.419.519.91619166916831698172920.120.520.720.921.310-Mar12-Mar14-Mar15-Mar18-Mar
207019.619.920.120.220.61693173617481760178620.921.321.521.722.06-Mar8-Mar9-Mar10-Mar13-Mar
2030-Base0.90.90.90.90.9123108104100982.31.81.61.51.1−7−8−9−10−14
2050-Base1.61.61.61.61.62081881821771703.52.62.42.31.9−12−14−14−15−21
2070-Base2.32.32.32.32.32822552472392274.33.43.23.12.6−16−18−19−20−26
WAPembertonBase17.417.617.818.018.51436150515311566163217.118.218.719.119.918-Mar23-Mar25-Mar28-Mar4-Apr
203018.218.518.618.819.31554161216331663171919.319.820.120.220.811-Mar15-Mar17-Mar19-Mar23-Mar
205018.919.219.419.520.11638169217101736178020.220.520.821.021.66-Mar10-Mar12-Mar13-Mar17-Mar
207019.619.820.120.220.81710175517711790182121.021.421.621.722.33-Mar6-Mar7-Mar8-Mar12-Mar
2030-Base0.80.90.80.80.811810710297872.21.61.41.10.9−7−8−8−9−12
2050-Base1.51.61.61.51.62021871791701483.12.32.11.91.7−12−13−13−15−18
2070-Base2.22.22.32.22.32742502402241893.93.22.92.62.4−15−17−18−20−23
VICBendigoBase16.217.518.018.519.01264145715121560161312.918.319.320.020.914-Mar17-Mar21-Mar27-Mar30-Apr
203017.118.318.919.319.91392155616061646169016.619.820.821.522.28-Mar11-Mar14-Mar18-Mar3-Apr
205017.819.119.620.120.61481163016721708174918.821.221.822.423.14-Mar7-Mar9-Mar13-Mar23-Mar
207018.519.820.320.821.41561169117281761179520.022.122.723.424.11-Mar3-Mar6-Mar8-Mar17-Mar
2030-Base0.90.80.90.80.9128999486773.71.51.51.51.3−6−6−7−9−27
2050-Base1.61.61.61.61.62171731601481365.92.92.52.42.2−10−10−12−14−38
2070-Base2.32.32.32.32.42972342162011827.13.83.43.43.2−13−14−15−19−44
WAGeographeBase17.418.518.919.620.41466159016361714176317.920.320.921.622.87-Mar11-Mar17-Mar20-Mar31-Mar
203018.319.419.720.421.31571168317201779181719.921.421.822.723.73-Mar6-Mar11-Mar13-Mar22-Mar
205019.020.120.521.222.11651174817771822185120.922.322.723.424.527-Feb2-Mar6-Mar8-Mar16-Mar
207019.720.921.221.922.91719179818181852187021.723.123.524.225.225-Feb27-Feb3-Mar4-Mar11-Mar
2030-Base0.90.90.80.80.9105938465542.01.10.91.10.9−4−5−6−7−9
2050-Base1.61.61.61.61.7185158141108883.02.01.81.81.7−9−9−11−12−15
2070-Base2.32.42.32.32.52532081821381073.82.82.62.62.4−11−13−14−16−20
VICMacedon RangesBase15.315.615.916.317.21118116212261287141612.014.014.815.317.21-Apr30-Apr30-Apr30-Apr30-Apr
203016.116.416.717.118.01268130713601411151812.914.816.217.219.321-Mar1-Apr11-Apr25-Apr30-Apr
205016.817.117.517.918.71384141414571502159916.117.018.119.020.615-Mar22-Mar26-Mar1-Apr7-Apr
207017.517.818.118.619.41475150415411581166318.318.819.320.221.610-Mar16-Mar19-Mar22-Mar25-Mar
2030-Base0.80.80.80.80.81501451341241020.90.81.41.92.1−11−29−19−50
2050-Base1.51.51.61.61.52662522312151833.13.03.33.73.4−17−39−35−29−23
2070-Base2.22.22.22.32.23573423152942475.34.84.54.94.4−22−45−42−39−36
VICSunburyBase16.917.017.117.418.01402142314461493157516.616.917.418.219.218-Mar26-Mar31-Mar3-Apr5-Apr
203017.717.817.918.218.81516153415531592166218.618.819.019.520.512-Mar17-Mar20-Mar22-Mar23-Mar
205018.418.518.618.919.51602161616321667172719.820.020.320.721.47-Mar11-Mar14-Mar15-Mar16-Mar
207019.019.119.319.620.21668168316971728178020.921.021.121.622.23-Mar7-Mar9-Mar10-Mar11-Mar
2030-Base0.80.80.80.80.811411110799872.01.91.61.31.3−6−9−11−12−13
2050-Base1.51.51.51.51.52001931861741523.23.12.92.52.2−11−15−17−19−20
2070-Base2.12.12.22.22.22662602512352054.34.13.73.43.0−15−19−22−24−25
VICYarra ValleyBase16.317.117.317.418.01322143814791497157814.417.317.918.219.318-Mar25-Mar27-Mar31-Mar22-Apr
203017.117.818.118.318.81453154915811599166317.418.919.319.720.611-Mar16-Mar17-Mar20-Mar30-Mar
205017.818.618.819.019.61553163416601673172918.920.220.620.821.56-Mar10-Mar11-Mar13-Mar19-Mar
207018.519.219.519.720.21632169917211734178020.121.121.521.722.33-Mar6-Mar7-Mar8-Mar13-Mar
2030-Base0.80.70.80.90.8131111102102853.01.61.41.51.3−7−9−10−11−23
2050-Base1.51.51.51.61.62311961811761514.52.92.72.62.2−12−15−16−18−34
2070-Base2.22.12.22.32.23102612422372025.73.83.63.53.0−15−19−20−23−40
VICPyreneesBase16.117.117.417.618.11259140614481474153414.416.817.918.519.520-Mar25-Mar28-Mar3-Apr30-Apr
203016.917.818.218.419.01381150615451569162416.119.019.620.021.013-Mar17-Mar19-Mar22-Mar7-Apr
205017.518.618.919.119.71473158716211640168618.220.220.921.221.99-Mar12-Mar14-Mar16-Mar26-Mar
207018.219.219.619.820.41552165316831700174119.421.321.722.122.85-Mar8-Mar9-Mar11-Mar19-Mar
2030-Base0.80.70.80.80.91221009795901.72.21.71.51.5−7−8−9−12−23
2050-Base1.41.51.51.51.62141811731661523.83.43.02.72.4−11−13−14−18−35
2070-Base2.12.12.22.22.32932472352262075.04.53.83.63.3−15−17−19−23−42
SARiverlandBase20.520.820.921.021.31767177917871793180722.923.123.323.523.927-Feb28-Feb1-Mar2-Mar3-Mar
203021.321.621.721.822.11817182618321836184623.824.124.224.324.625-Feb26-Feb26-Feb27-Feb28-Feb
205022.022.222.422.522.91847185418601864187424.424.624.824.925.223-Feb24-Feb24-Feb25-Feb26-Feb
207022.722.923.123.223.51873188018841886189024.925.225.425.625.922-Feb22-Feb23-Feb23-Feb24-Feb
2030-Base0.80.80.80.80.850474543390.91.00.90.80.7−2−2−4−4−4
2050-Base1.51.41.51.51.680757371671.51.51.51.41.3−4−4−6−6−6
2070-Base2.22.12.22.22.21061019793832.02.12.12.12.0−5−6−7−8−8
VICMornington PeninsulaBase15.716.416.717.017.31197133914021446149915.016.016.917.318.026-Mar1-Apr8-Apr19-Apr30-Apr
203016.517.217.517.818.11350147515261563160715.517.618.318.819.416-Mar20-Mar23-Mar29-Mar16-Apr
205017.117.818.218.418.81461156816141645168117.418.819.420.020.411-Mar13-Mar16-Mar19-Mar30-Mar
207017.718.418.819.119.41551164616821708174018.619.920.420.821.36-Mar8-Mar11-Mar13-Mar21-Mar
2030-Base0.80.80.80.80.81531361241171080.51.61.41.51.4−10−12−16−21−14
2050-Base1.41.41.51.41.52642292121991822.42.82.52.72.4−15−19−23−31−31
2070-Base2.02.02.12.12.13543072802622413.63.93.53.53.3−20−24−28−37−40
VICGeelongBase16.416.616.716.917.11334136613881417145115.015.716.016.817.431-Mar5-Apr10-Apr13-Apr19-Apr
203017.217.417.517.717.91459148915081533156117.618.018.318.619.020-Mar23-Mar25-Mar27-Mar30-Mar
205017.818.018.118.318.51548157515921615163718.819.219.419.720.214-Mar16-Mar18-Mar19-Mar21-Mar
207018.418.618.818.919.21620164616611683170220.020.320.520.721.09-Mar11-Mar12-Mar13-Mar15-Mar
2030-Base0.80.80.80.80.81251231201161102.62.32.31.81.6−11−13−16−17−20
2050-Base1.41.41.41.41.42142092041981863.83.53.42.92.8−17−20−23−25−29
2070-Base2.02.02.12.02.12862802732662515.04.64.53.93.6−22−25−29−31−35
VICGrampiansBase15.816.116.717.217.81212126713611423149713.115.516.617.718.923-Mar1-Apr12-Apr30-Apr30-Apr
203016.516.917.517.918.61340138914641520158515.116.218.119.120.216-Mar21-Mar27-Mar6-Apr17-Apr
205017.117.518.118.619.31429147515441596165517.118.219.420.421.411-Mar15-Mar20-Mar26-Mar1-Apr
207017.718.218.819.319.91512155416131660171218.619.420.621.422.27-Mar11-Mar14-Mar19-Mar23-Mar
2030-Base0.70.80.80.70.812812210397882.00.71.51.41.3−7−11−16−24−13
2050-Base1.31.41.41.41.52172081831731584.02.72.82.72.5−12−17−23−35−29
2070-Base1.92.12.12.12.13002872522372155.53.94.03.73.3−16−21−29−42−38
WAGreat SouthernBase15.717.517.818.119.51210151015481589174014.718.318.919.421.19-Mar21-Mar24-Mar27-Mar30-Apr
203016.518.318.618.920.31366161016431676179715.719.719.920.221.85-Mar15-Mar17-Mar19-Mar16-Apr
205017.219.019.219.620.91482168717141740183217.620.420.621.022.61-Mar10-Mar12-Mar14-Mar31-Mar
207017.819.619.920.221.51571174817681788185719.021.121.421.723.127-Feb6-Mar8-Mar9-Mar22-Mar
2030-Base0.80.80.80.80.81561009587571.01.41.00.80.7−4−6−7−8−14
2050-Base1.51.51.41.51.4272177166151922.92.11.71.61.5−8−11−12−13−30
2070-Base2.12.12.12.12.03612382201991174.32.82.52.32.0−11−15−16−18−39
SASouthern FlindersRangesBase17.118.919.520.021.81402160616651722183917.120.821.622.324.326-Feb6-Mar10-Mar14-Mar31-Mar
203017.919.620.220.822.61502168017341782187119.021.722.423.124.824-Feb2-Mar5-Mar9-Mar21-Mar
205018.620.420.921.523.21581173917831821189020.322.523.223.925.323-Feb27-Feb2-Mar5-Mar15-Mar
207019.321.021.622.123.91643178718211850189821.223.224.024.425.922-Feb25-Feb27-Feb1-Mar11-Mar
2030-Base0.80.70.70.80.8100746960321.90.90.80.80.5−2−4−5−5−10
2050-Base1.51.51.41.51.417913311899513.21.71.61.61.0−3−8−8−9−16
2070-Base2.22.12.12.12.1241181156128594.12.42.42.11.6−4−10−12−13−20
WAMargaret RiverBase18.118.618.718.819.41639167416921710174219.420.020.120.221.110-Mar13-Mar14-Mar15-Mar18-Mar
203018.919.319.519.620.21722175017651778180120.220.820.921.022.05-Mar7-Mar8-Mar9-Mar12-Mar
205019.620.020.120.320.91780180018101820183620.921.521.621.722.71-Mar3-Mar4-Mar5-Mar7-Mar
207020.220.720.820.921.61819183318411849186221.522.122.222.323.326-Feb27-Feb28-Feb1-Mar3-Mar
2030-Base0.80.70.80.80.883767368590.80.80.80.80.9−5−6−6−6−6
2050-Base1.51.41.41.51.5141126118110941.51.51.51.51.6−9−10−10−10−11
2070-Base2.12.12.12.12.21801591491391202.12.12.12.12.2−13−15−15−14−15
SAEden ValleyBase16.917.117.617.918.21390142714931527156716.317.118.519.019.618-Mar21-Mar24-Mar1-Apr6-Apr
203017.617.818.318.619.01488152015791609164518.318.919.720.320.912-Mar14-Mar17-Mar22-Mar25-Mar
205018.218.419.019.219.61562159316451671170219.519.920.921.121.68-Mar10-Mar12-Mar16-Mar18-Mar
207018.819.019.619.820.21628165417021725175320.520.921.521.822.25-Mar7-Mar8-Mar11-Mar13-Mar
2030-Base0.70.70.70.70.898938682782.01.81.21.31.3−6−7−7−10−12
2050-Base1.31.31.41.31.41721661521441353.22.82.42.12.0−10−11−12−16−19
2070-Base1.91.92.01.92.02382272091981864.23.83.02.82.6−13−14−16−21−24
SAClare ValleyBase17.418.318.518.819.01454155915801606162918.019.820.120.621.013-Mar15-Mar16-Mar18-Mar27-Mar
203018.219.019.219.519.71546163616551677169619.521.121.321.521.88-Mar10-Mar11-Mar12-Mar19-Mar
205018.819.719.920.220.41618169817131736175120.721.722.022.322.55-Mar6-Mar7-Mar8-Mar13-Mar
207019.420.320.520.821.01677174817621780179421.322.422.723.023.31-Mar2-Mar4-Mar5-Mar9-Mar
2030-Base0.80.70.70.70.792777571671.51.31.20.90.8−5−5−5−6−8
2050-Base1.41.41.41.41.41641391331301222.71.91.91.71.5−8−9−9−10−14
2070-Base2.02.02.02.02.02231891821741653.32.62.62.42.3−12−13−12−13−18
SABarossa ValleyBase17.518.318.618.819.31472156815971610165718.219.720.320.521.512-Mar15-Mar16-Mar18-Mar26-Mar
203018.219.019.319.520.01558164516691680171819.521.021.421.622.38-Mar10-Mar11-Mar12-Mar19-Mar
205018.819.720.020.120.61626170317221732176520.621.722.122.323.04-Mar7-Mar7-Mar8-Mar14-Mar
207019.420.320.620.721.21682175317691777180421.322.322.822.923.71-Mar3-Mar4-Mar5-Mar10-Mar
2030-Base0.70.70.70.70.786777270611.31.31.11.10.8−4−5−5−6−7
2050-Base1.31.41.41.31.31541351251221082.42.01.81.81.5−8−8−9−10−12
2070-Base1.92.02.01.91.92101851721671473.12.62.52.42.2−11−12−12−13−16
TASSouth TasmaniaBase13.613.914.214.715.37568258909941111N/AN/AN/AN/AN/A30-Apr*30-Apr*30-Apr*30-Apr*30-Apr*
203014.214.614.915.415.9895972103711361255N/AN/AN/AN/AN/A30-Apr*30-Apr*30-Apr*30-Apr*30-Apr*
205014.815.215.516.016.51016110011611267136314.014.2*15.0*15.2*15.5*15-Apr30-Apr*30-Apr*30-Apr*30-Apr*
207015.415.816.116.617.11133121812801381146414.315.315.8*16.4*17.0*31-Mar12-Apr30-Apr*30-Apr*30-Apr*
2030-Base0.60.70.70.70.6139147147142144N/AN/AN/AN/AN/A0*0*0*0*0*
2050-Base1.21.31.31.31.2260275271273252N/AN/AN/AN/AN/A−15*0*0*0*0*
2070-Base1.81.91.91.91.8377393390387353N/AN/AN/AN/AN/A−30*−18*0*0*0*
TASNorth TasmaniaBase14.614.815.015.215.69661020105511051180N/AN/AN/AN/AN/A30-Apr*30-Apr*30-Apr*30-Apr*30-Apr*
203015.215.515.715.916.31103116111981249132314.014.5*14.6*14.7*14.9*24-Apr30-Apr*30-Apr*30-Apr*30-Apr*
205015.816.116.316.516.91216128413211370143413.914.815.616.1*16.9*4-Apr14-Apr24-Apr30-Apr*30-Apr*
207016.316.716.917.117.51326138814201466152715.116.116.717.418.325-Mar1-Apr6-Apr12-Apr23-Apr
2030-Base0.60.70.70.70.7137141143144143N/AN/AN/AN/AN/A−6*0*0*0*0*
2050-Base1.21.31.31.31.3250264266265254N/AN/AN/AN/AN/A−26*−16*−6*0*0*
2070-Base1.71.91.91.91.9360368365361347N/AN/AN/AN/AN/A−36*−29*−24*−18*−7*
SAAdelaide PlainsBase19.119.719.920.020.11645171217211729174621.121.922.122.222.46-Mar7-Mar8-Mar8-Mar13-Mar
203019.720.420.620.720.81709176817751782179721.922.722.923.023.12-Mar3-Mar4-Mar4-Mar8-Mar
205020.321.021.221.321.41756180718131820183122.523.323.623.723.827-Feb28-Feb1-Mar1-Mar5-Mar
207020.921.621.821.821.91797183718421846185523.223.924.224.324.426-Feb26-Feb27-Feb27-Feb2-Mar
2030-Base0.60.70.70.70.764565453510.80.80.80.80.7−4−4−4−4−5
2050-Base1.21.31.31.31.3111959291851.41.41.51.51.4−8−8−7−7−8
2070-Base1.81.91.91.81.81521251211171092.12.02.12.12.0−9−10−10−10−11
SAAdelaide HillsBase16.216.716.917.218.21303138614231451155813.615.916.717.419.519-Mar29-Mar3-Apr8-Apr28-Apr
203016.817.417.517.818.91418148515181542163516.517.918.318.820.813-Mar20-Mar22-Mar25-Mar3-Apr
205017.317.918.118.419.51501156115881611169318.018.919.319.921.59-Mar14-Mar16-Mar18-Mar23-Mar
207017.918.418.719.020.11570162516501670174118.819.920.220.822.16-Mar10-Mar12-Mar13-Mar18-Mar
2030-Base0.60.70.60.60.7115999591772.92.01.61.41.3−6−9−12−14−25
2050-Base1.11.21.21.21.31981751651601354.43.02.62.52.0−10−15−18−21−36
2070-Base1.71.71.81.81.92672392272191835.24.03.53.42.6−13−19−22−26−41
SAWrattonbullyBase16.717.117.217.417.51379144614561470148415.817.417.718.018.226-Mar27-Mar29-Mar31-Mar10-Apr
203017.317.817.918.018.11472153115421556156717.818.819.019.219.419-Mar20-Mar21-Mar22-Mar28-Mar
205017.918.318.418.618.71546159916081621163118.919.719.920.220.514-Mar15-Mar16-Mar17-Mar21-Mar
207018.418.819.019.119.21609165716661676168519.820.721.021.121.210-Mar11-Mar11-Mar12-Mar16-Mar
2030-Base0.60.70.70.60.693858686832.01.41.31.21.2−7−7−8−9−13
2050-Base1.21.21.21.21.21671531521511473.12.32.22.22.3−12−12−13−14−20
2070-Base1.71.71.81.71.72302112102062014.03.33.33.13.0−16−16−18−19−25
SAPadthawayBase17.417.717.818.018.11479151715331552156918.218.718.919.119.418-Mar19-Mar21-Mar22-Mar26-Mar
203018.018.418.518.618.81561159816131630164519.319.820.120.420.612-Mar13-Mar15-Mar16-Mar19-Mar
205018.618.919.119.219.41626165916731689170320.420.921.021.121.38-Mar9-Mar11-Mar11-Mar14-Mar
207019.119.519.619.819.91681171217251738175121.121.421.621.821.95-Mar5-Mar7-Mar7-Mar10-Mar
2030-Base0.60.70.70.60.782818078761.11.11.21.31.2−6−6−6−6−7
2050-Base1.21.21.31.21.31471421401371342.22.22.12.01.9−10−10−10−11−12
2070-Base1.71.81.81.81.82021951921861822.92.72.72.72.5−13−14−14−15−16
SALanghorne CreekBase18.718.718.718.818.91676168016821685169420.320.320.420.420.59-Mar9-Mar10-Mar10-Mar10-Mar
203019.319.419.419.419.51739174217441746175421.021.021.121.121.25-Mar5-Mar5-Mar5-Mar6-Mar
205019.919.919.920.020.11786178917911792179921.621.621.721.721.91-Mar2-Mar2-Mar2-Mar2-Mar
207020.420.420.520.520.61823182518261828183322.122.222.322.322.427-Feb27-Feb27-Feb27-Feb27-Feb
2030-Base0.60.70.70.60.663626261600.70.70.70.70.7−4−4−5−5−4
2050-Base1.21.21.21.21.21101091091071051.31.31.31.31.4−8−7−8−8−8
2070-Base1.71.71.81.71.71471451441431391.81.91.91.91.9−11−11−12−12−12
SASouth FleurieuBase15.816.516.917.318.11224137414561514163614.916.317.217.919.1*14-Mar25-Mar31-Mar12-Apr30-Apr*
203016.417.117.517.818.71343148215541605170615.317.318.218.719.99-Mar16-Mar21-Mar28-Mar20-Apr
205016.917.618.118.319.21441156616261672176016.718.219.119.520.55-Mar11-Mar14-Mar19-Mar4-Apr
207017.318.118.618.819.71525163216841726180317.619.019.620.021.02-Mar7-Mar10-Mar14-Mar25-Mar
2030-Base0.60.60.60.50.61191089891700.41.01.00.80.8*−5−9−10−15−10*
2050-Base1.11.11.21.01.12171921701581241.81.91.91.61.4*−9−14−17−24−26*
2070-Base1.51.61.71.51.63012582282121672.72.72.42.11.9*−12−18−21−29−36*
SACoonawarraBase16.616.716.816.917.11377139714061419144615.816.216.516.717.431-Mar4-Apr6-Apr7-Apr11-Apr
203017.217.317.417.517.71471148914961507153117.618.018.218.318.722-Mar25-Mar26-Mar27-Mar29-Mar
205017.717.918.018.118.31543155915671578159918.718.919.219.319.617-Mar18-Mar19-Mar20-Mar22-Mar
207018.218.418.518.618.81605162016271637165719.519.920.120.220.712-Mar14-Mar14-Mar15-Mar16-Mar
2030-Base0.60.60.60.60.694929088851.81.81.71.61.3−9−10−11−11−13
2050-Base1.11.21.21.21.21661621611591532.92.72.72.62.2−14−17−18−18−20
2070-Base1.61.71.71.71.72282232212182113.73.73.63.53.3−19−21−23−23−26
VICHentyBase16.016.216.316.416.91271129113121338141213.414.415.015.6*16.7*5-Apr19-Apr26-Apr30-Apr*30-Apr*
203016.616.816.917.017.51377140014191441150115.816.216.717.018.425-Mar2-Apr5-Apr8-Apr13-Apr
205017.017.317.417.618.11458148315001519157117.117.818.018.319.319-Mar24-Mar26-Mar28-Mar1-Apr
207017.517.817.918.118.61526155115671585163118.118.719.019.220.415-Mar19-Mar20-Mar21-Mar24-Mar
2030-Base0.60.60.60.60.6106109107103892.41.81.71.4*1.7*−11−17−21−22*−17*
2050-Base1.01.11.11.21.21871921881811593.73.43.02.7*2.6*−17−26−31−33*−29*
2070-Base1.51.61.61.71.72552602552472194.74.34.03.6*3.7*−21−31−37−40*−37*
SAMcLaren ValeBase16.917.618.018.218.51429152715921609164216.818.419.119.520.113-Mar15-Mar16-Mar22-Mar2-Apr
203017.618.218.618.819.11524160716651681170918.419.520.120.320.78-Mar10-Mar11-Mar15-Mar21-Mar
205018.118.719.119.319.61595167017221736176019.320.320.620.921.34-Mar5-Mar7-Mar10-Mar16-Mar
207018.619.319.619.820.21656172317671779179920.020.821.221.421.92-Mar3-Mar4-Mar7-Mar11-Mar
2030-Base0.70.60.60.60.695807372671.61.11.00.80.6−5−5−5−7−12
2050-Base1.21.11.11.11.11661431301271182.51.91.51.41.2−9−10−9−12−17
2070-Base1.71.71.61.61.72271961751701573.22.42.11.91.8−11−12−12−15−22
SACurrency CreekBase17.818.318.418.518.61597165016661669168018.719.719.819.920.110-Mar11-Mar11-Mar12-Mar17-Mar
203018.418.919.019.119.21673171917321734174219.620.320.420.520.86-Mar6-Mar6-Mar7-Mar11-Mar
205018.919.419.519.619.81729176917801782178920.220.921.021.121.42-Mar3-Mar3-Mar4-Mar7-Mar
207019.419.920.020.120.31778181018191820182620.721.521.621.722.027-Feb28-Feb28-Feb1-Mar3-Mar
2030-Base0.60.60.60.60.676696665620.90.60.60.60.7−4−5−5−5−6
2050-Base1.11.11.11.11.21321191141131091.51.21.21.21.3−8−8−8−8−10
2070-Base1.61.61.61.61.71811601531511462.01.81.81.81.9−12−12−12−11−14
SARobeBase16.817.017.117.217.41413145014771491151316.316.917.417.618.025-Mar27-Mar29-Mar1-Apr7-Apr
203017.417.617.717.818.01509154115651576159517.818.218.418.619.017-Mar19-Mar20-Mar22-Mar26-Mar
205017.818.018.218.318.51580160716291638165418.619.019.319.519.813-Mar14-Mar15-Mar16-Mar19-Mar
207018.318.518.618.718.91638166216821690170519.519.820.020.120.49-Mar10-Mar11-Mar12-Mar14-Mar
2030-Base0.60.60.60.60.696918885821.51.31.01.01.0−8−8−9−10−12
2050-Base1.01.01.11.11.11671571521471412.32.11.91.91.8−12−13−14−16−19
2070-Base1.51.51.51.51.52252122051991923.22.92.62.52.4−16−17−18−20−24
SAMount BensonBase17.017.217.317.417.51454148814971511152217.017.517.717.918.124-Mar25-Mar26-Mar28-Mar1-Apr
203017.617.817.818.018.01546157515821594160218.218.618.718.919.117-Mar17-Mar18-Mar19-Mar22-Mar
205018.118.318.318.418.51610163616421653166119.019.519.619.820.012-Mar13-Mar13-Mar14-Mar16-Mar
207018.518.718.818.919.01666168916951704171219.920.120.220.320.49-Mar9-Mar10-Mar10-Mar12-Mar
2030-Base0.60.60.50.60.592878583801.21.11.01.01.0−7−8−8−9−10
2050-Base1.11.11.01.01.01561481451421392.02.01.91.91.9−12−12−13−14−16
2070-Base1.51.51.51.51.52122011981931902.92.62.52.42.3−15−16−16−18−20
SAKangaroo IslandBase15.716.617.017.418.11213138614681534162614.616.317.418.019.1*17-Mar26-Mar2-Apr12-Apr30-Apr*
203016.217.017.517.918.51307146915481609167614.717.118.118.719.813-Mar18-Mar24-Mar1-Apr28-Apr
205016.617.517.918.319.01387154216121669173816.118.118.819.420.38-Mar14-Mar18-Mar24-Mar13-Apr
207017.017.918.318.719.51460160316671719177716.918.719.519.920.85-Mar10-Mar14-Mar19-Mar3-Apr
2030-Base0.50.40.50.50.494838075500.10.80.70.70.7*−4−8−9−11−2*
2050-Base0.90.90.90.90.91741561441351121.51.81.41.41.2*−9−12−15−19−17*
2070-Base1.31.31.31.31.42472171991851512.32.42.11.91.7*−12−16−19−24−27*

Future projections for 2030 reveal an average GST wine region warming of 0.9°C with a range of 0.5°C in Henty and Kangaroo Island to 1.0°C in seven different regions (Table 2). By 2050, GST is projected to increase on average 1.6°C with the greatest warming of 1.9°C in New England, South Burnett and Perth Hills and the least in Kangaroo Island (0.9°C). GST warming by 2070 averages 2.3°C across all regions with a range from 1.3°C in Kangaroo Island to 2.8°C in Perth Hills. BEDD shows similar spatial changes to GST with average BEDD increases of 87, 152 and 207 units for the 2030, 2050 and 2070 projections, respectively. The capping of the heat accumulation to 9°Cdays per day results in heat accumulation totals not increasing on many days of the year for the already warm locations. The BEDD in regions with a cooler starting period, but with similarly large increases in GST, is modelled to increase at a faster rate. For example, the modelled BEDD increases for the Granite Belt region are 115, 186 and 236°Cdays for 2030, 2050 and 2070, respectively, even though the GCM forecasts a similar GST rise to that experienced by Perth Hills.

Furthermore, the maximum BEDD that can be accumulated for any region is 1899°Cdays, resulting from 9°Cdays being accumulated on every day of the 211 days between 1 October and 30 April. For those regions that have relatively high BEDD totals (especially South Burnett and Hastings River), increases in BEDD under different warming scenarios do not fully reflect the warmer conditions. Similarly, modelling increases in RPT and earlier season-end dates (assuming harvest takes place once 1300°Cdays is reached) are not appropriate for several regions because 1300°Cdays is reached at the earliest possible date (22 February), 9°Cdays being accumulated on every day after October 1. Any increase in temperature does not affect the BEDD accumulation and therefore cannot alter the season-end date under this modelling method.

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. References

Maps of mean GST are the simplest of the climate maps produced (Figure 4). Jones et al. (2005) suggest that the ‘simple’ GST value effectively defines spatial variations in varietal potential and growing season climates. If a GST of 21°C is considered as the upper limit of quality winegrape production (based on Jones 2006), then, under the temperature increase scenario for 2070, large areas of the northern viticultural regions of Australia may not be suitable for quality winegrape production. For the period 1971–2000, South Burnett, Swan District and Riverina are above a mean GST of 21°C. By 2030, Perth Hills, Hunter, Hastings River, Swan Hill and Murray Darling are modelled to have a mean GST above 21°C. By 2050, Peel, Perricoota, Cowra and Adelaide Plains, and by 2070, New England, Mudgee, Rutherglen, Glenrowan, Goulburn Valley, Gundagai, Shoalhaven Coast, Geographe, and Southern Flinders Ranges join this group.

If ripening periods occur earlier in a season, then the RPT is more likely to be warmer, which may lead to a decrease in quality. The level of impact of temperature on harvested fruit quality varies for different grapevine cultivars (Webb et al. 2006). The extrapolated maps of RPT for the period 1971–2000 suggest that most viticultural regions experienced RPTs that were on average below 24°C for varieties that required an accumulated BEDD total of 1300°Cdays. Clearly, fruit of varieties that require a greater number of BEDD would ripen later in the season and in conditions that are more likely to be cooler. For a variety such as Cabernet Sauvignon that is managed to mature at 1300 BEDD, the maps in Figure 4 can be used as a guide. The series of RPT maps clearly show a greater level of increase in RPT in inland areas than those that are closer to the coast. The same regions that become unsuitable for quality wine production using the threshold criterion of a GST above 21°C were also shown to become similarly unsuitable using the criterion of above an RPT of 24°C for grapevine varieties that ripen after accumulating 1300 BEDD. Nevertheless, those varieties that require more BEDD will have ripening periods that will more likely take place during cooler conditions later in a season.

In contrast to GST and RPT, the BEDD total is a less useful value for defining a region as being unsuitable for viticultural production due to high temperatures, because once a requisite total of BEDD has been reached, the grapes may be harvested and any further heat accumulation after the harvest date is irrelevant. BEDD is more useful in determining suitability of different regions to different grapevine varieties (Gladstones 1992). A viable variety for a particular region is one that requires a lower number of BEDD to ripen fruit than the total number of BEDD experienced at that location during a season. Assuming that a cooler ripening period is desired, the region's BEDD total should be close to the BEDD total required to ripen fruit of that variety so that the ripening stage occurs during a cooler period. However, due to natural season-to-season variability in average temperatures, there will be cool years that would lead to requisite BEDD not being reached for varieties that have required BEDD totals close to the average BEDD of the region leading to fruit not ripening within the season. Therefore, varieties should be selected for regions with the degree of climatic variability of the region in mind. The key changes due to increases in BEDD in Australia (Figure 4) under increasing future projections is the increasing viability of Tasmania and along the slopes of the Great Dividing Range. For example, by 2070, the projected warming in Northern Tasmania would likely result in more reliable viticultural production with a greater number of viable varieties. In particular, the modelling suggests that by 2070, Northern Tasmania would likely have a similar GST, BEDD, RPT and season-end date to that currently experienced in Coonawarra. Table 2 can be further used to compare regions' modelled future conditions with other regions' current conditions as indicated by the different indices similar to the way in which Northern Tasmania for 2070 can be compared with Coonawarra for 1971–2000.

Conclusion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. References

This study has used interpolated elevation-corrected maps of daily temperature data (temperature surfaces) for Australia for the period 1971–2000 to produce maps of GST, BEDD, RPT and season-end date of viticultural production. An error analysis of the temperature surfaces resulted in an estimated maximum error of about 0.3°C in any one location within the winegrape growing regions. The temperature surfaces were then altered by adding spatially modelled temperature anomalies for the years 2030, 2050 and 2070 using the CSIRO Mk3.0 GCM with a SRES scenario of A1B and a sensitivity of atmospheric temperature increases in response to a doubling of GHG of 2.6°C. The modified temperature surfaces were in turn used to produce similar maps of the temperature indices projected for 2030, 2050 and 2070. A correlation analysis of the spatial variation in the temperature indices demonstrated that maps of BEDD, RPT (for a variety requiring 1300 BEDD to ripen, such as Cabernet Sauvignon) and GST were sufficiently different, to show that GST alone does not fully characterise temperature differences that may be experienced due to future warming.

Using a very different methodological approach, the results of this study broadly validate the results and conclusions drawn by Webb et al. (2007) in that for established viticultural regions under warmer atmospheric conditions, shorter seasons would likely be experienced and harvest would occur in warmer conditions earlier in the year. The latitudinal location of Australia, being close to the equatorial limit of winegrape production for the Southern Hemisphere and with little land mass poleward, means that the total area of viable viticultural climates of Australia would be reduced, the level of reduction being proportional to the magnitude of the increase in temperature. The area of Australia estimated by the modelling process to experience GSTs between 13 and 21°C reduces from 986 000 km2 for the 1971–2000 base period to 736 000 km2 by 2030, 576 000 km2 by 2050 and 449 000 km2 by 2070. Of the 61 recognised wine regions (GIs) and two others in Tasmania, the median GSTs were found to be above 21°C for three regions for the period 1971–2000, for eight regions by 2030, 12 regions by 2050 and 21 regions by 2070. The spatial variation in the rate of temperature increase as derived by the CSIRO Mk3.0 model resulted in greater levels of change to the temperature indices for inland regions and lower levels of change to the temperature indices for coastal regions with southern areas of South Australia and western Victoria experiencing the least change. In addition, some regions that are apparently not suitable under 1971–2000 conditions could be suitable winegrape production regions under warmer temperature scenarios, such as areas of Tasmania and higher elevation areas of south-eastern Australia. Not only can the suitability of quality winegrape production in some regions be affected by the temperature changes, the grapevine varieties that are best suited to a given region may also change. Warm climate varieties may become suited to viticultural regions that are currently considered too cool for those varieties.

It should be noted that the warming scenarios used in this paper are based on estimates for warming produced by the IPCC and the CSIRO for the near future. Actual forecasts have large ranges of temperature changes because there is much uncertainty in the forecasting methods and in future human behaviour (IPCC 2007). The indices used in this study were designed to describe average growing conditions for long-term wine growing suitability. Temporal variability in inter-annual average temperatures must therefore be considered when interpreting the results. Derived viticultural temperature indices would also vary as a consequence of the inter-annual variance in temperature and may vary to a greater degree for different regions. Finally, mesoclimatic variation may not have been fully characterised. Data were derived from climate stations that may not fully represent the surrounding region. There are therefore likely to be sub-regions that experience significantly warmer or cooler overall conditions due to local factors (such as aspect) that affect the average temperature.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. References

This work was supported by the Wine Growing Futures Program, a joint initiative of the Grape and Wine Research and Development Corporation and the National Wine and Grape Industry Centre. The authors appreciate ongoing support provided by Charles Sturt University's Spatial Analysis Unit (CSU-SPAN).

This article incorporates data which is © Commonwealth of Australia (Geoscience Australia) 2003. The Data (GEODATA TOPO 250K, 2003) has been used in Figures 2, 3 and 4 with the permission of Geoscience Australia. Geoscience Australia has not evaluated the Data as altered and incorporated within this article, and therefore gives no warranty regarding its accuracy, completeness, currency or suitability for any particular purpose.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. References
  • Australian Bureau of Statistics (2006) Vineyards estimates, Australia, 2005–2006. http://www.abs.gov.au/ausstats/abs@.nsf/mf/1329.0.55.002[accessed 20/09/07.
  • Australian Wine and Brandy Corporation (2008) Australian geographical indications: Regions. Electronic resource (Australian Wine and Brandy Corporation, Adelaide).
  • Bindi, M., Fibbi, L. and Miglietta, F. (2001) Free Air CO2 Enrichment (FACE) of grapevine (Vitis vinifera L.): II. Growth and quality of grape and wine in response to elevated CO2 concentrations. European Journal of Agronomy 14, 145155.
  • Bindi, M., Fibbi, L., Gozzini, B., Orlandini, S. and Miglietta, F. (1996) Modelling the impact of future climate scenarios on yield and yield variability of grapevine. Climate Research 7, 213224.
  • Buttrose, M.S., Hale, C.R. and Kliewer, W.M. (1971) Effect of temperature on the composition of ‘Cabernet-Sauvignon’ berries. American Journal of Enology and Viticulture 22, 7175.
  • Caprio, J.M. and Quamme, H.A. (2002) Weather conditions associated with grape production in the Okanagan Valley of British Columbia and potential impact of climate change. Canadian Journal of Plant Science 82, 755763.
  • Christensen, J.H., Hewitson, B., Busuioc, A., Chen, A., Gao, X., Held, I., Jones, R., Kolli, R.K., Kwon, W.-T., Laprise, R., Magaña Rueda, V., Mearns, L., Menéndez, C.G., Räisänen, J., Rinke, A., Sarr, A. and Whetton, P. (2007) Regional climate projections. In: Climate change 2007: The physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Eds. S.Solomon, D.Qin, M.Manning, Z.Chen, M.Marquis, K.B.Averyt, M.Tignor and H.L.Miller (Cambridge University Press: Cambridge and New York) pp. 847940.
  • Coombe, B.G. and Iland, P.G. (2004) Grape berry development and winegrape quality. In: Viticulture volume 1 – Resources. Eds. P.R.Dry and B.G.Coombe (Winetitles: Adelaide) pp. 210248.
  • Coops, N., Loughhead, A., Ryan, P. and Hutton, R. (2001) Development of daily spatial heat unit mapping from monthly climatic surfaces for the Australian continent. International Journal of Geographical Information Science 15, 345361.
  • CSIRO (1996) OzClim: A climate scenario generator and impacts package for Australia. http://www.csiro.au/ozclim[accessed 05/03/08.
  • CSIRO (2001) Climate projections for Australia (CSIRO Atmospheric Research: Melbourne).
  • Donn, W.L. (1975) Meteorology (McGraw-Hill: New York).
  • Duchene, E. and Schneider, C. (2005) Grapevine and climatic changes: A glance at the situation in Alsace. Agronomy for Sustainable Development 25, 9399.
  • Environmental Systems Research Institute (2006) ArcMap 9.2. (Environmental Systems Research Institute: Redlands, CA).
  • Gladstones, J. (1992) Viticulture and environment (Winetitles: Adelaide).
  • Gladstones, J.S. (2004) Climate and Australian viticulture. In: Viticulture volume 1 – Resources. Eds. P.R.Dry and B.G.Coombe (Winetitles: Adelaide) pp. 90118.
  • Godwin, D.C., White, R.J.G., Sommer, K.J., Walker, R.R., Goodwin, I. and Clingeleffer, P.R. (2002) VineLOGIC – A model of grapevine growth, development and water use. In: Managing water. Eds. C.Dundon, R.Hamilton, R.Johnstone and S.Partridge (Australian Society of Viticulture and Oenology: Adelaide) pp. 4650.
  • Gordon, H.B., Rotstayn, L.D., McGregor, J.L., Dix, M.R., Kowalczyk, E.A., O'Farrell, S.P., Waterman, L.J., Hirst, A.C., Wilson, S.G., Collier, M.A., Watterson, I.G. and Elliott, T.I. (2002) The CSIRO Mk3 climate system model [Electronic publication]. CSIRO Atmospheric Research Technical Paper No. 60 (CSIRO Atmospheric Research: Aspendale).
  • Houghton, J.T., Ding, Y., Griggs, D.J., Noguer, M., Linden, P.J.v.d., Dai, X., Maskell, K. and Johnson, C.A. (2001) Climate Change 2001: The Scientific Basis: Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press: Cambridge, UK).
  • IPCC (2000) Emissions scenarios summary for policymakers. Intergovernmental Panel on Climate Change (A Special Report of IPCC Working Group III).
  • IPCC (2007) Summary for Policymakers. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Eds. S.Solomon, D.Qin, M.Manning, Z.Chen, M.Marquis, K.B.Averyt, M.Tignor and H.L.Miller (Cambridge University Press: Cambridge, UK and New York).
  • Jackson, D.I. and Lombard, P.B. (1993) Environmental and management practices affecting grape composition and wine quality – A review. American Journal of Enology and Viticulture 44, 409430.
  • Jet Propulsion Laboratory (2004) Seamless Shuttle Radar Topography Mission (SRTM) 3 arc second version 2. ftp://e0srp01u.ecs.nasa.gov/srtm/version2/SRTM3/Australia/[accessed 18/03/06.
  • Jones, G.V. (2005a) Climate change and wine: Observations, impacts and future implications. Australian and New Zealand Wine Industry Journal 21, 2126.
  • Jones, G.V. (2005b) Climate change in the western United States grape growing regions. Acta Horticulturae 689, 4159.
  • Jones, G.V. (2006) Climate and terroir: Impacts of climate variability and change on wine. In: Fine wine and terroir – The geoscience perspective. Eds. R.W.Macqueen and L.D.Meinert (Geological Association of Canada: St. John's, Newfoundland) pp. 203216.
  • Jones, G.V. and Davis, R.E. (2000) Climate influences on grapevine phenology, grape composition, and wine production and quality for Bordeaux, France. American Journal of Enology and Viticulture 51, 249261.
  • Jones, G.V., White, M.A., Cooper, O.R. and Storchmann, K. (2005) Climate change and global wine quality. Climatic Change 73, 319343.
  • Jones, K. (2003) The enhanced greenhouse effect and the Australian viticulture industry. Australian Viticulture March-April, 5968.
  • Keeling, C.D., Piper, S.C., Bacastow, R.B., Wahlen, M., Whorf, T.P., Heimann, M. and Meijer, H.A. (2001) Exchanges of atmospheric CO2 and 13CO2 with the terrestrial biosphere and oceans from 1978 to 2000: Observations and carbon cycle implications. I. Global aspects (SIO Reference Series, No. 01-06). (Scripps Institution of Oceanography: San Diego).
  • Kliewer, W.M. (1977) Influence of temperature, solar radiation and nitrogen on coloration and composition of Emperor grapes. American Journal of Enology and Viticulture 28, 96103.
  • Luo, W., Taylor, M.C. and Parker, S.R. (2008) A comparison of spatial interpolation methods to estimate continuous wind speed surfaces using irregularly distributed data from England and Wales. International Journal of Climatology 28, 947959.
  • McInnes, K.L., Whetton, P.H., Webb, L. and Hennessy, K.J. (2003) Climate change projections for Australian viticultural regions. The Australian & New Zealand Grapegrower & Winemaker February, 4047.
  • Mullins, M.G., Bouquet, A. and Williams, L.E. (1992) Biology of the grapevine (Cambridge University Press: Cambridge).
  • Rosen, P.A., Hensley, S., Joughin, I.R., Li, F.K., Madsen, S.N., Rodriguez, E. and Goldstein, R.M. (2000) Synthetic aperture radar interferometry. Proceedings of the Institute of Electrical and Electronics Engineers 88, 333382.
  • Schultz, H.R. (2000) Climate change and viticulture: A European perspective on climatology, carbon dioxide and UV-B effects. Australian Journal of Grape and Wine Research 6, 212.
  • Sturman, A.P. and Tapper, N.J. (1996) The weather and climate of Australia and New Zealand (Oxford University Press: Melbourne, Oxford, Auckland, New York).
  • Suppiah, R., Hennessy, K.J., Whetton, P.H., McInnes, K.L., Macadam, I., Bathols, J., Ricketts, J. and Page, C.M. (2007) Australian climate change projections derived from simulations performed for the IPCC 4th Assessment Report. Australian Meteorological Magazine 56, 131152.
  • Webb, L.B., Whetton, P.H. and Barlow, E.W.R. (2006) Potential impacts of projected greenhouse gas-induced climate change on Australian viticulture. Australian and New Zealand Wine Industry Journal 21, 1620.
  • Webb, L.B., Whetton, P.H. and Barlow, E.W.R. (2007) Modelled impact of future climate change on the phenology of winegrapesin Australia. Australian Journal of Grape and Wine Research 13, 165175.
  • Winkler, A.J., Cook, J.A., Kliewer, W.M. and Lider, L.A. (1974) General viticulture. (University of California Press: Berkeley, Los Angeles, London).