1

Fire weather in the Mount Lofty Ranges

Identifying trends to inform planning decisions

Analysis by Dr Warwick Grace, Grace Research Network

Report by Eilidh Wilson, Ruby Wake and Zafi Bachar

Natural Resources Adelaide and Mt Lofty Ranges

July 2018

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Executive summary

The Fire weather in the Mount Lofty Ranges - identifying trends to inform planning decisions study sought

to identify trends in the extent of historical fire danger seasons in the Mount Lofty Ranges Fire Ban District

(MLR Fire Ban District), and then project the trends into the near future in order to better understand

potential future fire weather patterns in the district and inform planning decisions made by the Adelaide

and Mount Lofty Ranges Natural Resources Management Board (the Board) and other agencies.

Trends of increased number of fire danger days were detected both in the Mount Lofty Ranges weather

station dataset and in the Adelaide (Kent Town) dataset, however only the Adelaide dataset had data of

sufficient length and quality to enable statistically valid trend analysis. Conditions of Forest Fire Danger

Index (FFDI) equal to or higher than 50 in the MLR Fire Ban District (the Total Fire Ban threshold) were

found to closely correlate to conditions of FFDI equal to or higher than 31 in Adelaide. This enabled the

use of the Adelaide-based threshold as a proxy for the Mount Lofty Ranges-based threshold.

The linear trend in FFDI≥31 found in forty years of Adelaide Kent Town data was applied to the MLR Fire

Ban District dataset and (linearly) extended to the year 2030 to generate a prediction for the number of

Total Fire Ban days that could be expected by that time in the MLR Fire Ban District. Using the same proxy

and linear trend extension methodology, the length of fire season (the period of time between the first

FFDI≥31 day and the last one within a season) was also modelled.

The modelling resulted in the following key findings:

1. Fire seasons in the MLR Fire Ban District are trending towards an earlier start, and an overall increase

in the length of the fire danger period can therefore be expected.

2. On current trends, the MLR Fire Ban District is predicted to experience 26 FFDI≥ 50 days by the year

2030, an increase of 10 days (or 60%) from the year 2000.

As the use of a weather station proxy and the extension of a historical trend line to infer future fire weather

represented a somewhat novel approach, the Board engaged the Bureau of Meteorology (BoM) to run a

localised model to assess changes in FFDI≥ 50 days for the MLR Fire Ban District as a point of comparison

with this study’s findings.

The BoM model’s output confirmed the validity of using the Adelaide Kent Town weather station as a proxy

for predicting the number of FFDI≥ 50 days at the MLR Fire Ban District. Using the International Panel on

Climate Change’s median emissions scenario known as RCP 4.5 (a conservative scenario deemed

appropriate for this analysis), the BoM’s model showed an increase from 16 FFDI≥ 50 days in 2000 to 25

by the year 2030, a 9 day increase quite in line with this study’s prediction of a 10 day increase over the

same period.

The MLR Region’s population and built asset increases of recent years, coupled with the increase in fire

danger weather predicted by this study, are likely to present significant challenges for emergency services

and other relevant agencies. A longer fire danger season may also reduce the window of opportunity for

hazard reduction, at the same time as the need for it grows.

It is important to note that while a Total Fire Ban is declared based on a BoM forecast, this study has solely

analysed trends in observed fire weather conditions, which if accurately forecast would trigger a Total Fire

Ban declaration. It is important to note that rainfall variability remains a key factor in fire weather, and will

continue to influence the MLR Fire Ban District’s fire weather conditions.

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Contents

Executive summary .......................................................................................................................... 2

1 Introduction ................................................................................................................................... 4

1.1 Project objectives and scope .................................................................................................. 4

1.2 Relevant fire ban districts and weather datasets ................................................................... 5

1.3 The science of predicting fire danger .................................................................................... 6

1.3.1 Fire danger day definition .................................................................................................... 6

1.3.2 Previous South Australian fire danger studies .................................................................... 7

2 Methodology ................................................................................................................................ 9

3 Results ......................................................................................................................................... 10

3.1 Mount Lofty Ranges fire weather findings ..................................................................... 10

3.2 Adelaide Kent Town fire weather findings........................................................................... 12

3.3 Using Adelaide Kent Town as a proxy for the MLR Fire Ban District .................................. 12

3.4 Projecting MLR’s fire weather using the observed Adelaide Kent Town trend .................. 14

3.5 BoM verification of this study’s results ................................................................................ 15

4 Conclusion and recommendations ........................................................................................ 17

References ...................................................................................................................................... 18

Appendix 1 – The use of Drought Factor (in calculating the Forest Fire Danger Index) and grass

curing (in calculating the Grassland Fire Danger Index) ............................................................... 19

Appendix 2 – Fire danger rating definition history ....................................................................... 20

Appendix 3 – MLR Fire Ban District weather station data issues .................................................. 21

Appendix 4 – BoM verification of this study’s findings ................................................................. 22

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1 Introduction

The Adelaide and Mount Lofty Ranges Natural Resources Management region (the region) encompasses

two fire ban districts, namely the Adelaide Metropolitan Fire Ban District and the Mount Lofty Ranges Fire

Ban District. Recognising the impact of Total Fire Ban days on its own operations and on those of its key

stakeholders in the region, as well as the potential impacts of climate change on fire weather throughout

the region, in 2017 the Adelaide and Mount Lofty Ranges Natural Resources Management Board (‘the

board’) commissioned a study of fire weather trends in the Mount Lofty Ranges (MLR) Fire Ban District.

This report documents the methodology and the findings of that study, carried out in partnership with the

Grace Research Network.

1.1 Project objectives and scope

The study sought to identify any statistically valid trends in the extent of historical fire danger seasons1 in

the MLR Fire Ban District, and then project such trends into the near future in order to gain a better

understanding of potential future fire weather patterns in this part of South Australia and inform planning

undertaken by the Board and other agencies into the future.

It is important to note that while a Total Fire Ban is declared based on a Bureau of Meteorology (BoM)

forecast, this study has solely analysed trends in observed fire weather conditions, which if accurately

forecast would trigger a Total Fire Ban declaration2.

As explained in Chapter 2, the study has used an observed trend in fire weather conditions to predict the

potential number of Total Fire Ban days in the Mount Lofty Ranges Fire Ban District by 2030. While climate

change projections have not been used in this study, it is recognised that the historical trends detected

through it are broadly consistent with typical climate change projections for the MLR area, with warmer

and drier conditions expected to lead to an increase in fire weather frequency and severity.

1 For the MLR FBD, the Fire Danger Season traditionally runs from 1 December to 1 April of the following year.

However under the Fire and Emergency Services Act 2005, fire restrictions apply throughout South Australia from a

date declared by the Country Fire Service Chief Officer prior to a Fire Danger Season commencing, with that date

varying from year to year according to the anticipated level of bushfire risk.

2 Weather forecasting is not an exact science, as it relies on modelling to generate forecasts on the best available

current data. While the BoM’s Total Fire Ban forecasting success has overall improved since 2001, some discrepancy

between predicted and eventuating fire ban days is to be expected into the future.

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1.2 Relevant fire ban districts and weather datasets

The region’s two Fire Ban District s designated by the Country Fire Service (CFS) are depicted in Figure 1,

alongside BoM’s weather stations.

Figure 1. Fire Ban Districts and BoM weather stations in the Adelaide metropolitan and MLR Fire Ban

Districts

The ten BoM-maintained weather stations used for fire weather predictions in those two Fire Ban Districts

are shown in Table 1. A data set comprising daily rainfall and maximum temperatures, as well as 3:00pm

temperature, relative humidity and wind speed readings was obtained from seven monitoring stations

(bolded in Table 1) and considered in this study as representative of the MLR Fire Ban District.

Table 1. BoM weather stations in the Adelaide and Mount Lofty Ranges’ Fire Ban Districts (bolded stations

are those used to represent the MLR Fire Ban District)

Station name BoM ID Data period

Adelaide Airport 023034 1956 - present

Adelaide Kent Town 023090 1978 - present

Mount Crawford 023878 1994 - present

Nuriootpa 023373 1952- present

Kuitpo 023887 1996 - present

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Station name BoM ID Data period

Parawa (West) 023875 1994 - present

Mount Lofty 023842 1985 - present

Hindmarsh Island 023894 2003 - present

Strathalbyn 024580 1996 – present

Mount Barker 023733 1861 - present

1.3 The science of predicting fire danger

In South Australia, two indices are used to predict the fire danger index (FDI), namely the Forest Fire Danger

Index (FFDI) and the Grassland Fire Danger Index (GFDI). Both indices seek to convey likely fire behaviour

and difficulty of suppression should a fire start (as opposed to the likelihood of a fire actually starting). FFDI

is calculated as follows:

DF stands for Drought Factor, which requires a calculated index (see Appendix 1). T stands for temperature

(in degrees Celsius), WS stands for wind speed (in kilometres per hour), and RH stands for relative humidity

(expressed as a percentage figure).

The GFDI calculation uses a similar equation, with DF replaced by a qualitative estimate of grass curing (as

explained in detail in Appendix 1).

The Adelaide metropolitan Fire Ban District uses GFDI only. For the Mount Lofty Ranges Fire Ban District

both indices are calculated, and the highest FDI is then used to determine the following day’s fire danger

rating (FDR).

1.3.1 Fire danger day definition

For the Mount Lofty Ranges Fire Ban District, a BoM-declared severe fire danger day (and a resultant Total

Fire Ban) occurs when the FDI is expected to be equal to or higher than 50. That declaration is later verified

against half-hourly weather observations from that Fire Ban District’s seven representative stations (bolded

in Table 1). Table 2 shows the relationship between FFDI figures and the FDRs used by the CFS. Note that

the method for calculating district FDRs has changed over recent years, as explained in appendix 2.

FFDI = 1.275 * DF0.987 * exp(0.0338T + 0.0234WS – 0.0345RH)

FFDI = 1.275 * DF0.987 * exp(0.0338T + 0.0234WS – 0.0345RH)

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Table 2. South Australia’s fire danger ratings – definitions and corresponding FDIs3

Fire danger rating

Grassland fire

danger index

(GFDI)

Forest fire

danger index

(FFDI)

What does it mean?

Catastrophic

Total Fire Ban Day

150+

100+

The worst conditions for a bush or grass fire.

If a fire starts and takes hold, it will be extremely difficult to control

and will take significant fire-fighting resources and cooler

conditions to bring it under control.

Spot fires will start well ahead of the main fire and cause rapid

spread of the fire. Embers will come from many directions.

Homes are not designed or constructed to withstand fires in these

conditions.

The safest place to be is away from bushfire prone areas.

Extreme

Total Fire Ban Day

100-149

75-99

Very hot, dry and windy conditions.

If a fire starts and takes hold, it will be unpredictable, move very

fast and be very difficult for fire fighters to bring under control.

Spot fires will start and move quickly. Embers may come from many

directions.

Homes that are prepared to the highest level, have been

constructed withstand bushfires and are actively defended may

provide safety.

You must be physically and mentally prepared to defend in these

conditions.

The safest place to be is away from bushfire prone areas.

Severe

Total Fire Ban Day

50-99

50-74

Hot, dry and possibly windy conditions.

If a fire starts and takes hold, it will be hard for fire fighters to bring

under control.

Actively defended, well prepared homes can provide safety.

You must be physically and mentally prepared to defend in these

conditions.

Very high 25-49 25-49 If a fire starts, it is likely to be controlled in these conditions and

homes can provide safety.

Be aware of how fires can start and reduce the risk.

High 12-24 12-24

Low - moderate 0-11 0-11

1.3.2 Previous South Australian fire danger studies

A study by Clarke et al. (2012) established a statistically significant increasing trend in fire danger weather

since the 1970s as measured by the FFDI. That study revealed a significant increase in the 90th percentile

FFDI in the period 1973-2010 at a number of stations across Australia (including in Adelaide). Significantly,

3 Modified from the Country Fire Service’s website -

http://www.cfs.sa.gov.au/site/fire_bans_and_ratings/more_about_fire_danger_rating.jsp

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none of the stations showed a decrease in FFDI during the study period. The most significant trends were

observed in seasonal FFDI during spring and autumn, with increases in fire weather conditions and an

extended fire danger season. In updating Clarke’s research for The State of the Climate 2016 report4, the

CSIRO and BoM investigated those fire weather trends for a 2015 timeline. That report again noted a

significant increase in extreme fire weather days since the 1970s, as well as a longer fire danger season

(CSIRO and Australian Bureau of Meteorology, 2016).

Drawing on global climate models, Hope et al. (2015) concluded that fire danger weather would continue

to become more severe. This study used Adelaide Kent Town weather station observations over the 1995-

2010 period to predict that severe fire danger days would increase from 1.7 days per annum to 2-3.5 by

2030. The report also predicted that days over 40°C would increase from 3.7 to 5.9 per annum and that

days over 35°C would increase from 20 to 26 per annum. The report concluded that the increase in fire

danger weather was primarily attributable to global temperature increases.

While both reports provide useful context to inform the board’s understanding of fire weather trends,

neither provides localised fire weather danger analysis for the MLR Fire Ban District.

4 Available on http://www.bom.gov.au/state-of-the-climate/

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2 Methodology

For this study, the Grace Research Network analysed publicly available BoM data using the multi-paradigm

numerical computing software MATLAB. Key steps in this study’s methodology were as follows:

1. Datasets from the representative MLR weather stations (bolded in Table 1) were inspected and found

generally lacking in length, weather parameter coverage and reliability. While a trend for increased

number of fire danger days was detected, it was not deemed statistically significant.

2. A rising trend in fire danger days detected in the Adelaide (Kent Town) dataset was deemed statistically

significant, and a comparison of that dataset to the MLR stations’ dataset demonstrated a clear

correlation between the two, enabling the use of the Adelaide dataset as a proxy for the MLR dataset.

3. FFDI≥50 (Total Fire Ban) conditions in the MLR Fire Ban District were found to closely correlate to

FFDI≥31 conditions at Adelaide. It was therefore determined that predictions for FFDI≥31 conditions at

Adelaide could be used as a proxy for Total Fire Ban conditions in the MLR Fire Ban District.

4. The linear trend in FFDI≥31 found in forty years of Adelaide data was applied to the MLR Fire Ban

District dataset and (linearly) extended to the year 2030 to come up with a prediction for the number

of Total Fire Ban days that could be expected by that time in the MLR Fire Ban District.

5. Using the same proxy and linear trend extension methodology, the length of fire season (the period

of time between the first FFDI≥31 day and the last one within a season) was also modelled.

Note that:

Historical MLR Total Fire Ban events were determined by the station with the highest FDI within

the Fire Ban District, as per the BoM’s pre-2011 methodology for forecasting a Total Fire Ban day

(as explained in Appendix 2).

Drought factor calculations in this study replicated the methodology used by Clarke (2012) and

Finkele (2006).

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3 Results

This chapter details the key results emerging from the study’s fire weather trend analysis, using the BoM’s

Adelaide and MLR weather data sets.

3.1 Mount Lofty Ranges fire weather findings

As shown in Figure 2, the following fire weather trends appear to emerge from an analysis of the seven

assessed MLR stations in the period 1994-2016:

an overall increase in the 95th percentile of maximum FFDI

a general increase (at any MLR station at any point in time) in the number of FFDI ≥50 occurrences

a slight lengthening of the fire danger period (that is the period between first and last day to

record FFDI ≥50).

Mainly due to the relatively short duration of the MLR dataset, it was considered that it could not be

confidently relied on to forecast future fire weather trends for the region (as further explained in Appendix

3).

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Figure 2 – Analysis of key fire weather parameters in the MLR Fire Ban District, using data from the seven

selected stations in the period 1994-2016

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3.2 Adelaide Kent Town fire weather findings

As shown in Figure 3, a time series analysis of Adelaide (Kent Town’s) 90th and 95th percentile FFDI figures

shows a clearly increasing trend over the past 40 years. Similarly, FFDI annual maxima show a generally

increasing trend over the same period.

Figure 3 – Trends in the occurrence of high FFDI days at the Adelaide Kent Town weather station (measured

at 3 pm)

3.3 Using Adelaide Kent Town as a proxy for the MLR Fire Ban District

With MLR fire weather data considered of insufficient length and reliability to establish statistically valid

regional fire weather trends, the study examined the relationship between the MLR dataset and the longer

and more robust Adelaide (Kent Town) dataset, in anticipation that the latter may act as a proxy for the

former.

A close relationship between these two datasets in the period 2004 – 2016 was identified by using root

mean squared error regression and correlation analysis. As shown in Figure 4, plotting the comparative

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cumulative distribution function (CDF) identifies a Kent Town 3pm FFDI of 31.22 as a proxy for an FFDI of

50 in the MLR Fire Ban District.

Figure 4 – The comparative cumulative distribution function establishing a correlation between an FFDI of

31 in Kent Town and an FFDI of 50 in the MLR Fire Ban District in the period 2004–16.

Figure 5 demonstrates the clear relationship between the annual number of FFDI≥31 days at Adelaide Kent

Town and FFDI≥ 50 at the MLR Fire Ban District, confirming Adelaide (Kent Town) as an acceptable proxy

for the MLR Fire Ban District for fire weather analysis purposes. The correlation was shown to have a p

value5 smaller than 0.001 and is therefore statistically highly significant.

5 In statistical hypothesis testing, a small p-value (typically ≤ 0.05) indicates strong evidence for a statistically

significant correlation, while a large p value (typically > 0.05) indicates weak evidence for such correlation.

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Figure 5 – Annual number of FFDI≥31 days at Adelaide Kent Town (using 3 pm data) plotted against the

annual number of FFDI≥ 50 days at any of the MLR Fire Ban District stations (using half-hourly data).

3.4 Projecting MLR’s fire weather using the observed Adelaide Kent Town trend

As shown in Figure 6(c), plotting the start and end dates of the MLR Fire Ban District’s fire danger period

(defined as the period between first and last day to record an FFDI ≥31 at Adelaide Kent Town) reveals a

trend towards an earlier start. Although no clear trend was observed for the end of the fire danger period

(possibly due to the unpredictability of autumn rainfall), Figure 6(d) shows an overall increase in the length

of the fire danger period.

Figure 6(b) shows that on the assumption that the observed trend continues at the same rate, the MLR Fire

Ban District is predicted to experience 26 FFDI≥ 50 days (the threshold for Total Fire Ban days) by the year

2030, an increase of 10 days (or 60%) from the year 2000.

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Figure 6 – Projected trends (to 2030) in MLR Fire Ban District’s fire danger period, as inferred from Kent

Town data

3.5 BoM verification of this study’s results

As the use of a weather station proxy and the extension of a historical trend line to infer future fire weather

represented a somewhat novel approach, the Board engaged the BoM to run a localised model to assess

changes in FFDI≥ 50 days for the MLR Fire Ban District as a point of comparison with this study’s findings.

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As further explained in Appendix 4, the BoM model’s output confirmed the validity of using data from

Adelaide (Kent Town) station as a proxy for predicting the number of FFDI≥ 50 days at the MLR Fire Ban

District (although at an FFDI of 28.7 rather than the figure of 31 used by this study).

Using the International Panel on Climate Change’s median emissions scenario known as RCP 4.5 (a

conservative scenario deemed appropriate for this analysis), the BoM’s model showed an increase from 16

FFDI≥ 50 days in 2000 to 25 by the year 2030, a 9 day increase quite in line with this study’s prediction of

a 10 day increase within the same period.

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4 Conclusion and recommendations

Analysis of this study’s results points to the following overall conclusions:

both the Adelaide Kent Town station dataset and the MLR Fire Ban District dataset clearly show an

upward trend in fire weather conditions over the period 1977-2016 and 1994-2016 respectively

fire seasons are trending towards an earlier start, and an overall increase in the length of the fire danger

period can therefore be expected

on current trends, the MLR Fire Ban District is predicted to experience 26 FFDI≥ 50 days by the year

2030, an increase of 10 days (or 60%) from the year 2000

BoM’s modelling showing a 9 day increase in FFDI≥ 50 days in the MLR Fire Ban District (from 16 in

2000 to 25 by the year 2030) is in line with this study’s finding of a 10 day increase.

It should be noted that rainfall variability remains a key factor in fire weather, and will continue to influence

the MLR Fire Ban District’s fire weather conditions.

The MLR region’s steady population and built asset increases of recent years, coupled with the increase in

fire danger weather predicted by this study, is likely to present significant challenges for emergency services

and other relevant agencies. A longer fire danger season may also reduce the window of opportunity for

hazard reduction, at the same time as the need for it grows.

This study concludes by offering the following recommendations:

1. Downscaled climate change projections now available (through the Climate Ready SA project) for all

of South Australia’s NRM regions could be used to corroborate this study’s findings, potentially

extending the prediction timeline further than 2030.

2. Where sufficient data are available, this study’s methodology should be considered for use in other

South Australian NRM regions.

3. The MLR’s weather station dataset could be used for other purposes, for example to inform winter

revegetation planning.

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References

Clarke, H., Lucas, C. and Smith, P. (2012) Changes in Australian fire weather between 1973 and 2010.

International Journal of Climatology, 33, 931-944.

CSIRO and Australian Bureau of Meteorology (2016) State of the Climate 2016. CSIRO and Bureau of

Meteorology, Melbourne.

Finkele, K., Mills, G., Beard, G and Jones D. (2006) National gridded drought factors and comparison of

two soil moisture deficit formulations used in prediction of Forest Fire Danger Index in Australia. Australian

Met Magazine, 55, 183-197.

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Appendix 1 – The use of Drought Factor (in calculating the

Forest Fire Danger Index) and grass curing (in calculating

the Grassland Fire Danger Index)

The Drought Factor (DF) is an estimate of fuel dryness, normally calculated on a day to day basis and

represented as a number between 0 and 10.

Although the Drought Factor is calculated through the same process throughout Australia, the Drought

Index differs between states.

It is calculated from a Soil Dryness Index such as:

the Keetch-Byram Drought Index (KBDI) which is used throughout many Australia States and in North

America

Mount’s Soil Dryness Index (SDI) which is used in South Australia, Tasmania and Western Australia.

Both KBDI and SDI require the use of daily rainfall and daily maximum temperature to calculate their values.

The Dryness Index (whether KBDI or SDI) is used in a formula to calculate the Drought Factor, being

modified by recent rainfall from the previous 20 days.

The GFDI does not use Drought Factor as an input, instead relying on a subjective quantification of grass

curing undertaken by individuals on a daily basis. An assessment of grass is made to determine how cured

(dry) it is on a scale of up to 100%, based on the yellow and dried nature of the grass. This measurement

is recorded by local government staff, collated by the CFS and forwarded to BoM.

In 2017-18, grass curing will be estimated using satellite data processed by the Country Fire Authority in

Victoria, with local observations used to validate the remotely sensed data. However, for this study, the

GFDI has not been used for back calculating a fire danger index, as there is no complete repository of

long-term data available for analysis. A comparison of available GFDI figures with the FFDI showed the

difference between the two to be minor when providing a fire danger index threshold for Total Fire Ban

declaration.6

6 For this study, available records for the past 7 years were used to compare the GFDI with the FFDI for the local weather

stations in the Adelaide and Mount Lofty Ranges. Only two occurrences were found where the observed GFDI was higher than

50 while the FFDI was lower than 50. It is acknowledged that there can be local fluctuations in GFDI which could trigger a Total

Fire Ban, however it is considered that in these situations there is a high probability that a FFDI ≥50 would already have

occurred elsewhere in the region.

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Appendix 2 – Fire danger rating definition history

Fire danger rating (FDR) definitions have changed over time, as follows:

prior to 2011, FDR was simply determined by the station with the highest FDI in the district

since 2011, district FDI is calculated for each 6x6 km grid using the Australian Digital Forecast

Database (known as ADFD); the District FDR is determined as the 90th percentile of all the grids

that fall within the district, hence a Severe FDR is declared if 10% of the grids within a district are

at 50-75

as of 2017, FFDI and GFDI (or both) will only be used for the grids with a majority of the respective

fuel type in the grid cell.

These changes have caused discontinuity in the FDI/FDR data.

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Appendix 3 – MLR Fire Ban District weather station data

issues

Nuriootpa weather station

Prior to 2011 Nuriootpa weather station data was not used by the BoM in fire weather calculations for the

MLR Fire Ban District. As Nuriootpa is overall prone to higher fire danger than the rest of the district, this

resulted in a lower annual number of declared fire danger days prior to 2011 than would have otherwise

been the case, an important consideration when reviewing historical trends and projecting them into the

future.

Hindmarsh Island weather station

BoM’s Hindmarsh Island weather station data are only available from 2003, while this study required a

complete, robust and representative MLR Fire Ban District dataset going back to 1997. A comparison made

between a 7 station data set (including Hindmarsh Island) and a 6 station data set (excluding Hindmarsh

Island) showed the inclusion of that station to result in a 9% increase in fire danger days. The derived data

set subsequently generated increased the MLR Fire Ban District’s historic number of fire danger days by

9% for the period 1997-2003.

Mount Lofty and Mount Barker weather stations

Time series plots for key variables (9am rainfall, KDBI, SDI, DF, FFDI, 3pm temperature, relative humidity

and wind speed) for all MLR Fire Ban District stations showed data anomalies in the Mount Barker and

Mount Lofty station data sets. As Mount Lofty station had gaps in its rainfall data set, it was replaced by

data sourced from the adjacent Cleland Conservation Park. The Mount Barker data set was altogether

excluded from the MLR Fire Ban District group of weather stations, due to its wind and rainfall data

anomalies, consistent with BoM’s procedures.

The FFDI was back calculated for the seven stations using half hourly historical data and the standard

formula. The group’s maximum FFDI indicates weather a severe fire danger day has occurred or not.

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Appendix 4 – BoM verification of this study’s findings

Revised fire weather projections for Adelaide, representative of Mt Lofty fire district

March 20th, 2017

Chris Lucas and Pandora Hope

Background and results The Climate Change in Australia, projections for natural resource management (NRM; www.climatechangeinaustralia.gov.au) project produced a table, presented in the Southern and South Western Flatlands cluster report (Hope et al. 2015) describing the average annual number of days with ‘severe’ fire weather, defined when the McArthur Forest Fire Danger Index (FFDI) is greater than 50. The FFDI was calculated from daily temperature, wind speed, humidity and a drought factor, with statistics tallied over a year defined from 1 July- 30 June year (Lucas 2010). The data for Adelaide, taken from the cluster report, are shown in Table 1.

For this report, climate projections for the grouped Mount Lofty fire district are desired based on an approach outlined in the cluster report. The Kent Town (Adelaide) station represents the closest station for which projections were produced; data records in the Mt Lofty district do not extend back far enough and the data quality is not as consistently high over the historical period. While Kent Town is a good proxy for the larger-scale trends in the region, the specifics of fire danger in the Mt. Lofty district differ considerably, owing to the differences in elevation and fuels. These differences effectively create a lower FFDI threshold for extreme fire weather to occur in the Mt Lofty district (Wilson and Grace, pers. comm., 2017). Using the Kent Town (Adelaide) historical data, it has been found that an FFDI in excess of 28.7 provides a good match to severe fire weather in the Mt Lofty district as measured by reported ‘total fire ban’ days issued by local fire authorities. For this work, we will apply this modified definition of severe fire weather to the projections for Adelaide presented in the cluster report to create a result more appropriate to the Mt Lofty district.

The results are shown in the last line of Table 1, and it is clear that lowering the threshold has greatly increased the number of days each year with severe fire weather.

Table 1. Annual values of maximum temperature (T; °C), RAINFALL (R; mm per year), DROUGHT FACTOR (DF; no units), the number of severe fire danger days with FFDI greater than 50 (SEV 50; days per year) and the cumulative FFDI (ΣFFDI; no units) for 1981/82 – 2009/10 baseline observations at Adelaide (Kent Town), and for each of three models at 2030 and 2090 under RCP4.5 or RCP8.5. The shaded line shows the new results, showing the number of days with FFDI greater than 28.7 (SEV 28.7; days per year).

Adelaide (SSWFE) 1982

-2010

2030 RCP4.5 2030 RCP8.5 2090 RCP4.5 2090 RCP8.5

Obs

CE

SM

GFD

L

MIR

OC

CE

SM

GFD

L

MIR

OC

CE

SM

GFD

L

MIR

OC

CE

SM

GFD

L

MIR

OC

T 22.1 23.2 23.3 23.2 23.4 23.1 23 24.7 23.7 23.7 26.4 25.4 24.6

R 544 511 309 537 516 400 549 479 409 547.3 434 267 594

DF 6.5 6.8 7.9 6.6 6.7 7.1 6.6 7.1 7.2 6.6 7.5 8.9 6.6

SEV 50

1.7 2.3 3.5 1.9 2.1 2.3 1.9 2.9 2.6 2.2 4.3 5.4 2.3

ΣFFDI 2942 3089 3885 3033 3067 3334 2985 3395 3373 3126 3835 4464 3085

SEV 28.7

16.1 18.8 25.4 17.4 18.3 18.9 17 21.7 20 19 27.1 31.9 19.4

Observed variability of Adelaide FFDI days over 28.7 As shown in Figure 1, the observed variability of the number of days with FFDI over 28.7 is large. This must be noted whenever discussing the ‘average’ number of severe fire weather days in a year. The variability is strongly linked to rainfall, although increasing mean temperatures can also have an impact.

Considering the current climate as estimated using the dataset described by Lucas (2010), there has been a large shift through time in the number of days with FFDI greater than 28.7. Consider the period from 1982-2006: the average number of days is 13. From 2006 through 2016, the average is 25 days per year. The current year has been relatively quiet through to mid-February. Other periods would show different averages. Whether this shift is due to climate change or decadal variability is unclear. It’s probably due to both, but the relative contribution of each is not known. Given the strong dependence of FFDI on rainfall, underlying natural variability must still be considered to be a factor now, and into the future.

Figure 1. The number of days at Adelaide with FFDI > 28.7. Data for 2016-17 only extend through mid-February and will not reflect any days that occurred after that time.

Detailed Projections More details have been provided beyond Table 1, at a number of different time horizons into the future in Table 2. Note that by creating the projections using the historical record, the interannual and decadal variability of the future is constrained to what occurred previously and is therefore probably an unrealistic representation of the future. Hence, the standard deviations noted below may not reflect the true variability that may be experienced.

As rainfall is so important, the mean modelled rainfall at these time horizons is also noted below. The change factor based on the 30-year mean is used, and thus only the mean rainfall is included. MIROC5 and CESM-CAM5 are generally in the middle to the wetter end of the range of rainfall projections and GFDL-ESM2M toward or at the dry end. Note that high rainfall in any one 30 year period aligns with lower fire risk.

Further details about the method are described below (from CSIRO and Bureau of Meteorology (2015)) . From the three models chosen, monthly-mean changes to maximum temperature, rainfall, relative humidity and wind speed were calculated for each sub-cluster over 30 year time-slices centred on 2030, 2050, 2070 and 2090. The mean changes from these models were applied to observation-based high-quality historical fire weather records (Lucas, 2010). The baseline of 1981/82-2009/10 was chosen, to best represent the current climate. The observed station records of maximum temperature, relative humidity, rainfall and wind speed were modified using the model-derived changes for each time-slice and emission scenario. From these adjusted records, future fire weather was estimated using the FFDI.

Being based on observed records, this methodology maintains the observed variability of past fire weather, thereby preserving the relationship between the variables and helping to avoid potential issues of model bias. However, by preserving this variability, any future changes that may be important to future fire weather, such as a change in the frequency of El Niño events, will not be captured; the observed interannual and decadal variability is not changed in the ranges of standard deviation ranges. This approach is good at capturing the mean changes to FFDI, while approaches based on direct use of climate model output (Clarke et al. 2011) or dynamical downscaling (Grose et al. 2014) may better capture changes to the extremes and overall variability of fire weather.

The baseline period for projections is 1981/2 to 2009/10. In the current climate, the mean annual number of days with FFDI > 28.7 is 16.1 days, with a standard deviation of 10.0 days. The basline rainfall taken for the dought factor calculation over that period is 544.0 mm.

Table 2. Annual number of days FFDI >28.7 and standard deviation for two scenarios. Also, mean rainfall over the same periods.

Scenario: RCP 4.5, site: Adelaide, Annual number of days FFDI >28.7, based on 81/82 – 2009/10 historical series (29 years), and annual rainfall (mm per year) below.

Model 2030 2050 2070 2090

MIROC 17.4 ± 10.6 17.9 ± 10.7 18.5 ± 10.9 19.0 ± 10.9

CESM 18.8 ± 11.3 20.0 ± 11.4 20.7 ± 11.6 21.7 ± 12.0

GFDL 25.4 ± 13.6 20.4 ± 12.0 25.4 ± 13.9 20.0 ± 12.7

Rainfall Model, RCP4.5

2030 2050 2070 2090

MIROC 537 559 559 547

CESM 510 497 488 479

GFDL 309 389 330 408

Scenario: RCP 8.5, site: Adelaide, number of days FFDI > 28.7, and annual rainfall (mm) below.

Model 2030 2050 2070 2090

MIROC 17.0 ± 10.6 18.9 ± 11.1 19.4 ± 11.0 19.4 ± 10.5

CESM 18.3 ± 11.2 21.1 ± 12.1 23.5 ± 12.5 27.1 ± 13.5

GFDL 18.9 ± 12.6 23.3 ± 12.9 25.7 ± 13.7 31.9 ± 14.4

Rainfall Model, RCP8.5 2030 2050 2070 2090

MIROC 549 534 577 594

CESM 516 447 467 433

GFDL 400 357 346 266

References Clarke, H. G., P. L. Smith, and A. J. Pitman, 2011: Regional signatures of future fire weather over eastern

Australia from global climate models. Int J. Wildl. Fire, 20, 550–562, doi:doi:10.1071/WF10070.

CSIRO, and Bureau of Meteorology, 2015: Climate Change in Australia - Information for Australia’s Natural Resource Management Regions: Technical Report. CSIRO and Bureau of Meteorology, Australia, 216 pp.

Grose, M. R., P. Fox-Hughes, R. M. B. Harris, and N. L. Bindoff, 2014: Changes to the drivers of fire weather with a warming climate – a case study of southeast Tasmania. Clim. Change, DOI 10.1007/s10584-014-1070-y.

Hope, P., and et al., 2015: Southern and South-Western Flatlands Cluster Report. Climate Change in Australia - Projections for Australia’s Natural Resource Management Regions: Cluster Reports, M. Ekstrom and Et al., Eds., CSIRO and Bureau of Meteorology, Australia.

Lucas, C., 2010: On developing a historical fire weather data-set for Australia. Aust. Meteorol. Oceanogr. J., 60, 1–14.