Spatial and temporal trends in wind erosion
of Australian rangelands during 1960 to 2005
using the Dust Storm Index (DSI)
A Report for the Australian Collaborative Rangeland Information
System [ACRIS]
Grant McTainsh,* Kenn Tews,* John Leys** and Gary Bastin ***
* Australian Rivers Institute (Catchment Group), The Griffith School of
Environment, Griffith University, Brisbane, Queensland, and Desert Knowledge
CRC.
** Department of Environment and Climate Change, Gunnedah, New South Wales,
(Adjunct Associate Professor - Griffith University), and Desert Knowledge CRC.
*** CSIRO, Sustainable Ecosystems, Alice Springs, and Desert Knowledge CRC.
TABLE OF CONTENTS
List of Tables ................................................................................................................. 3
List of Figures ................................................................................................................ 3
Summary ........................................................................................................................ 4
Introduction .................................................................................................................... 5
Methods.......................................................................................................................... 5
The Dust Storm Index ................................................................................................ 5
Limitations of the DSI data. ....................................................................................... 6
Results ............................................................................................................................ 9
DSI maps and statistics for rangeland bio regions (1992-2005). ............................... 9
Temporal trends in DSI for the period 1960-2005................................................... 11
Spatio-temporal trends of DSI (1960-2005). ........................................................... 12
Rainfall-adjusted DSI: an estimate of land use influences upon wind erosion........ 15
The institutional context of wind erosion monitoring and reporting ....................... 18
Acknowledgements ...................................................................................................... 18
References .................................................................................................................... 19
Appendix I: Observation Frequency Maps and DSI Maps 1992 to 2005. ................... 20
Appendix II: Bioregion Statistical Data and Interpretive Comments. ........................ 24
2
LIST OF TABLES
Table 1: Summary of DSI statistics for each bio region. DSI data are the spatial
average for each bio region time-averaged over the period 1992 to 2005........... 11
Table 2: Mean DSI at locations within three continental sectors of Australian
rangelands during five time periods from 1960 to 2005. ..................................... 16
Table 3: Mean rainfall-adjusted DSI at locations within three continental sectors
of Australian rangelands during five time periods from 1960 to 2005. ............... 16
LIST OF FIGURES
Figure 1: Meteorological stations used for long term wind erosion monitoring. .......... 7
Figure 2: Observation Frequency map 1992 to 2005 (number of observations
per day). Polygons show rangeland bio regions. .................................................. 8
Figure 3: Changes in spatial patterns of observation frequency during four
representative years from 1992 to 2005. ................................................................ 9
Figure 4: Map of mean DSI for the period 1992 to 2005 showing bioregions
within the rangelands and BoM recording stations.............................................. 10
Figure 5: Annual total DSI at 109 locations Australia-wide (1960-2005)................... 12
Figure 6: Mean DSI 1960 to 1965. ............................................................................. 12
Figure 7: Mean DSI 1966 to 1970. ............................................................................. 13
Figure 8: Mean DSI 1971 to 1980 .............................................................................. 13
Figure 9: Mean DSI 1981 to 2000 .............................................................................. 14
Figure 10: Mean DSI 2001 to 2005 ............................................................................ 14
Figure 11: Relationship between rainfall and wind erosion activity. .......................... 15
3
SUMMARY
Improvements in data quality control, data analysis and mapping techniques have
produced an improved version of the Dust Storm Index (DSI3); for wind erosion
mapping. DSI statistics for rangeland bio regions have been mapped and tabulated to
assist the Australian Collaborative Rangelands Information System with reporting
change for the period 1992 to 2005.
These statistics provide useful regional scale estimates of wind erosion activity,
however there are data limitations arising from the low and uneven spatial density of
stations which reduce the reliability of the data produced by this “cookie cutting”
technique. The temporal trend of DSI for the rangelands as a whole shows a high
level of episodicity which is driven mainly by drought, but as drought seldom affects
the whole continent DSI maps for 5 time periods between 1960 and 2005 are
informative.
A major rangeland monitoring challenge is to quantitatively discriminate the effects
of land management from natural drivers of wind erosion. A new measure examined
here is called rainfall-adjusted DSI; whereby wind erosion rates (expressed as DSI)
are normalised for rainfall and expressed as DSI per 10mm of rainfall. When this new
measure is compared with the standard DSI for three sectors of the continent (west,
centre and east) over the five time periods between 1960 and 2005, it is possible to
demonstrate that poor land management in the Alice Springs region is highly likely to
be responsible for the high wind erosion activity in this region during the 1960-1965
drought period and that the introduction of buffel grass in the 1970s played an
important role in stabilising these rangelands. There is much greater consistency of
rainfall-adjusted DSI in all sectors from 1965 to 2000, suggesting that in general
terms land management effects may have been similar in the 3 sectors. The higher
rainfall-adjusted DSI in the 2001 to 2005 period may partly reflect more erosive wind
conditions, but this needs verification.
This project demonstrates that ACRIS provides an effective institutional framework
for ongoing semi-regular updates of wind erosion within rangelands. There are also a
number of concurrent and planned developments within other government
institutions, including; the National Land and Water Resources Audit (NLWRA), the
National Landcare Program (NLP), and the Natural Heritage Trust (NHT). If these
developments can be sustained and if inter-institutional linkages can be improved, we
are optimistic that wind erosion monitoring and reporting can be embedded as part of
continued reporting of change in the rangelands.
4
INTRODUCTION
Wind erosion is an important component of the land degradation of Australia’s
rangelands. Because wind erosion is an episodic process which is strongly driven by
climate, long time series of data are needed. In addition, because wind erosion is
fundamentally a natural process that has been accelerated by some land use activities,
including pastoralism, agriculture and mining, a major challenge in wind erosion
monitoring is to differentiate these land use effects from the natural climate drivers.
Continental scale wind erosion in Australia is measured by the Dust Storm Index
(DSI). The DSI was first used in the National Collaborative Project on Indicators for
Sustainable Agriculture (NCPISA) (McTainsh, 1998) and has been used in all
National State of the Environment (SoE) Reports and all Queensland SoE reports.
The DSI is also the approved indicator of wind erosion rate by the Australia-New
Zealand Environment Conservation Council (ANZECC). The ACRIS project that this
paper is reporting on has improved the utility of the DSI methodology for monitoring
changes in rangeland condition in relation to wind erosion.
ACRIS is a partnership between government organisations responsible for rangeland
management, and is a coordinating mechanism for collating and synthesising
rangeland information. ACRIS is reporting change for a number of biophysical and
socio-economic themes related to managing the rangeland’s natural resources (e.g.
landscape function, biodiversity, fire regimes, water, components of total grazing
pressure, land values). Wind erosion and dust are being used to support reporting of
change in landscape function and sustainable management. Reporting is for the
period 1992 to 2005 as; that encompasses a range of seasonal conditions, is relevant
to most current rangeland managers and spans the period of generally available
ground-based monitoring data. Reporting is mainly to government (national and
state/NT) based on available data, rather than a literature review or ‘expert
assessment’.
The aims of this report are to:
1.
Describe and map changes in the amount of wind erosion between 1992 and
2005 for the Australian rangelands based upon the Dust Storm Index (DSI),
and DSI statistics for rangeland bio regions (v6.1).
2.
Provide temporal trends in DSI for the period 1960-2005.
3.
Make estimates of land management influences upon wind erosion using a
rainfall-adjusted DSI.
METHODS
The Dust Storm Index
The Dust Storm Index (DSI) provides a measure of the frequency and intensity of
wind erosion activity at continental scale from observations made at Bureau of
Meteorology (BoM) stations. The DSI provides a composite measure of the
contributions of: local dust events, moderate dust storms and severe dust storms using
5
weightings for each event type, based upon dust concentrations inferred from
visibility reduction during each of these event types.
The DSI is calculated using the following equation,
n
DSI   5  SD   MD  0.05  LDE  i
i 1
Where:
DSI = Dust Storm Index at n stations where i is the ith value of n stations for i=1 to n.
The number of stations (n) is the total number of stations recording a dust event
observation in the time period.
SD = Severe dust storm (BoM daily maximum weather codes: 33, 34, 35)
MD = Moderate dust storm (daily maximum weather codes: 09, 30, 31, 32 and 98)
LDE = Local dust event (daily maximum weather codes: 07 and 08)
The DSI was developed by McTainsh (1998) using uncorrected BoM weather code
data, but experience has revealed weaknesses in these data as a result of: changes to
the BoM definitions of dust event codes, inconsistent observer adherence to the codes
and other issues. DSI2 was developed in response which, among other things, uses
independently collected visibility data to define event types. DSI3 is a further
development, in which local dust events (weather codes 07 and 08) are re-assigned,
based on the visibility criteria, into severe dust storms, moderate dust storms or local
dust events. These and other technical developments are briefly discussed below, and
are examined in more detail by Tews et al., (2007).
Limitations of the DSI data.
Meteorological records are a valuable data resource for wind erosion monitoring,
however the data have a number of limitations which limit their utility. The low
spatial density of meteorological stations (Fig. 1) is a limitation upon accurate
mapping, particularly in the centre and west of the continent, and should be
considered when interpreting the DSI maps. The Natural Neighbour geometric
interpolation method (in the Vertical Mapper - v2.5 component of MapInfo - v7.5)
was used to produce the DSI maps. The low spatial density of meteorological stations
is also an issue for ”cookie cutting” data from these maps to provide DSI statistics for
bio regions. For example, in some bio regions there are no meteorological stations,
therefore the DSI statistics are based entirely upon interpolated data.
6
Figure 1: Meteorological stations used for long term wind erosion monitoring.
The establishment of DustWatch; a network of volunteer wind erosion observers in
2002 (Leys et al, 2007), was an attempt to increase the spatial density of observation
stations and development of this network is continuing. DSI maps cannot be viewed
as entrainment-only maps because the DSI value at a location may measure dust
entrainment and dust transport. This is because when dust storms traverse large tracts
of the continent, as for the 23 October, 2002 event (McTainsh et al 2005), they entrain
soil material from one area and transport the dust through downwind regions that
may, or may not, also be entraining dust.
The frequency with which BoM stations make observations ranges from 2 to 8
observations per day and this affects the spatial pattern of erosion. This effect is
confounded by changes in observation frequency at stations through time. While this
effect cannot be corrected, by producing observation frequency maps to accompany
DSI maps, allows the reader to better interpret the influence of observation frequency
on DSI. The indicator measure of observation frequency used here is visibility,
because it is recorded every day by stations contributing to the full surface
meteorological record. The observation frequency at BoM stations is presented as:
low (1-2 observations per day), medium (3-5 observations per day) and high (6-8
observations per day).
Figure 2 shows the spatial pattern of observation frequency averaged for the ACRIS
reporting period (1992 – 2005). The observation frequency pattern is patchy and is
generally better in the eastern sector of the continent.
7
Figure 2: Observation Frequency map 1992 to 2005 (number of observations per day). Polygons
show rangeland bio regions.
Of particular concern is that observation frequencies are generally decreasing through
time over the 1992-2005 reporting period. Figure 3 shows this trend over four
selected years, and the full suite of maps for 1992 – 2005 is shown alongside each
DSI map in Appendix I. This decline in observation frequency possibly reflects
developments within the BoM towards: (i) Converting BoM stations (particularly at
airports) from manual to automatic operation, (ii) Reduction in the number of BoM
stations keeping surface meteorological records and (iii) A reduction in the overall
number of BoM stations. This deterioration of the BoM record is a concern,
particularly at a time when the need for reliable environmental monitoring data is
increasing.
8
1992
1997
2000
2005
Figure 3: Changes in spatial patterns of observation frequency during four representative years
from 1992 to 2005.
RESULTS
DSI maps and statistics for rangeland bio regions (1992-2005).
Bio regions have become the accepted reporting region for a range of environmental
monitoring within the rangelands (Bastin et al. 2008), therefore it is appropriate to
provide DSI statistics for each bio region. Figure 4 is a map of average DSI for the
1992-2005 period with an overlay of the 51 numbered bio regions within rangelands
and the BoM stations. This map shows that the Simpson-Strzelecki Dunefields and
Channel Country bio regions (19 and 21) are the most actively eroding regions and
this activity extends into bioregions in western NSW, the NT and to a lesser extent
into SA. WA is in general less active.
9
Figure 4: Map of mean DSI for the period 1992 to 2005 showing bioregions within the rangelands
and BoM recording stations.
These bio region differences in erosion activity can be quantified by “cookie cutting”
the data within each bio region. Table 1 summarises the DSI statistics for the 51 bio
regions, and Appendix II provides more detailed DSI statistical data for each bio
region with accompanying comments to assist the interpretation of these DSI
statistics.
Although this approach provides useful regional estimates of wind erosion activity,
the sparse distribution of BoM stations throughout most of the rangelands, and
particularly in the ‘western deserts’ means that the data extracted from each bioregion
is strongly influenced by the model used to spatially interpolate between BoM
stations. Another factor affecting the DSI statistical values, and related to the
sparseness of recording stations, is that dust may have crossed a bio region boundary
before being observed. That is, the observation statistic for a bio region may not
exactly equate with the actual entrainment value.
10
Region
No.
1
8
17
18
19
21
22
24
25
28
29
30
31
32
33
34
36
38
39
40
41
44
45
46
IBRA name
Murray Darling Depression
Riverina
Darling Riverine Plains
Mulga Lands
Simpson Strzelecki
Dunefields
Channel Country
Brigalow Belt North
Cobar Peneplain
Broken Hill Complex
Central Ranges
Finke
Stony Plains
Gawler
Great Victoria Desert
Nullarbor
Hampton
Flinders Lofty Block
Mount Isa Inlier
Gulf Plains
Cape York Peninsula
Mitchell Grass Downs
Einasleigh Uplands
Desert Uplands
Gulf Fall and Uplands
47
48
MacDonnell Ranges
Burt Plain
Mean
DSI
3.03
4.13
1.40
3.15
Region
No.
49
50
51
52
8.25
8.44
0.53
1.64
2.50
1.00
2.91
4.49
1.75
1.98
1.64
1.00
1.42
1.56
0.76
1.28
1.69
0.47
0.86
0.74
53
54
55
56
58
59
60
63
65
66
68
71
72
73
75
76
77
79
81
82
2.90
1.86
84
IBRA name
Tanami
Sturt Plateau
Ord Victoria Plain
Victoria Bonaparte
Gascoyne
Carnarvon
Central Kimberley
Coolgardie
Dampierland
Gibson Desert
Great Sandy Desert
Little Sandy Desert
Murchison
Northern Kimberley
Pilbara
Yalgoo
Gulf Coastal
Daly Basin
Pine Creek
Brigalow Belt South
Central Arnhem
Darwin Coastal
Arnhem Coast
Arnhem Plateau
Davenport Murchison
Ranges
Mean
DSI
2.26
0.47
0.99
1.46
1.06
1.47
1.39
1.78
0.79
1.40
1.63
2.50
1.43
0.82
1.25
1.08
0.51
0.53
0.75
0.90
0.57
0.82
0.48
0.51
1.43
Table 1: Summary of DSI statistics for each bio region. DSI data are the spatial average for each
bio region time-averaged over the period 1992 to 2005.
Temporal trends in DSI for the period 1960-2005.
One of the major advantages of using meteorological data to measure wind erosion
activity is the relatively long temporal record available. The 46-year DSI record for
the continent shown in figure 5 evidences the episodic nature of wind erosion, which
is strongly driven by drought.
To examine this relationship in more detail, this record is divided into five time
periods: (i) 1960-1965, (ii) 1966-1970 (two periods of active erosion associated with
widespread drought), (iii) 1971-1980, (a period of low erosion activity associated with
above average rainfall), (iv) 1981-2000 (a period of variable erosion activity with
minor peaks associated with drought), and (v) 2001-2005 (a period of active erosion
associated with drought). DSI maps are produced for each of these periods to
examine how spatial patterns of wind erosion have changed.
11
450
400
350
300
DSI
250
200
150
100
50
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
0
Year
Figure 5: Annual total DSI at 109 locations Australia-wide (1960-2005).
Spatio-temporal trends of DSI (1960-2005).
Figures 6 to 10 are DSI maps for the five periods of wind erosion activity identified
from Figure 5. These show striking changes in the intensity and spatial patterns of
wind erosion activity. During 1960-1965 (Fig. 6) wind erosion was very active in the
Alice Springs region extending east across the Simpson Desert into W Queensland
and NSW, with isolated hotspots in the northern part of the NT and the mid coast of
WA.
Figure 6: Mean DSI 1960 to 1965.
12
In 1966-1970 (Fig. 7) overall erosion activity was significantly reduced and the area
affected retreated from semi-arid to arid regions and moved more into the eastern
sector of the continent.
Figure 7: Mean DSI 1966 to 1970.
The 1971-1980 period (Fig. 8) of reduced erosion activity appears to have affected the
east of the continent and SA more than the west, with some increased activity in the
Tanami Desert region of the NT.
Figure 8: Mean DSI 1971 to 1980
13
In 1981-2000 (Fig. 9) erosion was more widespread (similar in extent to the 19601965 period) but generally at low levels.
Figure 9: Mean DSI 1981 to 2000
The general pattern of 1981-2000 DSI continued into the 2001-2005 period (Fig. 10),
but with an increase in erosion activity in the east.
Figure 10: Mean DSI 2001 to 2005
14
Rainfall-adjusted DSI: an estimate of land use influences upon wind
erosion
The DSI record reflects the composite effect of the “natural” drivers of wind erosion:
climate (especially rainfall and wind conditions) and soil erodibility, plus land use
effects. Climate is by far the most important of the natural drivers; with rainfall
controlling erosion rates through its close relationship with vegetation cover. The
strong negative relationship between rainfall and dust storm frequency (McTainsh et
al., 1989) reproduced here in terms of annual DSI (Figure 11) is in effect a reflection
of vegetation controls upon wind erosion.
8
7
Mean Dust Storm Index
6
5
4
3
2
1
0
-1
0
200
400
600
800
1000
1200
1400
1600
1800
2000
2200
Annual Median Rainfall (mm)
Figure 11: Relationship between rainfall and wind erosion activity.
A major rangeland monitoring challenge is to discriminate the impacts of changing
rural land management from the strong rainfall driver of wind erosion. A new
measure is used called rainfall-adjusted DSI; in which wind erosion rates (ie DSI) are
normalised for rainfall by expressing erosion as DSI per 10mm of rainfall. Using this
rainfall-adjusted DSI as the measure of erosion activity provides an opportunity to
measure how factors other than rainfall are influencing erosion rates. Spatial and
temporal changes in rainfall-adjusted DSI will therefore reflect the other natural
drivers of wind erosion; wind conditions and soil erodibility, plus land management.
To provide an example of the utility of this approach the DSI and the rainfall-adjusted
DSI has been calculated for three sectors of the continent: the west (Western
Australian rangelands), the centre (Northern Territory and South Australian
15
rangelands) and the east (Queensland and New South Wales rangelands) during the
five time periods from 1960 to 2005. Between 5 and 10 stations were selected from
within each sector. In the west the stations are: Halls Creek, Broome, Port Hedland,
Carnarvon, Meekatharra, Kalgoorlie-Boulder and Giles. In the centre the stations are:
Tennant Creek, Alice Springs, Woomera, Marree and Ceduna, and in the east are:
Mount Isa, Longreach, Boulia, Birdsville, Charleville, Thargomindah, Tibooburra,
Broken Hill, Cobar and Mildura. Table 2 shows that the DSI, averaged for 19602005, is clearly highest in the centre followed by the east, then the west. This result
may not be surprising because the central Australia rangelands are driest. A closer
look at changes in DSI during the five time periods shows that in 1960-1965 the
centre was much higher than the west and east, whereas in the other time periods
although the centre remains highest, the difference is much less.
Period
1960-1965
1966-1970
1971-1980
1981-2000
2001-2005
1960-2005
West
9.0
8.4
2.9
1.4
1.3
4.6
Sector
Centre
22.2
9.6
7.1
3.6
4.2
9.3
East
13.0
8.1
3.1
4.0
6.6
7.0
Table 2: Mean DSI at locations within three continental sectors of Australian rangelands during
five time periods from 1960 to 2005.
The question remains unanswered from Table 2 as to whether these trends reflect
rainfall; the major natural driver of wind erosion, or land management. Table 3 shows
rainfall-adjusted DSI data for 1960-2005 in the three sectors. The mean rainfalladjusted DSI is the mean DSI of each rangeland location, divided by 0.1 of the mean
rainfall (in mm). This is expressed as the mean DSI per 10mm of mean rainfall.
Period
1960-1965
1966-1970
1971-1980
1981-2000
2001-2005
1960-2005
West
0.30
0.34
0.11
0.04
0.05
0.17
Sector
Centre
1.54
0.41
0.12
0.15
0.25
0.51
East
0.76
0.36
0.09
0.15
0.43
0.36
Table 3: Mean rainfall-adjusted DSI at locations within three continental sectors of Australian
rangelands during five time periods from 1960 to 2005.
The rainfall-adjusted DSI averaged for 1960-2005 remains highest in the centre
followed by the east, then the west. This result indicates that factors other than
16
rainfall are responsible for the very high erosion activity in the centre. It is possible
that the erodibility of soils is higher in the centre, or that wind conditions were more
erosive there. There is some support for erodibility being a factor, from Keetch
(1985) who indicated that the alluvial sediments of the Todd River, to the south of
Alice Springs, are highly erodible and are a major source of dust. These factors are
worthy of independent examination in future work, but based upon local knowledge
the most likely factor driving this high erosion rate in the centre was overstocking
during the severe drought of 1958-65, which left the soils with limited protective
vegetation cover (Condon et al., 1969, Chisholm, 1983 and McKeon et al. 2004). The
area to the south of the Alice Springs airport was particularly degraded.
McTainsh et al., (1989) compared dust storm occurrence during the 1960s in Alice
Springs and Kalgoorlie and found that although the rainfall at Alice Springs (255mm)
is slightly lower than at Kalgoorlie (262mm) and the number of rain days slightly
lower (40 and 65 raindays respectively), Alice Springs had twice the number of dust
storms as Kalgoorlie during the 1960s.
Table 3 also shows that in the 1965 – 2005 period, the rainfall-adjusted DSI values for
the three sectors were much more similar. An interpretation of this trend is that land
management in the immediate vicinity of Alice Springs during this period has
significantly improved (the area was proclaimed a Dust Control Area and effectively
destocked). There is evidence to support this conclusion. In an attempt to control the
wind erosion problem in the region to the south of the Alice Springs airport, buffel
grass was planted by the Land Conservation Unit of the Conservation Commission of
the Northern Territory (Keetch 1981). This rangeland rehabilitation project was the
first broadscale attempt in Australia to rehabilitate rangeland soils following wind
erosion.
The relative differences in rainfall-adjusted DSI values of the 3 sectors in Table 2
remained reasonably stable from 1965 to 2000, then in 2001-2005 the rainfalladjusted DSI values for the eastern rangelands were increased. This may reflect wind
erosivity as during this period there were some intense frontal systems that caused
widespread wind erosion in eastern Australia, especially during the 2002 drought year
(McTainsh et al., 2005). Further analysis of wind data is needed to verify this
hypothesis.
The rainfall adjusted DSI helps filter rainfall effects upon wind erosion and allows
identification of the accelerating effects of land management. In the Alice Springs
example, the rainfall adjusted DSI did quantify human impacts upon wind erosion
during the early 1960s and with good local information, these trends could be
validated.
The DustWatch program extends this concept of “citizen science” by formalising
access and storage of information from local people via the DustWatch website
(DustWatch.edu.au) and the associated DustWatch databases. This project will
improve capacity to link formal wind erosion measurements with local knowledge
across Australia.
17
The institutional context of wind erosion monitoring and reporting
There have been a number of recent positive institutional developments which may
herald a new more integrated approach to wind erosion monitoring and reporting, but
there are also new challenges ahead. The outcomes of the present project demonstrate
that ACRIS can provide an effective institutional framework for ongoing semi-regular
updates of wind erosion within rangelands. The DustWatch program which is
supported by the National Landcare Program in 2007-2008, provides a means for the
engagement of local people in wind erosion reporting, which in turn adds valuable
wind erosion monitoring data to the formal BoM data-derived Dust Event database.
The National Land and Water Resources Audit (NLWRA), through its National
Committee on Soil and Terrain (NCST), has also recently made significant progress
in testing and formalising wind erosion monitoring methodologies. The DSI has
recently been successfully tested as the formal measure of broadscale wind erosion
(McTainsh et al., 2007) and linked to a physically-based wind erosion model (Butler
et al., 2007). Significantly, NLWRA has also accepted the need for on-going
improvement in these methodologies.
A significant challenge to broadscale wind erosion monitoring and reporting has
arisen from the move towards automation of meteorological recording at major BoM
stations and a general decrease in the number of BoM stations, discussed earlier. This
change will reduce data quality for calculating the DSI because it relies upon a
meteorological observer identifying when a dust entrainment event is occurring. This
development was anticipated by McTainsh et al., (2007) and a development program
planned under the institutional umbrella of NLWRA. The planned development
involves a move from DSI to dust concentration as the formal measure of wind
erosion activity. The plan is to use BoM derived visibility observations and
measurements to calculate dust concentrations based upon an empirical relationships
from selected BoM stations where measured (and sampled) dust concentrations are
available. This methodological improvement will require a significant research effort,
however archival data are available from a long term dust monitoring program (by
McTainsh and Leys) at Buronga- Mildura and other short term data from Charleville,
Thargomindah and Birdsville in Queensland. This research effort is expected to be
funded by a Natural Heritage Trust (NHT) grant in 2007-2008. There is also potential
to compensate for the overall decline in the number of BoM stations by expanding the
DustWatch program to include former BoM stations and other BoM rainfall recording
sites. This development would be best implemented with the cooperation of the BoM.
ACKNOWLEDGEMENTS
The DustWatch concept was first tested within the NSW Department of the
Environment and Climate Change and the DustWatch program was developed with a
grant from the Desert Knowledge CRC. This reporting activity for the ACRIS was
partly supported by Natural Heritage Trust funding administered by the Desert
Knowledge CRC.
18
REFERENCES
Bastin G, and the ACRIS Management Committee, (2008) Rangelands 2007 – Taking
the Pulse. National Land and Water Resources Audit, Canberra.
Butler H.J., Shao Y., Leys J.F, and McTainsh G.H. (2007) “Modelling wind erosion at
national & regional scales” Report to the National Land and Water Resources
Audit (NLWRA) - National Monitoring and Evaluation Framework.
Chisholm, D.A. (1983) “Rural European Man as a Resource Manager” In Man in the
Centre. Proc. Symp. CSIRO, Alice Springs, 3-5 April 1979. pp. 189-198.
Condon et al. (1969) Soil erosion and pasture degeneration in central Australia. 1-4.
J. Soil Conserv. Serv. NSW vol. 25 part 1 47-92, part 2 161-182, part 3 225-250,
part 4 295-321.
Keetch, R.I. (1981) “Rangeland rehabilitation in central Australia”. Land
Conservation Commission of the Northern Territory, Alice Springs, 29pp.
Keetch, R.I. (1985) “Wind erosion on the Northern Territory rangelands”. In, Carter,
D.J (Ed) “Wind erosion research techniques workshop, Proceedings, 98-101.
Leys, J.F., and McTainsh, G.H, Strong, C.L., Heidenreich, S. and Biseaga, K.
(2007)DustWatch: community networks to improve wind erosion monitoring in
Australia”. EARTH SURFACE PROCESSES AND LANDFORMS (in press).
McKeon, G.M., Hall, W.B., Henry, B.K., Stone, G.S. and Watson, I.W. (2004)
Pasture Degradation and Recovery in Australia’s Rangelands: Learning from
History. Queensland Department of Natural Resources, Mines and Energy.
McTainsh, G.H. (1998). Dust Storm Index. In, Sustainable Agriculture: Assessing
Australia’s Recent Performance, Report of the National Collaborative Programme
on Indicators for Sustainable Agriculture, 56-62.
McTainsh, G. H., Burgess, R. and Pitblado, J. R. (1989). “Aridity, drought and dust
storms in Australia (1960-1984)”. JOURNAL OF ARID ENVIRONMENTS, 16,
11-22.
McTainsh, G.H., Chan. Y.C., McGowan, H.A., Leys, J.F. and Tews, E.K.(2005) The
23rd October, 2002 dust storm in eastern Australia: characteristics and
meteorological conditions. Atmospheric Environment 39, 1227-1236.
McTainsh G.H. and Tews E.K. (2007) “Soil erosion by wind: Dust Storm Index
(DSI). Report to the National Land and Water Resources Audit, National
Monitoring and Evaluation Framework
Tews, E.K. McTainsh, G.H and Leys, J.F. (2007) “The Dust Storm Index (DSI): a
method for monitoring broadscale wind erosion using meteorological records”.
Environmental Management (in preparation).
19
APPENDIX I: OBSERVATION FREQUENCY MAPS AND DSI
MAPS 1992 TO 2005.
20
21
22
23
APPENDIX II: BIOREGION STATISTICAL DATA AND
INTERPRETIVE COMMENTS.
Region
No.
IBRA name
Mean
DSI
95%
confidence
(+ / -)
Standard
deviation
Mean
observation
frequency /
day
1
Murray Darling Depression
3.03
0.07
1.05
3.4
8
Riverina
4.13
0.15
1.70
3.4
17
Darling Riverine Plains
1.40
0.06
0.92
2.5
18
Mulga Lands
3.15
0.11
2.64
4.1
19
Simpson Strzelecki Dunefields
8.25
0.15
3.80
3.5
21
22
Channel Country
Brigalow Belt North
8.44
0.53
0.18
0.03
4.44
0.28
5.1
3.4
24
Cobar Peneplain
1.64
0.04
0.54
3.8
25
28
29
30
31
32
33
34
Broken Hill Complex
Central Ranges
Finke
Stony Plains
Gawler
Great Victoria Desert
Nullarbor
Hampton
2.50
1.00
2.91
4.49
1.75
1.98
1.64
1.00
0.12
0.05
0.12
0.14
0.07
0.04
0.06
0.04
1.36
0.67
1.48
2.39
1.22
1.18
1.19
0.20
2.2
4.6
4.7
3.5
3.9
4.1
4.1
3.6
36
Flinders Lofty Block
1.42
0.11
1.28
2.6
38
39
40
Mount Isa Inlier
Gulf Plains
Cape York Peninsula
1.56
0.76
1.28
0.07
0.02
0.07
0.89
0.35
1.11
4.9
4.0
3.2
41
44
45
46
47
Mitchell Grass Downs
Einasleigh Uplands
Desert Uplands
Gulf Fall and Uplands
MacDonnell Ranges
1.69
0.47
0.86
0.74
2.90
0.05
0.02
0.02
0.03
0.14
1.30
0.37
0.29
0.49
1.31
4.8
4.3
3.3
4.0
4.6
48
Burt Plain
1.86
0.05
0.68
4.5
49
50
Tanami
Sturt Plateau
2.26
0.47
0.04
0.01
0.89
0.20
4.5
3.8
51
52
53
54
Ord Victoria Plain
Victoria Bonaparte
Gascoyne
Carnarvon
0.99
1.46
1.06
1.47
0.04
0.06
0.04
0.03
0.57
0.73
0.73
0.36
3.5
3.5
2.8
4.4
55
Central Kimberley
1.39
0.03
0.39
2.9
56
58
Coolgardie
Dampierland
1.78
0.79
0.06
0.02
1.00
0.26
4.5
2.6
59
60
63
Gibson Desert
Great Sandy Desert
Little Sandy Desert
1.40
1.63
2.50
0.03
0.03
0.04
0.52
0.86
0.60
4.4
2.8
2.9
Interpretive comments
Probable dust from the
north west
Probable dust from the
western sector
Probable dust from the
western sector
Probable dust from the
western sector
Probable dust from the
western sector
Probable dust from the
western sector
Probable dust from the
western sector
Probable dust from the
western sector
Probable dust from the
western sector
Probable dust from the
south
Possible dust from the
south in the western part of
the region
Possible dust from the
south and east
Possible dust from the
south and east
Possible dust from the
south and east
Possible dust from the
south and east
Probable dust from
western sector
Possible dust from the
north or west in the
western part of the region
24
Region
No.
65
66
68
71
72
73
75
76
77
79
81
82
84
Murchison
Northern Kimberley
Pilbara
Yalgoo
Gulf Coastal
Daly Basin
Pine Creek
Brigalow Belt South
Central Arnhem
Darwin Coastal
Arnhem Coast
Arnhem Plateau
Mean
DSI
1.43
0.82
1.25
1.08
0.51
0.53
0.75
0.90
0.57
0.82
0.48
0.51
95%
confidence
(+ / -)
0.03
0.04
0.03
0.03
0.02
0.05
0.03
0.03
0.03
0.13
0.04
0.04
Standard
deviation
0.68
0.50
0.53
0.27
0.14
0.35
0.24
0.33
0.22
0.97
0.34
0.28
Mean
observation
frequency /
day
3.4
2.5
3.6
3.2
3.7
3.8
3.4
2.5
3.5
4.0
3.7
3.0
Davenport Murchison Ranges
1.43
0.03
0.35
4.7
IBRA name
Interpretive comments
Possible dust from the
south and east
25