A Report for the Australian Collaborative Rangeland Information System ACRIS

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).

7 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.

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 Mean Region Mean No. IBRA name DSI No. IBRA name DSI 1 Murray Darling Depression 3.03 49 Tanami 2.26 8 Riverina 4.13 50 Sturt Plateau 0.47 17 Darling Riverine Plains 1.40 51 Ord Victoria Plain 0.99 18 Mulga Lands 3.15 52 Victoria Bonaparte 1.46 Simpson Strzelecki 19 Dunefields 8.25 53 Gascoyne 1.06 21 Channel Country 8.44 54 Carnarvon 1.47 22 Brigalow Belt North 0.53 55 Central Kimberley 1.39 24 Cobar Peneplain 1.64 56 Coolgardie 1.78 25 Broken Hill Complex 2.50 58 Dampierland 0.79 28 Central Ranges 1.00 59 Gibson Desert 1.40 29 Finke 2.91 60 Great Sandy Desert 1.63 30 Stony Plains 4.49 63 Little Sandy Desert 2.50 31 Gawler 1.75 65 Murchison 1.43 32 Great Victoria Desert 1.98 66 Northern Kimberley 0.82 33 Nullarbor 1.64 68 Pilbara 1.25 34 Hampton 1.00 71 Yalgoo 1.08 36 Flinders Lofty Block 1.42 72 Gulf Coastal 0.51 38 Mount Isa Inlier 1.56 73 Daly Basin 0.53 39 Gulf Plains 0.76 75 Pine Creek 0.75 40 Cape York Peninsula 1.28 76 Brigalow Belt South 0.90 41 Mitchell Grass Downs 1.69 77 Central Arnhem 0.57 44 Einasleigh Uplands 0.47 79 Darwin Coastal 0.82 45 Desert Uplands 0.86 81 Arnhem Coast 0.48 46 Gulf Fall and Uplands 0.74 82 Arnhem Plateau 0.51 Davenport Murchison 47 MacDonnell Ranges 2.90 84 Ranges 1.43 48 Burt Plain 1.86

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

250 I S D 200

0 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 6 6 6 6 7 7 7 7 7 8 8 8 8 8 8 9 9 9 9 9 0 0 0 6 6 6 6 6 7 7 7 7 7 8 8 8 8 9 9 9 9 9 0 0 0 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 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 1960- 1965 period) but generally at low levels.

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.

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.

x 5 e d n I

r 4 o t S

n a e 2 M

-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 1960- 2005, 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.

Sector Period West Centre East 1960-1965 9.0 22.2 13.0 1966-1970 8.4 9.6 8.1 1971-1980 2.9 7.1 3.1 1981-2000 1.4 3.6 4.0 2001-2005 1.3 4.2 6.6 1960-2005 4.6 9.3 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 rainfall- adjusted 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.

Sector Period West Centre East 1960-1965 0.30 1.54 0.76 1966-1970 0.34 0.41 0.36 1971-1980 0.11 0.12 0.09 1981-2000 0.04 0.15 0.15 2001-2005 0.05 0.25 0.43 1960-2005 0.17 0.51 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 rainfall are responsible for the very high erosion activity in the centre. It is possible

16 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 rainfall- adjusted 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.

Mean 95% observation Regio Mean confidenc Standard frequency / n No. IBRA name DSI e (+ / -) deviation day Interpretive comments Probable dust from the 1 Murray Darling Depression 3.03 0.07 1.05 3.4 north west Probable dust from the 8 Riverina 4.13 0.15 1.70 3.4 western sector Probable dust from the 17 Darling Riverine Plains 1.40 0.06 0.92 2.5 western sector Probable dust from the 18 Mulga Lands 3.15 0.11 2.64 4.1 western sector Probable dust from the 19 Simpson Strzelecki Dunefields 8.25 0.15 3.80 3.5 western sector Probable dust from the 21 Channel Country 8.44 0.18 4.44 5.1 western sector 22 Brigalow Belt North 0.53 0.03 0.28 3.4 Probable dust from the 24 Cobar Peneplain 1.64 0.04 0.54 3.8 western sector Probable dust from the 25 Broken Hill Complex 2.50 0.12 1.36 2.2 western sector 28 Central Ranges 1.00 0.05 0.67 4.6 29 Finke 2.91 0.12 1.48 4.7 30 Stony Plains 4.49 0.14 2.39 3.5 31 Gawler 1.75 0.07 1.22 3.9 32 Great Victoria Desert 1.98 0.04 1.18 4.1 33 Nullarbor 1.64 0.06 1.19 4.1 34 Hampton 1.00 0.04 0.20 3.6 Probable dust from the 36 Flinders Lofty Block 1.42 0.11 1.28 2.6 western sector Probable dust from the 38 Mount Isa Inlier 1.56 0.07 0.89 4.9 south 39 Gulf Plains 0.76 0.02 0.35 4.0 40 Cape York Peninsula 1.28 0.07 1.11 3.2 Possible dust from the south in the western part of 41 Mitchell Grass Downs 1.69 0.05 1.30 4.8 the region 44 Einasleigh Uplands 0.47 0.02 0.37 4.3 45 Desert Uplands 0.86 0.02 0.29 3.3 46 Gulf Fall and Uplands 0.74 0.03 0.49 4.0 47 MacDonnell Ranges 2.90 0.14 1.31 4.6 Possible dust from the 48 Burt Plain 1.86 0.05 0.68 4.5 south and east Possible dust from the 49 Tanami 2.26 0.04 0.89 4.5 south and east 50 Sturt Plateau 0.47 0.01 0.20 3.8 Possible dust from the 51 Ord Victoria Plain 0.99 0.04 0.57 3.5 south and east 52 Victoria Bonaparte 1.46 0.06 0.73 3.5 53 Gascoyne 1.06 0.04 0.73 2.8 54 Carnarvon 1.47 0.03 0.36 4.4 Possible dust from the 55 Central Kimberley 1.39 0.03 0.39 2.9 south and east Probable dust from western 56 Coolgardie 1.78 0.06 1.00 4.5 sector 58 Dampierland 0.79 0.02 0.26 2.6 Possible dust from the north or west in the 59 Gibson Desert 1.40 0.03 0.52 4.4 western part of the region 60 Great Sandy Desert 1.63 0.03 0.86 2.8 63 Little Sandy Desert 2.50 0.04 0.60 2.9 65 Murchison 1.43 0.03 0.68 3.4

24 Mean 95% observation Regio Mean confidenc Standard frequency / n No. IBRA name DSI e (+ / -) deviation day Interpretive comments 66 Northern Kimberley 0.82 0.04 0.50 2.5 68 Pilbara 1.25 0.03 0.53 3.6 71 Yalgoo 1.08 0.03 0.27 3.2 72 Gulf Coastal 0.51 0.02 0.14 3.7 73 Daly Basin 0.53 0.05 0.35 3.8 75 Pine Creek 0.75 0.03 0.24 3.4 76 Brigalow Belt South 0.90 0.03 0.33 2.5 77 Central Arnhem 0.57 0.03 0.22 3.5 79 Darwin Coastal 0.82 0.13 0.97 4.0 81 Arnhem Coast 0.48 0.04 0.34 3.7 82 Arnhem Plateau 0.51 0.04 0.28 3.0 Possible dust from the 84 Davenport Murchison Ranges 1.43 0.03 0.35 4.7 south and east