Australian dust storms have principally been studied using ground observations that are limited in being spatially sparse, and low Earth orbit satellites in turn limited by their relatively poor temporal resolution. In this study, the high temporal resolution of geostationary satellite monitoring was harnessed to assess the ability of Himawari-8 Advanced Himawari Imager (AHI) at detecting and mapping five case study Australian dust storm events between 2019 and 2020. AHI thermal bands (9μm,10μm and 11μm) were used to create Brightness Temperature Difference (BTD) indices which were then applied to a Threshold-based Dust Detection Algorithm (TBDDA). Event-specific index thresholds were tuned for each event at the time of the highest dust concentration as recorded by a ground observation network. Selection of event-specific thresholds showed considerable variation and subsequent evaluation of dust storm detection revealed a low average Probability of Detection (POD) of 0.01 and a False Alarm Ratio (FAR) of 0.3 for the five events. The variability within the thresholds for each index across the five dust storms emphasises the lack of a singly suitable threshold set for event detection. It highlights inefficiency in the TBDDA when observing individual Australian dust events. A method that does not rely on event-specific thresholds is seen as the next step in advancing dust storm detection in Australia.

Dust aerosols, recognized as hazardous atmospheric pollutants, significantly impact air quality, human health, and ecosystems. Understanding the dust's spatiotemporal dynamics, sources, and transport paths is crucial, especially in China, as it is home to major dust sources in East Asia and frequently experiences severe dust events. Substantial dust aerosols are produced during dust events, leading to sharp increases in particulate matter concentrations and resulting in health and environmental issues in both the dust source regions and downwind populated areas. Building on this context, this study analyzes the spatiotemporal distribution of dust aerosols in China and its six subregions between 2000 and 2023 using MERRA-2 reanalysis data. Unlike previous studies that focus on climatic drivers and anthropogenic factors influencing dust aerosol trends, this study provides new insights into dust aerosol trends in large provincial cities (population 2-20 million) within the dust-prone "Three-North" region, from the perspective of dust emissions, fluxes, and transport processes. Furthermore, the combination of MERRA-2 simulations and backward (forward) trajectory analyses offers complementary strengths. The results show that (1) spring Dust Aerosol Optical Depth (DAOD) decreased 2000-2023, with the exception of the Taklamakan Desert, where both dust emissions and DAOD increased; (2) increased dust emissions from the Saryesik-Atyrau Basin in eastern Kazakhstan resulted in higher DAOD over the southern Jungger Basin; (3) Taklamakan Desert emissions remained high across all seasons, sustaining elevated DAOD within the Tarim Basin; (4) MERRA-2 fails to capture anthropogenic dust emissions from cropland of the North China Plain, while the back-trajectory analysis based on measurements of particulate matter less than 10 μg/m3 in several cities across the North China and Northeast China suggests dust emissions from this region. This finding highlights the need to improve dust emission parameterizations in reanalysis models like MERRA-2, particularly when studying the impact of anthropogenic activities on dust emissions and air quality. Overall, the study provides essential references for managing the health and environmental impacts of dust aerosols in large densely populated cities of China.

Dust storms and wildfires occur frequently in south-eastern Australia. Their effects on the ecology, environment and population exposure have been the focus of many studies recently. Dust storms do not emit ground-sequestered carbon, but wildfires emit significant quantities of carbon into the atmosphere. However, both natural events promote phytoplankton growth in water bodies because carbon, and other trace elements such as iron, deposit on the surface water of oceans. Carbon dioxide is reabsorbed by phytoplankton via photosynthesis. The carbon balance cycle due to dust storms and wildfires is not well known. Recent studies on the carbon emission of the 2019–2020 summer wildfires in eastern Australia indicated that this megafire event emitted approximately 715 million tonnes of CO2 (195 Tg C) into the atmosphere from burned forest areas. This study focusses on the association of dust storms and wildfires in southeastern Australia with phytoplankton growth in the Tasman Sea due to the February 2019 dust storm event and the 2019–2020 Black Summer wildfires. Central Australia and western New South Wales were the sources of the dust storm emission (11 to 16 February 2019), and the Black Summer wildfires occurred along the coast of New South Wales and Victoria (from early November 2019 to early January 2020). The WRF-Chem model is used for dust storm simulation with the AFWA (Air Force Weather Agency of the US) dust emission version of the GOCART model, and the WRF-Chem model is used for wildfire simulation with FINN (Fire Emission Inventory from NCAR) emission data. The results show the similarities and differences in the deposition of particulate matter, phytoplankton growth and carbon reabsorption patterns in the Tasman Sea from these events. A higher rate of deposition of PM2.5 on the ocean surface corresponds to a higher rate of phytoplankton growth. Using the WRF-Chem model, during the 5-day dust storm event in February 2019, approximately ~1230 tons of total dust was predicted to have been deposited in the Tasman Sea, while ~132,000 tons of PM10 was deposited in the early stage of the wildfires from 1 to 8 November 2019.

Nutrient transported from soils to water bodies not only threatens agricultural productivity and food security but also causes the degradation of water quality and the environment in many parts of the world. However, nutrient transport through soil erosion is often ignored in nutrient cycle studies; there is little understanding of how much nutrient is lost through water and wind erosion. In this study, we attempted to assess soil nutrient transport due to both water and wind erosion and its spatial and temporal variability across New South Wales (NSW), Australia. We estimated the mass fraction (%) of total nitrogen (N), total phosphorus (P) and soil organic carbon (SOC) in the eroded soil, and the total nutrient stock of the soil layer down to 200 cm using water and wind erosion models and digital soil mapping. The estimated average N, P and SOC stocks in NSW topsoils (0-5 cm) are 160, 43, and 2970 kg ha(-1) respectively. There are great variations in the transport of nutrients by erosion in space and time, ranging from near zero in the Western region to 395 kg ha(-1) yr(-1) in the North Coast region. The average total nutrient transport rate is about 2.4 % of the surface soil (0-5 cm) total stock in NSW due to both water and wind erosion. The total cost of nutrient transport is estimated at 4.2 billion Australian dollars for the entire state of NSW and 0.2 billion dollars from the cropping lands per year. The areas with the highest nutrient transport rates are the North Coast and Hunter regions due to the relatively high water erosion and nutrient content of the soils. On average, water erosion contributes up to 98 % of the total nutrient transport across the state as a whole, but wind erosion can contribute up to 12 % of the total transport in Spring (i.e., September). This study is the first attempt to investigate nutrient transport from both water and wind erosion in Australia. The methodology and findings contribute to the knowledge of nutrient transport due to erosion in broad nutrient cycle studies.

The monitoring of land management practices is vital for the protection of soil resource and the environment. Here, we present a new approach to monitor sustainable land management using widely available remotely sensed data, such as MODIS fractional vegetation cover data. The method is based on the concept of maintaining sufficient vegetation cover to prevent hillslope water erosion beyond tolerable soil erosion targets. The targets were based on long-term natural erosion rates plus a small constant and are spatially and temporally variable, not static as in most reported studies to date. Where vegetation cover is more than that required to prevent nontolerable erosion under normal conditions for that calendar month, the site (pixel) is deemed to be managed sustainably. Monthly indices are then combined to form a yearly sustainable land management index (SLMI), presented as raster maps with a spatial resolution of 100 m. We explored this new method through case studies over New South Wales (NSW), Australia, over the period 2010 to 2021, with a particular examination of 2020. Results were further stratified by land uses and natural resource management regions, which revealed useful data and trends. The method is offered as an example of the potential use of readily available vegetation cover data to quantitatively assess and monitor levels of sustainable land management across landscapes. We believe it overcomes the limitations of previous methods to monitor vegetation cover and land management from remote-sensed data alone. Other users are encouraged to adapt the broad approach to meet local requirements.

Establishing mineral dust impacts on Earth's systems requires numerical models of the dust cycle. Differences between dust optical depth (DOD) measurements and modelling the cycle of dust emission, atmospheric transport, and deposition of dust indicate large model uncertainty due partially to unrealistic model assumptions about dust emission frequency. Calibrating dust cycle models to DOD measurements typically in North Africa, are routinely used to reduce dust model magnitude. This calibration forces modelled dust emissions to match atmospheric DOD but may hide the correct magnitude and frequency of dust emission events at source, compensating biases in other modelled processes of the dust cycle. Therefore, it is essential to improve physically based dust emission modules. Here we use a global collation of satellite observations from previous studies of dust emission point source (DPS) dichotomous frequency data. We show that these DPS data have little-to-no relation with MODIS DOD frequency. We calibrate the albedo-based dust emission model using the frequency distribution of those DPS data. The global dust emission uncertainty constrained by DPS data (±3.8 kg m-2 y-1) provides a benchmark for dust emission model development. Our calibrated model results reveal much less global dust emission (29.1 ± 14.9 Tg y-1) than previous estimates, and show seasonally shifting dust emission predominance within and between hemispheres, as opposed to a persistent North African dust emission primacy widely interpreted from DOD measurements. Earth's largest dust emissions, proceed seasonally from East Asian deserts in boreal spring, to Middle Eastern and North African deserts in boreal summer and then Australian shrublands in boreal autumn-winter. This new analysis of dust emissions, from global sources of varying geochemical properties, have far-reaching implications for current and future dust-climate effects. For more reliable coupled representation of dust-climate projections, our findings suggest the need to re-evaluate dust cycle modelling and benefit from the albedo-based parameterisation.

Large‐scale classical dust cycle models, developed more than two decades ago, assume for simplicity that the Earth's land surface is devoid of vegetation, reduce dust emission estimates using a vegetation cover complement, and calibrate estimates to observed atmospheric dust optical depth (DOD). Consequently, these models are expected to be valid for use with dust‐climate projections in Earth System Models. We reveal little spatial relation between DOD frequency and satellite observed dust emission from point sources (DPS) and a difference of up to 2 orders of magnitude. We compared DPS data to an exemplar traditional dust emission model (TEM) and the albedo‐based dust emission model (AEM) which represents aerodynamic roughness over space and time. Both models overestimated dust emission probability but showed strong spatial relations to DPS, suitable for calibration. Relative to the AEM calibrated to the DPS, the TEM overestimated large dust emission over vast vegetated areas and produced considerable false change in dust emission. It is difficult to avoid the conclusion that calibrating dust cycle models to DOD has hidden for more than two decades, these TEM modeling weaknesses. The AEM overcomes these weaknesses without using masks or vegetation cover data. Considerable potential therefore exists for ESMs driven by prognostic albedo, to reveal new insights of aerosol effects on, and responses to, contemporary and environmental change projections.

Since the mid-1970s, approximately 250 000 ha of rangelands in semi-arid far south-western NSW, Australia, have been converted to cropping land use, resulting in substantial wind erosion. Wind erosion is of concern because of its adverse impacts on soils, agricultural production, aviation, energy supply, human health and ecosystem function. Over recent decades, multifaceted extension programs have promoted sustainable land management practices to reduce wind erosion. Repeated biannual paddock surveys were undertaken between 2003 and 2022 to determine (1) the best land management practices (BMPs), that is, those that would reduce erosion while maintaining productivity and profitability, and (2) whether the use of the BMPs changed over time. The BMPs that reduce wind erosion included chemical fallow, standing stubble, and perennial pastures. Between 2003 and 2022, the proportion of sites with BMPs increased from 14% to 75%, and the proportion of sites with wind erosion fell from 23% to 9%. Practices that reduced ground cover and led to frequent erosion have declined, including tilled pasture, tilled stubble and grazed stubble. In drier years, livestock grazing crop stubble exacerbated the erosion hazard. In an attempt to understand where landholders obtain their information for land management practice change, the Western Local Land Services (LLS) conducted a social benchmarking survey in 2020 that indicated that the LLS was the third source of information for practice change after neighbours and other landholders, and stock and station agents. Although the LLS is a source of information, this study could not determine a causal link between LLS programs and practice change. Further research is required to test whether extension programs can reduce the overgrazing of stubble.

This study assessed whether dust-storm frequency during major droughts in New South Wales (NSW), Australia, has changed and what may have caused any change. The frequency of days with dust storms, i.e. when visibility is <1000 m, is presented for the dust storm year (July to June), with the maximum number of dust storms for three major droughts, namely, 2017/20, Millennial and World War II droughts. Community attitudes, government policy and land management practices have changed since the 1940s, and these factors were reviewed to determine whether they explain changes in dust-storm frequency. Two data sources were used: meteorological weather codes from the Australian Bureau of Meteorology and dust particulate matter <10 µm (PM10) from the DustWatch/Rural Air Quality Monitoring Network. The particulate-matter data were converted to dust-storm days (DSD) to create a yearly time series. The meteorological data records were coded as dust storms and required no modification. Results showed that 1944/45 was the dustiest year, with 4.4 times more DSD than in 2019/20 and 9.9 times more DSD than in 2009/10. One reason for the higher DSD in 2019/20 than in 2009/10 was the area protected from wind erosion by vegetation cover above 50%. In 2019/20, 69% of NSW was protected from wind erosion, compared with 79% in 2009/10. We suggest the primary reasons for lower DSD in 2019/20 and 2009/10 than in 1944/45 were community attitudes, government policy and land management practices; these, in combination, help maintain vegetation cover. Since the 1940s, the focus of land management has changed from ‘taming the land’ to ‘sustainably using the land’. Government policy in the 2000s is focused on supporting farming businesses and communities to manage and prepare so as to successfully manage drought. Land management practices that maintain ground cover are now widely practised.

Hillslope erosion, including sheet and rill erosion, is the dominant form of erosion in Australia and many parts of the world. Hillslope erosion improvement (HEI) targets are necessary for the evaluation of natural resource condition and sustainable land use and land management practices. The tolerable soil loss concept has been widely applied to agricultural lands for setting soil erosion targets. It is a static target that is difficult to apply to diverse land uses and soil conditions. In this study, we developed a new approach for setting HEI resource condition targets by considering seasonal erosion levels over a large climate gradient and range of land management practices across New South Wales (NSW), Australia. A 20-year fractional vegetation cover and rainfall erosivity time-series have been used to estimate hillslope erosion rates and assign monthly and annual HEI targets across NSW. To assist with land management action target, monthly vegetation cover levels have been set to meet the HEI targets on a pixel-by-pixel (100 m) basis. We further assessed the HEI targets against land and soil capability classes and their spatial and temporal variation across local land service regions. The findings can help identify the locations and periods of time with hillslope erosion exceeding a desirable threshold level and indicate management actions that might be required to improve the soil condition over areas of concern. The processes are fully automated in a geographic information system (GIS) environment, thus providing a useful tool to determine HEI targets for sustainable soil and land management at any location and period. The methods are globally portable as the input datasets are widely available.

Soil erosion caused by water and wind is a complicated natural process that has been accelerated by human activity. It results in increasing areas of land degradation, which further threaten the productive potential of landscapes. Consistent and continuous erosion monitoring will help identify the location, magnitude, and trends of soil erosion. This information can then be used to evaluate the impact of land management practices and inform programs that aim to improve soil conditions. In this study, we applied the Revised Universal Soil Loss Equation (RUSLE) and the Revised Wind Erosion Equation (RWEQ) to simulate water and wind erosion dynamics. With the emerging earth observation big data, we estimated the monthly and annual water erosion (with a resolution of 90 m) and wind erosion (at 1 km) from 2001 to 2020. We evaluated the performance of three gridded precipitation products (SILO, GPM, and TRMM) for monthly rainfall erosivity estimation using ground-based rainfall. For model validation, water erosion products were compared with existing products and wind erosion results were verified with observations. The datasets we developed are particularly useful for identifying finer-scale erosion dynamics, where more sustainable land management practices should be encouraged.

Dust storms originating from Central Australia and western New South Wales frequently cause high particle concentrations at many sites across New South Wales, both inland and along the coast. This study focussed on a dust storm event in February 2019 which affected air quality across the state as detected at many ambient monitoring stations in the Department of Planning, Industry and Environment (DPIE) air quality monitoring network. The WRF-Chem (Weather Research and Forecast Model—Chemistry) model is used to study the formation, dispersion and transport of dust across the state of New South Wales (NSW, Australia). Wildfires also happened in northern NSW at the same time of the dust storm in February 2019, and their emissions are taken into account in the WRF-Chem model by using Fire Inventory from NCAR (FINN) as emission input. The model performance is evaluated and is shown to predict fairly accurate the PM2.5 and PM10 concentration as compared to observation. The predicted PM2.5 concentration over New South Wales during 5 days from 11 to 15 February 2019 is then used to estimate the impact of the February 2019 dust storm event on three health endpoints, namely mortality, respiratory and cardiac disease hospitalisation rates. The results show that even though as the daily average of PM2.5 over some parts of the state, especially in western and north western NSW near the centre of the dust storm and wild fires, are very high (over 900 µg/m3), the population exposure is low due to the sparse population. Generally, the health impact is similar in order of magnitude to that caused by biomass burning events from wildfires or from hazardous reduction burnings (HRBs) near populous centres such as in Sydney in May 2016. One notable difference is the higher respiratory disease hospitalisation for this dust event (161) compared to the fire event (24).

Abstract. Dust emissions influence global climate while simultaneously reducing the productive potential and resilience of landscapes to climate stressors, together impacting food security and human health. Vegetation is a major control on dust emission because it extracts momentum from the wind and shelters the soil surface, protecting dry and loose material from erosion by winds. Many of the current dust emission models (TEM) assume that the Earth’s land surface is constantly devoid of vegetation, then adjust the dust emission using a vegetation cover reciprocal, and finally calibrate to dust in the atmosphere. We compare this approach with an albedo-based dust emission model (AEM) which calibrates Earth’s land surface shadow to shelter depending on wind speed, to represent aerodynamic roughness spatio-temporal variation. We also compare these dust emission models with estimates of dust in the atmosphere using dust optical depth frequency (DOD). Using existing datasets of satellite observed dust emission from dust point sources (DPS), we show that during the same period, DOD frequency exceeds DPS frequency by up to two orders of magnitude (RMSEDOD = 67 days). Relative to DPS frequency, both models over-estimated dust emission frequency by up to one order of magnitude (RMSETEM = 6 days; RMSEAEM = 4 days) but showed strong relations with DPS frequency suitable for calibrating models to observed dust emission. Theoretically, the TEM is incomplete in its formulation, which despite the pragmatic adjustment using the vegetation cover reciprocal, causes dust emission to be highly dependent on wind speed and over-estimates large (> 0.1 kg m−2 a−1) dust emission over vast vegetated areas. Consequently, the TEM produces considerable falsely positive change in dust emission, relative to the AEM. Since the main difference between the dust emission models is the treatment of aerodynamic roughness we conclude that its crude representation in the TEM has caused large, previously unknown, uncertainty in Earth System Models (ESMs). Our results indicate that tuning dust emission models to dust in the atmosphere has hidden for more than two decades, these TEM modelling weaknesses and its poor performance. The AEM overcomes these weaknesses and improves performance without tuning. In ESMs the AEM can be driven by available prognostic albedo to represent the fidelity of drag partition physics to reduce uncertainty of aerosol effects on, and responses to, contemporary and future environmental change.

The development of fractional vegetation cover products for Australia that resolve cover into photosynthetic vegetation (F-PV), non-photosynthetic vegetation (F-NPV) and bare soil/rock (F-BS) provides a new basis for examination of responses of vegetation cover to long term precipitation cycles, and to explore the interaction between these responses and land cover type, land use and other land surface properties. In this study, the relationship between accumulated antecedent precipitation (AAP) from 1 to 60 months and average monthly F-PV and F-NPV from the MODIS Fractional Cover Product is examined over a 17 year period from 2001 to 2018. The maximum R-2 value, regression coefficients for the maximum R-2, and number of accumulated months to the maximum R-2 were mapped for each month of the year for F-PV and F-NPV. Behaviour of responses in relation to land use, land cover, and soil water holding capacity was analysed based on pixel frequencies of classes at sub-region scale in the Interim Biogeographic Regionalisation of Australia. The analysis showed that F-PV is largely dependent upon AAP in the preceding 12 months, however responses to longer periods of AAP also occur in specific land use-vegetation type combinations. The study also showed that positive responses in F-NPV could be driven by AAP from as much as 60 months, but that F-NPV is reduced in many areas in response to increased AAP. The presence or absence of domesticated livestock grazing as defined by Australian land use mapping was a major influence on response to AAP of both F-PV and F-NPV with statistical analysis indicating interactions between major natural vegetation type and grazing. In highly responsive areas (R-2 > 0.6) monitoring of land condition could be enhanced by testing of regional cover levels for major deviations from the long terms responses that might suggest land management concerns.

There are numerous examples illustrating the integration of Aboriginal knowledge and participation in rangelands management. At the 2019 Australian Rangelands Conference we aimed to explore how Aboriginal culture and its core values have something deeper to contribute to rangelands management. We explore this through a Yungadhu (Malleefowl) cultural depiction and story. The depiction and story explain the often cited, but not well understood, concepts of Kinship, Country, Lore, and Dreaming. The story provides insight into Aboriginal people’s world view and is used in this paper to illustrate how well it aligns with current thinking about resilience in rangelands landscapes and communities. Significantly, we explain how the deep wisdom that resides in Aboriginal cultures has something meaningful to contribute to achieving the conditions for resilience.

Particle size distribution of dust at emission (dust PSD) is an essential quantity to estimate in dust studies. It has been recognized in earlier research that dust PSD is dependent on soil properties (e.g. whether soil is sand or clay) and friction velocity, u∗, which is a surrogate for surface shear stress and a descriptor for saltation-bombardment intensity. This recognition has been challenged in some recent papers, causing a debate on whether dust PSD is “invariant” and the search for its justification. In this paper, we analyse the dust PSD measured in the Japan Australian Dust Experiment and show that dust PSD is dependent on u∗ and on atmospheric boundary-layer (ABL) stability. By simple theoretical and numerical analysis, we explain the two reasons for the latter dependency, which are both related to enhanced saltation bombardment in convective turbulent flows. First, u∗ is stochastic and its probability distribution profoundly influences the magnitude of the mean saltation flux due to the non-linear relationship between saltation flux and u∗. Second, in unstable conditions, turbulence is usually stronger, which leads to higher saltation-bombardment intensity. This study confirms that dust PSD depends on u∗ and, more precisely, on the probability distribution of u∗, which in turn is dependent on ABL stability; consequently, dust PSD is also dependent on ABL. We also show that the dependency of dust PSD on u∗ and ABL stability is made complicated by soil surface conditions. In general, our analysis reinforces the basic conceptual understanding that dust PSD depends on saltation bombardment and inter-particle cohesion.

Wind erosion and blowing dust threaten food security, human health and ecosystem services across global drylands. Monitoring wind erosion is needed to inform management, with explicit monitoring objectives being critical for interpreting and translating monitoring information into management actions. Monitoring objectives should establish quantitative guidelines for determining the relationship of wind erosion indicators to management benchmarks that reflect tolerable erosion and dust production levels considering impacts to, for example, ecosystem processes, species, agricultural production systems and human well-being. Here we: 1) critically review indicators of wind erosion and blowing dust that are currently available to practitioners; and 2) describe approaches for establishing benchmarks to support wind erosion assessments and management. We find that while numerous indicators are available for monitoring wind erosion, only a subset have been used routinely and most monitoring efforts have focused on air quality impacts of dust. Indicators need to be related to the causal soil and vegetation controls in eroding areas to directly inform management. There is great potential to use regional standardized soil and vegetation monitoring datasets, remote sensing and models to provide new information on wind erosion across landscapes. We identify best practices for establishing benchmarks for these indicators based on experimental studies, mechanistic and empirical models, and distributions of indicator values obtained from monitoring data at historic or existing reference sites. The approaches to establishing benchmarks described here have enduring utility as monitoring technologies change and enable managers to evaluate co-benefits and potential trade-offs among ecosystem services as affected by wind erosion management.

Particle size distribution of dust at emission (dust PSD) is an essential quantity to be estimated in dust studies. It 10 has been recognized in earlier research that dust PSD is dependent on soil properties (e.g. whether soil is sand or clay) and 11 friction velocity, u*, a surrogate for surface shear stress and descriptor for saltation bombardment intensity. This recognition 12 has been challenged in some recent papers, causing a debate on whether dust PSD is “invariant” and the search for 13 justification. In this paper, we analyze dust PSD measured in the Japan-Australian Dust Experiment and show that dust PSD 14 is dependent on u* and on atmospheric boundary-layer stability. By simple theoretical and numerical analysis, we explain the 15 three reasons for the latter dependency. First, under similar mean wind conditions, the mean of u* is larger for unstable than 16 for stable conditions. Second, u* is stochastic and its probability distribution profoundly influences the magnitude of the 17 mean saltation flux due to the non-linear relationship between saltation flux and u*. Third, in unstable conditions, turbulence 18 is usually stronger, which leads to higher saltation-bombardment intensity. This study confirms that dust PSD depends on u*, 19 and more precisely, on the probability distribution of u*, which itself is stability dependent. We restate that for a given soil, 20 finer dust is released in case of stronger saltation. 21

Australia’s rangeland communities, industries, and environment are under increasing pressures from anthropogenic activities and global changes more broadly. We conducted a horizon scan to identify and prioritise key challenges facing Australian rangelands and their communities, and outline possible avenues to address these challenges, with a particular focus on research priorities. We surveyed participants of the Australian Rangeland Society 20th Biennial Conference, held in Canberra in September 2019, before the conference and in interactive workshops during the conference, in order to identify key challenges, potential solutions, and research priorities. The feedback was broadly grouped into six themes associated with supporting local communities, managing natural capital, climate variability and change, traditional knowledge, governance, and research and development. Each theme had several sub-themes and potential solutions to ensure positive, long-term outcomes for the rangelands. The survey responses made it clear that supporting ‘resilient and sustainable rangelands that provide cultural, societal, environmental and economic outcomes simultaneously’ is of great value to stakeholders. The synthesis of survey responses combined with expert knowledge highlighted that sustaining local communities in the long term will require that the inherent social, cultural and natural capital of rangelands are managed sustainably, particularly in light of current and projected variability in climate. Establishment of guidelines and approaches to address these challenges will benefit from: (i) an increased recognition of the value and contributions of traditional knowledge and practices; (ii) development of better governance that is guided by and benefits local stakeholders; and (iii) more funding to conduct and implement strong research and development activities, with research focused on addressing critical knowledge gaps as identified by the local stakeholders. This requires strong governance with legislation and policies that work for the rangelands. We provide a framework that indicates the key knowledge gaps and how innovations may be implemented and scaled out, up and deep to achieve the resilience of Australia’s rangelands. The same principles could be adapted to address challenges in rangelands on other continents, with similar beneficial outcomes.