This report provides background information about the Ginninderra controlled release Experiment 3 including a description of the environmental and weather conditions during the experiment, the groundwater levels and a brief description of all the monitoring techniques that were trialled during the experiment. The Ginninderra controlled release facility is designed to simulate CO2 leakage through a fault, with CO2 released from a horizontal well 2 m underground. Two previous subsurface CO2 release experiments have been conducted at this facility in early and late 2012, which have helped guide and develop the techniques that have been applied herein. The aim of the third Ginninderra controlled release experiment was to further the development of detection and quantification techniques, and investigate seasonal effects on gas migration. Particular focus was given to plant health as a diagnostic detection method, via physical, biochemical and hyperspectral changes in plant biomass in response to elevated CO2 in the shallow root zone. Release of CO2 began 8 October 2013 at 4:45 PM and stopped 17 December 2013 at 5:35 PM. The CO2 release rate during Experiment 3 was 144 kg/d CO2. Several monitoring and assessment techniques were trialled for their effectiveness to quantify and qualify the CO2 that was released. The methods are described in this report and include: - soil gas - eddy covariance - mobile surveys - Line CO2 concentrations - groundwater levels and chemistry - plant biochemistry - airborne hyperspectral - soil flux - electromagnetic (EM-31 and EM-38) - meteorology This report is a reference guide to describe the Ginninderra Experiment 3 details. Only methods are described in this report, with the results of the experiment published in conference papers and journal articles.

The ability of thermal and shortwave infrared spectroscopy to characterise composition and textural was evaluated using both particle size separated soil samples and raw soils. Particle size analysis and separation into clay, silt and sand sized soil fractions was undertaken to examine possible relationships between quartz and clay mineral spectral signatures, and soil texture. Spectral indices, based on thermal infrared specular and volume scattering features, were found to discriminate clay mineral-rich soil from mostly coarser quartz-rich sandy soil, and to a lesser extent, from the silty quartz-rich soil. Further investigations were undertaken using spectra and information on 51 USDA and other soils within the ASTER Spectral Library to test the application of shortwave, mid- and thermal infrared spectral indices for the derivation of clay mineral, quartz and organic carbon content. A non linear correlation between quartz content and a TIR spectral index based on the 8.62 im was observed. Preliminary efforts at deriving a spectral index for the soil organic carbon content, based on 3.4 - 3.5 im fundamental H-C stretching vibration bands were also undertaken with limited results.

A weathering intensity index (WII) over the Australian continent has been developed at 100 m resolution using regression models based on airborne gamma-ray spectrometry imagery and the Shuttle Radar Topography Mission (SRTM) elevation data. Airborne gamma-ray spectrometry measures the concentration of three radioelements - potassium (K), thorium (Th) and uranium (U) at the Earth's surface. The total gamma-ray flux (dose) is also calculated based on the weighted additions of the three radioelements. Regolith accounts for over 85% of the Australian land area and has a major influence in determining the composition of surface materials and in controlling hydrological and geomorphological processes. The weathering intensity prediction is based on the integration of two regression models. The first uses relief over landscapes with low gamma-ray emissions and the second incorporates radioelement distributions and relief. The application of a stepwise forward multiple regression for the second model generated a weathering intensity index equation of: WII = 6.751 + -0.851*K + -1.319* Relief + 2.682 * Th/K + -2.590 * Dose. The WII has been developed for erosional landscapes but also has the potential to inform on deposition processes and materials. The WII correlates well with site based geochemical indices and existing regolith mapping. Interpretation of the WII from regional to local scales and its application in providing more reliable and spatially explicit information on regolith properties is described.

pH is one of the more fundamental soil properties governing nutrient availability, metal mobility, elemental toxicity, microbial activity and plant growth. The field pH of topsoil (0-10 cm depth) and subsoil (~60-80 cm depth) was measured on floodplain soils collected near the outlet of 1186 catchments covering over 6 M km2 or ~80% of Australia. Field pH duplicate data, obtained at 124 randomly selected sites, indicates a precision of 0.5 pH unit (or 7%) and mapped pH patterns are consistent and meaningful. The median topsoil pH is 6.5, while the subsoil pH has a median pH of 7 but is strongly bimodal (6-6.5 and 8-8.5). In most cases (64%) the topsoil and subsoil pH values are similar, whilst, among the sites exhibiting a pH contrast, those with more acidic topsoils are more common (28%) than those with more alkaline topsoils (7%). The distribution of soil pH at the national scale indicates the strong controls exerted by precipitation and ensuing leaching (e.g., low pH along the coastal fringe, high pH in the dry centre), aridity (e.g., high pH where calcrete is common in the regolith), vegetation (e.g., low pH reflecting abundant soil organic matter), and subsurface lithology (e.g., high pH over limestone bedrock). The new data, together with existing soil pH datasets, can support regional-scale decision-making relating to agricultural, environmental, infrastructural and mineral exploration decisions.

Soil is a common evidence type used in forensic and intelligence operations. Where soil composition databases are lacking or inadequate, we propose to use publicly available soil attribute rasters to reduce forensic search areas. Soil attribute rasters, which have recently become widely available at high spatial resolutions, typically three arc-seconds (~90 m), are predictive models of the distribution of soil properties (with confidence limits) derived from data mining the inter-relationships between these properties and several environmental covariates. Each soil attribute raster is searched for pixels that satisfy the compositional conditions of the evidentiary soil sample (target value ± confidence limits). We show through an example that the search area for an evidentiary soil sample can be reduced to <10% of the original investigation area. This Predictive Soil Provenancing (PSP) approach is a transparent, reproducible and objective method of efficiently and effectively reducing the likely provenance area of forensic soil samples. <b>Citation:</b> de Caritat, P., Simpson, T. and Woods, B. (2019), Predictive Soil Provenancing (PSP): An Innovative Forensic Soil Provenance Analysis Tool. <i>J Forensic Sci</i>, 64: 1359-1369. https://doi.org/10.1111/1556-4029.14060

Our planet provides everything we need for our lives, including the food we eat. As the human population increases and expectations for lifestyle quality increases, so too do the pressures placed on our planet to provide that food. We therefore need to be better at producing food and understanding how that links to our scientific understanding of our planet. For National Science Week 2021, the Geoscience Australia public seminar (co-sponsored by the ACT Division of the Geological Society of Australia and the ACT Branch of the Australian Marine Sciences Association) will present four speakers to demonstrate how geoscience is integral to the provision of our food. Steve Hill – The Long View: Across many disciplines of geoscience and different spatial scales, geology, soils and even plate tectonics influence our food (and wine). Andrew Carroll – Finding Important Seabed Habitat (FISH): Did you know that seabed mapping data directly contributes $9 billion to the Australian economy each year and employs over 56,000 people? For the fishing and aquaculture sectors, seabed mapping is valued at $3 billion. However, only one quarter of Australia’s seabed is mapped! Learn how GA is addressing this challenge to support the rapid growth of Australia's Blue Economy. Claire Krause – Food at Scale: In a country as big and dynamic as Australia, producing food is no small task. Satellite imagery is being leveraged to map, monitor and understand Australia’s food production regions and to identify and manage challenges in the sector. Anna Riddell – From Paddock to Plate with Positioning: Have you ever wondered how your food arrives on your plate and the role that navigation satellites play? Positioning is becoming ubiquitous in everyday life and even has a part in enabling our food to be grown, harvested and transported.

A fundamental component of soils is its mineralogy which is a key driver/indicator of important soil properties/processes such as soil pH (acidity), metal availability (e.g. Al, K, Fe, Si, Ca, Mg) and water content/permeability/runoff. However, soil mineralogy is not routinely measured as part of current soil mapping programs at the paddock-, catchment- or continental-scales mainly because currently deployed measurement technologies are not able to deliver soil mineralogy directly, though remote radiometric and microwave sensing technologies do provide useful soil information. In contrast, mineralogy is now being efficiently delivered to the Australian minerals exploration industry through a new generation of field, airborne and spaceborne hyperspectral technologies (www.hyvista.com; nvcl.csiro.au/). This mineral information includes two of the three major soil mineral components, namely: clays (e.g. kaolinite, illite, smectite); and iron/aluminium oxyhydroxides (e.g. hematite. goethite, gibbsite), with specific information being delivered on their composition, abundances and physicochemistries (disorder and chemistry). The third dominant soil mineral component, quartz, is also spectrally measurable but has diagnostic features at wavelengths longer than current "operational" hyperspectral systems. These hyperspectral technologies thus provide an excellent opportunity to transfer mineral mapping capabilities being developed for the minerals industry into the soil mapping application, especially for establishing baseline inventories of soil mineral composition and providing a possible mechanism for quantitative monitoring of change in soil properties related to its mineralogy (e.g. pH, soil loss, water effects, metal activities and possibly soil carbon and salinity). This opportunity is explored using results from a collaborative geological remote sensing project between the CSIRO, the Geological Survey of Queensland and Geoscience Australia (www.em.csiro.au/NGMM, www.nrw.qld.gov.au/science/geoscience/projects/hyperspectral.html) which involves the collection and processing of 25,000 km2 of airborne HyMap imagery (~300 flight-lines at 5m pixel resolution and totalling >1 Terabyte of raw data) from across Queensland, including areas covered by airborne radiometrics and published geology at 1:100 000 scale around the Mount Isa region. The processed hyperspectral data show that lateritic materials in the Tick Hill area comprise relatively abundant iron oxides and kaolinite (poorly ordered) whereas the radiometrics shows these areas as being relatively high Th and U counts. This kaolinite is presumably developed in response to more acid conditions and/or better (downward percolating) drainage. The hyperspectral data also maps extensive areas of Al-smectite (montmorillonite) associated with the weathering of carbonate (calcite and dolomite) parent rocks or as "pedogenic" occurrences in alluvium/colluvium, with the latter sometimes associated with abundant opaline silica (also mapped using the hyperspectral data). These Al-rich smectites are formed in more alkaline conditions where there is sufficient Ca or Mg and water at the near surface and typically show in the radiometric as being poor in K and Th. Muscovite (water-poor, K-bearing white mica) is mapped over exposed parent rocks whereas illite (water-rich, K-bearing white mica) is typically mapped in weathered materials, including many soils and dried lake beds where there is sufficient available K. The radiometric data typically shows these areas as being K-rich. Note that the accuracy of the hyperspectral clay mineral maps was also validated by field sampling and associated laboratory spectral and X-Ray diffraction analyses.

This compilation data release is a selection of remotely sensed imagery used in the Exploring for the Future (EFTF) East Kimberley Groundwater Project. Datasets include: • Mosaic 5 m digital elevation model (DEM) with shaded relief • Normalised Difference Vegetation Index (NDVI) percentiles • Tasselled Cap exceedance summaries • Normalised Difference Moisture Index (NDMI) • Normalised Difference Wetness Index (NDWI) The 5m spatial resolution digital elevation model with associated shaded relief image were derived from the East Kimberley 2017 LiDAR survey (Geoscience Australia, 2019b). The Normalised Difference Vegetation Index (NDVI) percentiles include 20th, 50th, and 80th for dry seasons (April to October) 1987 to 2018 and were derived from the Landsat 5,7 and 8 data stored in Digital Earth Australia (see Geoscience Australia, 2019a). Tasselled Cap Exceedance Summary include brightness, greenness and wetness as a composite image and were also derived from the Landsat data. These surface reflectance products can be used to highlight vegetation characteristics such as wetness and greenness, and land cover. The Normalised Difference Moisture Index (NDMI) and Normalised Difference Water Index (NDWI) were derived from the Sentinel-2 satellite imagery. These datasets have been classified and visually enhanced to detect vegetation moisture stress or water-logging and show distribution of moisture. For example, positive NDWI values indicate waterlogged areas while waterbodies typically correspond with values greater than 0.2. Waterlogged areas also correspond to NDMI values of 0.2 to 0.4. Geoscience Australia, 2019a. Earth Observation Archive. Geoscience Australia, Canberra. http://dx.doi.org/10.4225/25/57D9DCA3910CD Geoscience Australia, 2019b. Kimberley East - LiDAR data. Geoscience Australia, Canberra. C7FDA017-80B2-4F98-8147-4D3E4DF595A2 https://pid.geoscience.gov.au/dataset/ga/129985

Geoscience Australia and CO2CRC have constructed a greenhouse gas controlled release reference facility to simulate surface emissions of CO2 (and other GHG gases) from an underground slotted horizontal well into the atmosphere under controlled conditions. The facility is located in a paddock maintained by CSIRO Plant and Industry at Ginninderra, ACT. The design of the facility is modelled on the ZERT controlled release facility in Montana, which conducts experiments to develop capabilities and test techniques for detecting and monitoring CO2 leakage. The first phase of the installation is complete and has supported an above ground, point source, release experiment, utilising a liquid CO2 storage vessel (2.5 tonnes) with a vaporiser, mass flow controller unit with a capacity for 6 individual metered gas outlet streams, equipment shed and a gas cylinder cage. Phase 2 involved the installation of a shallow (2m depth) underground 120m horizontally drilled slotted well, in June 2011, intended to model a line source of CO2 leakage from a storage site. This presentation will detail the various activities involved in designing and installing the horizontal well, and designing a packer system to partition the well into six CO2 injection chambers. A trenchless drilling technique used for installing the slotted HDPE pipe into the bore hole will be described. The choice of well orientation based upon the effects of various factors such as topography, wind direction and ground water depth, will be discussed. It is envisaged that the facility will be ready for conducting sub-surface controlled release experiments during spring 2011.

The use of airborne hyperspectral imagery for mapping soil surface mineralogy is examined for the semi-arid Tick Hill test site (20 km2) near Mount Isa in north-western Queensland. Mineral maps at 4.5 m pixel resolution include the abundances and physicochemistries (chemical composition and crystal disorder) of kaolin, illite-muscovite, and Al smectite (both montmorillonite and beidellite), as well as iron oxide, hydrated silica (opal), and soil/rock water (bound and unbound). Validation of these hyperspectral mineral maps involved field sampling (34 sites) and laboratory analyses (spectral reflectance and X-ray diffraction). The field spectral data were processed for their mineral information content the same way as the airborne HyMap data processing. The results showed significant spatial and statistical correlation. The mineral maps provide more detailed surface compositional information compared with the published soil and geology maps and other geoscience data (airborne radiometrics and digital elevation model). However, there is no apparent correlation between the published soil types (i.e. Ferrosols, Vertosols, and Tenosols) and the hyperspectral mineral maps (e.g. iron oxide-rich areas are not mapped as Ferrosols and smectite-rich areas are not mapped as Vertosols). This lack of correlation is interpreted to be related to the current lack of spatially comprehensive mineralogy for existing regional soil mapping. If correct, then this new, quantitative mineral mapping data has the potential to improve not just soil mapping but also soil and water catchment monitoring and modeling at local to regional scales. The challenges to achieving this outcome include gaining access to continental-scale hyperspectral data and models that link the surface mineralogy to subsurface soil characteristics/processes.