Lagrangian stochastic (LS) forward modelling of CO2 plumes from above-surface release experiments conducted at the GA-CO2CRC Ginninderra GHG controlled release facility demonstrated that small surface leaks are likely to disperse rapidly and unlikely to be detected at heights greater 4 m; this was verified using a rotorcraft to map out the plume. The CO2 sensing rotorcraft unmanned aerial vehicle (RUAV) developed at the Australian National University, Canberra, is equipped with a CO2 sensor (3 ppm accuracy and 2 s response time), a GPS, lidar and a communication module. It was developed to detect, locate and quantify CO2 gas leaks. The choice of a rotorcraft UAV allows slower flight speeds compared to speeds of a fixed-wing UAV; and the electric powered motor enables flight times of 12 min. During the experiments, gaseous CO2 (100 kg per day) was released from a small diffuse source located in the middle of the paddock of the controlled release facility, and the RUAV, flying repeatedly over the CO2 source at a few metres height, recorded CO2 concentrations up to 85 ppm above background. Meteorological parameters measured continuously at the site were input in the LS model. Mapped out horizontal and vertical CO2 concentrations established the need to be close to the ground in order to detect CO2 leakage using aerial techniques. Using the rotorcraft as a mobile sensor could be an expedient mechanism to detect plumes over large areas, and would be important for early detection of CO2 leaks arising from CO2 geological storage activities.

There is increasing recognition that minimising methane emissions from the oil and gas sector is a key step in reducing global greenhouse gas emissions in the near term. Atmospheric monitoring techniques are likely to play an important future role in measuring the extent of existing emissions and verifying emission reductions. They can be very suitable for monitoring gas fields as they are continuous and integrate emissions from a number of potential point and diffuse sources that may vary in time. Geoscience Australia and CSIRO Marine & Atmospheric Research have collected three years of continuous methane and carbon dioxide measurements at their atmospheric composition monitoring station ('Arcturus') in the Bowen Basin, Australia. Methane signals in the Bowen Basin are likely to be influenced by cattle production, landfill, coal production, and conventional and coal seam gas (CSG) production. Australian CSG is typically 'dry' and is characterised by a mixed thermogenic-biogenic methane source with an absence of C3-C6+ alkanes. The range of '13C isotopic signatures of the CSG is similar to methane from landfill gas and cattle emissions. The absence of standard in-situ tracers for CSG fugitive emissions suggests that having a comprehensive baseline will be critical for successful measurement of fugitive emissions using atmospheric techniques. In this paper we report on the sensitivity of atmospheric techniques for the detection of fugitive emissions from a simulated new CSG field against a three year baseline signal. Simulation of emissions was performed for a 1-year period using the coupled prognostic meteorological and air pollution model TAPM at different fugitive emission rates (i.e. estimates of <1% to up to 10% of production lost) and distances (i.e. 10 - 50 km) from the station. Emissions from the simulated CSG field are based on well density, production volumes, and field size typical of CSG fields in Australia. The distributions of the perturbed and baseline signals were evaluated and statistically compared to test for the presence of fugitive methane emissions. In addition, a time series model of the methane baseline was developed in order to generate alternative realizations of the baseline signal. These were used to provide measures of both the likelihood of detecting fugitive emissions at various emission levels and of the false alarm rate. Results of the statistical analysis and an indicative minimum fugitive methane emission rate that can be detected using a single monitoring station are presented. Poster presented at the American Geophysical Union meeting, December 2013, San Francisco

The ultimate purpose of carbon capture and storage is to keep CO2 out of the atmosphere. However there are some scenarios in which leakage to atmosphere may occur. Because of the large and variable level of naturallyoccurring CO2 , and rapid dispersion in the atmosphere, leakage to atmosphere can be difficult to detect from concentration measurements. By using prior information from risk assessments about plausible location of leaks, it is possible to design simple yet effective systems for identifying the location of a leak within a pre-defined area of surveillance. We have designed an inexpensive system of autonomous sensors that can locate leaks of CO2 , and have tested it during a controlled release at the CO2CRC Otway site. The system proved effective and it, and its associated workflow, could be adapted and implemented in a variety of storage settings.

<p>Geoscience Australia in collaboration with the CO2CRC hosted three controlled subsurface release experiments of CO2 during 2012 to 2013 at an agricultural research station managed by CSIRO Plant Industry Canberra. The facility was designed to simulate surface emissions of CO2 and other greenhouse gases from the soil into the atmosphere, and has deployed a range of near-surface monitoring techniques in the pursuit of improving detection and quantification methods and technologies. This product, which encompasses 4 geodatabases, a metadata report and a data dictionary, presents all the data collected during the experiments from over 10 research organisations, and is made to use with GIS software. The intention of this data release is make the data available for comparison with measurements taken at other controlled release experiments, CO2 storage projects and natural analogues. This will hopefully facilitate the further development of greenhouse gas monitoring technologies, methods and monitoring strategies and increase our understanding of the migration behaviour and impact of near surface CO2 leakage. <p>The contents of each geodatabase/experiment is summarised below: <p>Release 1 (Feb-May 2012): <p>- Soil microbial data <p>- Soil chemistry <p>- Free air CO2 concentration <p>- Eddy covariance <p>- Groundwater chemistry <p>- Soil gas <p>- Krypton tracers <p>- EM31 <p>- Soil flux <p>Release 2 (Oct-Dec 2012): <p>- Groundwater chemistry <p>- EM31 <p>- EM38 <p>- Soil gas <p>- Soil flux <p>- Airborne hyperspectral <p>- Ground hyperspectral <p>Release 3 (Oct-Dec 2013): <p>- Mobile CO2 surveys <p>- Groundwater depth <p>- Eddy covariance <p>- Plant physiology and chemistry <p>- EM31 <p>- EM38 <p>- Soil gas <p>- Soil flux <p>- Airborne hyperspectral <p>All Releases: <p>- Aerial images <p>- Groundwater depths <p>- Meteorological data <p>Bibliographic reference: <p>Feitz, A.J., Schroder, I.F., Jenkins, C.J., Schacht, U., Zegelin, S., Berko, H., McGrath, A., Noble, R., Palu, T.J., George, S., Heath, C., Zhang, H., Sirault, X. and Jimenez-Berni, J. 2016. Ginninderra Controlled CO2 Release Facility Dataset 2012-2013. eCat 90078, Geoscience Australia and CO2CRC, Canberra. https://pid.geoscience.gov.au/dataset/ga/90078. <p>Digital Object Identifier: http://dx.doi.org/10.4225/25/5823c37333f9d

This wind field was produced within v2.0 of TCRM, using data from the Bureau of Meteorology to constrain the wind field. Wind multipliers were calculated using a landcover dataset derived from Landsat and Digital Earth Australia. This wind field may be refined in the future as new data becomes available. This record includes - the track data from the Bureau of Meteorology used to model Tropical Cyclone Debbie - the landcover dataset produced for the Airlie Beach region - the preliminary local wind field

Here we report on the application of a new CO2 quantification and localization technique, called atmospheric tomography. The results of the study indicate that, through careful data processing, measurements from the comparatively inexpensive but lower accuracy and lower precision CO2 sensor array can provide useful data. Results from the application of the tomography technique will be presented and limitations of the technique discussed. From the 9th International Carbon Dioxide Conference, Beijing, China

There is increasing recognition that minimising methane emissions from the oil and gas sector is a key step in reducing global greenhouse gas emissions in the near term. Atmospheric monitoring techniques are likely to play an important future role in measuring the extent of existing emissions and verifying emission reductions. Geoscience Australia and CSIRO Marine & Atmospheric Research have collected three years of continuous methane and carbon dioxide measurements at their atmospheric composition monitoring station ('Arcturus') in the Bowen Basin, Australia. Methane signals in the Bowen Basin are likely to be influenced by cattle production, landfill, coal production, and conventional and coal seam gas (CSG) production. Australian CSG is typically 'dry' and is characterised by a mixed thermogenic-biogenic methane source with an absence of C3-C6+ alkanes. The range of ?13C isotopic signatures of the CSG is similar to methane from landfill gas and cattle emissions. The absence of standard in-situ tracers for CSG fugitive emissions suggests that having a comprehensive baseline will be critical for successful measurement of fugitive emissions using atmospheric techniques. In this paper we report on the sensitivity of atmospheric techniques for the detection of fugitive emissions for a simulated new CSG field against a three year baseline signal. Simulation of emissions was performed for a 1-year period using the coupled prognostic meteorological and air pollution model TAPM at different fugitive emission rates (i.e. 0.1 - 10 %) and distances (i.e. 10 - 50 km) from the station. Emissions from the simulated CSG field are based on well density, production volumes, and field size typical of CSG fields in Australia. The distributions of the perturbed and baseline signals were evaluated and statistically compared to test for the presence of fugitive methane emissions. In addition, a time series model of the methane baseline was developed in order to generate alternative realizations of the baseline signal. These were used to provide measures of both the likelihood of detecting fugitive emissions at various emission levels and of the false alarm rate. Results of statistical analysis and an indicative minimum fugitive methane emission rate that can be detected using a single monitoring station are presented. Submitted to AGU 2013, San Francisco

Deployment of Unmanned Aerial Vehicle during surface CO2 release experiments at the Ginninderra greenhouse gas controlled release facility H. Berko (CO2CRC, Geoscience Australia), F. Poppa (The Australian National University), U. Zimmer (The Australian National University) and A. Feitz (CO2CRC, Geoscience Australia) Lagrangian stochastic (LS) forward modelling of CO2 plumes from above-surface release experiments conducted at the GA-CO2CRC Ginninderra controlled release facility demonstrated that small surface leaks are likely to disperse rapidly and unlikely to be detected at heights greater 4 m; this was verified using a rotorcraft to map out the plume. The CO2 sensing rotorcraft unmanned aerial vehicle (RUAV) developed at the Australian National University, Canberra, is equipped with a CO2 sensor, a GPS, lidar and a communication module. It was developed to detect and locate CO2 gas leaks; and estimate CO2 concentration at the emission source. The choice of a rotor-craft UAV allows slower flight speeds compared to speeds of a fixed-wing UAV; and the electric powered motor enables flight times of 12 min. In experiments conducted at the Ginninderra controlled release facility, gaseous CO2 (100 kg per day) was released from a small diffuse source located in the middle of the paddock, and the RUAV was flown repeatedly over the CO2 source at a few meters height. Meteorological parameters measured continuously at the site at the time of the flight were input in the LS model. Mapped out horizontal and vertical CO2 concentrations established the need to be close to the ground in order to detect CO2 leakage using aerial techniques. Using the rotorcraft as a mobile sensor could be an expedient mechanism to detect plumes over large areas, and would be important for early detection of CO2 leaks arising from CCS activities.

In July 2010, Geoscience Australia and CSIRO Marine & Atmospheric Research jointly commissioned a new atmospheric composition monitoring station, named Arcturus, in sub-tropical Queensland, Australia. The facility is designed as a proto-type remotely operated `baseline monitoring station' that could be deployed in areas that are likely targets for commercial scale geological storage of carbon dioxide. A key question, given the ecosystem and anthropogenic sources of CO2 in the region, and the absence of a 'clean-wind' sector baseline, is how large would a CO2 leak have to be from a geological storage site before it can be detected above the background CO2 signal? To address this, CO2 leak simulation modelling was performed for 1-year period using the coupled prognostic meteorological and air pollution model TAPM at various locations, emission rates and distances (1-10 km) from the station.

Source The data was sourced from CSIRO (Victoria) in 2012 by Bob Cechet. It is not known specifically which division of CSIRO, although it is likely to have been the Marine and Atmospheric Research Division (Aspendale), nor the contact details of the person who provided the data to Bob. The data was originally produced by CSIRO for their input into the South-East Queensland Climate Adaptation Research Initiative (SEQCARI). Reference, from an email of 16 March 2012 sent from Bob Cechet to Chris Thomas (Appendix 1 of the README doc stored at the parent folder level with the data), is made to 'download NCEP AVN/GFS files' or to source them from the CSIRO archive. Content The data is compressed into 'tar' files. The name content is separated by a dot where the first section is the climatic variable as outlined in the table format below: Name Translation rain 24 hr accumulated precipitation rh1_3PM Relative humidity at 3pm local time tmax Maximum temperature tmin Minimum temperature tscr_3PM Screen temperature (2 m above ground) at 3pm local time u10_3PM 10-metre above ground eastward wind speed at 3pm local time v10_3PM 10-metre above ground northward wind speed at 3pm local time The second part of the name is the General Circulation Model (GCM) applied: Name Translation gfdlcm21 GFDL CM2.1 miroc3_2_medres MIROC 3.2 (medres) mpi_echam5 MPI ECHAM5 ncep NCEP The third, and final, part of the tarball name is the year range that the results relate to: 1961-2000, 1971-2000, 2001-2040 and 2041-2099 Data format and extent Inside each of the tarball files is a collection of NetCDF files covering each simulation that constitutes the year range (12 simulations for each year). A similar naming protocol is used for the NetCDF files with a two digit extension added to the year for each of the simulations for that year (e.g 01-12). The spatial coverage of the NetCDF files is shown in the bounding box extents as shown below. Max X: -9.92459297180176 Min X: -50.0749073028564 Max Y: 155.149784088135 Min Y: 134.924812316895 The cell size is 0.15 degrees by 0.15 degrees (approximately 17 km square at the equator) The data is stored relative to the WGS 1984 Geographic Coordinate System. The GCMs were forced with the Intergovernmental Panel on Climate Change (IPCC) A2 emission scenario as described in the IPCC Special Report on Emissions Scenarios (SRES) inputs for the future climate. The GCM results were then downscaled from a 2 degree cell resolution by CSIRO using their Cubic Conformal Atmospheric Model (CCAM) to the 0.15 degree cell resolution. Use This data was used within the Rockhampton Project to identify the future climate changes based on the IPCC A2 SRES emissions scenario. The relative difference of the current climate GCM results to the future climate results was applied to the results of higher resolution current climate natural hazard modelling. Refer to GeoCat # 75085 for the details relating to the report and the 59 attached ANZLIC metadata entries for data outputs.