Salinity of groundwater directly affects its suitability for different uses, from human consumption, agricultural use, to mineral and energy extraction. Traditionally, direct measurements of groundwater salinity at monitoring bores have been used to create salinity maps. However, drilling is expensive and logistically challenging, while leaving us with a small set of salinity measurements over large areas. Airborne electromagnetic (AEM) surveying provides a cost effective solution to this problem. We have developed a scripted geostatistical methodology, which can be repeated on a computer cluster as new AEM data are acquired or boreholes are drilled. We also provide uncertainties on the knowledge gained, allowing remote communities to manage their land and water resources effectively.

Groundwater is a critical resource for supporting human consumption, stock water, agricultural use, and mineral or energy extraction as well as the environment. However, the quality of groundwater varies enormously from potable to hyper-saline, particularly in the Australian context. To evaluate the suitability of a groundwater resource, the spatial distribution of salinity within an aquifer is typically estimated by measuring the electrical conductivity (EC) of groundwater samples from within boreholes. However, drilling is a logistically and economically challenging task, and hydrogeologists are usually left with a sparse set of measurements from which to infer groundwater salinity over large spatial extents. Airborne electromagnetic (AEM) surveying is a geophysical technique for estimating the bulk electrical conductivity of the near-surface. Where AEM bulk conductivity are well correlated with groundwater salinity in aquifers, AEM is a useful tool for modelling salinity in the data sparse areas between the boreholes. We present here a probabilistic method for modelling salinity and a case study from the Keep River Plains in the Northern Territory. Co-located probabilistic AEM inversions and EC measurements on pore fluids at coincident locations were fused to calculate an empirical joint probability density function. This function allowed us to estimate salinity away from the bores by sampling the ensemble of AEM conductivities. Unlike deterministic methods that provide a single estimate of salinity, our method generates an ensemble of estimates, which can be used to quantify predictive uncertainty. The results provided by our method can feed into decision making while accounting for uncertainty, enabling remote communities to manage their land and water resources more responsibly.

Demonstrates the application of modelling gamma-ray spectrometry and DEM for mapping regolith materials and in predicting salt stores.

The identification of suitable abiotic surrogates for biological diversity requires the collection of both physical and biological data. However, logistical constraints often preclude experimental designs that incorporate spatial and temporal replication. Given the quite limited resources normally available for surveys, the investigation of appropriate surrogates involves a trade-off between overall spatial coverage and replication. We have completed a survey in Jervis Bay in which environmental and infaunal data were collected contemporaneously in order to be combined with similar data from a previous winter survey (survey number GA309) to investigate variation across seasons. Because there will be a certain error in sampling at the exact location as the previous survey, the survey design also required that replicate samples be taken at a set number of stations in order to investigate fine-scale variability (at the scale of metres). We used grabs to collect paired geochemical and biological samples from thirty-two stations in a defined grid near Darling Rd; at eight of these stations we deployed three pairs of grabs to investigate fine-scale variability. Due to good weather and extra ship time available, we also deployed a CTD to investigate vertical temperature and salinity profiles at each station in the Darling Rd grid, as well as at stations throughout the entire bay. Samples are expected to be processed and analysed by late 2009, but preliminary results indicate that most physical variables and infaunal assemblages varied between seasons. In addition, variation among infaunal assemblages seems greater among stations (hundreds of meters) than within replicates at stations (meters).

In a land management context, conductivity can be related to quantities such as salt store, ground water salinity, clay content, hydraulic permeability, degree of groundwater saturation or more generally to definition of regolith and bedrock units. These relationships open the way for the use of electromagnetic methods to measure subsurface conductivity. From the very early trials, efforts have been made to transform the measurements to conductivity. The real world presents a myriad of difficulties, that translate into errors and artifacts in 2D sections or 3D volumes produced from individual 1D conductivity transformation solutions. Reconciliation of AEM conductivity predictions with other spatial information presents a deceptively difficult challenge. A variety of improvements in the future will lead to a more accurate portrayal of conductivity in 3D.

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The Ord Valley Airborne Electromagnetics (AEM) Interpretation Project was undertaken to provide information in relation to groundwater salinity management in the Ord River Irrigation Area (ORIA), and to assess the salinity hazard in areas of potential irrigation expansion. Salinity hazard maps were produced using an informed GIS-based approach. The salinity hazard maps combined AEM-derived maps of the shallow alluvial sediments, salt stored in the unsaturated zone and maps of groundwater salinity, with drilling data and maps of depth to the watertable. Hydrographic analysis showed that under current climate conditions, water tables were rising, and it was therefore assumed for GIS modeling purposes that water levels would continue to rise after land clearing and the onset of irrigation. It was also assumed that if shallow watertables developed at some time in the future, that salt accumulation through capillary rise (if within 2m of the surface) may lead to salinisation. This assumption was informed by prior geochemical modeling that inferred that if relatively modest groundwater salinity levels (>750 mg/l TDS) were evapo-concentrated that it may cause a significant salinity hazard to irrigated agriculture. Salinity hazard was assessed as high where there were significant quantities of salt stored in the alluvium in areas of shallow groundwater, and lowest where there is little or no salt stored in alluvium and groundwater tables are deep. The salinity hazard was forecast to be high to very high in the Mantinea Plain, Carlton Hill, Parry's Lagoon and lower Ord Floodplain areas. In the Knox Creek and Keep River Plains, the hazard was low in the north of the area, but moderate to high in the southern-central and areas of the southern Knox Creek Plain. In the priority development area (Weaber Plain), the salinity hazard was estimated to be highly variable.

An inventory of saline water disposal basins, Murray Basin : volume 3 additional basins in South Australia, Victoria and New South Wales 1998.

Airborne electromagnetic (AEM) systems are increasingly being used for mapping conductivity in areas susceptible to secondary salinity, with particular attention on near-surface predictions (ie those in the top 5 or 10 metres). Since measured AEM response is strongly dependent on the height of both the transmitter loop and receiver coil above conductive material, errors in measurements of terrain clearance translate directly into significant errors in predicted near-surface conductivity. Radar altimetry has been the standard in airborne geophysical systems for measuring terrain clearance. In areas of agricultural activity significant artifacts up to five metres in magnitude can be present. One class of error, related to surface roughness and soil moisture levels in ploughed paddocks and hence termed the ?paddock effect?, results in overestimation of terrain clearance. A second class of error, related to dense vegetation and hence termed the ?canopy effect?, results in underestimation of terrain clearance. A survey example where terrain clearance was measured using both a radar and a laser altimeter illustrates the consequences of the paddock and canopy effects on shallow conductivity predictions. The survey example shows that the combination of the dependence of AEM response on terrain clearance and systematic radar altimeter artefacts spatially coincident with areas of differing land-use may falsely imply that land-use practices are the controlling influence on conductivity variations in the near surface. A laser altimeter is recommended for AEM applications since this device is immune to the paddock effect. Careful processing is still required to minimise canopy effects.