Abstract Effective management of soil requires the spatial distribution of its various physical, chemical and hydrological properties. This is because properties, for example clay content, determine the ability of soil to hold cations and retain water. However, data acquisition is labour intensive and time‐consuming. To add value to the limited soil data, remote sensing (e.g. airborne gamma‐ray spectrometry) and proximal sensing, such as electromagnetic ( EM ) induction, are being used as ancillary data. Here, we provide examples of developing Digital Soil Maps ( DSM ) of soil physical, chemical and hydrological properties, for seven cotton‐growing areas of southeastern Australia, by coupling soil data with remote and proximal sensed ancillary data. A greater challenge is how to get these DSM to a stakeholder in a way that is useful for practical soil use and management. This study describes how we facilitate access to the DSM s, using a simple‐to‐use web GIS platform, called terra GIS . The platform is underpinned by Google Maps API , which is an open‐source development environment for building spatially enabled Internet applications. In conclusion, we consider that terra GIS and the supporting information, available on the sister web page ( http://www.terragis.bees.unsw.edu.au/ ), allow easy access to explanation of DSM of soil properties, which are relevant to cotton growers, farm managers, consultants, extension staff, researchers, state and federal government agency personnel and policy analysts. Future work should be aimed at developing error budget maps to identify where additional soil and/or ancillary data is required to improve the accuracy of the DSM s.
In Australia’s semi-arid and arid interior, groundwater resources provide water supply security for agriculture and community consumptive use and are critical for underpinning economic development. . The Southern Stuart Corridor Project in central Australia, is an inter-disciplinary study which aims to better characterise regional groundwater systems and identify the location, quantity and quality of new groundwater resources. The main aims of the project are(1) to de-risk investment in development of a potential agricultural precinct in the Western Davenport Basin, and expansion of horticulture in Ti-Tree Basin, (2) to identify future water supplies for Alice Springs and Tennant Creek, and (3) for regional water supplies for mineral resource development.The project is funded by Geoscience Australia (GA) as part of the Exploring for the Future (EFTF) Programme. The project integrates airborne electromagnetic (AEM), ground geophysics (ground magnetic resonance (GMR) and borehole geophysics (Induction, gamma and nuclear Magnetic Resonance (NMR)) with drilling and pump testing; hydrochemistry and geochronology; and geomorphic, geological, hydrogeological and structural mapping and modelling. Advancements in temporal remote sensing technologies for surface hydrology, vegetation and landscape mapping are also used to facilitate the identification of recharge and discharge zones and groundwater-dependent vegetation.This paper reports on initial AEM inversion results for the Alice Springs, Ti-Tree Basin, Western Davenport and Tennant Creek areas and the use of a machine learning approach for rapid geological and hydrogeological interpretation of the AEM data. These machine learning approaches have the potential to significantly reduce interpretation time and facilitate the rapid delivery of project results.
The presence of Neogene fault systems can have a significant impact on hydraulic connectivity of aquifers, juxtaposing otherwise disconnected aquifers, enhancing recharge and/or discharge or acting as barriers to flow and consequently compartmentalising groundwater resources. Previously, regional airborne electromagnetics (AEM) transects allied with groundwater investigations have pointed to the potential for localised compartmentalisation of the Daly River Basin groundwater systems. However, existing data is sparse, and equivocal.In this context, the main aim of the Daly River Basin Project is to determine if compartmentalisation of the aquifers is a significant factor and thus should be explicitly considered in groundwater modelling and water allocation planning. The objectives of the project main goals of the project are to: (1) map Neogene faults through the use of airborne electromagnetic (AEM) and morphotectonic mapping, and (2) assess the permeability and transmissivity of mapped fault zones and their role in potential groundwater system compartmentalisation. Data acquisition includes 3325 line-kilometres of new AEM and airborne magnetics, ground (ground magnetic resonance (GMR)), and borehole geophysics, drilling, groundwater sampling and hydrochemical analysis, geomorphic and morphotectonics mapping. Hydrogeophysical, geomorphic and hydrogeological data will also be used to better understand groundwater-surface water connectivity and the potential for managed aquifer recharge schemes to replenish extracted groundwater resources. The outcomes of this project will inform decisions on water allocations and underpin effective and efficient groundwater use. This paper specifically reports on the ability of AEM and morphotectonics mapping to identify Neogene fault systems in the Daly River Basin.
In the Howards East Aquifer (HEA) in Darwin’s Rural District, groundwater resources in a dolomitic and karstic aquifer system provide important water security for Darwin and a large horticultural industry. Previously (2011), a widely-spaced (550m) regional airborne electromagnetics (AEM) survey in this area mapped conductivity anomalies that were interpreted as potential zones of seawater intrusion (SWI) coincident with major fault zones. Subsequent drilling confirmed elevated groundwater salinities in some bores marginal to the main aquifer. It was recommended that more detailed investigations be undertaken to better define the SWI risk.The Howards East Project is an inter-disciplinary study which focussed on delineating and characterising the present SWI interface and potential future hazards from sea water intrusion. The Project is funded by Geoscience Australia (GA) as part of the Exploring for the Future (EFTF) Programme. New data acquisition includes 2,096 line-kilometres of 100 m line-spaced AEM and airborne magnetics data, ground magnetic resonance (GMR), and borehole nuclear magnetic resonance (NMR) data, drilling and pump testing; and hydrochemistry. The main aims of this study are to: (1) delineate potential SWI zones; (2) quantify the porosity, permeability and transmissivity of the Koolpinyah-Coomalie Dolomite aquifer along potential fault zones (coincident with magnetic anomalies) and (3) identify other structural and/or sedimentological preferential flow paths or barriers to ingress.This paper reports on: (1) initial AEM inversion results and spatio-temporal changes in groundwater quality arising since acquisition of previous AEM in 2011, and (2) the interplay between the sea water intrusion interface and structural/sedimentological flow paths/barriers.
The soil particle-size fractions (PSFs) are one of the most important attributes to influence soil physical (e.g., soil hydraulic properties) and chemical (e.g., cation exchange) processes. There is an increasing need, therefore, for high-resolution digital prediction of PSFs to improve our ability to manage agricultural land. Consequently, use of ancillary data to make cheaper high-resolution predictions of soil properties is becoming popular. This approach is known as “digital soil mapping.” However, most commonly employed techniques (e.g., multiple linear regression or MLR) do not consider the special requirements of a regionalized composition, namely PSF; (1) should be nonnegative (2) should sum to a constant at each location, and (3) estimation should be constrained to produce an unbiased estimation, to avoid false interpretation. Previous studies have shown that the use of the additive log-ratio transformation (ALR) is an appropriate technique to meet the requirements of a composition. In this study, we investigated the use of ancillary data (i.e., electromagnetic (EM), gamma-ray spectrometry, Landsat TM, and a digital elevation model to predict soil PSF using MLR and generalized additive models (GAM) in a standard form and with an ALR transformation applied to the optimal method (GAM-ALR). The results show that the use of ancillary data improved prediction precision by around 30% for clay, 30% for sand, and 7% for silt for all techniques (MLR, GAM, and GAM-ALR) when compared to ordinary kriging. However, the ALR technique had the advantage of adhering to the special requirements of a composition, with all predicted values nonnegative and PSFs summing to unity at each prediction point and giving more accurate textural prediction.
Despite its importance, accurate representation of the spatial distribution of water table depth remains one of the greatest deficiencies in many hydrological investigations. Historically, both inverse distance weighting (IDW) and ordinary kriging (OK) have been used to interpolate depths. These methods, however, have major limitations: namely they require large numbers of measurements to represent the spatial variability of water table depth and they do not represent the variation between measurement points. We address this issue by assessing the benefits of using stepwise multiple linear regression (MLR) with three different ancillary data sets to predict the water table depth at 100-m intervals. The ancillary data sets used are Electromagnetic (EM34 and EM38), gamma radiometric: potassium (K), uranium (eU), thorium (eTh), total count (TC), and morphometric data. Results show that MLR offers significant precision and accuracy benefits over OK and IDW. Inclusion of the morphometric data set yielded the greatest (16%) improvement in prediction accuracy compared with IDW, followed by the electromagnetic data set (5%). Use of the gamma radiometric data set showed no improvement. The greatest improvement, however, resulted when all data sets were combined (37% increase in prediction accuracy over IDW). Significantly, however, the use of MLR also allows for prediction in variations in water table depth between measurement points, which is crucial for land management.
In irrigated areas of central and northern New South Wales secondary soil salinisation is of increasing concern. This is particularly the case for the Bourke Irrigation District (mid-Darling River valley) where incipient traces of point-source salinisation are evident. Much of this is attributed to significant changes in the water balance and in particular increased deep drainage and mobilisation of primary salts. In the first instance, near-surface prior-stream channels and subsurface paleochannels evident within Cenozoic alluvial deposits act as significant hydrological features. Owing to the porosity and connectivity of the Cenozoic sediments, deep draining water interacts with ancient and highly saline Cretaceous marine mudstones. Unfortunately, natural resource data on the spatial distribution of near-surface and subsurface stratigraphy are not readily available to understand these processes or determine best management. In this paper, we demonstrate how fuzzy k-means (FKM) analysis of EM34 signal data collected across the Bourke Irrigation District can be used to identify and map common near-surface and subsurface stratigraphic units. EM34 survey was carried out in the horizontal mode of operation with measurements made at coil spacing of 10, 20 and 40 m (i.e. EM34-10, -20 and -40, respectively). In all, 1236 sites were visited on a ∼0.5–1 km grid. The EM34 signal data were classified numerically using the FKM clustering algorithm. The iteration of fuzzy exponents (f) and various indices, including the fuzziness performance index (FPI) and normalised classification entropy (NCE) enabled the determination of f = 1.6 and k = 4 classes to be selected for further investigation. Using fuzzy canonical analysis we find that the EM34-10 and EM34-20 signal data best discriminate the classes. The resulting k = 4 classes (i.e. Class A, B, C and D) are then mapped using a method that ensures summation of class membership (m) values to unity and using local ordinary kriging. Class A represents saline and clay-rich sediments associated with outcrops of the Cretaceous marine mudstone. Class D represents interbedding of sand within the clay units indicative of the Cenozoic alluvial deposits that overly the Cretaceous mudstone. Similarly, Class B represents Cenozoic sediments with the presence of paleochannel(s). Class C characterises the eolian dune and alluvial floodplain physiographic units. The use of a confusion index (CI) highlights areas of uncertainty in the FKM classification mapping and indicates where the collection of additional information is appropriate. Validation of the FKM approach is confirmed by calculating near-surface (0–6 m) and subsurface (6–12 m) class average soil physical and chemical property (i.e. clay content, cation exchange capacity and salinity) variance (i.e. S2 Z ) and total within-class variance (i.e. S 2 T ) of a FKM map. Overall, the average subsurface ECe data produces the greatest reduction in variance. This suggests the FKM classification of EM34 signal data is influenced by saline groundwater-tables in the Cenozoic sediments which lie above the Cretaceous marine mudstone. The FKM class memberships and CI along a short but detailed transect across the Bourke Irrigation District are interpreted with respect to the spatial distribution of near-surface and subsurface soil property data. We conclude the FKM analysis of EM34 provides a better understanding of the spatial distribution and horizontal layering of the various stratigraphic units which characterise the Bourke Irrigation District. The results help explain the broad hydrological processes driving point source secondary soil salinisation and provides a framework for the deployment of measuring and monitoring sites.
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