J. Triantafilis

Mapping Water Table Depth Using Geophysical and Environmental Variables

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.

Comparison of statistical prediction methods for estimating field-scale clay content using different combinations of ancillary variables

The need for spatial information on soil properties at the field level is increasing, particularly for its applications in precision agriculture and environmental management. One important soil property is clay content; however, costs involved with obtaining soil data at the field scale are prohibitive. Geostatistical techniques have been used with some success to improve the accuracy of spatial prediction of soil properties, especially those based on easy-to-obtain ancillary information. There is also, however, the need to determine optimal spacing for generating the ancillary data for spatial prediction. In this paper, we used ancillary variables along with spatial prediction models to determine an optimal method for estimating clay content at the field scale. We also determined the optimal spacing for generating the ancillary data for spatial prediction. The ancillary variables used were apparent soil electrical conductivity (ECa) obtained with EM38 and EM31 and digitized bands (red, green, and blue) of aerial photographs of the bare soil. The spatial prediction models tested are generalized additive models using various combinations of ancillary data (e.g., ECa and red, green, and blue data) and the geostatistical methods of ordinary-, regression- and co-kriging. The results suggest that the linear regression of average clay content with ECa (EM38) data used in combination with kriging of regression residuals was most accurate (RMSE = 3.03). The generation of ECa data on 24-m transect spacing was optimal for prediction. Doubling and tripling the transect spacing (i.e., 48 and 72 m) cause relative reductions in precision of 17% and 12%, respectively.

Resolving the spatial distribution of the true electrical conductivity with depth using EM38 and EM31 signal data and a laterally constrained inversion model

The ability to map the spatial distribution of average soil property values using geophysical methods at the field and district level has been well described. This includes the use of electromagnetic (EM) instruments which measure bulk soil electrical conductivity (σa). However, soil is a 3-dimensional medium. In order to better represent the spatial distribution of soil properties with depth, various methods of inverting EM instrument data have been attempted and include Tikhonov regularisation and layered earth models. In this paper we employ a 1-D inversion algorithm with 2-D smoothness constraints to predict the true electrical conductivity (σ) using σa data collected along a transect in an irrigated cotton field in the lower Namoi valley. The primary σa data include the root-zone measuring EM38 and the vadose-zone sensing EM31, in the vertical (v) and horizontal (h) dipole modes and at heights of 0.2 and 1.0 m, respectively. In addition, we collected σa with the EM38 at heights of 0.4 and 0.6 m. In order to compare and contrast the value of the various σa data we carry out individual inversions of EM38v and EM38h collected at heights of 0.2, 0.4, and 0.6 m, and EM31v and EM31h at 1.0 m. In addition, we conduct joint inversions of various combinations of EM38 σa data available at various heights (e.g. 0.2 and 0.4 m). Last we conduct joint inversions of the EM38v and EM38h σa data at 0.2, 0.4, and 0.6 m with the EM31v and EM31h at 1.0 m. We find that the values of σ achieved along the transect studied represent the duplex nature of the soil. In general, the EM38v and EM38h collected at a height of 0.2, 0.4, and 0.6 m assist in resolving solum and root-zone variability of the cation exchange capacity (cmol(+)/kg of soil solids) and the electrical conductivity of a saturated soil paste extract (ECe, dS/m), while the use of the EM31v and EM31h at 1.0 m assists in characterising the vadose zone and the likely location of a shallow perched-water table. In terms of identifying an optimal set of EM σa data for inversion we found that a joint inversion of the EM38 at a height of 0.6 m and EM31 signal data provided the best correlation with electrical conductivity of a saturated soil paste (ECp, dS/m) and ECe (respectively, 0.81 and 0.77) closely followed by a joint inversion of all the EM38 and EM31 σa data available (0.77 and 0.56).

SPATIAL PREDICTION OF SOIL PARTICLE-SIZE FRACTIONS AS COMPOSITIONAL DATA

Particle-size fractions (psf) of mineral soils and, hence, soil texture, are the most important attributes affecting physical and chemical processes in the soil. More often, psf data are available only at a few locations for a given area and, therefore, require some form of interpolation or spatial prediction. However, psf data are compositional and, therefore, require special treatment before spatial prediction. This includes ensuring positive definiteness and a constant sum of interpolated values at a given location, error minimization, and lack of bias. In order to meet these requirements, this study applied two methods of data transformation prior to kriging of the psf of soils in two regions of eastern Australia. The two methods are additive log-ratio transformation of the psf (ALROK) and modified log-ratio transformation (m ALROK). The performance of the transformed values by ordinary kriging was compared with the spatial prediction of the untransformed psf data using ordinary kriging, compositional kriging (CK) (UTOK), and cokriging, based on the criteriaprediction bias or mean error (ME) and precision (root mean square error (RMSE)), and validity of textural classification. ALROK and m ALROK outperformed UTOK and CK in terms of prediction ME and RMSE. Because of the closure effect on the psf data, UTOK, and, to a lesser extent, CK, did not meet all of the requirements for spatially predicting compositional data and, therefore, performed poorly. m ALROK outperformed all of the interpolation methods in terms of misclassification of soils into textural classes. The results show that without considering the special requirements of compositional data, spatial interpolation of psf data will necessarily produce uncertain and unreliable interpolated psf values.

Field level digital soil mapping of cation exchange capacity using electromagnetic induction and a hierarchical spatial regression model

At the field level the demand for spatial information of soil properties is rapidly increasing owing to its requirements in precision agriculture and soil management. One of the most important properties is the cation exchange capacity (CEC, cmol(+)/kg) because it is an index of the shrink–swell potential and hence is a measure of soil structural resilience to tillage. However, CEC is time-consuming and expensive to measure. Various ancillary datasets and statistical methods can be used to predict CEC, but there is little scientific literature which implements this approach to map CEC or addresses the issue of the amount of ancillary data required to maximise precision and minimise bias of spatial prediction at the field level. We compare a standard least-squares multiple linear regression (MLR) model which includes 2 proximally sensed (EM38 and EM31), 3 remotely sensed (Red, Green and Blue spectral brightness), and 2 trend surface (Easting and Northing) variables as ancillary data or independent variables, and a stepwise MLR model which only includes the statistically valid EM38 signal data and the Easting trend surface vector. The latter is used as the basis for developing a hierarchical spatial regression model to predict CEC. The reliability of the model is analysed by comparing prediction precision (root mean square error) and bias (mean error) using degraded EM38 transect spacing (i.e. 96, 144, 192, 240, and 288 m) and comparing these with predictions achieved with the 48-m spacing. We conclude that the EM38 data available on the 96- and 144-m spacing are suitable at a reconnaissance level (i.e. broad-scale farming) and 24- or 48-m spacing are suitable at smaller levels where detailed information is necessary for siting the location of water reservoirs. In terms of soil management, CEC predictions determine where suitable subsoil exists for the purpose of soil profile inversion to improve the structural resilience of a topsoil that is susceptible to dispersion and surface crusting.

2-Dimensional soil and vadose-zone representation using an EM38 and EM34 and a laterally constrained inversion model

The network of prior streams and palaeochannels common across the Riverine Plains of the Murray-Darling Basin act as conduits for the redistribution of water and soluble salts beneath the root-zone. To improve scientific understanding of these hydrological processes there is the need to better represent and map the connectivity and spatial extent of these physiographic and stratigraphic features. Groundbased electromagnetic (EM) instruments, which measure bulk soil electrical conductivity (sa), have been used widely to map their areal distribution across the landscape. However, methods to resolve their location with depth have rarely been attempted. In this paper we employ a 1-D inversion algorithm with 2-D smoothness constraints to predict the true electrical conductivity (s) at discrete depth increments using EM data. The EM data we use include the root-zone measuring EM38 and the deeper sensing EM34. We collected EM38 data in the vertical (EM38v) and horizontal (EM38h) dipole modes and EM34 data in the horizontal mode and coil spacing of 10, 20, and 40m (respectively, EM34-10, EM34-20, and EM34-40). In order to compare and contrast the value of the various EM data we carried out multiple inversions using different combinations, which include: independent inversions of (i) EM38 (root-zone) and (ii) EM34 data (vadose-zone), and in combination using (iii) EM38v, EM38h, and EM34-10 (near-surface), and (iv) all 5 EM datasets (regolith) available. The general patterns of s are shown to compare favourably with the known pedoderms, physiographic, and stratigraphic features and soil particle size fractions collected from calibration cores drilled across the lower Macquarie Valley study area. In general we find that the EM38 assists in resolving root-zone variability, specifically duplex soil profiles and physiographic features such as prior streams, while the use of the EM34 assists in resolving the stratigraphic nature of the vadose-zone and specifically the likely location of palaeochannels and subsurface anomalies that may indicate the location of good quality groundwater and/or clay aquitards. In this case, our potential to use s to predict clay content is limited by the non-linearity of the cumulative functions. In order to improve on the non-linearity of our inversion we need to develop a full solution of the forward problem.

Estimating and mapping deep drainage risk at the district level in the lower Gwydir and Macquarie valleys, Australia

In the Murray–Darling Basin, irrigated agriculture, which produces rice, dairy, cotton and citrus, is a large consumer of water resources. Effective management of the water resource is therefore important to ensure sustainability of irrigated agriculture. In the lower Gwydir and Macquarie valleys, respectively located in northern and central New South Wales of Australia, extensive irrigated-cotton production is an important contributor to the nation’s export earnings. However, there are problems of excessive deep drainage (DD) in these regions. To address them requires soil and water quality information, but there is little quantitative information to plan for and implement improved water use efficiency. In this paper, we explore methods that could efficiently generate data on natural resources. First, we carried out an electromagnetic induction (EM38) survey to characterise broad soil profile types in the Ashley (lower Gwydir valley) and Trangie (lower Macquarie valley) districts. From the resulting apparent electrical conductivity (ECa, mS/m) data collected using an EM 38 (vertical mode of operation), soil profile sites were selected and sampled, followed by laboratory analysis carried to determine exchangeable cations and clay content. The soil data collected were analysed with a salt and leaching fraction (SaLF) model, based on specific water quality and quantity parameters, such as electrical conductivity of irrigation water (ECiw, dS/m) and rainfall (R, mm/year). Various water application rates (I) were also considered, to simulate irrigated cotton (I = 600 mm/year) and rice production (I = 1200 mm/year) as well as shallow water reservoirs (I = 1800 mm/year). For each irrigation scenario, DD values (mm/year) were estimated. An exponential function was used to describe the relationships between ECa values obtained with the EM38 and estimated DD. These relationships were then used to estimate DD at each of the EM38 survey sites, whereupon cut-off (zc) values were used for indicator transforms of the data. Using indicator kriging (IK) and various irrigation scenarios, we demonstrate the usefulness of this approach in identifying areas of high risk of DD exceeding various cut-off values (zc = 50, 75, 100 and 200 mm/year). Thus, we show where improvements in water-use efficiency could be achieved in the irrigated cotton growing districts of Ashley and Trangie.

Elucidation of physiographic and hydrogeological features of the lower Namoi valley using fuzzy k-means classification of EM34 data

Abstract In the irrigated regions of New South Wales and Victoria, Australia, secondary soil salinisation is becoming of increasing concern. However, natural resource data are not available to elucidate the threat. What is required is information on stratigraphy and spatial location and quality (i.e. low, intermediate and high salinity) of groundwater. Electromagnetic (EM) induction instruments have been used successfully to obtain this information because they measure bulk electrical conductivity (ECa), which is a function of clay content, mineralogy, salinity and moisture. In this paper, we show how data collected from an EM survey can be used to infer this information in the cotton growing area of the lower Namoi valley, Australia. The survey involved taking EM34 measurements at three coil spacings in the horizontal mode of operation (i.e. 10, 20 and 40 m). In all 1869 locations were visited on an approximate 1-km grid. In order to objectively classify the ECa data into natural resource management units, we used fuzzy k-means (FKM). The classes obtained were subsequently mapped using a method that ensured summation of class membership values to unity and using local ordinary kriging. The use of a confusion index highlighted areas where the collection of additional information may be appropriate. Using fuzzy linear discriminant analysis we found that measurements obtained at the 10 m coil spacing reflect the shallow stratigraphy and physiography, whilst the 40 m coil spacing clearly differentiated parts of the clay plain underlain by saline aquifers. We conclude that the use of EM34 data and fuzzy k-means provide a good and non-destructive approach to representing the lower Namoi valley landscape.

A spatially constrained 1D inversion algorithm for quasi-3D conductivity imaging: Application to DUALEM-421 data collected in a riverine plain

The efficient use of water in irrigated agricultural systems is of increasing importance given the changes in climatic patterns currently being experienced in the irrigated areas of the Murray-Darling Basin (MDB) in Australia. In previous research, electromagnetic (EM) induction instruments have been used to map the distribution of the clay content in those areas. However, describing their vertical extent and connectivity with groundwater tables or stratigraphic features such as paleochannels has not been studied adequately. One of the reasons for the paucity of research is the lack of suitable instrumentation or software to invert apparent conductivity (σa) data. The aim of this research is to demonstrate how DUALEM-421 equipment, which operates using electromagnetic induction theory, can be used to map not only the areal distribution of a prior stream channel but its vertical extent by inputting the data into a 1D spatially constrained algorithm for quasi-3D conductivity imaging. We discovered how the i...

STATUS AND TRENDS OF SOIL SALINITY AT DIFFERENT SCALES : THE CASE FOR THE IRRIGATED COTTON GROWING REGION OF EASTERN AUSTRALIA

This paper reports on how prior information was used as a source of data for sampling schemes as well as a foundation for further salinity studies at different scales. The results at each of the scale levels are useful to the degree of sampling intensity at which the information was obtained. While the regional study revealed the salinity pattern is closely associated with climatic trend, the pattern of salinity at the county scale is less well-defined. The salinity information at the field scale revealed high saline areas coinciding with an abandoned creek channel. The salinisation process at this scale is probably due to deposition of soluble salts that have been flushed from the upper reaches of an abandoned creek. There is preponderance of saline subsoil layers in and around Mungindi which needs further investigation. Visualisation of information transfer through the scale continuum, as demonstrated by this study, is presented and discussed.