Chris Moran

Large-scale patterns of erosion and sediment transport in river networks, with examples from Australia

This paper examines the patterns of sediment transport in rivers in terms of the sources of sediment and its transport and deposition through the river network. The analysis is in the context of dramatic human influences on river sediment transport and how they might influence freshwater ecosystems. The review of Australian work shows that erosion of hillslopes and stream banks has greatly increased in historical times, supplying vast quantities of sediment to rivers, much of which is still stored within the river system. The stored sediment will continue to effect in-stream and estuarine ecosystems for many decades. In most Australian catchments the dominant source of sediment is streambank erosion. An analysis of historical channel widening suggests that a conceptual framework of relative stream power can explain the diversity of behaviour observed in the numerous case studies. Sediment delivery through catchments is considered first in a generic whole network sense, which emphasizes the crucial role played by riverine deposition in determining catchment sediment budgets. A method is then presented for analysing the diverse spatial patterns of sediment storage in any river network. Finally, the paper considers the temporal changes to channel morphology in response to a human-induced pulse of sediment.

3D reconstruction and quantification of macropores using X-ray computed tomography and image analysis

Axial X-ray Computed tomography (CT) scanning provides a convenient means of recording the three-dimensional form of soil structure. The technique has been used for nearly two decades, but initial development has concentrated on qualitative description of images. More recently, increasing effort has been put into quantifying the geometry and topology of macropores likely to contribute to preferential now in soils. Here we describe a novel technique for tracing connected macropores in the CT scans. After object extraction, three-dimensional mathematical morphological filters are applied to quantify the reconstructed structure. These filters consist of sequences of so-called erosions and/or dilations of a 32-face structuring element to describe object distances and volumes of influence. The tracing and quantification methodologies were tested on a set of undisturbed soil cores collected in a Swiss pre-alpine meadow, where a new earthworm species (Aporrectodea nocturna) was accidentally introduced. Given the reduced number of samples analysed in this study, the results presented only illustrate the potential of the method to reconstruct and quantify macropores. Our results suggest that the introduction of the new species induced very limited chance to the soil structured for example, no difference in total macropore length or mean diameter was observed. However. in the zone colonised by, the new species. individual macropores tended to have a longer average length. be more vertical and be further apart at some depth. Overall, the approach proved well suited to the analysis of the three-dimensional architecture of macropores. It provides a framework for the analysis of complex structures, which are less satisfactorily observed and described using 2D imaging

Spatial data mining for enhanced soil map modelling

The principle of using induction rules based on spatial environmental data to model a soil map has previously been demonstrated. Whilst the general pattern of classes of large spatial extent and those with close association with geology were delineated, small classes and the detailed spatial pattern of the map were less well rendered. Here we examine several strategies to improve the quality of the soil map models generated by rule induction. Terrain attributes that are better-suited to landscape description at a resolution of 250 m are introduced as predictors of soil type. A map sampling strategy is developed. Classification error is reduced by using boosting rather than cross-validation to improve the model. Further, the benefit of incorporating the local spatial context for each environmental variable into the rule induction is examined. The best model was achieved by sampling in proportion to the spatial extent of the mapped classes, boosting the decision trees, and using spatial contextual information extracted from the environmental variables.

Managing the cumulative impacts of coal mining on regional communities and environments in Australia

The expansion and contraction of the coal mining industry in Australia has placed pressure on regional communities and environments and multiplied the extent, magnitude and profile of cumulative impacts. While some mining communities have benefited from the expansion of the coal industry through the creation of jobs and the investment in economies, the compounding impacts of multiple mining operations have stretched environmental, social, human and economic systems and rendered conventional mine-by-mine governance approaches ineffective. In this paper we draw from examples in the Bowen Basin, Hunter Valley and Gunnedah Basin to traverse the range of cumulative impacts resulting from mining activities, and detail working examples of management and assessment practices that aim to enhance positive, and avoid and mitigate negative, cumulative impacts.

ASRIS: the database

The Australian Soil Resources Information System (ASRIS) database compiles the best publicly available information available across Commonwealth, State, and Territory agencies into a national database of soil profile data, digital soil and land resources maps, and climate, terrain, and lithology datasets. These datasets are described in detail in this paper. Most datasets are thematic grids that cover the intensively used agricultural zones in Australia.

Predicting sheetwash and rill erosion over the Australian continent

Soil erosion is a major environmental issue in Australia. It reduces land productivity and has off-site effects of decreased water quality. Broad-scale spatially distributed soil erosion estimation is essential for prioritising erosion control programs and as a component of broader assessments of natural resource condition. This paper describes spatial modelling methods and results that predict sheetwash and rill erosion over the Australian continent using the revised universal soil loss equation (RUSLE) and spatial data layers for each of the contributing environmental factors. The RUSLE has been used before in this way but here we advance the quality of estimation. We use time series of remote sensing imagery and daily rainfall to incorporate the effects of seasonally varying cover and rainfall intensity, and use new digital maps of soil and terrain properties. The results are compared with a compilation of Australian erosion plot data, revealing an acceptable consistency between predictions and observations. The modelling results show that: (1) the northern part of Australia has greater erosion potential than the south; (2) erosion potential differs significantly between summer and winter; (3) the average erosion rate is 4.1 t/ha. year over the continent and about 2.9 x 10(9) tonnes of soil is moved annually which represents 3.9% of global soil erosion from 5% of world land area; and (4) the erosion rate has increased from 4 to 33 times on average for agricultural lands compared with most natural vegetated lands.

Disaggregation of polygons of surficial geology and soil maps using spatial modelling and legacy data

Examples from the Murray-Darling basin in Australia are used to illustrate different methods of disaggregation of reconnaissance-scale maps. One approach for disaggregation revolves around the de-convolution of the soil-landscape paradigm elaborated during a soil survey. The descriptions of soil ma units and block diagrams in a soil survey report detail soil-landscape relationships or soil toposequences that can be used to disaggregate map units into component landscape elements. Toposequences can be visualised on a computer by combining soil maps with digital elevation data. Expert knowledge or statistics can be used to implement the disaggregation. Use of a restructuring element and k-means clustering are illustrated. Another approach to disaggregation uses training areas to develop rules to extrapolate detailed mapping into other, larger areas where detailed mapping is unavailable. A two-level decision tree example is presented. At one level, the decision tree method is used to capture mapping rules from the training area; at another level, it is used to define the domain over which those rules can be extrapolated

Modelling sediment delivery ratio over the Murray Darling Basin

This paper presents a scientific and technical description of the modelling framework and the main results of modelling the long-term average sediment delivery at hillslope to medium-scale catchments over the entire Murray Darling Basin (MDB). A theoretical development that relates long-term averaged sediment delivery to the statistics of rainfall and catchment parameters is presented. The derived flood frequency approach was adapted to investigate the problem of regionalization of the sediment delivery ratio (SDR) across the Basin. SDR, a measure of catchment response to the upland erosion rate, was modeled by two lumped linear stores arranged in series: hillslope transport to the nearest streams and flow routing in the channel network. The theory shows that the ratio of catchment sediment residence time (SRT) to average effective rainfall duration is the most important control in the sediment delivery processes. In this study, catchment SRTs were estimated using travel time for overland flow multiplied by an enlargement factor which is a function of particle size. Rainfall intensity and effective duration statistics were regionalized by using long-term measurements from 195 pluviograph sites within and around the Basin. Finally, the model was implemented across the MDB by using spatially distributed soil, vegetation, topographical and land use properties under Geographic Information System (GIs) environment. The results predict strong variations in SDR from close to 0 in floodplains to 70% in the eastern uplands of the Basin. (c) 2005 Elsevier Ltd. All rights reserved.

Macropore sheath: quantification of plant root and soil macropore association

Plants require roots to supply water, nutrients and oxygen for growth. The spatial distribution of roots in relation to the macropore structure of the soil in which they are growing influences how effective they are at accessing these resources. A method for quantifying root-macropore associations from horizontal soil sections is illustrated using two black vertisols from the Darling Downs, Queensland, Australia. Two-dimensional digital images were obtained of the macropore structure and root distribution for an area 55 × 55 mm at a resolution of 64 μm. The spatial distribution of roots was quantified over a range of distances using the K-function. In all specimens, roots were shown to be clustered at short distances (1–10 mm) becoming more random at longer distances. Root location in relation to macropores was estimated using the function describing the distance of each root to the nearest macropore. From this function, a summary variable, termed the macropore sheath, was defined. The macropore sheath is the distance from macropores within which 80% of roots are located. Measured root locations were compared to random simulations of root distribution to establish if there was a preferential association between roots and macropores. More roots were found in and around macropores than expected at random.

A strategy to fill gaps in soil survey over large spatial extents: an example from the Murray–Darling basin of Australia

We re-mapped the soils of the Murray-Darling Basin (MDB) in 1995-1998 with a minimum of new fieldwork, making the most out of existing data. We collated existing digital soil maps and used inductive spatial modelling to predict soil types from those maps combined with environmental predictor variables. Lithology, Landsat Multi Spectral Scanner (Landsat MSS), the 9-s digital elevation model (DEM) of Australia and derived terrain attributes, all gridded to 250-m pixels, were the predictor variables. Because the basin-wide datasets were very large data mining software was used for modelling. Rule induction by data mining was also used to define the spatial domain of extrapolation for the extension of soil-landscape models from existing soil maps. Procedures to estimate the uncertainty associated with the predictions and quality of information for the new soil-landforms map of the MDB are described