Abstract:In this study, suspended sediment concentration (SSC) and discharge (Q) hysteresis patterns recorded at the outlets of two nested upland catchments in south-eastern Australia were examined. Detailed monitoring of sediment flux was undertaken in a 1Ð64 km 2 sub-catchment located within a 53Ð5 km 2 catchment for which sediment yield was measured and the extent of incised channels mapped. The analysis of SSC-Q hysteresis patterns was supplemented by these additional datasets to contribute to the explanation of observed patterns. Clockwise SSC-Q hysteresis loops (with the suspended sediment peak leading the discharge peak) were recorded most frequently at both sites. This was attributed to initial rapid delivery of sediment from channel banks, the dominant sediment source in the sub-catchment and probably also for the catchment, in conjunction with remobilization of in-channel fine sediment deposits. Sediment exhaustion effects were considered to enhance clockwise hysteresis, with reduced SSC on the falling limb of event hydrographs. Pronounced exhaustion effects were observed on some multi-rise events, with subsequent flow peaks associated with much reduced sediment peaks. To compare SSC-Q hysteresis patterns between the two catchments, a dimensionless similarity function (SF) was derived to differentiate paired-event hysteresis patterns according to the extent of pattern similarity. This analysis, coupled with the other datasets, provided insight into the function of erosion and sediment delivery processes across the spatial scales examined and indicated the dependency of between-scale suspended sediment transfer on defined flow event scenarios. Quantitative measures of event SSC-Q hysteresis pattern similarity may provide a mechanism for linking the timing and magnitude of process response across spatial scales. This may offer useful insights into the between-scale linkage of dominant processes and the extent of downstream suspended sediment delivery.

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Appropriate land management decisions are important for current and future use of the land to ensure its sustainability. This requires that land management units (LMUs) be specified to enable the identification of specific parameters employed in decision making processes. This paper presents the development of a conceptual model, within geographic information systems (GIS), for defining and assessing LMUs from available biophysical information. The model consists of two main components (sub-models): land quality-based suitability analysis and soil erosion estimation. Using a fuzzy set methodology, the first sub-model was constructed to derive a land suitability index (LSI) for a cropping land utilization type. The LSI thus highlights the suitability grades of every pixel in the study area on a continuous basis. A submodel of soil erosion was established based on the Revised Universal Soil Loss Equation (RUSLE) utilising the same spatial data bases employed for structuring the LSI. Using a soil loss tolerance principle, a fuzzy membership function of average annual soil loss (called soil loss index, SLI) was established, leading to compatibility between LSI and SLI for data integration. LMUs were then derived from various combinations of LSI and SLI. The methodology developed shows the significance of the model for refining available land suitability evaluation systems, which take no account of expected land degradation (from erosion) due to a nominated land use. It also provides a valuable guideline for cost-effective GIS applications in the identification and assessment of homogeneous land units, using available spatial information sets, at a finer scale.

Today, competing land use is continuing to occur in many developed regions. In the Agricultural Development Zone of Western Sydney Region, which is characterised by complex landscape patterns, land use competition is widespread. From a land use planning perspective, identification of suitable locations for a given type of land use is necessary for decision makers to formulate land use alternatives in different locations, based on existing land potential and constraints. For such a region, use of a simple method that implements a categorical system and considers only inherent land characteristics in the analysis is often inadequate to arrive at an optimal spatial decision. The primary aim of this paper is to develop spatial modelling procedures for agricultural land suitability analysis using compromise programming (CoPr) and fuzzy set approach within a geographical information systems (GIS) environment. Five main sets of spatial data for use as decision criteria were developed by using fuzzy set methodology: a land suitability index (LSI) for maximising the land productivity objective; an erosion tolerance index (ETI) for minimising the erosion risk objective; a runoff curve number (CN) for maximising the water discharge regulation objective; an accessibility (RP) measure for maximising the land accessibility objective; and the proximity to water body (WP) for minimising the water pollution objective. An L p -metric was used in the analysis utilising different strategies with representative indices ranging from a situation where full tradeoff among criteria occurs to a noncompensatory condition. Different weighting combinations were also applied, and decision analysis was carried out by using values ranging from 0 to 1.0, where 1.0 is considered as an ideal point. The CoPr model demonstrated in this paper yielded a promising result, as several different techniques of sensitivity analysis show reasonably good results. Likewise, an overlay of that result with the present land use/land cover indicates a good corresponding spatial matching between existing land use (orchard and cultivated land), and the cells (land parcels) classified as the best in CoPr. The results are amenable to various map display techniques, either using continuous values or by defining different cut off points in the data space within a raster GIS environment.

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