Master of Engineering (Honours)
Faculty of Engineering
Morgan, Marcus John, Optimal prediction of coastal acid sulfate soil severity using geographic information systems, MEng thesis, Faculty of Engineering, University of Wollongong, 2006. http://ro.uow.edu.au/theses/572
Coastal Acid Sulfate Soil (CASS) is a soil-water phenomenon that causes soil and water pollution resulting from the exposure, typically human-initiated, of pyrite to atmospheric and biotic oxygen. Structural deformation of capital works, combined with loss to flora and fauna (biodiversity) resulting from CASS has caused major concern to environmental managers, industries that rely directly on high quality water conditions for day-to-day operations, and landholders who experience characteristic scalding and other associated environmental problems on land adjacent to disturbed areas.
Areas of CASS in Australia have been identified by Department of Natural Resources (DNR) using a combination of expert knowledge, geomorphologic principles and Geographic Information Systems (GIS) known as Acid Sulfate Soil Risk Maps. These maps have been applied by local managers in planning and natural resource management to identify areas showing the highest probability of being severely affected by CASS.
In this project, with the DNR model as a starting point, the aim was to improve the way CASS severity is assessed. This included using five major soil-chemical parameters and/or relationships in a number of geostatistical models. The five parameters included were: Total Actual Acidity (TAA), pH, Chloride to Sulfate ratio (Cl-:SO4 2-), Depth to actual CASS layer (Jarosite layer), and Exchangeable Aluminium per cent of total Cation Exchange Capacity. Other parameters such as depth to Potential CASS layer (Pyrite layer) and Sulfur per cent (S%), also have weight but not as significant as the other parameters and were subsequently removed from further detailed analysis.
Ordinary Kriging (OK) was identified as the most suitable geostatistical method to predict CASS severity using the aforementioned soil-chemical principles. The resulting 3-Dimensional model was compared to the 2-Dimensional DNR Risk Maps with similarities in both models validating both approaches in determining severity using different methods. The CASSOK model put a greater emphasis on soil parameters down the soil profile and how they relate to surface elevation across a finite study area (Broughton Creek floodplain, New South Wales).
Applying the new CASSOK model to broader areas of New South Wales will be dependent on available data to input into the model. Using the current DNR risk maps is a broad indication of an area, using CASSOK will give a greater indication of what can be expected 2m below the surface. The ability to create a method that can be applied across the entire state of New South Wales, and then to a national level will be an invaluable resource to land managers in future planning and risk management.
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