Expansion of landscape characterisation methods within the Hydrogeological Landscape Framework: application in the Australian Capital Territory

• Published in: Australian journal of earth sciences Vol. 64; no. 8; pp. 1073 - 1084
• Main Authors: Cowood, A. L., Moore, C. L., Cracknell, M. J., Young, J., Muller, R., Nicholson, A. T., Wooldridge, A. C., Jenkins, B. R., Cook, W.
• Format: Journal Article
• Language: English
• Published: Hoboken Taylor & Francis 17.11.2017; Wiley Subscription Services, Inc
• Subjects: Biological properties; Case studies; Confidence; End users; expert system; Frameworks; Geology; Groundwater; Groundwater flow; Hazard assessment; Hazard identification; Hydrogeological Landscapes Framework; Hydrogeology; Hydrology; land management; landform modelling; Landforms; Landscape; landscape characterisation; Machine learning; Mathematical models; Modelling; Regolith; Resource management; Self-Organising Maps; Soil; Statistical analysis; Territory; Water flow; Water quality; Australia; Australian Capital Territory Australia
• ISSN: 0812-0099; 1440-0952
• DOI: 10.1080/08120099.2017.1255656

The Hydrogeological Landscape (HGL) Framework is a landscape-characterisation tool that is used to discern areas of similar physical, hydrogeological, hydrological, chemical and biological properties, referred to as HGL Units. The HGL Framework facilitates prioritisation of natural-resource management investment by identifying current and potential hazards in the landscape. Within prioritised regions, on-ground management actions are tailored for specific Management Areas within individual HGL Units. The HGL Unit boundaries are determined through expert interpretation of spatial and field based datasets, such as climate, landform, geology, regolith, soil, stream network, groundwater flow systems, water quality and vegetation assemblages. The resulting HGL Units are validated by an interdisciplinary team using field assessment and biophysical testing. The use of the HGL Framework for new applications creates opportunities for refinement of the existing methodology and products for end users. This paper uses an application in the Australian Capital Territory as a case study to illustrate two enhanced techniques for the landscape characterisation component of the HGL Framework: use of an unsupervised statistical learning algorithm, Self-Organising Maps (SOM), to further validate HGL Units; and landform modelling to assist in delineation of Management Areas. The combined use of SOM and landform modelling techniques provides statistical support to the existing expert and field-based techniques, ensuring greater rigour and confidence in determination of landscape patterns. This creates a more refined HGL Framework landscape-characterisation tool, facilitating more precise hazard assessment and strategic natural-resource management by end users.