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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.

The Hydrogeological Landscape (HGL) framework divides geographic space into regions with similar landscape characteristics. HGL regions or units are used to facilitate appropriate management actions tailored to individual HGL units for specific applications such as dryland salinity and climate-change hazard assessment. HGL units are typically constructed by integrating data including geology, regolith, soils, rainfall, vegetation and landscape morphology, and manually defining boundaries in a GIS environment. In this study, we automatically construct spatially contiguous regions from standard HGL data using Self-Organising Maps (SOM), an unsupervised statistical learning algorithm. We compare the resulting SOM-HGL units with manually interpreted HGL units in terms of their spatial distributions and attribute characteristics. Our results show that multiple SOM-HGL units successfully emulate the spatial distributions of individual HGL units. SOM-HGL units are shown to define subregions of larger HGL units, indicating subtle variations in attribute characteristics and representing landscape complexities not mapped during manual interpretation. We also show that SOM-HGL units with similar attributes can be selected using Boolean logic. Selected SOM-HGL units form regions that closely conform to multiple HGL units not necessarily connected in geographic space. These SOM-HGL units can be used to establish generalised land management strategies for areas with common physical characteristics. The use of SOM for the construction of HGL units reduces the subjectivity with which these units are defined and will be especially useful over large and/or inaccessible regions, where conducting field-based validation is either logistically or economically impractical. The methodology presented here has the potential to contribute significantly to land-management decision-support systems based on the HGL framework.

In Australia, salinity has the potential to affect up to 17million hectares of agricultural and pastoral land. For many degraded sites, biophysical hazards are often poorly understood and consequently poorly managed. Attempts to remediate areas affected by salinity have met with varying degrees of success. The New South Wales (NSW) Office of Environment and Heritage, NSW Department of Primary Industries, University of Canberra and Geoscience Australia have collaborated to develop a biophysical expert-based approach for the assessment and management of salinity within landscapes. The Hydrogeological Landscape (HGL) framework provides a structure for understanding how salinity manifests in the landscape, how differences in salinity are expressed across the landscape and how salinity may best be managed. The HGL framework merges the flow dynamics of the groundwater flow system with the landscape elements of the soil landscape or regolith landform approaches. This is the first approach to specifically address all three manifestations of salinity: land salinity, in-stream salt load and in-stream salt concentration. The HGL framework methodology recognises the interplay between surface and subsurface flow systems, as well as the capacity for water to interact with salt stores in the landscape, and identifies biophysical landscape characteristics (e.g. amount and type of vegetation cover, typical land use practice) that affect these interactions. The HGL framework is an expert system that integrates the spatial variability of landscape characteristics and salinity processes to produce a salinity hazard assessment for any given area.

A globally relevant change taxonomy and evidence-based change framework for land monitoring