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

Abstract

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.

Constraining water age dynamics in a south‐eastern Australian catchment using an age‐ranked storage and stable isotope approach

“…We show that this yields a relatively constant streamflow travel time with little temporal fluctuation. These findings agree with the idea that streamflow in the Corin catchment receives contributions of water from relatively long and stable groundwater flowpaths, as indicated in the geological surveys of the region (Cowood et al, 2017 ;Evans, 1987). The Australian climate and geological history has led to large variations in topography and local weathering in the Corin catchment.…”

“…Fractured rock aquifers of the region typically contain large open fractures with a depth limit of 100 m and below this depth the aquifers are considered impermeable (Evans, 1987). The distance from recharge zone to discharge zone in the fractured‐rock aquifers can range up to 10 km and flow times are estimated to be around months to years for the shorter flow paths, and 10 to 20 years for the longer flow paths (Cowood et al, 2017; Evans, 1987).…”

“…Alluvial soils, organosols and acid peats are found on areas of imperfect drainage, drainage lines and the swampy valley floor. Hydraulic conductivities typically range from 10 −2 to 10 metres per day within the catchment (Cowood et al, 2017).…”

“…The dominant geologic parent materials are Silurian granitoids (adamellite, leucoadamellite, leucogranite) and Ordovician metasediments ( Figure 1) (Abell, 1992). Depth to regolith is shallow on the crests, upper slopes and mid slopes (<5 m), trending deeper towards the valley floor (Cowood et al, 2017). Fractured rock aquifers of the region typically contain large open fractures with a depth limit of 100 m and below this depth the aquifers are considered impermeable (Evans, 1987).…”

Runoff Threshold Responses in Continental Boreal Catchments: Nexus of Subhumid Climate, Low‐Relief, Surficial Geology, and Land Cover

“…Intercatchment comparisons have increased our understanding of how topography (Dreps et al., 2014), soil pedology (Gannon et al., 2014; Lin et al., 2006; Testzlaff et al., 2014), and surficial or bedrock geology (Cowood et al., 2017; O’Sullivan et al., 2020; Pfister et al., 2017) interact with overlying vegetation to influence the spatial distribution of soil–groundwater S and catchment (antecedent) moisture states. These characteristics, in turn, define differences or similarities in the memory of wetting and drying periods, and thus surface and subsurface hydrological connectivity, and the degree of nonlinearity or abruptness of the threshold response to current P events (Buttle & Eimers, 2009; McNamara et al., 2005; Nippgen et al., 2011).…”