The Maranoa-Balonne-Condamine preliminary assessment extent (PAE) contains a diverse range of assets that span ecological, sociocultural and economic values. Landscape classification is used to characterise the nature of water dependency among a diverse range of assets, based on key landscape properties associated with geology, geomorphology, hydrology and vegetation (natural and human-modified ecosystems). The primary objective of the landscape classification is to conceptualise the main biophysical and human systems at the land surface and describe their hydrological connectivity. Hydrological connectivity describes how different biophysical factors, such as flow regime, influence the spatial and temporal patterns of connection between elements of the water cycle, including surface water and/or groundwater systems . For example, surface water connectivity can be longitudinal along the river channel itself, lateral during overbank flows and, vertical where surface water is in contact with underlying groundwater . Assets are grouped based on functional criteria depending on their association with a particular landscape class. This section describes the methodology and datasets used to classify the landscape in the Maranoa-Balonne-Condamine PAE, following methods outlined in the companion submethodology M03 (as listed in Table 1) for assigning receptors to water-dependent assets .
Many different classification approaches and methodologies have been developed to provide consistent and functionally relevant representations of ecosystems such as the Australian National Aquatic Ecosystem (ANAE) Classification Framework . Where appropriate, the approach outlined in this section is integrated with and builds on these existing classification systems. The landscape classification for the Maranoa-Balonne-Condamine PAE predominantly uses existing classes within data associated with aquatic ecosystems, GDEs, remnant vegetation and land use mapping. The landscape classification uses data layers consisting of polygons (e.g. remnant vegetation, terrestrial/surface GDEs; subsurface GDEs or wetlands), polylines (stream network) and points (springs and spring complexes). The Assessment team describes 34 landscape classes, which are aggregated into five landscape groups with similar habitats, topography, water dependency and water regime. This initial classification helps to place the landscape classes within a common biophysical system that aids in formulating conceptual models and patterns in water dependency across the landscape.
Classification of remnant and human-modified landscape elements
The approach was formulated in close collaboration with experts who have extensive experience with the landscapes of the PAE and have contributed to the development of similar classification systems such as the ANAE . The classification uses broad geomorphological, soil, hydrological and habitat information to derive classes of water-dependent, remnant and human-modified landscape features to produce landscape classes that capture key distinctions using the following rule sets (Table 3):
- broad habitat type (remnant/non-remnant)
- geomorphology (floodplain/non-floodplain)
- groundwater source (Great Artesian Basin (GAB)/non-GAB/non-groundwater dependent)
- wetland (wetland/non-wetland)
- water regime (near-permanent/temporary/null).
Table 3 Landscape classification rule sets used for the landscape elements (polygons)
Vegetation is classed as either ‘remnant’, if it is mapped in the current Queensland remnant vegetation mapping (Queensland Herbarium, ), or ‘non-remnant’ if it is defined using the Queensland pre-clearing vegetation mapping (Queensland Herbarium, ). This classifier delineates between ‘human-modified’ landscapes and those that are relatively intact. This distinction has important consequences for defining where important habitats and biota may occur when considering assets and their likely distribution. Ecological assets, such as potential species and community distributions, will often be confined to those areas that have been mapped as ‘remnant’ and assist in focusing the analysis of potential impacts on assets within the landscape.
The PAE is also divided into floodplain and non-floodplain areas based on the Land Zones of Queensland , where Land Zone 3 is defined as recent Quaternary alluvial systems (Queensland Herbarium, ). This helps to broadly characterise which landscape features, such as wetlands, might be influenced by flooding regimes that are more likely to support water-dependent vegetation and habitats.
All wetland types (palustrine, lacustrine or riverine) are classed as wetlands and given the water regime described in the Queensland wetlands dataset (DSITIA, ). This means that only those elements classed as ‘wetland’ receive a ‘water regime’ class. These two classifiers are necessary for identifying typically water-dependent features and their associated water regimes. The distribution and persistence of aquatic ecosystems is influenced largely by water regime and is a key attribute for differentiating and characterising habitats and ecosystems.
The distinction between GDEs based on their likely groundwater source (GAB versus non-GAB) is done using the Queensland GDE mapping and classification (DSITIA, ). GAB GDEs associated with unweathered sandstones (excluding springs) tend to occur on sandstone outcrop areas or sandstone ridges that are important GAB recharge areas. Expression of groundwater around these outcrop areas occurs along foot slopes and channels due to fractures and weathered zones in these otherwise low-permeability rocks. Those remaining GDEs falling into the ‘non-GAB’ class include GDEs associated with floodplain alluvia (floodplain, non-GAB GDE) or non-floodplain landscapes including permeable rock or basalt systems and inland sand ridges (non-floodplain, non-GAB GDE).
Land use mapping data (ABARES, ) are used to classify all landscape elements identified as ‘non-remnant’ into six land use types (Table 4) that comprise the human-modified group of landscape classes:
- conservation and natural environments
- intensive uses
- modified water bodies
- production from dryland agriculture and plantations
- production from irrigated agriculture and plantations
- production from relatively natural environments.
Table 4 Landscape classification rule sets used for the human-modified landscape polygons
The landscape classification (Bioregional Assessment Programme, ) covers the entire PAE and includes all remnant and non-remnant vegetation polygons. Landscape classes are defined using the five classifiers described in Table 3 with their nomenclature reflecting key water-dependency attributes. For example, a landscape element classified as ‘remnant’, ‘floodplain’, ‘non-GAB GDE’, ‘non-wetland’ and ‘null’ has the landscape class of ‘Floodplain, non-GAB GDE’. In other words, this element is remnant vegetation that is a GDE associated with a non-GAB floodplain aquifer, but is not classed as a wetland.
Classification of the stream network
Streams in the PAE are classified based on their catchment position, water regime and association with GDEs. Catchment position (i.e. upland versus lowland) exerts a strong influence on stream geomorphology, flow patterns and associated biota. Water regime is critical in determining habitat suitability for stream biota and is influenced by the geomorphology and hydrology of the stream channel and riparian zone. Rivers and streams can also receive significant baseflow inputs from local and regional groundwater systems and act as recharge sources to support GDEs. Differentiating between GDEs associated with GAB flow paths and aquifers and other non-GAB GDEs is also considered to be important given the regional-scale hydrological connectivity between GAB aquifers and coal measures under development. The level of human modification was not explicitly included in the classification system of the stream network and is therefore confined to the landscape classes included in the polygon data layers (see detail provided earlier in this section). The influence of different landscape classes adjacent to the in stream or riverine landscape classes is critical, yet difficult to distinguish based on the available data on the stream network.
The Assessment team classified the entire stream network in the Maranoa-Balonne-Condamine PAE to provide a consistent classification of the riverine landscapes (Bioregional Assessment Programme, ). Two classifiers (catchment position and water regime) from the ANAE rule sets are used to classify the stream network (Table 5). The stream network data are based on the Geofabric v2 cartographic mapping of river channels derived from 1:250,000 topographic maps (Bureau of Meteorology, ). The Geofabric is a purpose-built geographic information system (GIS) that maps Australian rivers and streams and identifies how stream features are hydrologically connected. The water regime of these stream networks is also defined (near-permanent or temporary) using associated attributes in the Geofabric dataset (Bureau of Meteorology, ) (see Table 5 for details). Mapping of valley bottom flatness (MrVBF) (CSIRO, ) is used to classify streams as either upland or lowland following methods outlined in (Table 5). The presence of GDEs associated with individual stream segments is classified as GAB, non-GAB or non-GDE using the Queensland GDE data (DSITIA, ). Spatial analysis is used to identify surface expression of GDEs contained in the Queensland eastern Murray-Darling Basin mapping (polylines; DSITIA, ) that intersects with the stream network.
Table 5 Landscape classification rule sets used for the stream network polylines
Classification of springs
The springs and spring complexes contained in the asset database for the Maranoa-Balonne-Condamine subregion (Bioregional Assessment Programme, ) are classified based on groundwater source: GAB or non-GAB. Information on source aquifer is derived from the most recent database for spring vents in this region (OGIA, ). GAB springs tend to be associated with sandstone aquifers such as Springbok and Precipice sandstones, whereas non-GAB springs have source aquifers in basalt aquifers associated with the Main Range Volcanics or other non-GAB sediments (Table 6).
Table 6 Landscape classification rule sets used for springs and spring complexes
The datasets used for this classification approach are derived from on-ground mapping and other approaches (e.g. remote sensing) done at different spatial resolutions. Thus, the precision and accuracy of different elements comprising the landscape class varies depending on the source data. Furthermore, integrating these data sources into a consistent and complete landscape classification of the PAE inevitably produces some mismatches in the element boundaries – an issue that was minimised by giving preference to those spatial data layers with the greatest level of accuracy and confidence when producing a union of the final landscape elements. Thus, the potential for misclassification is most likely within datasets mapped at lower resolution such as the land use mapping (ABARES, ), whereas the wetland mapping boundaries (DSITIA, ), a layer given priority over the land use data, remained intact and were reclassified according to the landscape classification rule set discussed above.
The logic of the landscape classification rule sets used in the Maranoa-Balonne-Condamine subregion is shown in Figure 17.
GAB = Great Artesian Basin, GDE = groundwater-dependent ecosystem, GAB GDEs… = GAB GDEs (riverine, springs, floodplain, non‑floodplain), Non-floodplain… = Non-floodplain or upland riverine (including or non-GAB GDEs)
Product Finalisation date
- 2.3.1 Methods
- 2.3.2 Summary of key system components, processes and interactions
- 2.3.3 Ecosystems
- 2.3.4 Baseline and coal resource development pathway
- 184.108.40.206 Developing the coal resource development pathway
- 220.127.116.11 Water management for the coal resource developments
- 18.104.22.168 Gaps
- 2.3.5 Conceptual model of causal pathways
- 22.214.171.124 Methodology
- 126.96.36.199 Hazard analysis
- 188.8.131.52 Causal pathways
- 184.108.40.206 Gaps
- Contributors to the Technical Programme
- About this technical product