3.2.4 Assessing potential impacts for landscape classes and assets

The approach for assessing potential impacts on landscape classes and water-dependent assets is discussed in M10 (as listed in Table 1) for analysing impacts and risks (Henderson et al., 2018). The principal focus of BAs is water-dependent assets that are nominated by the community. These assets may have a variety of values, including ecological, sociocultural and economic values.

The water-dependent asset register (see companion product 1.3 for the Hunter subregion (Macfarlane et al., 2016) and Bioregional Assessment Programme, 2017, Dataset 13) provides a simple and authoritative listing of the assets within the assessment extent. The register is a compilation of assets identified in Local Land Services (formerly Catchment Management Authorities) databases and Commonwealth and state databases, and through the Hunter assets workshop. The identified assets were assessed by the Assessment team for fitness for BA purpose, location within the assessment extent and water-dependency. Assets that satisfy the requirements are considered in the impact and risk analysis reported in this product.

Landscape classification discretises the heterogeneous landscape into a manageable number of landscape classes for impact and risk analysis. Landscape classes represent key surface ecosystems, having broadly similar physical, biological and hydrological characteristics that are expected to respond similarly to changes in groundwater and/or surface water due to coal resource development. They are used to reduce some of the complexity inherent in assessing impacts on a large number of water-dependent assets by focusing on the hydrological drivers and interactions relevant to a regional-scale assessment. The landscape classes provide a meaningful scale for understanding potential ecosystem impacts and communicating them through their more aggregated system-level view. The landscape classification for the Hunter subregion is described in companion product 2.3 (Dawes et al., 2018) and the methodology that underpins it is described in companion submethodology M05 (as listed in Table 1) for developing a conceptual model of causal pathways (Henderson et al., 2016).

Potential hydrological changes are assessed by overlaying the extent of a landscape class or asset on the zone of potential hydrological change. For the landscape classes or assets that lie outside the zone, the magnitude of the hydrological changes are considered very unlikely to result in adverse impacts, and thus they can be ruled out in terms of further assessment. Section 3.4.2 identifies landscape classes in the Hunter subregion that can be ruled out on this basis.

Where an asset or landscape class wholly or partially intersects the zone of potential hydrological change, there is the potential for impact. This does not mean there will be an impact, but rather based on the magnitude of the hydrological change, the possibility of an impact cannot be ruled out and further investigation is required. The nature of the water dependency of the landscape class can be important for informing the assessment. For example, if the water dependence of a landscape class relates to overbank flows to support seedling establishment, but the predicted hydrological changes in the nearby stream relate only to low-flow variables (i.e. flows within the bank), then it may be possible to rule out the landscape class from further consideration because it is very unlikely to be impacted.

Six receptor impact models were built, representing five landscape classes in the Hunter subregion (Table 5), and are used to quantify the impact of the predicted hydrological changes on one or more receptor impact variables within the receptor impact model (see companion product 2.7 for the Hunter subregion (Hosack et al., 2018a)). Meaningful hydrological response variables and receptor impact variables (Table 5) were elicited from experts (listed in Table 3 in companion product 2.7 (Hosack et al., 2018a)) during qualitative and receptor impact model building workshops and subsequent follow-up by email. The receptor impact variables serve as indicators of ecosystem response for the landscape class or ecosystem represented in the model. Within a landscape class at a specific location, local information, such as condition of the associated habitat, species diversity and abundance, presence of other stressors (e.g. agricultural or urban land uses) and recovery potential will influence the perception of risk and whether risk management measures are required to minimise potential impacts. The assumptions and limitations of the receptor impact modelling are described in Table 4 in companion product 2.7 (Hosack et al., 2018a).

A full description of the receptor impact modelling is provided in companion submethodology M08 (as listed in Table 1) for receptor impact modelling (Hosack et al., 2018b).

Table 5 Receptor impact models and their variables

Receptor impact model

Landscape class

Hydrological response variable

Receptor impact variable

Perennial streams – riffle-breeding frog

Permanent or perennial streams

Number of zero-flow days per year, averaged over a 30-year period (ZQD)

Maximum length of spells (in days per year) with zero flow over a 30-year period (ZMA)

Probability of presence of riffle-breeding frog (over 100 m transect)

Perennial streams – Hydropsychidae larvae

Permanent or perennial streams

Mean number of Hydropsychidae larvae (per m2 of riffle habitat)

Intermittent streams – riffle-breeding frog

Lowly to highly intermittent streamsa

Number of zero-flow days per year, averaged over a 30-year period (ZQD)

Maximum length of spells (in days per year) with zero flow over a 30-year period (ZMA)

Probability of presence of riffle-breeding frog (over 100 m transect)

Intermittent streams – hyporheic invertebrate taxa

Lowly to highly intermittent streamsa

Mean hyporheic taxa richness (in 6 L of water pumped from 40 cm below the streambed)

Wet and dry sclerophyll forests

Wet sclerophyll forest

Maximum drawdown (dmax)

Year of maximum change (tmax)

Projected foliage cover (m2/m2)

Dry sclerophyll forest

Forested wetland – riverine forest

Forested wetland

Overbank events (EventsR3.0)

Overbench events (EventsR0.3)

Maximum drawdown (dmax)

Year of maximum change (tmax)

Projected foliage cover (m2/m2)

aThe ‘Lowly to moderately intermittent’ and ‘Moderately to highly intermittent’ landscape classes are treated as one landscape class.

Potential impacts are reported in Section 3.4 for landscape classes and in Section 3.5 for assets. Given the large number of assets, the focus of Section 3.5 is on identifying the assets that are ‘more at risk of hydrological changes’ within the zone of potential hydrological change. These are the assets that overlap with areas in the zone that have at least a 50% chance of a hydrological change larger than the threshold hydrological response variable values used to define the zone. Local information is necessary to improve upon the regional-scale risk predictions at any given site.

In addition, impact profiles for landscape classes and assets are available at www.bioregionalassessments.gov.au. Each profile summarises the hydrological changes and potential impacts that pertain to that landscape class or asset (e.g. increase in the number of low-flow days for the ‘Permanent or perennial streams’ landscape class in the zone of potential hydrological change). Users can aggregate and consider potential impacts for their own scale of interest.

Users can also explore the results for landscape classes and assets using a map-based interface at www.bioregionalassessments.gov.au/explorer/HUN/landscapes and www.bioregionalassessments.gov.au/explorer/HUN/assets. Information management and processing

A very large number of multi-dimensional and multi-scaled datasets is used in the impact and risk analysis for each BA, including model outputs, and ecological, economic and sociocultural data from a wide range of sources. To manage these datasets and produce meaningful results, a consistent spatial framework is needed that permits rapid spatial and temporal analyses of impacts without compromising the resolution of the results.

The datasets for this BA are organised into an impact and risk analysis database (Bioregional Assessment Programme, Dataset 14) to enable efficient management. The purpose of this database is to produce result datasets that integrate the available modelling and other evidence across the assessment extent of the BA. These datasets are required to support three types of BA analyses: analysis of hydrological changes, impact profiles for landscape classes, and impact profiles for assets. The results of these analyses are summarised in this product, with more detailed information available at www.bioregionalassessments.gov.au. The impact analysis database is also available at data.gov.au.

The datasets used in the impact and risk analysis database (Bioregional Assessment Programme, Dataset 14) include the assets, landscape classes, modelling results (groundwater, surface water and receptor impact modelling), coal resource development ‘footprints’ and other relevant geographic datasets, such as the boundaries of the subregion, assessment extent and zone of potential hydrological change. All data in the impact and risk analysis database (and the results derived from it) meet the Programme requirements for transparency.

The impact and risk analysis requires the geoprocessing of complex queries on very large spatial datasets. To overcome the computational overload associated with this task a relational approach, rather than a geospatial approach, was utilised. All dataset geometries are split against a universal grid of assessment units that exhaustively cover the assessment extent (Figure 13). An assessment unit is a geographic area represented by a square (1 km2) polygon with a unique identifier. Assessment units were used to partition asset and landscape class spatial data for impact analysis. The gridded data can be combined and recombined into any aggregation supported by the conceptual modelling, causal pathways and model data.

The gridded data were normalised and loaded into the impact and risk analysis database (Bioregional Assessment Programme, Dataset 14).

Impact area, length and counts are calculated for individual features (e.g. stream reaches, individual assets, groups of assets or landscape classes) at the assessment unit level. An individual analysis result is executed by selecting the assessment units of interest and summing the pre-calculated values of area, length or count for the required dataset. This approach of front-loading the geospatial analysis through grid-base attribution is fundamental to enabling the volume of calculations required to complete the assessment. The approach uses the source geometries in calculation and hence does not impact on the analysis calculations. In a few cases, source geometries were found to create geospatial errors and were removed from the analysis. The removing of invalid geometries did not, in any case, affect the analysis results more than a combined total area of one assessment unit per geospatial item.

The interpolated modelled groundwater drawdowns are at the same resolution as the assessment unit and contain a single value per assessment unit. However, the surface water modelling generates results at points that are extrapolated to links (see Section, which must then be mapped to assessment units. For assessment units with only a single stream reach, the assessment unit stores the information associated with this stream segment. However, where the assessment unit contains multiple stream reaches (e.g. at the confluence of two streams), it is necessary to prioritise which stream reach is used to inform the value of the assessment unit for representing the surface water modelling results. The general rules for prioritising a stream reach take into account:

  • whether the modelled reaches show a hydrological change (i.e. a reach with a potential hydrological change takes priority over a reach predicted to have no significant change)
  • whether the stream reach is represented in the model (i.e. modelled reaches take priority)
  • reach length (i.e. where two streams in an assessment unit are of equally high stream order, priority is given to the longer of the two).

Figure 13

Figure 13 Assessment units across the assessment extent

Data: Bioregional Assessment Programme (Dataset 15)

Last updated:
15 March 2019
Thumbnail of the Hunter subregion

Product Finalisation date