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 companion submethodology 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 (Bioregional Assessment Programme, 2017, Dataset 1; companion product 1.3 for the Namoi subregion (O’Grady et al., 2015) 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 Namoi assets workshop. The Assessment team identified these assets for fitness for BA purpose, location within the assessment extent and water-dependency. Assets that satisfy the requirements form part of the impact and risk analysis.

Landscape classification discretises the heterogeneous landscape into a manageable number of landscape classes for impact and risk analysis. A landscape class is a surface ecosystem with characteristics that are expected to respond similarly to changes in groundwater and/or surface water due to coal resource development. There is expected to be less heterogeneity in the response within a landscape class than between landscape classes. 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 Namoi subregion is located in companion product 2.3 (Herr et al., 2018), and companion submethodology M05 (as listed in Table 1) provides the methodology for developing a conceptual model of causal pathways (Henderson et al., 2016).

Assessing potential hydrological changes involves 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 is 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 the ruled-out landscape classes in the Namoi subregion.

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 exists 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.

Experts built eight receptor impact models and these represent two landscape groups in the Namoi subregion (Table 3), which formed the basis for quantifying 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 Namoi subregion (Ickowicz et al., 2018)). Expert elicitation provided meaningful hydrological response variables and receptor impact variables (Table 3, see also Table 5 in companion product 2.7 (Ickowicz et al., 2018)) during qualitative and receptor impact model building workshops and subsequent follow-up. While the groundwater and surface water models operate on different time steps, coupling of individual runs ensured dependence between the groundwater and surface water runs for the receptor impact modelling. 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.

A full description of the receptor impact modelling is provided in companion submethodology M08 (as listed in Table 1) for recept impact modelling (Hosack et al., 2018). This includes Table 4 in Section 2.7.1.2.6, which summarises some of the assumptions made for the receptor impact modelling, the implications of those assumptions for the results, and how those implications are acknowledged through the BA workflow and ultimately within this product. Examples of the main assumptions include the simplification of complex ecological systems, the segregation of the system into discrete classes that are assumed to respond similarly to hydrological changes, and the assumption that areas of landscapes classes remain constant over time (see Table 4 in companion product 2.7 for the Namoi subregion (Ickowicz et al., 2018) for the complete list of assumptions). The specific implications or flow-on effects of these assumptions are further explained in the respective sections for individual landscape classes in companion product 2.7 (Ickowicz et al., 2018). It is also important to note that the outputs from receptor impact modelling (which translate potential hydrological change into potential change in ecosystem indicators) are only one line of evidence used in this impact and risk analysis, and these outputs need to be considered in the context of the assumptions made and the availability and quality of local data.

Table 3 Receptor impact models and their variables


Landscape class group

Receptor impact model

Hydrological response variable

Receptor impact variable (RIV)

Floodplain or lowland riverine

Floodplain riparian forest

  • Maximum difference in drawdown under the baseline future or under the coal resource development pathway future relative to the reference period (1983 to 2012)
  • The mean annual number of events with a peak daily flow exceeding the threshold (the peak daily flow in flood events with a return period of 3.0 years as defined from modelled baseline flow in the reference period (1983 to 2012)). This metric is designed to be approximately representative of the number of overbank flow events in future 30-year periods.

Projected foliage cover of dominant riparian trees (river red gum)

Floodplain or lowland riverine

Floodplain wetland (GDE and non-GDE)

  • The mean annual number of events with a peak daily flow exceeding the threshold (the peak daily flow in flood events with a return period of 3.0 years as defined from modelled baseline flow in the reference period (1983 to 2012)). This metric is designed to be approximately representative of the number of overbank flow events in future 30-year periods.

Probability of presence of tadpoles from Limnodynastes genus (L. dumerilii, L. salmini, L. interioris and L. terraereginae) in pools and riffles

Floodplain or lowland riverine

Permanent and temporary lowland streams (GDE and non-GDE)

  • The number of zero-flow days per year, averaged over a 30-year period
  • The maximum length of spells (in days per year) with zero flow, averaged over a 30-year period

Average number of families of aquatic macroinvertebrate in edge habitat

Floodplain or lowland riverine

Pilliga riverine

(two models, one for each RIV)

  • The number of zero-flow days per year, averaged over a 30-year period
  • The maximum length of spells (in days per year) with zero flow, averaged over a 30-year period
  • Maximum difference in drawdown under the baseline future or under the coal resource development pathway future relative to the reference period (1983 to 2012)
  • Projected foliage cover
  • Average number of families of aquatic macroinvertebrates in instream pool habitat

Non-floodplain or upland riverine

Upland riparian forest GDE

  • Maximum difference in drawdown under the baseline future or under the coal resource development pathway future relative to the reference period (1983 to 2012)
  • The mean annual number of events with a peak daily flow exceeding the threshold (the peak daily flow in flood events with a return period of 3.0 years as defined from modelled baseline flow in the reference period (1983 to 2012)). This metric is designed to be approximately representative of the number of overbank flow events in future 30-year periods.

Projected foliage cover of riparian trees

Non-floodplain or upland riverine

Permanent and temporary upland streams (GDE and non-GDE)

Upland riverine

  • The number of zero-flow days per year, averaged over a 30-year period
  • The maximum length of spells (in days per year) with zero flow, averaged over a 30-year period
  • Average number of families of aquatic macroinvertebrates in instream pool habitat
  • Probability of presence of tadpoles from Limnodynastes genus (L. dumerilii, L. salmini, L. interioris and L. terraereginae)

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 assets that are at ‘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 greater 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 landscape classes containing temporary or lowland streams 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/NAM/landscapes and www.bioregionalassessments.gov.au/explorer/NAM/assets.

Last updated:
6 December 2018
Thumbnail of the Namoi subregion

Product Finalisation date

2018
PRODUCT CONTENTS

ASSESSMENT