2.7.3.1 'Floodplain or lowland riverine' landscape group

Table 6 Areas and/or lengths of the ‘Floodplain or lowland riverine’ landscape classes within the entire Namoi subregion assessment extent and the non-Pilliga region of the zone of potential hydrological change

na = not applicable

GDE = groundwater-dependent ecosystem

Figure 7 Location of the 'Floodplain or lowland riverine' landscape group within the zone of potential hydrological change in the Namoi assessment extent

Data: Bioregional Assessment Programme (Dataset 2, Dataset 4); Bureau of Meteorology (Dataset 3)

Riverine environment

Lowland streams in the assessment extent include the Namoi River and its tributaries and are low‑gradient channels typically incised into alluvium with silt or sandy beds (Figure 8). There are limited riffles and fast-water habitat in these streams, and in those stream reaches with more temporary water regimes, habitat is mostly in pools. In streams such as Maules Creek, the channel is incised into sands and sandy gravels with some riffles and cobble-bottomed stretches.

Figure 8 Namoi River 20 km north of Gunnedah on the Liverpool Plains

Credit: Bioregional Assessment Programme, Patrick Mitchell (CSIRO), January 2016

Lowland stream systems in the Namoi subregion encompass a range of flow regimes (Table 7). Riverine landscape classes classified as ‘permanent’ have surface flows greater than 80% of the time and are mostly found along the Namoi River and the lower reaches of Mooki Creek and the Peel River. Streams classified as ‘temporary’ have surface flows less than 80% of the time and cover a large collection of small tributaries to the Namoi River on the Liverpool Plains and Castlereagh-Barwon regions (Bioregional Assessment Programme, Dataset 1). These landscape classes broadly relate to the classification of Kennard et al. (2010). The ‘permanent’ streams correspond to the ‘stable baseflow’ classes (Classes 1, 2 and 3) (Kennard et al., 2010) and have flow at least 80% of the year, and a baseflow index of 0.15 to 0.40. The riverine landscape classes classified as ‘temporary’ correspond broadly to the ‘unpredictable baseflow’ and ‘intermittent’ classes (Classes 4 and 5 to 8) (Kennard et al., 2010). Rarely to highly intermittent streams are characterised by streams that cease flowing more often than perennial streams and have a lower (0.10 to 0.35) baseflow contribution (Kennard et al., 2010). Highly intermittent or ephemeral streams are characterised by small baseflow contributions (<0.15) and large numbers of zero-flow days (>50) (Kennard et al., 2010).

Table 7 Maximum, median, 10th, 25th, 75th and 90th percentile flows (ML/d) for the three streamflow gauging stations along lowland streams in the Namoi assessment extent

Water resource development along the Namoi River has affected the water regime of the riverine and floodplain environments and their ecological character. For example, there has been an increase in the average and maximum period between flooding of off-channel water bodies (e.g. palustrine wetlands) (CSIRO, 2007) of 27% and 50%, respectively. These changes in frequency have been accompanied by a reduction in annual flooding volume (28% less) (CSIRO, 2007). Despite these changes, the hydrologic condition based on the Hydrology Index score (HI) was deemed to be good across most of the catchment (OEH, 2010). However, the fish condition in terms of both ‘nativeness’ (the proportion of the fish assemblage that is native versus introduced) and ‘expectedness’ (the proportion of species collected during sampling that were expected to have occurred in each basin zone before European colonisation) was reported as being poor in the NSW State of the Catchments 2010 report in the Namoi region (OEH, 2010). Macroinvertebrate condition was poor to moderate. The pressures from alien fish species, changes in water temperature from dam releases (Lake Keepit), artificial barriers to movement and other land use and climate change effects were seen as important for these results.

The following section provides a summary of the current state of knowledge of the linkages between surface water and groundwater hydrological regimes in lowland streams and floodplains to ecological function and composition. It helps to inform the discussion on the nature of the ecological modelling and choice of hydrological response variables associated with this landscape group as discussed in subsequent sections of this report.

Surface water flow regimes are defined by the timing, frequency, duration, magnitude, discharge volume and rates of the rise and fall of flow events (Boulton et al., 2014; Poff et al., 2010). Connectivity between the floodplain and stream channel riverine environments arises from longitudinal, lateral and vertical exchange of water. This connectivity can be described by surface water hydrological response variables that span the flow regime captured by the flow duration curve. Ecologically important components of the surface water regime can be broadly summarised (Dollar, 2004) as cease-to-flow periods, periods of low flows and base flows (or those intermediate of low flows and freshes), freshes, and periods of high flow (including overbench and overbank flows). These are illustrated in Figure 9.

Figure 9 The spectrum of flow types in a river or stream segment

Source: Symbols courtesy of the Integration and Application Network, University of Maryland Center for Environmental Science (ian.umces.edu/symbols/).

Larger pulses of river flow increase lateral connectivity within the streambed and provide access to new habitats, including river benches (Robson et al., 2009) (Figure 9). An increase in localised velocity profiles, especially around snags and along river banks creates new habitat for fish and stimulates downstream drift in macroinvertebrates (Boulton et al., 2014). Greater exchange in surface water and groundwater through the hyporheic zone also tends to occur, promoting improved water quality of the alluvial aquifers and increasing recharge to groundwater.

Connectivity between the river channel and the floodplain is essential for ecosystem health and is dependent on the timing, duration and frequency of overbank flows (Watts et al., 2009). These flows are capable of inundating the adjacent floodplain and filling off-channel water bodies and provide opportunities for the migration and exchange of riparian and floodplain biota and nutrients (Boulton et al., 2014). The presence of off-channel water bodies is a distinguishing feature of lowland systems compared to upland streams in the Namoi subregion assessment extent. Overbank flows also modify channel and floodplain geomorphology. Overbank flooding leads to deposition of nutrients and sediments on floodplains (Watts et al., 2009) and provides important wetland habitat for fish (NSW DPI, 2014) and frogs (Watts et al., 2009). Floodplain vegetation growth and life cycles are highly dependent on the depth, duration, frequency and timing of inundation (Roberts and Marston, 2011; Rogers, 2011). For example, regeneration of river red gum (Eucalyptus camaldulensis) is intimately linked to patterns of inundation along the riparian and floodplain zones, with regular flooding events required for seedling establishment (Roberts and Marston, 2011). The understorey composition in these forests is also closely linked to the flooding regime, with more regularly flooded sites having a sedge (Carex, Eleocharis spp.) dominated community and sites flooded less frequently containing native grasses (Bren and Gibbs, 1986). Overbank flooding maintains the health of floodplain vegetation through provision of freshwater, leaching of soil salinity and regeneration of floodplain species (Doble et al., 2012; Roberts and Marston, 2011). However, there is considerable uncertainty associated with the degree of connection between floodplains and alluvial aquifers at local and regional scales.

Freshes or pulse flows, characterised by moderate increases in streamflow, increase within-stream flow variability and play an important role in the regulation of water quality through the input of freshwater and flushing of deeper pools (Robson et al., 2009). Two main types of pulse flows are identified by the review of Watts et al. (2009): small and large pulses. Small pulses exceed baseflow and inundate some or all of the streambed (Figure 9) for time periods ranging from hours to days. Large pulses can reach flows up to bankfull stage and typically occur over periods of days to weeks (Figure 9). Flow pulses reset several key processes in the stream environment through active bedload transport, maintaining channel dimensions and scouring streambeds and banks (Watts et al., 2009). Larger stream pulses can represent important spawning triggers (King et al., 2009; NSW DPI, 2014) or inundate benches, anabranches and snags increasing habitat availability (Watts et al., 2009).

Mackay et al. (2014) outline 35 metrics that address the magnitude, frequency, duration and timing of low flows. Dominant low-flow metrics for ephemeral streams based on the analysis of Mackay et al. (2014) include:

Low flows and the cessation of flow play a critical role in maintaining longitudinal connectivity and linking of instream habitats. Cease-to-flow events dry out shallow habitats and can create chains of pools, isolated pools or completely dry riverbeds, depending on riverbed morphology (Robson et al., 2009). Rolls et al. (2012) propose four key principles outlining the mechanistic links between the flow-related attributes at the low end of the hydrograph and the attendant ecosystem processes. Low-flow and zero-flow attributes affect the composition, abundance and structure of aquatic biota by influencing the:

The ecologically relevant low-flow attributes are related to antecedent conditions, duration, magnitude, timing and seasonality, rate of change, and frequency that operate within a temporal hierarchy of the flow regime (Rolls et al., 2012).

The response of aquatic biota to declines in streamflow can be linear while longitudinal connectivity is maintained; but if flow ceases, a more severe threshold response is likely, corresponding to an abrupt loss of specific habitat, change in physicochemical conditions and ecosystem fragmentation (Boulton, 2003). The concept of ‘ramped’ and ‘stepped’ changes in biota in response to declining flows was proposed for macroinvertebrate assemblages by Boulton (2003). The drying river system moves through several discharge thresholds, involving firstly the isolation of riparian habitat, the cessation of flow and the eventual disappearance of surface water (Boulton, 2003). Flow intermittence, the temporary loss of surface water (Datry et al., 2014), prevents the transport of nutrients, biota and organic material downstream, and creates pool environments along the river channel, the quality of which may vary considerably depending on geomorphic condition, health of the extant riparian vegetation, length of the dry period and input of organic matter (Bond and Cottingham, 2008). Extended periods of no flow or an increase in the frequency of no-flow periods is likely to increase the levels of stress in the system through deteriorating water quality (e.g. increases in turbidity, reduced dissolved oxygen and increased temperatures), crowding of biota and reduced hydrological connectivity (Bond and Cottingham, 2008; Marsh et al., 2012) . These no-flow periods can affect seasonal habitat for some species and remove important refugia for other species (Dollar, 2004). Diversity of macroinvertebrates is closely linked to river drying, with the largest drops in species richness occurring in the early stages of drying (Leigh and Datry, 2017). The decline in taxon richness often results in invertebrate communities becoming dominated by ubiquitous taxa (Datry et al., 2014). Chessman et al. (2012) reported that macroinvertebrate assemblages in riffle habitats with fast, flowing water were dominated by aerophilic and rheophilic species, while riffle habitats exposed to severe flow reductions or cessation were dominated by thermophilic species. Marsh et al. (2012) also concluded that communities in streams that are usually perennial but cease to flow for short periods (weeks) will mostly recover the following season but that the community will decline if cease-to-flow periods recur over consecutive years. Alterations to flow intermittence has potentially cascading effects on adjacent ecosystems such as riparian and hyporheic zones (Datry et al., 2007; McCluney and Sabo, 2012).

Mackay et al. (2012) detail a low-flow classification based on 35 low-flow metrics calculated for 830 stream gauge records. Their work concluded that four low-flow metrics provided meaningful biological information in most situations:

Their recommendations include the caveat that these are general guidelines and the broad generalisations associated with refining this subset should only be used as a general guide to ecological conditions (Mackay et al., 2012).

Groundwater and river system interactions

While the interconnections between groundwater and river systems are poorly understood in many catchments globally (Ivkovic, 2009), the Namoi river basin has had several detailed studies that have investigated these relations. In general, the contribution of groundwater to baseflow has declined in the lower sections of the Namoi River because of groundwater abstraction from the surrounding alluvium (Giambastiani et al., 2012). The decrease in hydraulic head in recent decades has reversed the flow of groundwater, shifting the once ‘gaining’ condition of the river to a ‘losing’ one (CSIRO, 2007; Giambastiani et al., 2012). These trends in surface water – groundwater interactions may have significant implications for the riverine environment, particularly during low-flow periods. This section focuses on ecohydrological processes relevant to the ecological outcomes and complements the discussion of surface and groundwater interactions in Section 2.1.5 in the companion product 2.1-2.2 for the Namoi subregion (Aryal et al., 2018).

Andersen and Acworth (2009) studied the nature of surface water and groundwater exchange along a section of the Namoi River and a zone of perennial pools along Maules Creek (see Figure 10). This study detected zones of groundwater discharge along Maules Creek (reaches classified as ‘Temporary lowland stream’ or ‘Permanent lowland stream’ under the BA landscape classification; Bioregional Assessment Programme, Dataset 1) that flow between pools in the streambed sediments (sand and coarse gravel) (Andersen and Acworth, 2009). Further downstream, stream water appears to be recharging the aquifer when it comes into contact with highly permeable paleochannels (Andersen and Acworth, 2009). In a recent report concerning the assessment of ecohydrological responses to coal seam gas and coal mining (Andersen et al., 2016), much of Maules Creek (excluding the most downstream ~2 km) was characterised as predominantly a ‘losing transition’ stream with intermittent surface flow (Brunner et al., 2009). This definition implies that the capillary fringe of the watertable remains in contact with the stream and that floodplain and riparian vegetation can access this groundwater. However, lowering of the watertable can make this groundwater unavailable to vegetation (Andersen et al., 2016).

Figure 10 Riffle habitat along Maules Creek ('Temporary lowland stream' landscape class, ~15 km upstream from its junction with the Namoi River)

Credit: Bioregional Assessment Programme, Patrick Mitchell (CSIRO), January 2016

Stygofauna live in groundwater systems and are particularly sensitive to groundwater environmental disturbance because they are adapted to near steady-state environmental conditions and have very narrow spatial distributions (Hose et al., 2015). The shallow aquifers of the Namoi River and Peel River alluvium support diverse invertebrate and microbial assemblages (Korbel et al., 2013; Tomlinson, 2008). The most important factors influencing the community composition of stygofauna in the region were land use, soil type and carbon availability (Korbel et al., 2013). The microbial assemblages were more affected by ionic water quality and season of sampling (Korbel et al., 2013). Studies of stygofauna in the nearby Gwydir River alluvial aquifer concluded that human impacts on streamflow and aquifer conditions had a large influence on stygofauna community structure, with those bores exhibiting minimal riverine influence having higher richness and abundance (Menció et al., 2014). Experimental studies of drawdown in the Peel Valley alluvium found different responses among stygofauna to changes in the watertable, with copepods showing vertical movement to changing water availability in contrast to amphipods that did not increase their movement (Tomlinson, 2008).

Floodplain environment

The floodplain environment extends from the riparian zone adjacent to the stream channel back across the alluvial plain that receives flood waters at various intervals. The riparian environment is represented by the ‘Floodplain riparian forest’ and ‘Floodplain riparian forest GDE’ landscape classes and is dominated by tree species such as river red gum (Eucalyptus camaldulensis) and river sheoak (Casuarina cunninghamiana) (Benson et al., 2010). These are represented by the ‘Eastern Riverine Forests’ and ‘Inland Riverine Forests’ classes in the Keith vegetation classification system (Keith, 2004).

Adjacent to the riparian zone is the floodplain environment, representing the transition between the frequently flooded river channel and the upland environment. Landscape classes occurring in the floodplain environment include ‘Floodplain grassy woodland’ and ‘Floodplain grassy woodland GDE’. This floodplain environment contains woodlands and various types of off-channel water bodies or wetlands with varying degrees of groundwater dependency (Holloway et al., 2013). The back plain environment tends to be dominated by woodland species such as poplar box (Eucalyptus populnea), black box (E. largiflorens), coolibah (E. coolabah), river coobah (Acacia stenophylla) and other Eucalyptus spp., shrubs and grasses (most commonly plains grass – Austrostipa aristiglumis) (Eco Logical, 2009). Off-channel water bodies are also interspersed along the floodplain and include the ‘Floodplain wetland’ and ‘Floodplain wetland GDE’ landscape classes. These tend to be palustrine wetlands, typically described as swamps, bogs, marshes and prairies (Aquatic Ecosystems Task Group, 2012). Flooding frequency, duration and depth tend to be reduced for the floodplain wetland landscape classes that tend to have a temporary water regime. Several listed ecological communities are found in the floodplain landscape group areas including the ‘Coolibah - Black Box Woodlands of the Darling Riverine Plains and the Brigalow Belt South Bioregions’, listed under the Commonwealth’s Environment Protection and Biodiversity Conservation Act 1999 (EPBC Act).

Alluvial aquifers form in deposited sediments such as gravel, sand, silt and/or clay within paleochannels, stream channels and the adjacent floodplain. Water is stored and transmitted to varying degrees through inter-granular voids, meaning that aquifers are generally unconfined, shallow and have localised flow systems (DSITIA, 2012). Groundwater expressed at the surface supports ecosystems occupying drainage lines, riverine water bodies, and lacustrine and palustrine wetlands. The riparian forests along much of the lowland streams in the assessment extent were mapped as being groundwater dependent under the NSW DPI GDE mapping approach (NSW Office of Water, Dataset 5). This is consistent with observations of E. camaldulensis that show clear dependency on groundwater (Kath et al., 2014; Thorburn and Walker, 1994). Groundwater uptake in the dominant tree species of floodplain woodland environments along the Liverpool Plains (i.e. ‘Floodplain woodland’ landscape class) such as E. populnea has not been as intensely studied. One study of E. populnea inferred likely groundwater uptake by comparing the water status of these trees compared to co-occurring shrubs and grasses (Anderson and Hodgkinson, 1997). Other woodland species, more widespread on the Castlereagh-Barwon floodplain, such as black box, have been studied in detail (Jolly and Walker, 1996), where groundwater uptake on the Chowilla anabranch region was closely related to groundwater salinity (Thorburn et al., 1993). When floodplain soils are flooded or receive significant rainfall, E. largiflorens switches its water uptake to shallower soil water sources, emphasising its ability to take advantage of the most energetically available soil water (Jolly and Walker, 1996). A study of water use by E. populnea in the Namoi Valley made similar findings in regard to water use patterns (Kalaitzis et al., 2000).

The nature of groundwater dependency in these floodplain tree species will determine how these species respond to potential drawdown of the watertable. Previous research has revealed links between groundwater depth and tree condition, but the nature of the response in terms of being ‘ramped’ (i.e. linear changes in condition in response to drawdown) versus ‘stepped’ (i.e. tree condition rapidly changes across critical thresholds) is not clearly defined for most floodplain species. Eucalyptus spp. are capable of exploiting large soil volumes, and rooting depths of some species such as E. marginata are capable of achieving depths of greater than 20 m (Dell et al., 1983). It is likely that roots are capable of tracking available soil water from deep alluvial aquifers where shallow sources are limited (Thorburn et al., 1993). Kath et al. (2014) used data from two dominant floodplain species, river red gum and poplar box, at 118 sites in the Condamine river basin to present evidence for a critical drawdown threshold in the range from 12.1 to 22.6 m for river red gum and 12.6 to 26.6 m for poplar box, beyond which canopy condition declined abruptly. Another study in a water-limited riparian environment found that transpiration decreased in response to a 9-m decline in groundwater levels, but that changes to foliage density were more influenced by variability between seasons and site conditions (Pfautsch et al., 2015). Tree water uptake of groundwater when growing over deeper watertables is generally less than where the watertable is shallower (e.g. O'Grady et al., 2006; Zencich et al., 2002). The rate of drawdown can also be critical to vegetation survival. Plant roots can remain in contact with a declining watertable if the rate of decline does not exceed the potential root growth rate; 3 to 15 mm/day for arid shrub and grass species (Naumburg et al., 2005).

Floodplain wetlands or off-channel water bodies are perhaps most affected by water resource development along Australia’s intensively managed river basins (Kingsford, 2000). These ecosystems are sustained by flood sequences that drive booms in their productivity (Leigh et al., 2010) and therefore any reduction in flows, particularly overbank flow events, will diminish the source of water and nutrients for these habitats. Despite being important sites of high biodiversity and providing key habitat for waterbirds (Kingsford, 1995), native fish (Closs et al., 2005), invertebrate species (Boulton and Lloyd, 1991) and microbes (Boon et al., 1996), their ecohydrological interactions are not as well understood as the riverine system (Kingsford, 2000). Flooding events provide water and organic matter that trigger a cascade of biological processes driven by the activity of microbes (Boon et al., 1996), zooplankton (Boulton and Lloyd, 1992) and plants (Bunn and Boon, 1993). Colonisers such as fish larvae and insects are brought into the wetland habitats when water conditions are suitable. Frogs (burrowing and non-burrowing) utilise the open-water habitats and produce tadpoles in the newly established water bodies (Jansen and Healey, 2003; Ocock et al., 2014). Waterbirds attracted to the abundant food sources move in to the temporary wetland habitats from more permanent water bodies elsewhere (Kingsford, 1995). Lignum (Muehlenbeckia florulenta) is an important element in these off-channel water bodies and can quickly respond to flooding or heavy rainfall through rapid leaf growth and/or flowering (Capon et al., 2009). The germination of aquatic macrophytes may also occur in response to inundation (Britton and Brock, 1994). Fringing floodplain trees also respond to flooding events through increased growth and recruitment with some species such as E. camaldulensis requiring a flooding sequence to ensure seedling survival (Roberts and Marston, 2011).

2.7.3.1.2 Qualitative mathematical model

A model for the floodplain or lowland riverine ecosystem (Figure 11) was developed based on the model for the ‘Non-floodplain or upland riverine’ (non-Pilliga) landscape classes (see Section 2.7.4.1.2) with additional components and processes associated with the floodplain system. The low-gradient stream channels in this floodplain landscape class lack significant amounts of fast-water habitats; therefore fast-water habitat variables, such as associated populations of fast-water invertebrates, tadpoles and native fishes (which are part of the model in Section 2.7.4.1.2) are omitted from the model here.

The major feature of the floodplain is the existence of off-channel water bodies, which are filled by connections to overbank floods and groundwater. These water bodies provide habitat for plankton (Boon et al., 1996), macrophytes (Bunn and Boon, 1993) and populations of fish (Closs et al., 2005), off-channel frogs (Ocock et al., 2014), and still-water invertebrates such as shrimps and snails (Boulton and Lloyd, 1991). Slow-water native fishes and some waterbird species are major predators of the off-channel frogs. Floodplain grasses, shrubs and trees provide inputs of coarse particulate organic matter to the stream system, and habitat for mammals, reptiles, frogs and birds (Bunn and Boon, 1993).

The volume of overbank flow (VOBF) determines the magnitude of flood events. VOBF was defined as the maximum daily streamflow during, or cumulative volume of, an overbank event. These flood events facilitate the transport of organic matter from the floodplain into the stream channel. Hypoxic blackwater events (so-called because high concentrations of dissolved organic matter leached from inundated detritus darkens the water) occur when the first flush of a flood event coincides with a peak in accumulated floodplain organic matter (Whitworth et al., 2012). Blackwater events severely lower pH and dissolved oxygen in floodwaters, adversely affecting many fish and aquatic invertebrates such as crayfish (Hladyz et al., 2011; McCarthy et al., 2014). The inundation period of the floodplain is an important determinant of populations of long-lived tadpoles, the composition of and emergence from the microinvertebrate ‘egg bank’ (Jenkins and Boulton, 2007), and the proportions of families of aquatic invertebrate communities (e.g. the richness of Ephemeroptera, Plecoptera and Trichoptera relative to Odonata, Coleoptera and Hemiptera; EPT/OCH ratio). Flood events can increase the relative dominance of hyporheic fauna over phreatic fauna (obligate stygobites) in aquifers alongside stream channels.

Riparian trees in this landscape (e.g. river red gum) access groundwater in the alluvium and are heavily dependent on overbank flows for their recruitment success (Roberts and Marston, 2011). Floodplain trees (i.e. trees outside of the riparian zone on top of river terraces such as black box) were described as being dependent on groundwater for their growth and survival, but their recruitment was not dependent on any specific overbank flow regime. A hydrologic flow regime (HR2) related to overbank flood flows was considered to be key in maintaining the soil moisture of floodplain soils for riparian trees.

Figure 11 Signed digraph model of the 'Floodplain or lowland riverine' landscape group

Model variables are: algal bloom (AB), biological oxygen demand (BOD), bank stability (BS), blackwater flood event (BWFE), cyanobacteria (Cyan), coarse particulate organic matter and biofilm (COM BF), catchment vegetation (CV), dissolved oxygen (DO), Ephemeroptera, Plecoptera and Trichoptera richness relative to Odonata, Coleoptera and Hemiptera richness (EPT/OC), fine particulate and dissolved organic matter (F&DOM), flood event (FE), floodplain grasses (FPG), floodplain trees (FPT), fine sediments (FS), flood velocity (FV), groundwater connectivity (GWC), hyporheic biota (HB), hyporheic fauna relative to phreatic fauna (HF/PF), inundation period (IP), land clearing and grazing (LC&G), long-lived tadpoles, shrimps, crayfish, Odonata and snails (LLTSS), mammals, reptiles, frogs and birds (MRFB), off-channel frogs (OCF), off-channel water body (OCWB), acidity or basicity of water (pH), phosphorous runoff (PRO), riparian habitat structure (RHS), riparian trees (RT), riparian vegetation (e.g. sedges & rushes) (RV), salinity (Sal), stream habitat structure (SHS), submerged macrophytes (SMP), suspended sediment (SS), slow-water invertebrates and tadpoles (SW I&T), surface water connectivity (SWC), slow-water habitat (SWH), still-water invertebrates, plankton and macrophytes (SWIPM), slow-water native fishes (SWNF), volume of overbank flow (VOBF), water temperature (WT), maximum difference in drawdown (Dmax), low-flow days (LFD), zero-flow days (ZFD), hydrological regime 2 (HR2).

Data: Bioregional Assessment Programme (Dataset 6)

Surface water and groundwater modelling predict significant potential impacts to hydrological regime 2, low-flow days, zero-flow days, and maximum depth to groundwater level. Combinations of these impacts were considered in seven scenarios (Table 8).

Table 8 Summary of the (cumulative) impact scenarios (CISs) for the ‘Floodplain or lowland riverine’ landscape group

Pressure scenarios are determined by combinations of no-change (0), increase (+) or a decrease (–) in the following signed digraph variables: hydrological regime 2 (HR2), low-flow days (LFD), zero-flow days (ZFD), and maximum difference in drawdown (Dmax).

Data: Bioregional Assessment Programme (Dataset 6)

Qualitative analyses of the signed digraph model (Figure 11) generally indicate a negative, neutral (zero) or ambiguous response prediction for biological variables within the floodplain or lowland riverine ecosystem (Table 9). The only biological variables that were predicted to respond positively to any of the cumulative impact scenarios was cyanobacteria (to six of the seven cumulative impact scenarios), algal blooms (to the first and seventh scenarios), and also off-channel frogs (OCF), the latter of which can be attributed, in part, to a predicted decline in their native fish predators. Riparian trees were predicted to decrease across all of the seven cumulative impact scenarios, while riparian vegetation (sedges and rushes) was predicted to decrease only in response to an increase in zero-flow days. Hyporheic fauna were predicted to decrease in proportion to phreatic fauna in cumulative impact scenarios that included a decrease in hydrological regime 2. Note that some variables in the signed digraph were isolated from any impacts due to the four scenarios, as there was no interaction pathway leading to them from the input variables of HR2, LFD, ZFD or Dmax (e.g., EPT/OC, pH, GWC). In all cases their predicted response was zero or no change, but for brevity these zero predictions were not included in Table 9 below.

Physical and habitat variables were also predicted to change in the cumulative impact scenarios, and with a decrease in riparian trees there as an associated increase in flood velocity, decline in bank stability and increase in fine sediments. Fine particulate and dissolved organic matter was generally predicted to increase, and coarse particulate organic matter and biofilm to decrease.

Table 9 Predicted response of the signed digraph variables in the floodplain or lowland riverine ecosystem to (cumulative) changes in hydrological response variables

Qualitative model predictions that are completely determined are shown without parentheses. Predictions that are ambiguous but with a high probability (0.80 or greater) of sign determinancy are shown with parentheses. Predictions with a low probability (less than 0.80) of sign determinancy are denoted by a question mark. Zero denotes completely determined predictions of no change.

Data: Bioregional Assessment Programme (Dataset 6)

2.7.3.1.3 Choice of hydrological response variables and receptor impact variables

In BAs, the potential ecological impacts of coal resource development are assessed in two simulation periods – 2013 to 2042 and 2073 to 2102. These are labelled as the short- and long-assessment years, respectively. Potential ecological changes are quantified in BAs by predicting the state of a select number of receptor impact variables in the two simulation periods. These predictions are made conditional on the values of certain groundwater and surface water statistics that summarise the outputs of numerical model predictions in that landscape class in an interval of time that precedes the assessment year. In all cases these predictions also allow for the possibility that changes in the future may depend on the state of the receptor impact variable in the reference year 2012, and consequently this is also quantified by conditioning on the predicted hydrological conditions in a reference interval that precedes 2012 (companion submethodology M08 (as listed in Table 1) for receptor impact modelling (Hosack et al., 2018)).

For surface water and groundwater variables in the Namoi subregion, the reference assessment interval is defined as the 30 years preceding and including 2012 (i.e. 1983 to 2012). For surface water variables in the Namoi subregion, the short-assessment interval is defined as the 30 years preceding the short-assessment year (i.e. 2013 to 2042), and the long-assessment interval is defined as the 30 years that precede the long-assessment year (i.e. 2073 to 2102). For groundwater, maximum drawdown (metres) and time to maximum drawdown are considered across the full 90-year window (i.e. 2013 to 2102).

In BAs, choices of receptor impact variables must balance the project’s time and resource constraints with the objectives of the assessment and the expectations of the community (companion submethodology M10 (as listed in Table 1) for analysing impacts and risks (Henderson et al., 2018)). This choice is guided by selection criteria that acknowledge the potential for complex direct and indirect effects within perturbed ecosystems, and the need to keep the expert elicitation of receptor impact models tractable and achievable (companion submethodology M08 (as listed in Table 1) for receptor impact modelling (Hosack et al., 2018)).

For a subset of landscape classes within the ‘Floodplain or lowland riverine’ group, the qualitative modelling workshop identified five hydrological response variables as key drivers of the surface water and groundwater regimes that were thought to (i) be instrumental in maintaining and shaping the ecosystem, and (ii) have the potential to change due to coal resource development. All of the ecological components and processes represented in the qualitative model are potential receptor impact variables and all of these are predicted to vary as the hydrological factors vary either individually or in combination (Table 10).

Following advice received from participants during (and after) the qualitative modelling workshop, and guided by the availability of experts for the receptor impact modelling workshop, the scope of the BA numerical modelling and the receptor impact variable selection criteria, the receptor impact models focused on the following relationships:

The hydrological factors identified by the participants in the qualitative modelling workshops have been interpreted as a set of hydrological response variables. The hydrological response variables are summary statistics that: (i) reflect these hydrological factors, and (ii) can be extracted from BA’s numerical surface water and groundwater models during the reference, short- and long-assessment intervals defined previously. The hydrological factors and associated hydrological response variables for the relevant classes in the ‘Floodplain or lowland riverine’ landscape group are summarised in Table 10. The precise definition of each receptor impact variable, typically a species or group of species represented by a qualitative model node, was determined during the receptor impact modelling workshop.

Using this interpretation of the hydrological response variables, and the receptor impact variable definitions derived during the receptor impact modelling workshop, the relationships identified in the qualitative modelling workshop were formalised into three receptor impact models (Table 11).

Table 10 Summary of the hydrological response variables used in the receptor impact models for the floodplain or lowland riverine landscape classes, together with the signed digraph variables that they correspond to

Table 11 Summary of the three receptor impact models developed for the floodplain or lowland riverine landscape classes in the Namoi subregion

Hydrological response variables are as defined in Table 10.

2.7.3.1.4 Receptor impact models

Floodplain riparian forests

Table 12 summarises the elicitation design matrix for the projected foliage cover of riparian trees in the ‘Floodplain riparian forest’ and ‘Floodplain riparian forest GDE’ landscape classes. The design point identifiers are simply index variables that identify the row of the elicitation design matrix. They are included here to maintain an auditable path between analysis and reporting.

The first design point provides for an estimate of the uncertainty in mean projected foliage cover across the landscape class in the reference year 2012 (Yref). The remaining design points represent hydrological scenarios that span the uncertainty in the values of the hydrological response variables in the relevant time period of hydrological history associated with the short- (2042) and long- (2102) assessment years.

Table 12 Elicitation design matrix for annual mean projected foliage cover of river red gum in the ‘Floodplain or lowland riverine’ landscape group in the Namoi subregion zone of potential hydrological change

Receptor impact modelling elicitation design matrix for annual mean projected foliage cover, over a 100 m x 100 m transect in floodplain riparian forests. Design points for Yref in the future (short- and long-assessment periods) were calculated during the receptor impact modelling elicitation workshop using elicited values for the receptor impact variable in the reference period. All other design points (with identifiers) are either default values or values determined by groundwater and surface water modelling. Hydrological response variables are as defined in Table 10. na = not applicable

Data: Bioregional Assessment Programme (Dataset 6)

Design point identifiers 15 through to 108 (as listed in Table 12) represent combinations of the three hydrological response variables (dmaxRef, tmaxRef, EventsR3.0), together with high and low values of Yref (see companion submethodology M08 (as listed in Table 1) for receptor impact modelling (Hosack et al., 2018)). The high and low values for Yref were calculated during the receptor impact modelling workshop following the experts’ response to the first design point, and then automatically included within the design for the elicitations at the subsequent design points.

The receptor impact modelling methodology allows for a very flexible class of statistical models to be fitted to the values of the receptor impact variables elicited from the experts at each of the design points (companion submethodology M08 (as listed in Table 1) for receptor impact modelling (Hosack et al., 2018)). The model fitted to the elicited values of mean foliage projected cover for the floodplain riparian forest landscape classes is summarised in Figure 12 and Table 13. The fitted model takes the form:

where is an intercept term (a vector of ones), is a binary indicator variable scored 1 for the case of an assessment in the short- or long-assessment year, is a binary indicator variable scored 1 for the case of an assessment in the long-assessment year, is a continuous variable that represents the value of the receptor impact variable in the reference year (Yref, set to zero for the case of an assessment in the reference year), are the (continuous or integer) values of the three hydrological response variables (dmaxRef, tmaxRef and EventsR3.0), is the linear predictor, h is an invertible link function and y is the expected response (Hosack et al., 2018). Note that the modelling framework provides for more complex models, including quadratic value of, and in interactions between, the hydrological response variables but in this instance the simple linear model (Equation 1) was identified as the most parsimonious representation of the experts’ responses.

The model estimation procedure adopts a Bayesian approach. The model coefficients are assumed to follow a multivariate normal distribution. The Bayesian estimation procedure quantifies how compatible different values of the parameters of this distribution are with the data (the elicited expert opinion) under the model. The (marginal) mean and 80% central credible intervals[1] of the three hydrological response variable coefficients are summarised in partial regression plots in Figure 12, whilst Table 13 summarises the same information for all seven model coefficients.

The model indicates that the experts’ opinion provides strong evidence for Yref having a positive effect on average projected foliage cover (Figure 12, Table 13). This suggests that given a set of hydrological response variable values in the future, a site with a higher projected foliage cover at the 2012 reference point is more likely to have a higher projected foliage cover in the future than a site with a lower projected foliage cover value at this time point. This reflects the lag in the response of projected foliage cover to changes in hydrological response variables that would be expected of mature trees with long life spans.

The model also indicates that the experts’ opinion provides strong evidence for dmaxRef having a negative effect on average percent projected foliage cover (Figure 12). This suggests that percent projected foliage cover will decrease as groundwater drawdown increases due to coal resource development. The model predicts that (holding all other hydrological response variables constant at the midpoint of their elicitation range) the mean of the average percent projected foliage cover will drop from just under 15% without any change in groundwater level, to about 10% if the levels decrease by 20 m relative to the reference level in 2012 (Figure 12). There is, however, considerable uncertainty in these predictions, with an 80% chance that the projected foliage cover will lie somewhere between approximately 5% and 30% on the short-assessment period, and somewhere between roughly 2% and 20% in the long-assessment period. In relation to dmaxRef, Yrs2tmax is also found significant (Figure 12). The interpretation is that long-term drawdown will cause a larger decrease in projected foliage cover than short-term drawdown.

The model indicates that the experts’ opinion provides strong evidence for EventsR3.0 having a positive effect on average percent projected foliage cover (Figure 12). The model predicts that (holding all other hydrological response variables constant at the midpoint of their elicitation range) the mean of the average percent foliage cover will increase from just under 12% without any change in overbank events frequency, to about 18% if the frequency increases to 0.7 (relative to the reference level of 0.33 in 2012).

Finally, the model also indicates some diverging influence between short-term and long-term influence (Figure 12). For the short-assessment period, experts believe in a relative increase of projected foliage cover, while they expect a decrease in the long-term assessment. An interpretation is that the effects of changes in hydrology are not immediate on projected foliage cover, and a worsening of the conditions starting today will only be observed by 2102.

Figure 12 (Top row) Predicted mean (black dot) and 80% central credible interval (grey line) of annual mean projected foliage cover, over a 100 m x 100 m transect in floodplain riparian forest landscape classes under reference hydrological conditions. (Middle and bottom rows) Predicted future effect (mean = black line, 80% central credible interval = grey polygon) of each hydrological response variable on annual mean projected foliage cover, holding all other hydrological response variables constant at the midpoint of their elicitation range (during risk estimation all vary hydrological response variables simultaneously)

Dashed vertical lines show hydrological response variable range used in the elicitation. EventsR3.0 and dmaxRef are as defined in Table 10. Yrs2tmaxRef is the difference between tmaxRef and the assessment year that is relevant for the prediction (2012, 2042 or 2102). The numbers on the y-axis range from 0 to 1 as the receptor impact model was constructed using the proportion for the statistical modelling. They should be interpreted as a percent foliage cover ranging from 0 to 100%.

Data: Bioregional Assessment Programme (Dataset 6)

Table 13 Mean, 10th and 90th percentile of the coefficients of the receptor impact model for annual mean projected foliage cover in floodplain riparian forest landscape classes

Future1 is the indicator variable for the short-assessment year (2042). Long1 is the indicator variable for the long-assessment year (2102). Yref quantifies the value of the receptor impact variable during the reference period. Hydrological response variables EventsR3.0 and dmaxRef are as defined in Table 10. Yrs2tmaxRef is the difference between tmaxRef and the assessment year that is relevant for the prediction (2012, 2042 or 2102).

Data: Bioregional Assessment Programme (Dataset 6)

Floodplain wetlands

Table 14 summarises the elicitation matrix for the probability of the presence of tadpoles for the floodplain wetland landscape classes. The first design point – design point identifier 1 – addresses the predicted variability (across the landscape class in the reference interval) in overbank flows (EventsR3.0), capturing the lowest and highest predicted values together with two intermediate values (Table 14). This design point provides for an estimate of the uncertainty in probability of presence of tadpoles across the floodplain wetland landscape classes in the reference year 2012 (Yref; Table 14).

Table 14 Elicitation design matrix for probability of the presence of tadpoles from Limnodynastes genus (dumerilii, salmini, interioris and terraereginae) in pools and riffles in the ‘Floodplain or lowland riverine’ landscape group in the Namoi subregion zone of potential hydrological change

Receptor impact modelling elicitation design matrix for probability of presence of tadpoles in pools and riffles habitat in floodplain wetlands, sampled using standard 30 cm dip net. Design points for Yref in the future (short- and long-assessment periods) were calculated during the receptor impact modelling elicitation workshop using elicited values for the receptor impact variable in the reference period. All other design points (with identifiers) are either default values or values determined by groundwater and surface water modelling. Hydrological response variable EventsR3.0 is as defined in Table 10. na = not applicable

Data: Bioregional Assessment Programme (Dataset 6)

Design points 2 to 61 inclusive (as listed in Table 14) represent scenarios that span the uncertainty in the predicted values of overbank flood events in the relevant time period of hydrological history associated with the short- (2042) and long- (2102) assessment years, combined with high and low values of Yref. Again, the high and low values for Yref were calculated during the receptor impact modelling workshop.

The fitted model for probability of the presence of tadpoles takes the form:

where the terms and are as before and is the value of EventsR3.0 ( relates to the quadratic term). The (marginal) mean and 80% central credible interval of the coefficient for this hydrological response variable are summarised in the partial regression plots in Figure 13, whilst Table 15 summarises the same information for all five model coefficients.

The hydrological response variable in the tadpole model varies during the reference interval and the future interval. The model indicates that the experts’ elicited information supports the hypothesis that an increase in EventsR3.0 will have a positive effect on the probability of the presence of tadpoles (with a quadratic term suggesting a plateau past a number of overbank flood events, then a decrease). The model suggests that the probability of tadpoles is fairly uncertain across the floodplain wetland landscape classes with values between 0.35 to 0.80 under historical conditions (EventsR3.0 = 0.33), holding all other covariates at their mid-values. As the number of overbank flow events increases, however, experts were of the opinion that the probability of tadpoles would increase with values 1 and 0.60 falling within the 80% credible interval under highly flooded conditions (EventsR3.0 >0.6 day) (Figure 13).

There was very little evidence in the elicited data to suggest that this effect would be substantially different in the future assessment years. Again, this is indicated by the almost identical partial regression plots in the reference, short- and long-assessment years (Figure 13), and the relatively large negative 10th and positive 90th percentiles for the long and future coefficients in Table 15. The model does, however, suggest that the experts’ uncertainty increased for predictions in the future assessment years relative to the reference year.

The best-fitting model in this case is unable to eliminate the possibility that the probability of tadpoles in the reference years has no influence on the probability of tadpoles in the future years. This is indicated by the fact that the model automatically dropped this variable from the model. This suggestion is consistent with the hypothesis that there is likely to be very little lag in the response of this short-lived group to changes in the hydrological response variables.

Figure 13 (Top row) Predicted mean (black dot) and 80% central credible interval (grey line) of probability of the presence of tadpoles in pools and riffle habitat in floodplain wetland landscape classes under reference hydrological conditions. (Middle and bottom panels) Predicted future effect (mean = black line, 80% central credible interval = grey polygon) of hydrological response variable EventsR3.0 on probability of the presence of tadpoles in pools and riffle habitat in floodplain wetlands

Dashed vertical lines show hydrological response variable range used in the elicitation. EventsR3.0 is as defined in Table 10.

Data: Bioregional Assessment Programme (Dataset 6)

Table 15 Mean, 10th and 90th percentile of the coefficients of the receptor impact model for probability of the presence of tadpoles in pools and riffle habitat in floodplain wetlands

Future1 is the indicator variable for the short-assessment year (2042). Long1 is the indicator variable for the long-assessment year (2102). Yref quantifies the value of the receptor impact variable during the reference period. EventsR3.0 is as defined in Table 10. I(EventsR3.0^2) quantifies the quadratic effect of EventsR3.0.

Data: Bioregional Assessment Programme (Dataset 6)

Lowland riverine landscape classes

Table 16 summarises the elicitation matrix for the average number of families of aquatic macroinvertebrates in edge habitat sampled using the NSW AUSRIVAS method or referred to here as simply the average number of families of aquatic macroinvertebrates. The first six design points – design points 1 to 7 as shown in the table – address the predicted variability (across the lowland riverine landscape class in the reference interval) in ZQD (zero-flow days (averaged over 30 years) subsequently referred to in this section as ‘zero-flow days’) and ZME (maximum length of spells with zero flow, averaged over a 30-year period), capturing the lowest and highest predicted values together with two intermediate values. These design points provide for an estimate of the uncertainty in aquatic macroinvertebrate family abundance across the landscape classes in the reference year 2012 (Yref).

Design points 10 to 27 inclusive (as listed in Table 16) represent scenarios that span the uncertainty in the predicted values of ZQD and ZME in the relevant time period of hydrological history associated with the short- (2042) and long- (2102) assessment years, combined with high and low values of Yref. Again, the high and low values for Yref were calculated during the receptor impact modelling workshop.

The fitted model for number of families of macroinvertebrates takes the form:

where the terms and are as before and is the integer value of ZQD and is the integer value of ZME. The (marginal) mean and 80% central credible interval of the coefficient for these hydrological response variables are summarised in the partial regression plots in Figure 14, whilst Table 17 summarises the same information for all six model coefficients.

Table 16 Elicitation design matrix for average number of families of aquatic macroinvertebrate in edge habitat sampled using the NSW AUSRIVAS method for edges in the ‘Floodplain or lowland riverine’ landscape group in the Namoi subregion zone of potential hydrological change

Receptor impact model elicitation design matrix for average number of families of aquatic macroinvertebrate in edge habitat in permanent and temporary lowland streams. Design points for Yref in the future (short- and long-assessment periods) were calculated during the receptor impact modelling elicitation workshop using elicited values for the receptor impact variable in the reference period. All other design points (with identifiers) are either default values or values determined by groundwater and surface water modelling. Hydrological response variables ZQD and ZME are as defined in Table 10. na = not applicable

Data: Bioregional Assessment Programme (Dataset 6)

The hydrological response variable in the macroinvertebrates model varies during the reference interval and the future interval. The model indicates that the experts’ elicited information strongly supports the hypothesis that an increase in ZQD and/or ZME will have a negative effect on the number of families of macroinvertebrates. The model suggests that it can vary substantially across the landscape class from less than 5 to almost 20 when ZQD >0, holding all other covariates at their mid-values. As the number of zero-flow days increases, however, experts were of the opinion that the number of families would drop quite dramatically with values less than 0.5 falling within the 80% credible interval under very intermittent flow conditions (ZFD >300 days) (Figure 14).

There was very little evidence in the elicited data to suggest that this effect would be substantially different in the future assessment years. Again, this is indicated by the almost identical partial regression plots in the reference, short- and long-assessment years (Figure 14), and the relatively large negative 10th and positive 90th percentiles for the long and future coefficients in Table 17. The model does, however, suggest that the experts’ uncertainty increased for predictions in the future assessment years relative to the reference year.

The best-fitting model is unable to eliminate the possibility that the average number of families of macroinvertebrates in the reference years has no influence on its number in the future years. This is indicated by the fact that the model automatically dropped this variable from the model. This suggestion is consistent with the hypothesis that there is likely to be very little lag in the response of this measure of aquatic macroinvertebrate family richness in response to changes in the hydrological response variables.

Figure 14 (Top row) Predicted mean (black dot) and 80% central credible interval (grey polygon) of average number of families of aquatic macroinvertebrate in edge habitat in lowland riverine landscape classes under reference hydrological conditions. (Middle and bottom rows) Predicted future effect (mean = black line, 80% central credible interval = grey polygon) of each hydrological response variable on average number of families of aquatic macroinvertebrate in edge habitat in lowland riverine landscape classes, holding all other hydrological response variables constant at the midpoint of their elicitation range (during risk estimation all hydrological response variables vary simultaneously)

Dashed vertical lines show hydrological response variable range used in the elicitation. ZQD and ZME are as defined in Table 10.

Data: Bioregional Assessment Programme (Dataset 6)

Table 17 Mean, 10th and 90th percentile of the coefficients of the receptor impact model for average number of families of aquatic macroinvertebrates in edge habitat in lowland riverine landscape classes

Future1 is the indicator variable for the short-assessment year (2042). Long1 is the indicator variable for the long-assessment year (2102). ZQD and ZMA are as defined in Table 10. I(ZQD^2) quantifies the quadratic effect of ZQD.

Data: Bioregional Assessment Programme (Dataset 6)