2.7.4.2 Forested wetlands


In the Hunter subregion zone of potential hydrological change, the 57.7 km2 of the ‘Forested wetland’ landscape class consists of ‘Coastal floodplain wetlands’ (3.8 km2), ‘Coastal swamp forests’ (25.2 km2), ‘Eastern riverine forests’ (18.2 km2) and ‘Coast and tableland riverine forests’ (10.6 km2). The riverine forests are located in the central and western uplands of the Hunter subregion while the coastal swamp forests and floodplain wetlands are located near the coast in the south-east of the subregion. The qualitative model applies to the whole ‘Forested wetland’ landscape class while the quantitative model applies to the riverine forests only.

2.7.4.2.1 Qualitative mathematical model

A qualitative model was developed to describe the ecological community associated with the ‘Forested wetland’ landscape class (Figure 23). The following description of the class is adapted from the NSW Office of Environment and Heritage ‘Forested wetlands’ webpage (OEH, 2017a). Forested wetlands are restricted to fertile soils along riverine corridors and floodplains, mostly at low altitude. They are dominated by sclerophyllous trees (including several Eucalyptus and Melaleuca species as well as Casuarina species) but are distinguished from sclerophyll forests by the presence of hydrophytes in the understorey, which are adapted to periodic inundation by floodwaters. Floodwaters are also an important factor in the nutrient cycle of forested wetlands. Invertebrates are numerous, with insects dominating forest floors while streams and standing water have an abundance of crustaceans, aquatic insects and other invertebrates. Many species have both aquatic and terrestrial life stages. A complex food web exists in forested wetlands; submerged debris is a substrate for algae, microbes and filter-feeders, which in turn are food for larger animals. Many fauna species are also reliant on trees for feeding, nesting, shelter, and hunting.

The qualitative model was based upon the biological processes and environmental factors that regulate competition of Casuarina with other tree species, shrubs and herbs, and draws upon the extensive knowledge of the ecology and water requirements of river sheoak (Casuarina cunninghamiana) and other wetland tree species (e.g. Chalmers et al., 2009; Erskine et al., 2013; Morris and Collopy, 1999; Woolfrey and Ladd, 2001). The suspected allelopathic properties of leaf litter from Casuarina trees generally (Hozayn et al., 2015) is thought to suppress the germination of other species of vegetation. The competitive advantage that Casuarina has in suppressing understory vegetation is regulated by the level of disturbance (Dis) to, or removal of, the leaf-litter layer through such processes as wind, fire and floods. Casuarina seeds (CS) are a principal food resource for the glossy black-cockatoo (Calyptorhynchus lathami) (GBC), and the root structure of mature trees are key in providing stream bank stability (SBS) (Erskine et al., 2013; Erskine et al., 2012). Within forested wetlands, tree canopies provide shade and forest structure that contribute to habitat suitability for the Australian water dragon (Intellagama lesueurii) (AWD), as well as species of frogs and skinks (F&S). Nectar (Nec) provided by flowering trees (Tre), shrubs (Shr) and herbs (Her) is a key resource for numerous species of nectar consumers (NC) (e.g. bats, birds, bees, beetles), which in turn support populations of predators (Pre) such as quolls, snakes, owls and raptors. All vegetation components of this system support various species of sap- and leaf-eating insects (S&LEI), and the shrubs and herbaceous vegetation are a key resource for wombat (Wom) populations, which are also dependent on particular aspects of the stream and floodplain morphology for the construction of burrows.

Land clearing and grazing (LC&G) were identified as drivers of hydrological changes that could lead to the development of saline soils (SS), which generally suppress the growth of trees and shrubs. This process is not within the scope of the bioregional assessment (BA) modelling, but was identified in the qualitative model as a factor influencing the recruitment and condition of tree and shrub species in this landscape class. In the model, these nodes are not affected by hydrological changes from coal resource development.

Three water regimes were described as being important to specific life history stages of forested wetland vegetation and the production of nectar. While these were denoted by groundwater regime (GWR) in the workshop, the specific water requirements are met through the interaction of groundwater and streamflow in and upon the floodplain alluvium.

  1. GWR1 pertains to the ecohydrology of tree seedlings (Casuarina (CSI) and non-Casuarina (SI)), which cannot survive permanent inundation or saturation (from overbench and overbank flows or elevated watertables), or permanent drying. Seedling survival also requires that the rate of the seasonal lowering of groundwater does not outpace the growth rate of seedling roots. This regime was described as being relevant to seedling year-class success over a two-to-three-year time window (Roberts and Marston, 2011).
  2. GWR2 is a water regime that provides sufficient groundwater stores during the flowering period of wetland forest vegetation.
  3. GWR3 is similar to GWR1, but pertains to the canopy of mature tree and shrub species. The requirement is that the soil is not permanently saturated or permanently dry, especially during the season of major growth, and that the maximum depth to which groundwater falls in a season does not exceed the maximum root depth of dominant tree species.

The dependence of terrestrial vegetation on groundwater is difficult to predict or even quantify (Eamus et al., 2006); riparian and near-riparian vegetation may have an absolute dependency on groundwater (obligate phreatophyte), whereas vegetation growing above deeper groundwater, especially if shallow-rooted, may make only occasional use of groundwater (facultative phreatophyte). In stream reaches where the hydraulic gradient is from the stream to the adjoining floodplains (i.e. a ‘streamflow in losing reach’; SFLR), streamflow replenishes the alluvial groundwater store and thus is important in the ecohydrology of the ‘Forested wetland’ landscape class.

Figure 23

Figure 23 Signed digraph of forested wetland communities of the Hunter Valley

Australian water dragon (Intellagama lesueurii) (AWD), casuarina seeds (CS), casuarina seedlings (CSI), casuarina trees (CT), disturbance (Dis), frogs and skinks (F&S), glossy black-cockatoo (Calyptorhynchus lathami) (GBC), groundwater regime 1 (for seedling ecohydrology) (GWR-1), groundwater regime 2 (for nectar production) (GWR-2), groundwater regime 3 (for tree and shrub canopy cover) (GWR-3), herbs (Her), insects (Ins), land clearing and grazing (LC&G), nectar consumers (NC), nectar (Nec), orchids and fungi (O&F), predators (Pre), shade and habitat structure (S&HS), sap- and leaf-eating insects (S&LEI), stream bank stability (SBS), streamflow in losing reach (SFLR), shrubs (Shr), seedling (SI), stream morphology (SM), saline soils (SS), tree (Tre), wombats (Wom). Hydrological variables added subsequent to the qualitative modelling workshop are flow regimes 1 and 2 (FR1, FR2) and groundwater level (GW).

Data: Bioregional Assessment Programme (Dataset 2)

The hydrological variables that maintain and support the forested wetlands ecosystem were identified subsequent to the qualitative modelling workshop. Based on experience gained during the Gloucester subregion assessment, and comments received by experts following the Hunter qualitative modelling workshop, groundwater levels (GW), overbench flow (flow regime 1, FR1) and overbank flow (flow regime 2, FR2) were identified as the critical hydrological determinants of the condition of the forested wetlands ecosystem. In the qualitative models for this landscape class, a diminishment in overbench flows (FR1) and overbank flows (FR2) were both projected to have a negative effect on the replenishment of streamflow for each groundwater regime (via SFLR). A decrease in groundwater levels was also described as having a negative impact on the three groundwater regimes. Based on combinations of these potential impacts, three cumulative impact scenarios were developed for qualitative analyses of response predictions (Table 21).

Table 21 Summary of the (cumulative) impact scenarios (CISs) for forested wetlands in the Hunter subregion


CIS

FR1

FR2

GW

C1

0

0

+

C2

0

C3

+

Pressure scenarios are determined by combinations of no change (0), increase (+) or decrease (–) in hydrological response variables. Scenario C1 shows the expected impacts under the coal resource development pathway (CRDP).

FR1 = flow regime 1 (overbench flow), FR2 = flow regime 2 (overbank flow), GW = groundwater level

Data: Bioregional Assessment Programme (Dataset 2)

Qualitative analysis of the signed digraph model (Figure 23 and Table 22) generally indicates a negative predicted response of casuarina trees, seeds and seedlings (CT, CS and CSI, respectively) to each of the cumulative impact scenarios, with a corresponding decline in shade and habitat structure (S&HS), stream bank stability (SBS) and orchids and fungi (O&F). While most of the other variables have a zero or ambiguous response prediction, the predicted decline in casuarina trees would result in competitive release that benefits shrubs (Shr) and herbaceous vegetation (Her), with flow-on benefits to wombats (Wom), nectar (Nec) and nectar consumers (NC).

Table 22 Predicted response of the signed digraph variables of forested wetlands to (cumulative) changes in hydrological responses variables


Signed digraph variable (full name)

Signed digraph variable (shortened form)

C1

C2

C3

Casuarina trees

CT

Casuarina seeds

CS

Casuarina seedlings

CSl

Glossy black-cockatoo

GBC

Shade and habitat structure

S&HS

(–)

(–)

(–)

Stream bank stability

SBS

Orchids and fungi

O&F

Saline soils

SS

0

0

0

Land clearing and grazing

LC&G

0

0

0

Tree

Tre

?

?

?

Seedling

Sl

?

?

?

Disturbance

Dis

0

0

0

Shrubs

Shr

?

?

(+)

Australian water dragon

AWD

?

?

(–)

Herbs

Her

+

+

+

Frogs and skinks

F&S

?

?

(–)

Insects

Ins

?

?

?

Nectar

Nec

?

?

?

Sap- and leaf-eating insects

S&LEI

?

?

?

Wombats

Wom

(+)

(+)

(+)

Nectar consumers

NC

?

?

(+)

Predators

Pre

?

?

?

Stream morphology

SM

0

0

0

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 a completely determined prediction of no change.

Data: Bioregional Assessment Programme (Dataset 2)

2.7.4.2.2 Temporal scope, hydrological response variables and receptor impact variables

The temporal scope for the forested wetlands is the same as that described for the riverine models described in Section 2.7.3. For surface water and groundwater variables the reference assessment interval is defined as the 30 years preceding 2012 (i.e. 1983 to 2012). For surface water variables, the short-assessment interval is defined as the 30 years preceding the short-assessment year (i.e. 2013 to 2042), and similarly 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: 2013 to 2102.

For forested wetlands, the qualitative modelling workshop identified three ecologically important water regimes that were thought to: (i) be instrumental in maintaining and shaping the components, functions and processes provided by the landscape class ecosystem and, (ii) have the potential to change due to coal resource development. Most of the ecological components and processes represented in the qualitative model are potential receptor impact variables and are predicted to vary as the hydrological factors vary either individually or in combination (Table 22). The exceptions are saline soils (SS), land clearing and grazing (LC&G), disturbance (Dis) and stream morphology (SM), which in the model are completely determined predictions of no change.

Following advice from participants during (and after) the qualitative modelling workshop, and guided by the availability of experts for the receptor impact modelling workshop, and the receptor impact variable selection criteria (Section 2.7.1.2.3), the receptor impact models were designed to focus on the relationship between trees (Tre) and changes to flow regimes 1 and 2 and groundwater.

The key hydrological components of the model, identified during the Hunter subregion qualitative modelling workshop, were subsequently reinterpreted in terms of hydrological response variables that could be derived from the modelled groundwater and streamflow time series (Table 23). The precise definitions of the receptor impact variables, typically a species or group of species represented by a qualitative model node, were determined by the experts during the receptor impact modelling workshop.

Using the 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 a forested wetland receptor impact model that describes the response of:

  • mean annual projected foliage cover of woody riparian vegetation (predominately Eucalyptus tereticornis, Casuarina cunninghamiana and E. camaldulensis) in a 0.25-ha transect extending from the channel to the top of the bank (including floodplain overbank), to changes in dmaxRef, tmaxRef, EventsR0.3 and EventsR3.0 (Table 23).

Table 23 Summary of the hydrological response variables used in the receptor impact models, together with the signed digraph variables that they correspond to, for the ‘Forested wetland’ landscape class in the Hunter subregion


Hydrological response variable

Definition of hydrological response variable

Signed digraph variable

dmaxRef

Maximum difference in drawdown under the baseline future or under the coal resource development pathway future relative to the reference period (1983 to 2012)

GW

tmaxRef

The year that the maximum difference in drawdown relative to the reference period (1983 to 2012) (dmaxRef) occurs

GW

Yrs2tmaxRef

The difference between tmaxRef and the assessment year that is relevant for the prediction (2012, 2042 or 2102)

GW

EventsR0.3

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 0.3 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 overbench flow events in future 30-year periods. This is typically reported as the maximum change due to additional coal resource development.

FR1

EventsR3.0

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. This is typically reported as the maximum change due to additional coal resource development.

FR2

2.7.4.2.3 Receptor impact model

2.7.4.2.3.1 Projected foliage cover of woody riparian vegetation

Elicitation scenarios

Table 24 summarises the elicitation design matrix for the projected foliage cover of woody riparian vegetation in the ‘Forested wetland’ landscape class. The first six design points – design point identifiers 1, 2, 4, 5, 6 and 7 – address the predicted variability (across the perennial streams in the landscape class during the reference interval) in the overbench (EventsR0.3) and overbank (EventsR3.0) flows, defined as events with a return interval of 3.3 events and 0.33 events per year, respectively. The design points 1, 5 and 7 capture the combination of the extremes of each hydrological response variable, whilst design points 2, 4 and 6 capture intermediate points in each hydrological response variable axis.

The first six design points provide 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 (2012) assessment years.

Table 24 Elicitation design matrix for the receptor impact model of mean projected foliage cover in the riparian-dependent community in the ‘Forested wetland’ landscape class for the Hunter subregion


Design point identifiers

EventsR0.3

EventsR3.0

dmaxRef

tmaxRef

Yref

Year

5

3.3

0.330

0.0

na

na

2012

6

3.3

0.330

0.0

na

na

2012

4

3.3

0.330

0.0

na

na

2012

7

3.3

0.330

0.0

na

na

2012

1

3.3

0.330

0.0

na

na

2012

2

3.3

0.330

0.0

na

na

2012

44

2.9

1.100

5.8

2041

0.07

2042

55

1.1

0.033

0.2

2102

0.07

2042

9

9.4

1.100

0.2

1980

0.07

2042

25

1.1

1.100

34.0

1980

0.07

2042

78

9.4

0.320

34.0

2102

0.07

2042

21

9.4

0.033

34.0

1980

0.07

2042

148

1.1

0.320

5.8

2102

0.22

2042

155

2.9

0.033

34.0

2102

0.22

2042

82

1.1

0.033

0.2

1980

0.22

2042

111

9.4

0.033

0.2

2041

0.22

2042

306

9.4

1.100

0.2

2102

0.22

2102

167

2.9

0.320

0.2

1980

0.07

2102

208

1.1

0.033

34.0

2041

0.07

2102

270

9.4

1.100

34.0

1980

0.22

2102

277

1.1

1.100

0.2

2041

0.22

2102

228

9.4

0.033

5.8

2102

0.07

2102

241

1.1

1.100

34.0

2102

0.07

2102

Receptor impact model (RIM) elicitation design matrix for percentage of projected foliage. Design points for Yref in the future (short- and long-assessment periods) are calculated during the RIM 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 23.

na = not applicable

Data: Bioregional Assessment Programme (Dataset 2)

Design point identifiers 9 through to 306 (as listed in Table 24) represent combinations of the four hydrological response variables (dmaxRef, tmaxRef, EventsR0.3 and EventsR3.0), together with high and low values of Yref, that respect certain logical constraints, for example the number of overbank flood events (EventsR3.0) cannot be greater than the number of overbench flood events (EventsR0.3) (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 six design points, and then automatically included within the design for the elicitations at the subsequent design points.

Receptor impact model

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) (Hosack et al., 2018)). The fitted model takes the form

eta equals h open parentheses y close parentheses equals beta subscript 0 x subscript 0 plus beta subscript f x subscript f plus beta subscript l x subscript l plus beta subscript r x subscript r plus sum from j equals 1 to 4 of beta subscript h subscript j end subscript x subscript h subscript j end subscript

(5)

where x subscript 0 is an intercept term (a vector of ones), x subscript f is a binary indicator variable scored 1 for the case of an assessment in the short- or long-assessment year,x subscript l is a binary indicator variable scored 1 for the case of an assessment in the long-assessment year, x subscript r is a continuous variable that represent the value of the receptor impact variable in the reference year (Yref, set to zero when the design case is in the reference year), and x subscript h subscript j end subscript comma j equals 1 dot dot dot 4 are the (continuous or integer) values of the four hydrological response variables (dmaxRef, tmaxRef, EventsR0.3 and EventsR3.0). Note that the modelling framework provides for more complex models, including the quadratic value of, and in interactions between, the hydrological response variables but in this instance the simple linear model above was identified as the most parsimonious representation of the experts’ responses.

The model estimation procedure adopts a Bayesian approach. The model coefficients (beta subscript 0 end subscript comma beta subscript f end subscript comma beta subscript l end subscript comma beta subscript r end subscript comma beta subscript subscript h subscript j end subscript 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. Table 25 summarises information for all eight model coefficients, whilst Figure 24 summarises the (marginal) mean and 80% central credible intervals of the four hydrological response variable coefficients in partial regression plots.

Table 25 Mean, 10th and 90th percentile of the coefficients of the receptor impact model for projected foliage cover


Mean

q10

q90

(Intercept)

–2.23

–3.45

–1.02

Yref

1.09

0.817

1.37

future1

2.09

0.772

3.41

long1

–0.364

–0.768

0.0397

EventsR0.3

0.0405

0.00202

0.079

EventsR3.0

0.472

0.105

0.839

dmaxRef

–0.0144

–0.0248

–0.00401

Yrs2tmaxRef

–0.00244

–0.00562

0.000749

Yref is value of receptor impact variable in the reference assessment year; it has no value if the design case is in the reference assessment year. Future is a binary variable scored 1 if the design case is in a short- or long-assessment year. Long is a binary variable scored 1 if the design case is in the long assessment year. EventsR0.3, EventsR3.0 and dmaxRef are defined in Table 23. 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 2)

Figure 24

Figure 24 (Top row) Predicted mean (black dot) and 80% central credible interval (grey line) of projected foliage cover 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 mean projected foliage cover for the Hunter subregion

In the middle and bottom rows, all other hydrological response variables are held constant at the midpoint of their elicitation range (during risk estimation all hydrological response variables vary simultaneously). Dashed vertical lines show the hydrological response variable range used in the elicitation. EventsR0.3, EventsR3.0 and dmaxRef are defined in Table 23. 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 2)

The model reflects the experts’ opinion that the projected foliage cover in the reference year (Yref) has a positive effect on average projected foliage cover in the future. That is, for the same changes in hydrology, a site with a high foliage cover in 2012 is likely to have a higher foliage cover in the future than a site with a low foliage cover in 2012. This reflects the lag in the response of canopy cover to changes in hydrological response variables that would be expected of mature trees with long life spans.

The model reflects the experts’ opinion that groundwater drawdown (as indicated by dmaxRef) has a negative effect on average projected foliage cover. In other words, 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 mid-point of their elicitation range) the mean of projected foliage cover will drop from about 10% to about 6.5% for a decrease in groundwater levels of 35 m relative to the reference level in 2012. There is, however, considerable uncertainty in these predictions, with an 80% chance that the foliage cover will lie somewhere between approximately 47% and 5% in the short-assessment period, and somewhere between roughly 52% and 5% in the long-assessment period, with a 35-m drop in groundwater level.

The mean negative coefficient associated with the covariate Yrs2tmaxRef suggests that projected foliage cover is higher in the assessment year if the assessment year occurs after the time of maximum drawdown (Yrs2tmaxRef<0 in Figure 24). One interpretation is that the more time that has elapsed since tmaxRef, the greater the groundwater recovery and, in turn, the greater the projected foliage cover in the assessment year. Again, there is considerable uncertainty around the predicted effect on percent projected foliage cover, reflecting uncertainty around the magnitude of and rate at which groundwater levels decline due to pumping and the trees’ ability to adjust to these changes. This is reflected in the large credible intervals in Figure 24.

The model represents the frequency of overbench flows as having a positive relationship with projected foliage cover – an increase in the average frequency of overbench flood events, from 3.3 (by definition) in the 30 years preceding the reference year, to a maximum average value of 9 over the future period, causes an increase in average projected foliage cover of about 4%. The uncertainty in the projected foliage cover in the short- and long-assessment years is driven by the relative large uncertainty in the intercept, Yref and future coefficients (indicated by the relatively large 80% credible interval, Table 24). There is relatively small variation in expert’s belief about the positive effect of over bench flows (indicated by the relatively small 80% credible interval, Table 24).

The model also suggests that the frequency of overbank flows will have a positive effect on average projected foliage cover – the predicted slight increase in the average frequency of overbench flood events, from 0.3 (by definition) in the 30 years preceding the reference year, to a maximum average value of 1.2 over the future period, causes the average percentage projected foliage cover to increase by about 7%. Again the uncertainty in the projected foliage cover in the future assessment periods is driven by uncertainty in the intercept, reference year and future terms, not by large uncertainty in the effect of overbank flows.

Finally, the model suggests that we can expect differences in projected foliage cover in the future compared to the reference period, and even between short-term and long-term assessment. While ‘future’ is associated with a positive coefficient, ‘long’ is estimated to have a negative effect on projected foliage cover. This indicates that greater effects are expected in the short-term period than in the long-term period, as can be observed in Figure 24.

Last updated:
18 January 2019
Thumbnail of the Hunter subregion

Product Finalisation date

2018

ASSESSMENT