Journal of Environmental Management 157 (2015) 127e138

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Assessing climate change impacts on wetlands in a flow regulated catchment: A case study in the Macquarie Marshes, Australia Baihua Fu a, *, Carmel A. Pollino b, Susan M. Cuddy b, Felix Andrews a a b

Fenner School of Environment and Society, The Australian National University, Canberra, ACT 0200, Australia CSIRO Land and Water, Canberra, ACT 2601, Australia

a r t i c l e i n f o

a b s t r a c t

Article history: Received 15 January 2014 Received in revised form 8 April 2015 Accepted 14 April 2015 Available online

Globally wetlands are increasingly under threat due to changes in water regimes as a result of river regulation and climate change. We developed the Exploring CLimAte Impacts on Management (EXCLAIM) decision support system (DSS), which simulates flow-driven habitat condition for 16 vegetation species, 13 waterbird species and 4 fish groups in the Macquarie catchment, Australia. The EXCLAIM DSS estimates impacts to habitat condition, considering scenarios of climate change and water management. The model framework underlying the DSS is a probabilistic Bayesian network, and this approach was chosen to explicitly represent uncertainties in climate change scenarios and predicted ecological outcomes. The results suggest that the scenario with no climate change and no water resource development (i.e. flow condition without dams, weirs or water license entitlements, often regarded as a surrogate for ‘natural’ flow) consistently has the most beneficial outcomes for vegetation, waterbird and native fish. The 2030 dry climate change scenario delivers the poorest ecological outcomes overall, whereas the 2030 wet climate change scenario has beneficial outcomes for waterbird breeding, but delivers poor outcomes for river red gum and black box woodlands, and fish that prefer river channels as habitats. A formal evaluation of the waterbird breeding model showed that higher numbers of observed nest counts are typically associated with higher modelled average breeding habitat conditions. The EXCLAIM DSS provides a generic framework to link hydrology and ecological habitats for a large number of species, based on best available knowledge of their flood requirements. It is a starting point towards developing an integrated tool for assessing climate change impacts on wetland ecosystems. © 2015 Elsevier Ltd. All rights reserved.

Keywords: Climate change Ramsar wetland Habitat condition Decision support system

1. Introduction Inland wetlands support high biodiversity that are dependent on a regime of flows from rivers (Bunn and Arthington, 2002). Irrigation infrastructure and land use changes across the landscape have led to widespread decline in the extent and health of wetlands through clearing and regulation of river flows (Ward and Stanford, 1995). Water stress caused by climate change is considered as one of the factors contributing to the decline of wetlands (Palmer et al., 2008; Pittock and Finlayson, 2011). For example, the recent Millennium Drought (1997e2009) in the Murray-Darling Basin, Australia has led to record low flows in extensive areas, severely impacting water-dependent ecosystems and causing extensive loss of riparian vegetation (Bond et al., 2008) and a reduction in

* Corresponding author. E-mail address: [email protected] (B. Fu). http://dx.doi.org/10.1016/j.jenvman.2015.04.021 0301-4797/© 2015 Elsevier Ltd. All rights reserved.

waterbirds (Saintilan et al., 2010). Adaptive measures that restore the natural capacity of the rivers are vital to protect wetlands and their function within the landscape (Richter et al., 2003). Previously, modelling climate change impacts for environmental flow management has been undertaken using hydrological indicators that have ecological relevance, often by linking climate modelling and river systems modelling (Path 1 in Fig. 1) (e.g. Mortsch and Quinn, 1996). Scenarios can be evaluated using hydrological indicators based on the ‘natural flow paradigm’ (Poff et al., 1997), where natural (or a reference) flow provides the ideal condition for ecosystems. More than one hundred hydrological indicators have been identified and applied to describe a flow regime (Kennard et al., 2010; Olden and Poff, 2003). However, using hydrological indicators alone has its limitations. These include: i) accurate representation of the ‘natural’ flow regime is often not available and difficult to define; ii) the approach relies on the assumption that hydrological indicators are directly associated with ecological outcomes; and iii) the ecological outcomes are not

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Fig. 1. Two paths of modelling climate change impacts for environmental flow management.

modelled, making it difficult to evaluate environmental water management outcomes or to facilitate an informed compromise between environmental and human water needs. To overcome these limitations, including ecological models has been recommended (Path 2 in Fig. 1). These ecological models quantify the ecological responses of flow, such as changes in habitat condition, species distribution and composition (e.g. Dawson et al., 2003; Johnson et al., 2005). An integrated climate model, hydrologichydraulic model and ecological model allows the evaluation of climate change or water management scenarios based on ecological outcomes, either through scenario testing (e.g. Lester et al., 2011a) or optimisation (Szemis et al., 2012). In Australia, a wetland system that is vulnerable to hydrological change is the Macquarie Marshes. This is one of the biggest semipermanent wetlands in southeast Australia, covering over 2000 km2. The Macquarie Marshes have high ecological and cultural values, despite having experienced changes in vegetation distribution and community composition due to agricultural activities, water regulation and drought (Bowen and Simpson, 2010). In this paper we introduce EXCLAIM (Exploring CLimAte Impacts on Management), a Decision Support System (DSS) developed to assist natural resource managers explore climate change impacts, where the focus is on ecological outcomes. The DSS is underpinned by an integrated climate, hydrologic-hydraulic and ecological models. This paper describes the modelling framework that underlies the EXCLAIM DSS, presents results of ecological outcomes under five climate change and water management scenarios, and evaluates the performance of the integrated model through sensitivity analysis and model testing. 2. EXCLAIM DSS for the Macquarie Marshes 2.1. The Macquarie Marshes The Macquarie Marshes are located at the lower end of the Macquarie River in New South Wales, southeast Australia (Fig. 2). Mean annual rainfall at the Macquarie Marshes is 400e450 mm, with higher summer rainfall. The importance of the Macquarie Marshes is recognised under the Ramsar Convention, with approximately 200 km2 of its private and public lands (or about 10% of its total area) collectively being listed. The wetlands are also recognised in migratory bird agreements that Australia has with China, Korea and Japan (Kingsford and Thomas, 1995). The Macquarie Marshes sustain a wide range of floodplain woodland vegetation species. The core wetlands support river red gum (Eucalyptus camaldulensis), river cooba (Acacia stenophylla), common reed (Phragmites australis), lignum (Muehlenbeckia

florulenta) and water couch (Paspalum distichum). Ephemeral parts of the wetlands are characterised by black box (Eucalyptus largiflorens) and coolibah (Eucalyptus coolabah). These vegetation communities provide important habitats for waterbirds, and the Macquarie Marshes have supported some of the largest waterbird breeding events recorded in Australia (Thomas et al., 2010). The Macquarie River provides refuge for many freshwater fish communities with a total of 16 species recorded within the Macquarie Marshes (Brock, 1998), that is about 30% of the species in the Murray-Darling Basin (Lintermans, 2007). A past assessment of vegetation in the Macquarie Marshes suggested that the extent and condition of river red gum and river cooba have declined since 1991 due to insufficient flooding (Bowen and Simpson, 2010). The understory of these areas was dominated by chenopod shrubs, a terrestrial vegetation type (Bowen and Simpson, 2010). Accompanying declines in river red gum is the loss of common reed (approx. 43% decline in extent between 1991 and 2008), water couch (approx. 92% decline in extent between 1991 and 2008) and lignum (approx. 89% decline in extent between 1991 and 2008) (Bowen and Simpson, 2010). Prior to the flooding of the Macquarie Marshes in 2010, waterbird breeding habitats had reduced dramatically, with only one successful waterbird breeding year recorded since 2001 (NSW DECCW, 2010). Hydrologically, the Macquarie Marshes are predominantly supported by discharge from the upper Macquarie River, maintained by water released from Burrendong Dam (operational in 1967), Windamere Dam (operational in 1984), unregulated tributaries downstream of the dams and local rainfall (Fig. 2). Mean annual flow at Dubbo and Warren is approximately 1,200,000 and 700,000 ML/year, respectively. Profound change to flows has occurred over the last few decades. During 1944e1953, on average 51% of flows passing through Dubbo reached the Macquarie Marshes. This proportion has decreased to 21% between 1984 and 1993 (Kingsford and Thomas, 1995). As a result, both the frequency and area of flooding have been reduced in the Macquarie Marshes (Thomas et al., 2009). Climate change forecasts for 2030 suggest that moderate to high flows will be reduced further in the Macquarie River, the average period between inundation events in the Macquarie Marshes will increase by 10%, the average volume of events will decrease from 328,000 ML to 278,000 ML, and the number of events will decrease by 5% (CSIRO, 2008a). In this paper, we use the EXCLAIM DSS to explore the likely ecological consequences of these climate change forecasts and river management. 2.2. Model framework underlying the EXCLAIM DSS The scope of the DSS was defined in consultation with stakeholders. A workshop was conducted with regional natural resource managers, agricultural scientists, environmental flow managers, hydrologists, ecologists and local landholders. The primary concern of stakeholders was how climate change was likely to impact the availability of water resources and the ecology of the catchment, with a focus on the Macquarie Marshes (Pollino et al., 2007a; Tighe et al., 2007). Based on the consultation process, the EXCLAIM framework was developed to explore impacts of change in climate on water availably and ecology. This needed to be done in the context of current water management in the Macquarie catchment. The model framework underlying the EXCLAIM DSS was designed as a hierarchy of climate scenarios, river flows, wetland inundation and conditions of ecological habitat (Fig. 3). The model framework was designed to be an integrator of existing knowledge and data, which enables scenario testing. The climate and river system component was derived using data from the MurrayDarling Basin Sustainable Yields Project (MDBSY) (CSIRO, 2008a; b). MDBSY predictions focus on scenarios of a 2030 climate, and

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Fig. 2. The Macquarie river Catchment and Macquarie Marshes.

thus the scenarios presented in this paper are for possible climates in 2030. A simple inundation model (Simple Wetland Inundation Model using Poweroids; SWIMP) was developed to predict inundation within the Macquarie Marshes. Values for inundation metrics from SWIMP were used as inputs to the ecological models for vegetation maintenance and regeneration and waterbird breeding. A model for fish was directly coupled to the river system component of the framework. Each of the components is described in the following sections.

2.3. Climate and hydrology The climate-hydrology component is based on simulation data generated in the MDBSY project (CSIRO, 2008a; b). Global climate models were statistically downscaled to a region, and climate predictions were used as inputs to existing river system models. The scenarios presented in this paper focus on historical and future (2030) climates, and consider levels of water resource development, which refer to the extent of water extraction within the region:






historical climate with current development (Scenario A) historical climate without development (Scenario P) 2030 wet climate with current development (Scenario 2030wet) 2030 dry climate with current development (Scenario 2030dry) 2030 median climate with current development (Scenario 2030med).

Scenario A uses a historical climate (1895e2006), with data sourced from the SILO Data Drill (an Australian daily meteorological database from 1889 to present, hosted by the Queensland Climate Change Centre of Excellence) (CSIRO, 2008b). The development level in Scenario A accounts for recent land use condition

(1975e2005) and the establishment of dams, weirs and license entitlements on the river system. The historical climate without development scenario (Scenario P) is similar to Scenario A, except it removes the influence of water infrastructure and consumptive use (i.e. no dams, weirs and licence entitlements). Future (2030) climate scenarios were derived from 15 global climate models for three global warming scenarios (CSIRO, 2008a). The MDBSY climate projections were inputs to the MacquarieCastlereagh river system model (Hameed and O'Neill, 2005) which contains the current rules for water allocations. A 111year daily flow time series (June 1895 to June 2006) was simulated for each scenario; for future climate scenarios these were adjusted to match projected global temperature at about 2030. The simulated daily flows at Marebone (421090), Warren (421004) and Oxley (421022) were provided by the NSW Office of Water. 2.4. Inundation model Information on flood attributes, such as duration, timing and area, are inputs for the prediction of ecological habitat for vegetation and waterbirds. We used a simple water balance model, SWIMP, to simulate flood behaviour in the Macquarie Marshes and calculate flood attributes. In SWIMP, the water balance for the wetland is described as:

Vt ¼ Vt1 þ Qt þ ðPt  Et  Iwt ÞAt1  Dt

(1)

where, at time t (day), V is volume of surface water (ML), Q is inflow (ML), P is precipitation (mm), E is evaporation (mm), Iw is infiltration from the wetland into the soil (mm), A is the inundated area (km2), and D is the drainage of water above the drain level (ML). The SWIMP model flood extent outputs were validated using simple linear regression against reported annual maximum flood

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Fig. 3. EXCLAIM model framework, showing three major components: 1) Murray-Darling Basin Sustainable Yields (MDBSY) climate change scenarios and hydrological outcomes; 2) inundation; and 3) flow-related ecological habitat models for vegetation, waterbird and fish.

extents during 1988e2000 (Kingsford and Auld, 2005), and reported 6 days of large flood extents in 1990, 1998 and 2000 (Hogendyk, 2007). Using Kingsford and Auld (2005), we had a good fit with a regression coefficient of 1.08 and a coefficient of determination (R2) value of 0.94. The model underestimated flood extents when comparing model outputs with the observed data reported in Hogendyk (2007) (regression coefficient ¼ 0.88, R2 ¼ 0.97). It is because the model underestimated daily flows from rating curves for large events. For a large and flat area like the Macquarie Marshes, accurately estimating flood areas remains problematic even for purposely built and more complex inundation model (e.g. MIKE Flood model (Atiquzzaman et al., 2010)). See Andrews and Fu (2009) for more detailed descriptions of the SWIMP model including the parameterisation and validation methods and results. Simulated mean daily flow data at Marebone (421090) for each scenario (derived from the river system model, see Section 2.3) were used as input to SWIMP. SWIMP predicted distributions of four flood attributes: flood duration (number of months), flood timing (month), flood area (km2) and inter-flood dry period (number of months). These attributes were used as inputs to the vegetation and waterbird ecological habitat models.

2.5. Ecological habitat models The ecological models estimate the flow-related habitat

condition for vegetation, waterbirds and fish, and are based on Rogers and Ralph (2010), the Murray Flow Assessment Tool (MFAT) (Young et al., 2003), and a purpose built carp model (Fu et al., 2009). Vegetation and waterbird outcomes are based on flood attributes, whilst fish outcomes are based on flow preferences. 2.5.1. Vegetation and waterbird habitat condition models The structure of the vegetation Bayesian network model includes flood duration, flood timing, flood area and inter-flood dry period as inputs. The output describes the condition of habitats for a vegetation species, given the flood attributes. The waterbird model is similar in structure, but also includes the rate of fall of water as an input, as this is believed to relate to abandonment of nests (Kingsford and Auld, 2005). The flood-habitat relationships were parameterised in Bayesian networks, linking flood attributes with habitat condition for vegetation and waterbirds. Bayesian networks are graphical probabilistic models useful in integration (Pollino and Henderson, 2010). The conditional probability tables that represent flood-habitat relationships were derived using species flood requirements for the maintenance and regeneration of vegetation and waterbird breeding (Rogers and Ralph, 2010). For each species, to describe the predicted vegetation and waterbird habitat condition we used three ‘states’ e good, moderate, poor. These describe each flood attribute as:
good: requirements for the flood attribute are met;

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moderate: the flood attribute is outside the preferred thresholds for maintenance or reproductive processes, but the survival/ minimum requirements are met;
poor: survival/minimum requirements are not met. We used equal weights to aggregate conditions of all flood attributes and obtain the probabilities of habitat condition being good, moderate and poor for each species. The approach on how best to aggregate attributes in habitat models lacks consensus (Bovee, 1986; Lester et al., 2011b), and this approach has the least bias. Sixteen vegetation and 13 waterbird species were included in EXCLAIM (Table 1). Habitat conditions for maintenance and survival of vegetation species were modelled separately from regeneration and reproduction due to their different flood requirements. Habitat conditions for species were further aggregated to a functional group, which was estimated by averaging the habitat condition across species which belong to that group.

2.5.2. Fish habitat condition model The fish model estimates the suitability of flow regime in supporting fish communities at two locations: Warren and Oxley. The model considers habitats for three groups of native fish: wetland specialists (e.g. Nematalosa erebi, Retropinna semoni), main channel generalists (e.g. Philypnodon grandiceps), and main channel specialists (e.g. Maccullochella peelii), and one pest species: common carp (Cyprinus carpio). For the three native fish groups, habitat preference curves which define the relationships between flow attributes and habitat indices were adopted from MFAT (Young et al., 2003). For the common carp, the preference curves were generated by the authors using the MFAT style of approach (Fu et al., 2009). The input to the fish model is simulated daily flow from the river system model. Other variables such as woody debris, fish passage, thermal pollution due to upstream dam and riparian condition were not included in the model because they were not considered to be climate change or flow related drivers. Wetland specialists spawn opportunistically in floodplain wetlands and in anabranches and billabongs during in-channel flow. Thus it was assumed that this group of fish does not have particular flow related spawning requirements. Instead, the model focuses on adult habitat condition, which is determined by channel condition (which is regulated by flow magnitude and variability) and maintenance flow (which ensures food supply). The channel condition index was calculated based on two year return flood peak and flow variability (represented using S80: (90th percentile e 10th percentile)/median), and the maintenance flow index was calculated based on daily flow percentile and flow timing. Details of the

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model can be found in Young et al. (2003). In contrast, main channel generalists and specialists models focus on fish recruitment, which is considered to be the most important factor for these groups of native fish (Young et al., 2003). The model firstly identified a larval development period based on daily flow. A larval development period starts from the second day of flow fall during October to April for the main channel generalists, and during September to January for the main channel specialists. Then, the habitat suitability for the main channel generalists and specialists was estimated based on the preference for daily flow percentile (FPL). If the flow remains less than natural mean daily flow (i.e. mean flow from Scenario P) during the larval development period (8 weeks), then the habitat condition is the mean value of the FPL index over those 8 weeks. However, if the flow exceeds the natural mean flow, then it is considered that such a flow will flush out the larvae and thus the habitat condition index is 0. If there are multiple possible larval development periods in a year, then the one with the maximum habitat condition is used to represent the habitat condition index for that year. Habitat condition index for common carp was estimated based on timing of spawning, flow rise and flow duration:

H ¼T F D

(2)

where H is habitat condition index for common carp, T is spawn timing index, F is flow rise index and D is flood duration index. The spawn timing index was based on Smith (2004). The most favourable months for spawning are February and November, while spawning events cannot occur between May and August. A successful spawning event starts when the flow starts to rise during the preferred spawn timing window. The flow rise index is ‘1’ if the flow continues to rise for 3 days or more. Otherwise, the flow rise index is ‘0’. The flow duration index defines a successful hatching event. It was assumed that if the flow starts to fall within 4 days of spawning, then the eggs would not hatch. Therefore, the flow duration index is ‘0’ when the flow falls within 4 days of spawning. Otherwise, the flood duration index is ‘1’. Finally, the estimated annual habitat condition index time series for each fish species at each location are classified into five states: 0e0.2, 0.2e0.4, 0.4e0.6, 0.6e0.8 and 0.8e1. Probability distributions of fish habitat condition index under each scenario were generated based on the frequency of each state over the modelled period (111 years). 2.6. Integrated model The EXCLAIM DSS was implemented in the Interactive Component Modelling System (ICMS) (Cuddy et al., 2002). ICMS is a

Table 1 Groups and species of vegetation and waterbirds in EXCLAIM. Type

Group

Species

Vegetation

Aquatic macrophytes association Black box woodland Cumbungi association Lignum shrubland Reed association River red gum forest River red gum woodland Rush and sedge association Water couch association

Vallisneria species (ribbonweed), Nymphoides crenata (wavy marshwort) Eucalyptus largiflorens (black box), Eucalyptus coolabah (coolibah), Typha species (cumbungi), Juncus species (rush), Eleocharis species (spike-rush), Nymphoides crenata (wavy marshwort) Muehlenbeckia florulenta (lignum) Phragmites australis (common reed), Eleocharis species (spike-rush), Cyperus species (flat-sedge) Eucalyptus camaldulensis (river red gum), Muehlenbeckia florulenta (lignum) Eucalyptus camaldulensis (river red gum), Eucalyptus largiflorens (black box) Eleocharis species (spike-rush), Bolboschoenus caldwellii (marsh club-rush), Cyperus species (flat-sedge) Paspalum distichum (water couch), Isotoma species (isotome), Marsilea species (nardoo), Pratia species

Waterbirds Egrets Grebes Herons Ibis Spoonbills

Great egret, intermediate egret, little egret Great crested grebe, hoary-headed grebe Pacific heron, white-faced heron, rufous night heron Glossy ibis, Australian white ibis, straw-necked ibis Yellow-billed spoonbill, royal spoonbill

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software platform designed for the modelling of interacting processes. It is an object-oriented modelling environment that allows the building and compiling of different models with temporal, spatial and other data (Reed et al., 1999). The model application that sits within EXCLAIM is a Bayesian network. The Bayesian network consists of a series of variables and causal links that conceptualise a system, where probabilities reflect the strength of relationships. In EXCLAIM, the Bayesian network was implemented as nodes in ICMS, described as either ‘decision class’ or ‘state class’. The decision class defines MDBSY climate change scenarios. The state class reflects the causal relationship between two objects, which contains data templates for storing the conditional probability table for that node. Using the conditional probability tables, the probability distribution of habitat condition is calculated. This generic structure allows extension of the model to include other components for the same system or to use the existing components for other modelling applications. 3. Investigating climate change impacts in the Macquarie Marshes Indicators of Hydrological Alteration (IHA) analysis, as documented in Richter et al. (1996), was employed as the initial attempt to explore climate change impacts on aquatic ecosystems in the Macquarie Marshes, under the five MDBSY scenarios. Daily flow from Scenario P was used as a surrogate for ‘natural’ flow regime. Five groups of hydrological indicators were analysed (Richter et al., 1996):
Group 1: magnitude of monthly water conditions (12 indicators);
Group 2: magnitude and duration of annual extreme water conditions (12 indicators);
Group 3: timing of annual extreme water conditions (2 indicators);
Group 4: frequency and duration of high and low pulses (4 indicators);
Group 5: rate and frequency of water condition changes (3 indicators). Details of the IHA method and IHA indicators for each group can be found in Richter et al. (1996). The median and coefficient of dispersion (i.e. (75th percentile e 25th percentile)/50th percentile) of the 33 IHA indicators over time series are summarised in the supplementary file. The differences between Scenario P and other four scenarios are shown in Fig. 4. All four scenarios exhibit significant flow alterations for all IHA groups, although Group 3 (timing) indicators are relatively less altered when compared to ‘natural’ flow regime. Alteration of inter-annual variation, represented by the coefficient of dispersion, is high, particularly for IHA Group 2 indicators (magnitude and duration of extreme flows). The degrees of alteration are similar across scenarios; however the change in inter-annual variation of IHA Group 5 indicators (rate and frequency of flow changes) for Scenario 2030dry is significantly higher than other scenarios. Although the IHA analysis provides insight on how flow regimes under different scenarios differ from ‘natural’ flow, the impact of these alterations on ecosystems is not quantified. As a result, interpreting scenario outcomes remains difficult. EXCLAIM DSS was then used to estimate habitat condition in order to better understand potential ecological outcomes of flow alteration as a result of climate change and river management. The probabilities of ‘good’ habitat conditions for vegetation and waterbird species under the five modelled scenarios are compared using heat maps (Fig. 5), where the darker the colour in a heat map, the higher the probability of having good habitat conditions. The

colours are centred and scaled by the difference in scenario values for each species. This is necessary due to the small differences in outcomes between scenarios. Note that this method masks the absolute differences between scenarios by not scaling the colour between the minimum and maximum possible probabilities (i.e. between 0 and 100%). The absolute differences in model outcomes are evaluated through sensitivity analysis as presented in the next section. Unsurprisingly, Scenario P produces the highest probability of having good habitat condition for over 80% of the modelled vegetation species (Fig. 5). Exceptions are trees such as black box, coolibah and river red gum, which prefer drier climate or historical climate with development (i.e. Scenario A). Scenario 2030dry has the worst outcomes for all vegetation species except black box. The worst outcome for river red gum is Scenario 2030wet, presumably due to prolonged flooding that lead to the decline of river red gum. In terms of waterbird breeding, both Scenario P and Scenario 2030wet provide the highest probability of good habitat condition for all modelled waterbird species, with Scenario P slightly better than Scenario 2030wet (Fig. 5). Scenario 2030dry produces the lowest probability of good habitat condition for most waterbird breeding. In terms of vegetation communities, it is estimated that Scenario P produces the highest probability of good habitat conditions for aquatic macrophytes association, cumbungi association, lignum shrubland, reed association, river red gum forest, rush and sedge association and water couch association (Fig. 6). Drought tolerant communities such as river red gum woodland and black box woodland are predicted to have the poorest conditions under Scenario 2030wet. Habitats for the breeding of all waterbird groups are most likely to thrive under Scenario P and Scenario 2030wet, but be stressed under Scenario 2030dry (Fig. 6). Mixed results are found for fish groups (Fig. 7). Scenario P provides the highest probability of very good (i.e. index value >0.8) habitat condition for wetland specialists. Scenario P is also the least likely to provide very good habitat condition for common carp. The worst scenario for main channel specialists and generalists is Scenario 2030wet. This is likely to be due to main channel specialists and generalists recruitment requiring low flow (Young et al., 2003). 4. Model evaluation The model was evaluated by analysing sensitivity of model outputs to scenarios, sensitivity to flood attributes and comparisons between observed waterbird nest data and predicted habitat condition for waterbird breeding. 4.1. Sensitivity to climate change scenarios The sensitivity of model outputs to climate change scenarios was analysed by running each scenario and recording the probability distributions of the habitat condition outputs. Scenario P was removed from the analysis because this scenario is related to development only, not climate change. To summarise the results, the probability distributions over discrete states were assumed to be a linear scale and the mean of each score was calculated (termed the expected value). This allowed the set of scenarios to be ranked according to the expected value of each endpoint. For each expected value, the maximum difference between any two scenarios was calculated. This is referred to as the ‘sensitivity’ to scenario choice. One unit of sensitivity is the difference between adjacent discrete states (e.g. ‘good’ to ‘moderate’ or ‘moderate’ to ‘poor’); thus it represents the transition between one state of habitat condition to another. The higher the unit of sensitivity, the more sensitive the model outputs to climate change scenarios.

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Fig. 4. Percentage changes of IHA indicators between four scenarios (2030dry, 2030med, 2030wet, A) and Scenario P. The box in a boxplot marks median, 25th percentile and 75th percentile.

Fig. 5. Heat maps of good habitat condition for vegetation and waterbird species, for five MDBSY scenarios: 2030 wet climate (2030wet), 2030 median climate (2030med), 2030 dry climate (2030dry), historical climate with development (A), and historical climate without development (P). The darker the colour, the higher the probability of having good habitat conditions. The colours are centred and scaled by the difference in scenario values for each row (i.e. cannot compare between rows). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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Fig. 6. Heat maps of good habitat condition for vegetation and waterbird groups, for five MDBSY scenarios: 2030 wet climate (2030wet), 2030 median climate (2030med), 2030 dry climate (2030dry), historical climate with development (A), and historical climate without development (P). The darker the colour, the higher the probability of having good habitat conditions. The colours are centred and scaled by the difference in scenario values for each row (i.e. cannot compare between rows). See Table 1 for the species associated with each vegetation and waterbird groups.(For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

As shown in Fig. 8, model outputs for fish are most sensitive to climate change scenarios, with an average of 0.53 units of sensitivity. This is unsurprising given that the fish model used flow attributes as inputs while the vegetation and waterbird models used flood attributes. This reflects the nature of the system: a small change in flow may trigger response from fish but may not be enough to cause flooding and subsequently responses from flooddependent vegetation and waterbirds. The most sensitive output to climate change scenarios is the habitat condition of main channel specialists at Warren, in which 1.04 units of sensitivity was reported between the best (Scenario 2030dry) and worst (Scenario 2030wet) scenarios. Sensitivities of fish habitat conditions at Oxley are lower than those at Warren for the equivalent scenarios. This is due to changes in flow regime between scenarios at Oxley being less than that at Warren. The least sensitivity fish model output is Carp (0.09 unit of sensitivity). Model outputs for vegetation and waterbirds are significantly less sensitive to climate change scenarios (Fig. 8). The average sensitivity levels for vegetation habitat condition and waterbird habitat condition variables are 0.07 (standard deviation SD ¼ 0.02) and 0.09 (SD ¼ 0.02), respectively. This is possibly because of reduced model sensitivity as a result of the discretisation of

continuous variables (e.g. flood duration) and averaging of flood requirements. It is also possible that projected climate change at 2030 has minor impacts on the habitat condition for vegetation and waterbirds at the Macquarie Marshes. 4.2. Sensitivity to flood attributes The sensitivity of the vegetation and waterbird model outputs to flood attributes was analysed using Netica's ‘Sensitivity to Findings’ function (Norsys, 2009; Pollino et al., 2007b). The outputs from the sensitivity analysis in Netica are presented as mutual information, which describes the strength of relationship between variables, where zero indicates independence between variables. As the mutual information value increases, so does the strength of the linkage. Mutual information should be interpreted as a relative comparison among the selected variables. Thus for each species, the hydrological variables can be ranked according to the mutual information, with Rank 1 corresponding to being the most sensitive variable for that species. The results of the sensitivity analysis for vegetation and waterbird species in EXCLAIM are shown in Fig. 9, which displays for each flood attributes, the proportion of species that has certain

Fig. 7. Heat maps of very good habitat condition (i.e. index value >0.8) for native fish and common carps at Warren and Oxley, for five MDBSY scenarios: 2030 wet climate (2030wet), 2030 median climate (2030med), 2030 dry climate (2030dry), historical climate with development (A), and historical climate without development (P). The darker the colour, the higher the probability of having good habitat conditions. The colours are centred and scaled by the difference in scenario values for each row (i.e. cannot compare between rows).(For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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months then the rate of fall is very slow). For vegetation regeneration, flood timing becomes the most sensitive input for 9 vegetation species. Flood timing is the second most sensitive input for the maintenance of 9 vegetation species and the third most sensitive input for the breeding of 8 waterbird species. Inter-flood dry period is generally the least sensitive factor for most vegetation and waterbirds. These outcomes show the importance of flood duration and timing inputs. These inputs require robust hydrological and inundation models. Despite the relative importance of one flood attribute over another, the sensitivity analysis indicates that all of the existing flood attribute inputs are important in estimating habitat conditions of the vegetation and waterbird species, and thus none of them can be neglected in the habitat models. 4.3. Model evaluation: waterbird breeding habitat

Fig. 8. Sensitivity of the model outputs (fish, vegetation, and waterbird breeding habitat condition) to input climate change scenarios. One unit of sensitivity is the equivalent to a change to an adjacent state, such as ‘good’ to ‘moderate’ or ‘moderate’ to ‘poor’.

sensitivity ranking. A maximum of four flood attributes (duration, timing, inter-flood dry period and flood area) were used for the vegetation model and a maximum of five attributes for the waterbird model. Note that not all species have all four or five flood attributes as inputs, as documented in Rogers and Ralph (2010). Flood duration is ranked the most sensitive input for describing habitat condition for the maintenance and survival of nearly half of the modelled vegetation species, and for the breeding of 16 waterbird species. Rate of fall, which is also identified as a sensitive input for most waterbirds, was estimated from flood duration (using a simple conversion rule, e.g. if duration is greater than 12

EXCLAIM outputs on waterbird breeding habitat conditions were compared with observed waterbird nest counts in the Macquarie Marshes between 1986 and 2000, as reported by Kingsford and Auld (2005). A positive relationship between the two was expected given that breeding of waterbirds is stimulated by flooding (Rogers and Ralph, 2010). The nest count data were estimated from the published graphs in Kingsford and Auld (2005), and we assumed there is a ±10% reading error using this method. Observed daily flow data (1986e2000) at Marebone gauge (421090) were extracted from PINNEENA 9.2 (DWE, 2008), and used as inputs to the SWIMP inundation model. Except for 1997 (no flood), one flood event was identified by the inundation model for each year between 1986 and 2000, allowing the habitat conditions for waterbird species to be estimated on an event basis for the corresponding year. Breeding habitat conditions for seven species (straw-necked ibis, Australian white ibis, glossy ibis, great egret, intermediate egret, little egret, rufous night heron) were estimated using EXCLAIM. Values of 0, 1 and 2 were assigned to ‘poor’, ‘moderate’ and ‘good’ states, respectively, and the averaged habitat

Fig. 9. Proportion of vegetation and waterbird species that has the corresponding sensitivity ranking for the five flood attributes: flood duration, flood timing, inter-flood dry period, flood area and rate of fall. Rank 1 is the highest sensitivity ranking, suggesting the corresponding flood attribute being the most sensitivity input. Some attributes were not used in the model due to lack of knowledge or identified as irrelevant.

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condition for each species for each year was then calculated using the following equation:

H ¼ Pp  0 þ Pm  1 þ Pg  2

Pp þ Pm þ Pg ¼ 1




(3)

where H is the modelled average habitat condition for a species, and Pp, Pm and Pg are the model outputs on the probabilities of poor, moderate and good habitat conditions for that species. Thus, H ranges from 0 (worst case) to 2 (best case). The results show that higher numbers of observed nest counts are typically associated with higher modelled average breeding habitat conditions (Fig. 10). Ninety percent of the recorded nobreeding years are captured by less than average habitat conditions. Of the seven tested waterbird species, modelled breeding habitat condition for little egrets are least in accord with the observed nest counts. Other factors may contribute to the numbers of waterbird nests, such as wetland vegetation composition and condition and as waterbirds are meta populations, they could be nesting at other wetland sites. 5. Discussion Whilst climate change is widely recognised as an emerging threat to aquatic ecosystems (Meyer et al., 1999), in our analyses we found that the predicted ecological outcomes as a result of alternative climate change scenarios demonstrate little difference from baseline. For the majority of species, Scenario P (the ‘natural’ surrogate) produces the most distinct ecological outcomes compared

to other scenarios. We suggest that the current river operations in the Macquarie River Catchment have a greater impact on catchment hydrology, relative to predictions used here for climate change. Subsequently flow-related ecological outcomes are impacted by current river operations, and 2030 climate change scenarios have a relatively minor additional impact. This argument is supported by CSIRO (2008a:65): “The seasonality in the middle of the [Macquarie] river has been significantly altered by river regulation … .The impacts of climate change and development are less than the impacts of regulation in rivers other than the Castlereagh.” River operations and provision of water specifically for environmental purposes is needed to maintain and restore ecological habitats in the Macquarie Marshes. The analysis of hydrologyeecology relationships is a key science and water management challenge. We believe that the EXCLAIM approach provides a framework to link hydrology and ecological habitats for a large number of species, based on best available knowledge of their flood requirements. This framework can be applied to similar systems without significant modification in the parameters (e.g. the conditional probability tables in the Bayesian networks which specify flood requirements of each species). This approach has an advantage over statistical-derived models (e.g. Kingsford and Auld, 2005; Overton et al., 2009), which tend to rely on relating limited spatial and temporal observed ecological data to flow data, without considering flood attributes. Such models are difficult to generalise to other systems. In contrast, the EXCLAIM DSS has an inundation model, which is transferable, and hydrologyeecology relationships that are generalisable for semi-arid

Fig. 10. Comparing estimated habitat conditions (with ‘2’ being 100% good, and ‘0’ being 100% bad) for waterbird breeding and the observed nest counts between 1986 and 2000 (Kingsford and Auld, 2005). The error bars account for assumed 10% reading errors from the published graphs in Kingsford and Auld (2005).

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wetland species of the Murray-Darling Basin. This approach also provides a means of quantifying the impacts of climate change and water management scenarios for different ecological indicators. The implication of the findings is that specifying ecological indicators should be incorporated into the processes of developing management objectives; for example the best option for maintaining river red gum is not necessary the best for promoting the breeding of glossy ibis. As with any model, there are limitations of the EXCLAIM DSS. A major limitation is that habitat condition is determined only by flow availability. Other important factors can include groundwater input, water quality, predation and competition, as well as land management influences. The assumption that hydrological regime is the single most important driving factor in maintaining the ecological habitats in the Macquarie Marshes is consistent with the environmental flow methodologies as they are applied globally (Tharme, 2003), as supported by recent comprehensive reviews of ecological responses to flow (Lloyd et al., 2003; Poff and Zimmerman, 2010). However, if other factors are found to be critical, incorporating additional components is relatively straightforward in EXCLAIM due to its modular structure design. EXCLAIM allows updating of the structure and parameters as we learn more of the complexity and diversity of wetland ecosystems and how they respond to flow. Additional factors and/or indicators can be incorporated, depending on the system in question, the scale of a study, and/or the advance of our knowledge in system behaviour. Incremental knowledge of requirements of wetlands species, articulated as frequency, duration, and timing is being gained, and has been drawn upon in this paper. However, as yet we do not have sufficient information to quantify the interaction between these variables. For instance, the relative importance of flood attributes in achieving outcomes has been less studied. Is there an overriding factor for which if a threshold is exceeded or not met, an outcome will not be achieved? In the EXCLAIM DSS, we used weight to combine flood attributes, where it is assumed that the impacts of each flood attribute to species habitat conditions are independent and can counteract each other. That is consistent with one of the composite approaches used in PHABSIM (Bovee, 1986). As this knowledge is gained through field observations and research, the underpinning hydrologyeecology relationships can be revised and tested. This is an example of an adaptive approach to modelling, which we contend is critical as part of the adaptive management approach. Despite the limitations and the uncertainties in ecological knowledge, we have demonstrated that EXCLAIM provides a robust framework for assessing climate change impacts on the Macquarie Marshes. It is a starting point towards developing an integrated tool for assessing climate change impacts on wetland ecosystems. It has the potential to be updated as new knowledge and information becomes available, and can be tailored for use in other wetland systems in the Murray-Darling Basin in Australia. It can input any hydrological sequence, should it be the consequence of climate change, and/or river regulation and management. Acknowledgements Funding for this project was provided by the Australian Government's Caring for our Country Program and the Water for the Future Program, and the NSW Government's Catchment Action NSW Program and the Rivers Environmental Restoration Program, through NSW Central West Catchment Management Authority and NSW Office of Environment and Heritage. We would like to thank Dr Barry Croke for his advice on inundation modelling, and Ambrose Andrews for his work on developing the user interface for the EXCLAIM DSS.

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