From hydrodynamic to hydrological modelling: Investigating long-term hydrological regimes of key wetlands in the Macquarie Marshes, a semi-arid lowland floodplain in Australia

Journal of Hydrology 500 (2013) 45–61

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From hydrodynamic to hydrological modelling: Investigating long-term hydrological regimes of key wetlands in the Macquarie Marshes, a semi-arid lowland floodplain in Australia Li Wen a,⇑, Rohan Macdonald b, Tim Morrison c,1, Tahir Hameed b, Neil Saintilan a, Joanne Ling a a b c

Science Division, NSW Office of Environment and Heritage, Sydney, Australia NSW Office of Water, Department of Primary Industries, Sydney, Australia DHI Water and Environment Pty Ltd., Sydney, Australia

a r t i c l e

i n f o

Article history: Received 28 October 2011 Received in revised form 5 June 2013 Accepted 10 July 2013 Available online 24 July 2013 This manuscript was handled by Konstantine P. Georgakakos, Editor-in-Chief, with the assistance of Michael Brian Butts, Associate Editor Keywords: MIKE FLOOD IQQM Hydrological indicators Wetland restoration Flow-ecology relationship

s u m m a r y The Macquarie Marshes is an intermittently flooded wetland complex covering nearly 200,000 ha. It is one of the largest semi-permanent wetland systems in the Murray–Darling Basin, Australia, and portions of the Marshes are listed as internationally important under the Ramsar Convention. Previous studies indicate that the Marshes have undergone accelerated ecological degradation since the 1980s. The ecological degradation is documented in declining biodiversity, encroaching of terrestrial species, colonisation of exotic species, and deterioration of floodplain forests. There is strong evidence that reduction in river flows is the principal cause of the decrease in ecological values. Although the streams are relatively well gauged and modelled, the lack of hydrological records within the Marshes hampers any attempts to quantitatively investigate the relationship between hydrological variation and ecosystem integrity. To enable a better understanding of the long-term hydrological variations within the key wetland systems, and in particular, to investigate the impacts of the different water management policies (e.g. environmental water) on wetlands, a river system model including the main wetland systems was needed. The morphological complex nature of the Marshes means that the approximation of hydrological regimes within wetlands using stream hydrographs would have been difficult and inaccurate. In this study, we built a coupled 1D/2D MIKE FLOOD floodplain hydrodynamic model based on a 1 m DEM derived from a LiDAR survey. Hydrological characteristics of key constituent wetlands such as the correlation between water level and inundation area, relationships between stream and wetlands and among wetlands were estimated using time series extracted from hydrodynamic simulations. These relationships were then introduced into the existing river hydrological model (IQQM) to represent the wetlands. The model was used in this study to simulate the daily behaviours of inflow/outflow, volume, and inundated area for key wetlands within the Marshes under natural conditions and recent water management practices for the period of July 1 1991 to June 30 2009. The results revealed that the recent water management practices have induced large changes to wetland hydrology. The most noticeable changes include the dramatic reductions in high flows (i.e. flows with less than 25% exceedence, reduction ranges from 85% to 98% of the high flow peak depending on the location), areal inundation extent (ranging from 13% to 79% depending on climatic conditions), and flow rising/falling rates (over 90% for high flows). Our analysis also highlighted that the impacts of water management practices on some of the flow variables for wetland habitats contrasted with those for instream habitats. For example, we did not find any evident alterations in the low flows (i.e. 75% exceedence) attributable to water management. Crown Copyright Ó 2013 Published by Elsevier B.V. All rights reserved.

1. Introduction ⇑ Corresponding author. Address: Science Division, Office of Environment and Heritage, NSW Department of Premier and Cabinet, 59-61 Goulburn Street, Sydney 2000, Australia. Tel.: +61 (02) 9995 5054; fax: +61 (02) 9995 5924. E-mail address: [email protected] (L. Wen). 1 Current address: Regional Operations, Office of Environment and Heritage, NSW Department of Premier and Cabinet. 10 Valentine Ave, Parramatta NSW 2150, Australia.

There is an increasing recognition of the ecological and economic values and services of wetlands within the lowland floodplain landscape (Ehrlich and Ehrlich, 1992; MEA, 2005; Duranel et al., 2007; Acreman and Ferguson, 2010). Notwithstanding, the losses and degradation of wetlands have been exceptional during the last two centuries (MEA, 2005; Dudgeon et al., 2006). For

0022-1694/$ - see front matter Crown Copyright Ó 2013 Published by Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jhydrol.2013.07.015

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L. Wen et al. / Journal of Hydrology 500 (2013) 45–61

example, estimates of wetland loss exceed 50% for the conterminous United States and Europe (Finlayson and Davidson, 1999; Dahl, 2000). In the Murray–Darling Basin (MDB) of Australia, an estimated 90% of the floodplain wetlands within the regulated river systems were lost as a result of flow regulation (Arthington, 2002), and the remaining wetlands are under increasing pressure due to the lack of flooding (Kingsford and Thomas, 2004; Wen et al., 2009; MDBA, 2010). The massive loss and widespread degradation of floodplain wetlands has prompted Australian Governments to take a range of actions to arrest the trend of ecological degradation observed in lowland floodplains, and the restoration of the remaining key wetlands through the provision of environmental water has become a high priority (MDBA, 2010). The ecological significance of wetland hydrology is widely acknowledged, particularly for semi-arid regions, such as the MDB in Australia. A number of studies have reported that the wetland hydrological regimes exerted a primary control on ecological processes such as plant productivity and diversity (Boudewijin et al., 2007; Alexander et al., 2008), aquatic microinvertebrate colonisation (Jenkins and Boulton, 2007), fish recruitment (Tonkin et al., 2008), waterbird breeding (Kingsford and Thomas, 2004), and nutrient transformation and cycling (Kobayashi et al., 2011). Quantification of the relationships between wetland hydrological and ecological attributes, therefore, is vital for the success of river environmental restoration (Poff et al., 2010), which often aims to (partially) restore natural hydrological regimes. To quantify the ecological response to flow regimes, as outlined by Poff et al. (2010), the first step is to build a hydrological foundation that describes the flow regimes under reference and developed conditions. Benefiting from streamflow gauging networks and river hydrological modelling, time series of hydrological metrics are readily available for most of the large river channels worldwide. For example, the Global Runoff Database Centre of the World Meteorological Organisation comprises monthly discharge data of more than 7800 gauging stations from all over the world (GRDC, 2010). However, hydrological data, particularly long-term data on depth, extent of inundation and how these variables have been altered as a result of human activities, rarely exist for wetlands. In addition, because the temporal and spatial variations in inundation extent and water movement are extremely complex in large floodplains, the hydrological regimes in the intermittently flooded wetlands cannot be readily established by linking flooding patterns with stream gauge records. Therefore, hydrological data with proper spatial and temporal resolutions for ecological studies are sparse for floodplain wetlands, even given the recent developments in remote sensing that have significantly improved the accuracy of reconstruction of past flooding patterns (Milzow et al., 2009; Overton et al., 2009; Thomas et al., 2011). Hydrodynamic models have been increasingly used in wetland restoration and management (Thompson et al., 2004; Loucks, 2006; Hammersmark et al., 2008; Paudel et al., 2013). For example, a large-scale spatially distributed hydraulic model (90,000 km2 with grid size of 1 km2) has been developed for the Okavango Delta Wetlands, Botswana for evaluating the ecological impacts of water resources development and climate change (Bauer et al., 2006; Milzow et al., 2010). In the Florida Everglades, the United States, a two dimensional (2D), spatially distributed numerical model was developed to provide the hydrodynamic foundation for hydro-ecological studies (Min et al., 2010). The model domain is relatively small (1.5 km by 4 km) but with a very fine spatial resolution. Chen et al. (2012) built a larger scale (over 50,000 ha) but coarser resolution (400 m) MIKE FLOOD model to investigate the constituent transport with the Arthur R. Marshall Loxahatchee National Wildlife Refuge in the same wetland system. Despite rapid developments in software, hardware and computing algorithms related to floodplain modelling, long computational

time remains the main impediment for wide use of hydrodynamic models, especially 2D models, in particular for water resource planning, which often needs long-term (decadal to centurial) data to understand the impacts of changing seasons, droughts and climate change. Issues including the high computational cost, numerical instability, inadequate input data and the volume of output data mean hydrodynamic model is not suitable for water resource planning. Hence there is a need to link the hydrodynamic model to the hydrological model. The New South Wales (NSW) Office of Water has developed IQQM models (Integrated Quantity and Quality Modelling) for the major river systems including the Macquarie River (NSW Office of Water, 2010). The historic driver for development of these models has been water usage associated with irrigation and town supply as opposed to use associated with the environment (Hameed and Podger, 2001). As a consequence, wetlands were either not represented in the models or represented so coarsely as to provide hydrologic information of limited value. Given the well documented decline in important wetland systems, it was important to extend the IQQM models to include major floodplain wetlands to significantly improve water and the ability of wetland manager to make transparent and scientifically rigorous decisions on the better use of environmental water for the targeted wetlands. The data required to build the enhanced IQQM models include wetland hydrological characteristics (for example, the volume– area relationship of a wetland) along with relationships between inflows and outflows for the array of hydrological components of the floodplain (e.g. precipitation, potential evapotranspiration, and infiltration). Of the available data, ground observations of water level and discharge are spatially infrequent, flow gauges are located only on main channels, and the floodplains are rarely gauged. Remote sensing observations of inundation extent do not provide a solution as they can be limited by either spatial resolution or temporal coverage (reviewed by Schumann et al., 2009). For example, passive microwave instruments have good temporal coverage but limited spatial resolution (as large as 20–100 km), and synthetic aperture radars have good spatial resolution (25 m) but limited temporal coverage. An option is to develop a 2D hydrodynamic model with sufficient spatial details to simulate inundation over the floodplains. The hydrodynamic model can provide a more detailed view of the dynamics of the inundation process (Wilson et al., 2007), and provide the essential data for hydrological modelling as demonstrated in Rayburg and Thoms (2009). In this study, hydrodynamic modelling, which made the maximum use of available data, was conducted to provide information and data of sufficient quality for hydrological modelling (Fig. 1). The hydrodynamic model presented in this paper is among the first attempts in terms of large spatial scale (more than 2400 km2) and relative fine resolution (90 m grid) to investigate the detailed water movement within a highly regulated floodplain (Wilson et al., 2007). Simulations of the developed hydrological model were conducted for three scenarios to investigate the impacts of the recent water management on wetland hydrological parameters such as inflow and water area. The simulation results will contribute to the building of a hydrological foundation for the Macquarie Marshes.

2. Methods 2.1. Study site – the Macquarie Marshes The Macquarie Marshes (hereafter referred as the Marshes) are located in the north west of NSW within the lower Macquarie Catchment (Fig. 2). The Marshes are an extensive wetland system covering an area of 220,000 ha, representing one of the largest semi-permanent wetlands in south-eastern Australia. The area

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L. Wen et al. / Journal of Hydrology 500 (2013) 45–61

Climatic data

Inundation Mapping

Storage hydraulic characteristics

Inundation Mapping

Vegetation Mapping

Calibration

Digital Elevation Model (DEM)

Floodplain hydrodynamic model

Time series of influx to storage

Calibration

Soil Mapping

Calibration

Time series of flows at certain locations

Floodplain Hydrological model

Field Survey

Hydrological records

Time series of efflux from storage

Calibration

Hydrological records

Daily output of inflow, outflow, area and volume for each storage Fig. 1. Graph shows the data and information flows of the project. The hydrodynamic model (MIKE FLOOD), which has made maximum use of all available information and data, provided the vital data to develop the hydrological model (IQQM).

has a semi-arid climate with low rainfall, hot summers and cold winters. The average annual rainfall is 447.4 mm. The daily mean maximum summer and mean minimum winter temperatures are 34.6 °C and 4.0 °C, respectively (BoM, 2010). The Marshes start in the south at Marebone Weir, which is situated 50 km north of the town of Warren. As the Macquarie River reaches the flat alluvial plain, the floodplain transforms into a maze of interconnected streams, lagoons, distributary creeks and anabranching channels (Paijmans, 1981), and extends to the north a further 100 km until all the channels unite to form a single channel near Carinda (Fig. 2). The Marshes sustain a wide range of floodplain woodlands, including river red gum (Eucalyptus camaldulensis), black box (Eucalyptus largiflorens), coolabah (Eucalyptus coolabah) and river cooba (Acacia stenophylla). There are also large areas of wet meadows including species such as common reed (Phragmites australis), water couch (Paspalum distichum), and Cumbungi (Typha sp.) (Bowen and Simpson, 2010). These vegetation communities provide important habitats for many colonial waterbirds, which are the most iconic fauna of the Macquarie Marshes (Kingsford and Auld, 2005; DECCW, 2010). The Marshes also provide refuge for many aquatic taxa, including fish, macroinvertebrates and herpetofauna such as frogs, lizards, snakes and turtles (Brock et al., 1999).

2.2. Identification of key wetland systems within the Marshes The Marshes consists of a number of constituent wetlands that may be identified as discrete but linked eco-hydrological units. The key wetlands within the Marshes were delineated based on vegetation maps (Bowen and Simpson, 2010), historical waterbird breeding (Kingsford and Thomas, 1995), inundation history (Thomas et al., 2010), and the location of Ramsar-listed component wetlands (DECCW, 2010) using ArcGIS. A total of 36 key wetlands were identified within the Marshes, as shown in Fig. 2. These wetlands represent the existing ecological assets (e.g. vegetation communities, waterbird breeding sites) in the Marshes. They are also the targeted sites for restoration through environmental water provision. The 36 delineated wetlands will be represented as ‘‘storages’’ in the hydrological model (to be consistent, they are referred to as wetlands). In this paper, we will focus our analysis on three wetlands: wetland 1 (the Jungle) at the start of the Marshes,

wetland 16 (Pillicawarrina) in the middle and wetland 26 (North Marsh river red gum woodland) at end of the system (Fig. 2). The three wetlands are chosen as representative of the spatial variability of wetland types across the Marshes. 2.3. Available data The success of hydrodynamic and hydrological simulations to reproduce the flow conditions and flooding patterns observed in the Marshes depends largely on the availability and accuracy of data, including topography, soil texture and structure, vegetation distribution, climate (rainfall and evaporation), river flows, and flooding patterns (Fig. 1). The sources, methods of data collection applications and accuracy of the data were summarised in Table 1. 2.4. Hydrodynamic modelling 2.4.1. Model setup A physically-based, spatially distributed hydrodynamic model for the Marshes was developed in DHI Software’s MIKE FLOOD modelling system. MIKE FLOOD is a comprehensive toolbox for floodplain modelling that combines the dynamic coupling of the one dimensional river model MIKE 11 and two dimensional MIKE 21 model systems (DHI, 2010). MIKE 11 uses the traditional Saint Venant equations to govern the mass and momentum conservation. The solution of the equations is based on an implicit finite difference scheme developed by Abbott and Ionescu (1967). MIKE 21 is a modelling system for 2D free-surface flows. We used the single grid simulation engine (one of the three available numerical engines) which solves the full time-dependent non-linear equations of continuity and conservation of momentum by implicit finite difference techniques with the variables defined on a space-staggered rectangular grid. MIKE FLOOD uses an integrated approach that enables the water fluxes between the 1D and 2D domains by defining linkages between the two modelling systems. MIKE FLOOD provides five types of link for the coupling of MIKE 11 and MIKE 21: standard, lateral, structure, side structures, zero flow links. The standard links are the connection between the end of a MIKE 11 branch and a series of MIKE 21 cells. Lateral links, which allows a string of MIKE 21 elements (cells) to be laterally connected to a given reach in MIKE

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L. Wen et al. / Journal of Hydrology 500 (2013) 45–61

Fig. 2. The modelled 36 key wetlands in Macquarie Marshes. Water enters the Southern Marshes through gauges 421088 and 421090, flows north and leaves the Northern Marshes through gauges 421012 and 421011 near Carinda. Inset shows the location of the Macquarie Marshes within the Murray–Darling Basin in Australia.

11, are used as the coupling mechanism in our model. A detailed description of the MIKE FLOOD package including the components and scientific background and references is provided in the MIKE FLOOD User Guide (DHI, 2010). 2.4.1.1. Rivers and channels network (MIKE 11). The 1D river network was developed based on a 1 m digital elevation model (DEM), representing the major channels (497.5 km) in the Marshes (Fig. 3). Using the MIKE 11 GIS tool, the channel network was digitised and defined in the model by extracting cross-sections at regular interval (approximately 3 km) from the 1 m DEM. Given that all the modelled channels are wider than 10 m, and the LiDAR survey was taken during the extreme drought when the majority of the Marshes was dry, the 1 m DEM provided an relatively accurate profile of the channel cross sections. Further cross-sections were extracted where the channel has a sudden change in direction, shape and grade. For each channel reach in Fig. 3, at least three cross-sections, at the start, middle and end of the channels, were digitized. In some areas, notably the North marsh area for both the Bora Channel and the Macquarie River (Fig. 3), the main river

channel becomes braided into a large number of smaller meandering channels. Therefore, it is difficult to form a single main flow channel in the model. The most representative flow path through such highly braided areas was determined by reviewing the DEM and was represented as a single channel within the 1D model. Structures (a total of 70) such as bridges, culverts and gates, were incorporated in the model along the channels (Fig. 3). These structures were modelled using MIKE 11 structure tools with specifications, such as location, length and geometrical type of the structures obtained from the NSW State Water and ground surveys. The bed resistance was described with the conventional Manning’s n. The value of n is typically in the range 0.01 (smooth channel) to 0.10 (densely vegetated channel) (DHI, 2010). As the channels in the Marshes are vegetated with various densities, an initial global value for Manning’s ‘n’ of 0.05 was defined as channel roughness coefficients, which is appropriate for earth channels with some vegetation (Mohamed et al., 1992; Ladson et al., 2003). The values of Manning’s ‘n’ for the various 1D channels were subject to refinement during the calibration (see below).

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L. Wen et al. / Journal of Hydrology 500 (2013) 45–61 Table 1 Summary of data used in the hydrodynamic and hydrological models. Type

Description

Application

Source

Topography

Digital elevation model (DEM) derived from LiDAR survey with 0.05 m vertical and 1 m horizontal accuracy. Flying height 1671 m ASL Digital photos with 0.6 m accuracy obtained as LiDAR survey. Flying height 2897 m ASL

1 m DEM provides channel cross-sections for 1D model establishment; 90 m DEM; resampled from the 1 m DEM for 2D model grid ground elevation Identification of flow regulation structures, roads and bridges for 1 D model setup

NSW Lands Department

Some cross-sections, specifications on hydraulic control structures

1 D model establishment

Soil map

Soil types with hydraulic properties

To develop soil infiltration parameters

Inundation maps

Inundation maps with spatial resolution of 30 m were derived from Landsat satellite imagery. The overall accuracy of the mapped inundation extent is approximately 80%. The maps cover a numbers of flooding events from 1989 to 2008 Vegetation map in 2005 developed from aerial photography. A total of 26 types of communities were identified ranging from cleared to bush to tree Water level and river discharge for 18 gauges across the Marshes

For calibrating hydrodynamic and hydrological models

NSW State Water and field surveya Jenkins and James (2009) Thomas et al. (2010)

Orthorectified aerial digital photography Ground survey data

Vegetation map

Stream gauging records Climate a

To develop the surface roughness map for 2D model grid

River flows at two gauges were used as model inputs, the others for model calibration Model inputs

Rainfall and evaporation

NSW Lands Department

Bowen and Simpson, 2010 NSW Office of Water Australian BOM

Field survey for areas that were under water during LiDAR surveys to correct the DEM.

River hydrographs at the Macquarie River downstream of the Marebone Weir (Gauge No. 421090) and the Marebone Break downstream of the Marebone Regulator (Gauge No. 421088) (Fig. 2) were the upstream 1D model boundaries. At the downstream, model boundaries discharge ratings table were based on the channel cross-section, which meant the model represented flows leaving the model area. Such a boundary condition was assigned to the most downstream chainage of the Macquarie River, Terrigal Creek and Ginghet Creek where the channels meet the boundary of the model area. 2.4.1.2. Flows in floodplain (2D MIKE 21). A 90 m grid cell resolution was chosen to keep model run time within a practical range without losing the spatial details. Therefore, the MIKE 21 model has a total of 213,443 active cells. The 1 m DEM was resampled to 90 m using the raster resampling tool in ArcMap using the cubic convolution algorithm. It is expected that the reduction of spatial resolution will smooth out the sub-grid sized flood ways, and will have impacts on depth distribution. However, the impacts on the inundation extent and flood propagation across the Marshes are not significant because the main flood paths are handled by the 1D model. In addition, the flood progression is also controlled by floodplain roughness, which is dependent on the type of vegetation cover. The hydraulic roughness of the floodplain varies depending on the vegetation type (Kadlec and Wallace, 2009). Generally, the denser the vegetation the higher the roughness coefficients, as defined by Manning’s ‘n’. A roughness map was developed by assigning a Manning’s n to each of the 26 vegetation types in the 2008 vegetation map (Bowen and Simpson, 2010). The Manning’s n values range from 0.02 to 0.08 representing bare ground to dense vegetation cover (Hickin, 1984; Ladson et al., 2003). We did not define upstream boundary for the 2D model as inflows entering the model domain were specified in the MIKE 11. At downstream, constant water level boundaries were defined to allow any overland flows reaching the boundaries to propagate out of the model domain and minimise boundary effects in the model. Representation of soil infiltration using the recently developed MIKE 21 HD – Infiltration and Leakage Module (DHI, 2009) was included in the initial model set up. The infiltration and leakage

model implements a simple approach to describe the infiltration from the free surface zone to the unsaturated zone and from the unsaturated zone to the saturated zone. The model assumes the following:  The unsaturated zone is modelled as an infiltration layer with homogeneous porosity over its full depth.  The flow between the free surface zone and the infiltration layer is based on a constant flow rate (independent of flow depth), i.e. Vtotal = a  Dt, where a is the infiltration rate and Dt is the length of time it takes to fill the effective volume in the infiltration layer.  The flow between the saturated and unsaturated zone is defined using a constant leakage rate (b).  The net rate (a  b) is used to determine the length of time it takes to fill the effective volume in the infiltration layer, defined as a function of grid size, layer depth, porosity and initial volume.  When the infiltration layer volume is filled, the infiltration rate reverts to the leakage rate (b). The vertical flow from the free surface zone can therefore be calculated by specifying the following six parameters (Table 2). 1. The depth of the free surface zone (simplified to dry or wet in the current version i.e. independent of depth). 2. Flow rate between the free surface zone and infiltration layer (a). 3. The porosity of the infiltration layer. 4. The depth of the infiltration layer. 5. A leakage rate from the infiltration layer to the saturated zone (b). 6. Initial volume of water in the infiltration layer. The values of infiltration rate in Table 2 were based on a recent soil survey for the Marshes (Jenkins and James, 2009), and standard tables from Argonne National Laboratory (ANL, 2010). 2.4.1.3. Coupling 1D and 2D flows (MIKE FLOOD). The 1D and 2D components of the model were coupled to provide flow exchange mechanisms between the two model domains. Lateral links were

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L. Wen et al. / Journal of Hydrology 500 (2013) 45–61

Fig. 3. The 1D river network setup in the MIKE 11 showing the 31 channels and location of flow regulating structures.

Table 2 Parameters specified in the soil infiltration model and statistics.

a b

Parameter

Unit

Mean values Range

Standard deviation

Infiltration rate (a) Porosity Depth Leakage rate (b)a Leakage rate (b)b Initial volume

mm h1 % m mm h1 mm h1 m3

4.23 0.17 0.06 1.06 0.53 0

0.85 0.014 0.02 0.21 0.11 0

1.25–12.05 0.09–0.33 0.02–0.23 0.31–3.12 0.16–1.56 0

b for low flow. b for medium to high flows.

made between the 1D cross-sections and the 2D model grid cells. These links were created by digitising the top-of-bank locations along the river channels using the LiDAR and ground survey data coupled with the aerial photography images. The linkages were carefully checked to ensure compatibility between the 1D and 2D topography levels.

2.4.2. Model calibration The MIKE FLOOD model has been calibrated against six historical flow events, comprising two low flow events, two medium flow events and two high flow events. The duration of these events varies from 4 to 6 months (Table 3), and covering the full range of flow conditions in the Marshes. Data used for calibration included recorded river flows at 10 gauges and remote sensing inundation extent (Thomas et al., 2010). During the calibration, a number of model parameters, including channel roughness, initial conditions, 2D grids ground levels and 1D cross-sections, were adjusted to reproduce the observed inundation extent and recorded hydrographs at gauge stations.

2.4.2.1. Channel roughness. Initially, a global roughness for Manning’s ‘n’ of 0.05 was applied to all the 1D channels. The values were manually adjusted to modify the flow behaviour in the channels and to account for potential vegetation along the channels, such as water couch and common reeds. Increasing the roughness resulted in retardation of the flood wave along the channels

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L. Wen et al. / Journal of Hydrology 500 (2013) 45–61 Table 3 Historical flooding events for MIKE FLOOD calibration. Event category

Duration

Inflow statistics*

Inundation map dates

Median (m3 s1) Low flow

01/09/97–28/02/98 09/09/05–29/01/06

11/09/97, 13/10/97, 01/01/98 03/10/05, 20/11/05

5.83 9.82

33.63 55.90

Medium flow

21/06/96–15/11/96 01/06/01–31/10/01

26/06/01, 28/07/01, 14/09/01

25.72 8.04

62.02 35.24

14/07/98–18/11/98 24/09/00–20/02/01

29/08/98, 16/10/98, 01/11/98 16/12/00, 01/01/01

67.67 51.60

89.70 105.15

High flow

*

Max (m3 s1)

Combination mean daily flows at Macquarie River at Marebone Weir (421088) and Marebone Break Weir (421090).

causing more flow out of the channels onto the floodplain. Roughness changes were also made at bifurcations between two channels to change flow distribution, with flows preferentially following the smoother channel. Table 4 summarises the resulting roughness values that were changed from the global value of 0.04. 2.4.2.2. Initial water depth in MIKE 11 channels and water level in MIKE 21 cells. A number of tests with varying the initial water depth in the 1D channels and over the 2D grid were undertaken. However, it was found that the overall result was relatively insensitive to the initial inundation values. Differences were noted only during the early stage of the simulation and in small areas of the floodplain where pooling of the water occurred. These differences were not significant for the calibration, particularly as the inundation maps were generally not available for the early parts of the event (Table 3). The final model set up was to define a 0.5 m initial depth in Buckiinguy Creek, which resulted in earlier flooding of the swamp area. Further testing of the initial water level values could be undertaken as part of the scenario testing, when differing times of flooding for the different wetland areas could be tested. 2.4.2.3. 2D grid ground level and 1D channel cross-sections. In some areas, there was significant discrepancy between simulated and remote sensing flood extents during the calibration. A review of these areas found that there were high elevation areas that were unexpectedly above the predicted flood levels. These areas have

Table 4 Final bed resistance (Manning’s ‘n’) in the MIKE FLOOD.

a

Channel sectiona

Chainage

Buckinguy Creek Buckinguy Creek Breakaway Breakaway Macquarie_River Macquarie_River Macquarie River Macquarie River Monkey Creek South Monkey Creek South Monkey Creek North Monkey Creek North Monkey Creek West Monkey Creek West Monkey West Monkey West Monkeygar Creek Monkeygar Creek Monkeygar Creek Monkeygar Creek Monkeygar Creek Monkeygar Creek

0 12,506 0 6010 0 80,000 136,559 153,773 0 3295 0 3555 0 8380.3 0 9067 0 600 0 18,494 18,494 24,094

b

2.5. Hydrological modelling Manning’s n 0.045 0.045 0.055 0.045 0.055 0.055 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.055 0.055 0.035 0.035 0.07 0.07

Refer to Fig. 3 location. For the same channel section, the values of Manning’s ‘n’ changes linearly from low to high chainages. b

significant aquatic vegetation such as common reed and water couch. It is supposed that there were problems with LiDAR penetrating through dense vegetation. Therefore, the LiDAR is likely to falsely report the vegetation canopy as the ground levels. In an attempt to correct the errors in LiDAR DEM, an eight point moving average was put through the problematical areas to lower the ground level, which resulted in an average 0.2 m reduction in ground level. A further 0.5 m reduction in these areas was made to reproduce the remote sensing inundation patterns during the following calibrations. In addition, adjustments were made to the cross sections of the affected 1D channels to force water onto the floodplain and to match the remote sensing inundation patterns. Ground survey is recommended for these areas to reduce the uncertainty so that the flood simulation is based on actual topography rather than inferred values based on vegetation height and observed flow patterns. The calibrated MIKE FLOOD model was further validated against three more events: low (14/08/2003–16/10/2003), medium (17/ 08/1999–17/01/2000) and high flow (03/03/1990–17/09/1990). As the results of validation runs (i.e. the performances) were similar to those of calibration runs, the result was not presented to avoid duplication. Sensitivity tests for evaporation and roughness, as represented by Manning’s ‘n’, were also carries out by globally increasing and decreasing these parameters by 20%. The results (the changes in inundation extent caused by increase/decrease 20% is generally less than 1%) showed that the two parameters (i.e. evaporation and roughness) had low effects on inundation extent.

2.5.1. Model setup The hydrological model was developed in the Integrated Quality and Quantity Modelling (IQQM) framework, which is the main platform used in NSW for water resource sharing and management (NSW Office of Water, 2010). In IQQM, a river system is portrayed as a schematic network of nodes and links that represent key system components (e.g. river channels, storages and water users) and the flow between them. Physical and operational constraints such as dams, weirs and regulators) are also specified with a simple scripting language (i.e. IQQM decision tree, see below). The hydrological model was updated from the existing Macquarie IQQM model (NSW Office of Water, 2010) by adding 36 storages representing the key wetlands identified in the Marshes (Fig. 2). The schematic of the Marshes was developed by analysing the MIKE FLOOD animation of the 2000–2001 high flow flooding event (Table 3). Each of the 36 wetlands was modelled as an un-gated onriver storage node whose water balance can be written as:

DQ ¼

X

Q ii 

X

Q oi þ P  E

ð1Þ P

where DQ is the daily change in volume of the storage, Qii is the P total daily inflows, Qoi is the total daily outflows, P is daily rainfall and E is daily evaporation.

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The inflow is modelled as a demand node which orders water from reference locations (river flow or other storages). A storage can have a number of inflows from multiple locations and a total of 88 such inflows were identified for the IQQM. The order is controlled by an IQQM decision tree (IDT), which specifies the reference location (i.e. where the water comes from) and flow rate (flow control table – FCT) (Fig. 4A is an example of such relationship). The IDT is a flexible tool in IQQM to model particular operator behaviours (Hameed and O’Neil, 2005). By specifying the volume–spillway relationships of the storages (Fig. 4B shows such a relationship for wetland 4), the total outflow can be modelled. Depending on the flood paths, a storage could have several outlets (Fig. 4C illustrates an example outflow from wetland 1). Each of the outflows is controlled by an IDT, and the sum of the outflows equals to the total spillway in each modelling step (i.e. 1 day). Finally, the relationship between storage volume and surface water area was specified for each storage (Fig. 4D). All information and data for building the above relationships were extracted from MIKE FLOOD simulation, and these relationships are subjected to change during calibration. The 2000–2001 event was chosen because it represented a large range of flow conditions from the initially low flows to the very high flows that resulted in extensive overland flooding (Table 3).

relationship of storages based on remote sensing inundation patterns as the 90 m spatial resolution in MIKE FLOOD might be too coarse to accurately describe the wetlands. 2.5.2.1. Flow calibration. For flow calibration the river system was divided into routing segments, and the routing parameters and transmission loss relationships were calibrated for each segment independently. The objective was to match the observed hydrograph shape and volume at the downstream limit of each segment based on recorded inflows from upstream, tributary inflows and diversions. Each routing segment requires a gauging station at the upstream and downstream limits. The calibration procedure involved routing flows from the upstream gauging station to the downstream gauging station. Any tributary inflows were input along the reach and extractions were removed according to observed records. Observed flows (01/07/1991–30/06/2009) were input at each of the Marebone gauges (421088 and 421090) for the calibration process. Flow routing parameters were adjusted to obtain a match in the observed and simulated time series, and achieved a satisfactory flow duration curve. Linear, non-linear and flow-travel time table are the three main routing types to link upstream–downstream flow nodes. Non-linear (Eq. (2)) is the exclusive routing mechanism in the study.

2.5.2. Model calibration Model calibration was an iterative process, involving a number of stages. The main aims of the calibration were to match observed flows at gauge locations in the main channels and to match the hydrodynamic model outputs and remote sensing inundation results for the storages. The process comprised the following steps: (1) calibrate flows in the main channels; (2) calibrate storages to the hydrodynamic results; and (3) refine the volume–area

S ¼ k  Qm

where S is the channel storage capacity, Q is flow rate, and k and m are constants (the routing parameters).Transmission losses were then estimated using a relationship between river discharge and loss rate, through effluent nodes, with the aim being to match the flow duration curves between simulated and observed flows, through the entire flow range.

-1

Out flow to wetland 4(10 m day )

(C) Relationship between Outflow to wetlands 4 and total outflow from wetland 1

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Fig. 4. Hydrological relationships specified for storage one in the wetland hydrological model (IQQM) based on hydrodynamic model (HDM) outputs, FCT = flow control table and IDT = IQQM decision tree.

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2.5.2.2. Storage calibration. The storage calibration aimed to match the inflow, outflow, volume and surface area results from the IQQM simulation with results from the hydrodynamic modelling. This was achieved by adjusting the flow distribution and storage relationships, and where necessary refining the IQQM schematic. These storage relationships were further refined to match various stream gauging stations within the Marshes and to match the surface water area for each storage from remote sensing. 2.5.3. Model evaluation We used both statistical and graphic techniques to evaluate the final MIKE FLOOD and IQQM models. Graphic techniques provide visual comparisons of observed and simulated data (hydrographs and percent exceedence probability for IQQM and flood patterns for MIKE FLOOD). It would be ideal to perform quantitative statistical evaluations during model calibration and validation. However, as both the hydrodynamic and hydrological models have a large number of parameters (for example, the IQQM has 780 nodes and each node has many parameters to be calibrated), the calibration process involves numerous iterations. It is impractical to calculate the evaluation statistics for each round of calibration. Therefore, statistics evaluation was carried out and reported only for the final models. Three statistics, Pearson’s coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE) and percent bias (PBIAS), were calculated for river flows for both MIKE FLOOD and IQQM at a number of gauge locations. These locations were selected based on the consideration of their spatial distribution through the Marshes (Fig. 3) and the availability of gauge records. For IQQM, we also included two gauges (421011 and 421012) outside of the MIKE FLOOD boundary to evaluate the accuracy of the modelled flows leaving the Marshes. Pearson’s coefficient of determination describes the degree of collinearity between simulated and measured data. The NSE is a normalised statistic that determines the relative magnitude of the residual variance compared to the measured data variance (Nash and Sutcliffe, 1970). NSE indicates how well the observed versus simulated data fits the 1:1 line. PBIAS measures the average tendency of the simulated data to be larger or smaller than their observed counterparts (Gupta et al., 1999). 2.5.4. Scenario runs Three scenarios, natural, actual and current water sharing plan (hereafter referred as planned), were simulated for the period 1991–2009, during which the flows into the Marshes were

recorded and available. The current water sharing plan for the Macquarie Catchment was introduced on 1 July 2004 and applies for a period of 10 years to 30 June 2014 (NSW Office of Water, 2011). The water sharing rules in the Plan provide water for the environmental needs of the regulated rivers and direct how the water available for extraction is to be shared. Water sharing rules, include water allocations, the extraction of water, the operation of dams, and the management of water flows are all included in the planned scenario. To model the natural condition, the calibrated model was modified by removing all the structures and water diversions (e.g. irrigation and town water suppliers). Effluent–river flow relationship at regulators was also adjusted to represent a more natural flow splitting at divergent points. The adjustments were based on the resulting flow rates from the hydrodynamic simulation. In the actual scenarios, actual flow hydrographs for the Macquarie River at Marebone and Marebone Break (Fig. 2) were used as model inputs to re-construct the actual wetland hydrology over the simulation period. 3. Results and discussion 3.1. MIKE FLOOD model performances It is noteworthy to point out that the results from the two calibration events were consistent in terms of the geographic locations of good matches and misrepresentations, especially for high flow events. Therefore, we only reported results from one calibration event. 3.1.1. Simulation of flow For low flow events, the MIKE FLOOD performances were satisfactory to very good in terms of Pearson’s R2 and NSE (Table 5). However, the PBIAS indicated that the model performed poorly at two locations: the model underestimated by more than 50% at gauge 421111 and overestimated by a little less than 50% at gauge 421147 (Table 5). Considering the simulated river flow matched the recorded hydrograph very well at the upstream gauge 421022 with an NSE value of 0.96 and PBIAS < 5% (Table 5), the upstream weir (Fig. 3), which controls eastern and western river flow, is the likely cause of the large discrepancy at gauge 421111. The overestimation at gauge 421147 may also due to the operational diversions though the six weirs upstream (Fig. 3). Simulation of low flow event is obviously sensitive to water extractions because even small diversions can be significant proportionally to the total river flow. Because the management of weirs and other structures

Table 5 The performance indicatorsa of MIKE FLOOD for river flowsb at various gauges within the Macquarie Marshes. Gaugec

Low flow 2

421111 421129 421132 421022 421118 421135 421147 421152 421169 421116

High flowd

Medium flow

R

NSE

PBIAS

0.94 0.98

0.38 0.91

51.74 7.36

0.98

0.96

4.29

0.95

0.59

47.80

0.98 0.95

0.92 0.91

5.92 1.37

R

2

0.64 0.90 0.81 0.86 0.97 0.85 0.81 0.77 0.83 0.86

NSE

PBIAS

R2

NSE

PBIAS

0.08 31.48 0.37 0.21 0.95 0.60 0.19 0.27 100.50 0.59

1.51 52.82 5.19 3.88 2.59 12.04 5.82 1.37 27.93 14.00

0.71 0.78 0.77 0.90 0.27 0.29 0.50 0.55 0.56 0.35

46.36 7.08 3.70 0.49 0.37 3.55 0.11 0.28 22.56 21.83

39.88 28.57 10.46 4.24 7.39 28.33 1.40 2.86 23.66 43.07

a Performance indicators based on Henriksen et al. (2008) and Moriasi et al. (2007): R2 is the Pearson’s coefficient of determination: R2 P 0.85 very good, 0.85 > R2 P 0.65 good, 0.65 > R2 P 0.5 satisfactory, and R2 < 0.5 poor; NSE = Nash–Sutcliffe efficiency, NSE P 0.75 very good, 0.75 > NSE P 0.65 good, 0.65 > NSE P 0.5 satisfactory, and NSE < 0.5 poor; and PBIAS = percent bias, NSE P ±10 very good, ±10 > NSE P ±15 good, ±15 > NSE P 25, satisfactory, and NSE < ±25 poor; b Statistic based on simulation results from 2005–2006 event (low flow); 2001 event (medium flow); and 2000–2001 event (high flow). c Refer to Fig. 2 for locations of gauge; blank indicates gauge data not available. d Based on water levels as gauged river discharge is unreliable during high flows.

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was not well documented in the Marshes, their operation may not be efficiently modelled. For medium flow events, the Pearson’s coefficient of determination (R2) between modelled and observed river flows is greater

than 0.5 for all locations, suggesting acceptable modelling results (Van Liew et al., 2003). However, the negative NSE values indicate unacceptable performance at gauges 421129 and 421169. The unsatisfactory performance at the two locations is further

Fig. 5. Comparison between modelled (right) and remote sensing (left) flooded extents for (A) low flow (20/11/2005), (B) medium flow (14/09/2001), and (C) high flow (16/ 12/2000) events.

L. Wen et al. / Journal of Hydrology 500 (2013) 45–61 Table 6 The performance indicatorsa of IQQM for river flows at various gauges within the Macquarie Marshes.

a b c

Gaugeb

Years of record

R2

NSE

PBIAS

421022 Macquarie R at Oxley Station 421147 Macquarie R at Pillicawarrina 421135 Macquarie R at Miltara 421116 Macquarie R at Gibsomn way 421118 Bulgeraga Ck at Gibson Way 421011 Marthaguy Ck at Carindac 421152 Gum Cowal at Oxley 421012 Macquarie R at Carindac

15 18

1.00 0.87

0.95 0.86

0.24 1.23

18 5

0.67 0.91

0.61 0.91

1.93 1.24

3 11 8 16

0.68 0.97 0.84 0.76

0.62 0.83 0.84 0.76

4.38 37.60 6.59 1.45

See table 5 for classification of performance. Refer to Fig. 2 for locations of gauge. Gauge situated outside of the MIKE FLOOD model domain.

evidenced in the PBIAS values (i.e. greater than ±25, Table 5). The two gauges are both located at Monkeygar Creek (Fig. 3) and the poor results reflected the technical difficult to accurately represent the complex hydraulic relationships among the braided channels such as flow distribution at bifurcations and the operation of flow control structures. Nevertheless, the simulation results are acceptable for all locations on the main streams including Macquarie River at Oxley Station, Macquarie River at Piccicawarrina, and Gum Cowal at Oxley (Table 5). Furthermore, the modelled river flows at Macquarie River at Miltara (421135) and Gum Cowal at Oxley (421152), which represented the water flows out of the MIKE FLOOD domain, are acceptable according to the three statistics. For high flow events, the simulation results are acceptable at three locations according to all three statistics (i.e. at gauges 421022, 421147 and 421152, Table 5). At the other locations, the model either under- or over-simulated flows (i.e. PBIAS greater than ±25). Even when the PBIAS is acceptable, the negative NSE

55

indicates that the mean of the observed value is a better predictor than the simulated value therefore indicating unsatisfactory performance. A number of reasons were identified for this poor performance. Firstly and most importantly, not all the water entering the model domain was accounted for during the modelling period. The gauged river flows at Marebone (421090) and Marebone Break (421088) were the only inflows that entered the model domain but there was a large amount of ungauged water unaccounted for (for example, tributary flow from Marra Creek and river flows overtopping Marebone Weir and Marebone Break). In addition, a large portion of the Gum Cowal-Terrigal river system in the east part of the Marshes (Fig. 3) is not included in the model domain and, runoff from this and other local regions into the Macquarie Marshes, is not modelled. Secondly, the differences in gauge and model datum are believed to be significant for a number of locations – this has no implication for actual flood simulations. The limitations with high flow simulations cannot be adequately addressed without accurate estimation of the missing inflows. Once the missing inflows are gauged and recorded, it would be relatively easy to incorporate them into MIKE FLOOD by adding point sources and/or amending boundary conditions. However, for the purposes of this project, i.e. providing data and information to specify hydrological relationships in IQQM model (Fig. 4), the misrepresentation has to be rectified. An important strategy is to use a diversity of data for IQQM calibration (Fig. 1 and Section 2.5.2). Another strategy is to add dummy input upstream of the problematical areas for the ungauged inflows and calibrate to the downstream hydrograph. 3.1.2. Simulation of inundation extent The spatial comparisons between simulated and remote sensing inundation extents are presented in Fig. 5. For the low and medium flow events, the model predicted the major water compounding areas relatively accurate (Fig. 5A and B), indicating that the main flood paths and the bank breach locations are well represented in

Fig. 6. IQQM output shows the comparison of modelled and observed river flows for (A) Macquarie River at Pillicawarrina (gauge 421147); (B) Macquarie River at Carinda (gauge 421012); (C) Macquarie River at Oxley Station (gauge 421022); and (D) Bulgeraga Creek at Gibson Way (gauge 421118). Locations of gauge can be found in Fig. 2. Note the missing observation data.

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the MIKE FLOOD model. However, compared to satellite data, the simulated inundation extent was lower (about 65% for the whole model domain, but varies across the landscape). In particular, some small patches of mapped inundation in the southern and central parts of the Marshes were not shown in the simulation results. This may be caused by the un-recorded flow diversions which were not included in the model. Furthermore, the 90 m grid size in the 2D MIKE 21 is likely to be too coarse to represent all flow paths, particularly in the braided areas, where water can be spread over a large area and therefore flood extent can be underestimated. For high flow events, the model produced a more accurate flood patterns (Fig. 5C), and the simulated inundation extents were within 75% of the satellite data. This is somehow contrary with the flow comparison. As discussed in Section 3.1.1, the underestimation is more likely due to the inadequate representation of inflows within the model rather than the model setup.

3.2. IQQM model performance 3.2.1. Simulation of flow The fully calibrated model performs well against all gauge records (Table 6). The NSE ranges from 0.61 to 0.95, suggesting satisfactory to very good simulations at these locations (Moriasi et al., 2007). Similarly, the PBIAS ranges from 1.93% to 4.38% for gauges located within the Marshes, indicating good model performance at these locations. In addition, there is no systemic over- or underestimation spatially (Table 6 and Fig. 6) and temporally (Fig. 6). For gauge 421011, the PPBIAS value of 37.60% is out of the ±25% range, suggesting the model unsatisfactorily underestimated river flows at this location (Moriasi et al., 2007) even though the Pearson’s coefficient (R2 = 0.97, Table 6) indicated a very good performance. The underestimation is expected as river flows at this

gauge also include inflows from Merri Merri Creek (Fig. 3), which is not calibrated. For gauge 421012, which measures the river flows leaving the Marshes, the three evaluation statistics indicate a good to very good simulation (R2, NSE and PBIAS is 0.76, 0.76 and 1.45, respectively, Table 6). 3.2.2. Simulation of inundation extent The inundation maps derived from satellite imagery include three subclasses: open water, inundated vegetation (i.e. water can be interpreted as vegetation is sparse), the vigorous vegetation (i.e. dense vegetation areas, the response of vegetation could be interpreted as being flooded recently) (Thomas et al., 2010). The areas of the three classes were summed and the term ‘‘mixed’’ was used to define the maximum inundation extent. The open water and the mixed represent the minimum and maximum inundation extent, respectively. In most cases (more than 80% of the paired comparisons), the simulated inundation area was less than the maximum but greater than the minimum watered area interpreted from satellite images for the majority of storages (representative examples are illustrated in Fig. 7). Therefore, the model results are considered reliable. 3.3. The impacts of development on the wetland hydrology 3.3.1. Spatial and temporal variations in inundation extent The Water Sharing Plan (WSP) was implemented in 2004, and was developed to address the general wetland deterioration within the Marshes. However, even with the introduction of WSP, the inundated areas were substantially reduced when compared to natural conditions for all of the wetlands. Overall the reduction ranged from 11.3% (storage 2) to 56.6% (storage 31) with an average of 35.9% (Fig. 8). Nonetheless, as intended in the WSP, the inun-

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Fig. 7. The comparisons between modelled and remote sensing derived inundation area of (A) wetland 2, (B) wetland 10, (C) Wetland 8, and (D) wetland 16 in the Macquarie Marshes. Note that remote sensing data are discrete, and was interpreted as the minimum and maximum possible inundated area. There is no remote sensing inundation for 2004 and 2006.

L. Wen et al. / Journal of Hydrology 500 (2013) 45–61

dated areas were generally larger under WSP rules when compared to actual areas for low to medium flow conditions. The actual inundated areas were slightly smaller (within 95%) but similar to those predicted under current water sharing rules (Fig. 9). Therefore, only the re-constructed actual inundated areas will be used in further discussion. Spatially, the reduction in inundation area was greater for wetlands on the western side of the Marshes. This was caused by the diversions of river flow to the east through the Marebone Break regulator. The reduction in inundation area was much greater during dry periods (79.2% and 13.0% for dry and wet periods, respectively, Table 7). Intra-annually, greater reductions occurred during the peak agricultural production season of March to September (Table 8). The inter-annual and intra-annual variations demonstrate that the prior to WSP, the water management practices were tilted

57

towards the benefit of crop irrigation. During wet periods, the demand for irrigation is much lower. Therefore, more water is left in the river system, and allows larger wetland areas to be inundated. On the other hand, during dry periods, more water proportionally is diverted to cropping regions. Therefore, most of wetland areas are denied of water, and remained dry. The WSP was developed to improve the Macquarie wetlands by allowing more for environmental benefits. However, it appears that more environmental flow should be allocated in future. 3.3.2. Impacts on wetland hydrological indictors of great ecological influences The inflows to wetlands from IQQM scenario simulations were used to investigate changes to hydrological indictors of ecological importance (The Nature Conservancy, 2009) under natural and

Fig. 8. Reduction (%) in mean inundation extent for key wetlands in the Macquarie Marshes under natural and actual conditions for the period of 1991–2009.

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Fig. 9. Comparison of water areas (ha) in (A) wetland 1, (B) wetland 16, and (C) wetland 26 under natural, planed and actual conditions.

Table 7 The total inundated wetland area (ha) in the Macquarie Marshes during dry (01/07– 1998–30/12/2000) and wet (01/07/2003–30/12/2006) periods under natural and actual conditions. Perioda

Wet Dry

Mean

Maximum

Reduction (%)

Natural

Actual

Natural

Actual

19446.8 7443.5

16922.2 1547.5

62637.0 38668.7

57081.7 15962.0

13.0 79.2

a Wet = the annual flow at Marebone (the total of gauges 421088 and 421090) is greater than the 75th percentile and dry = the annual flow less than the 25th percentile.

current conditions. A number of high flow indicators were divergent from their natural states although some of the impacts were location specific (Table 9). The most noticeable alteration

Table 8 Monthly mean inundated area (ha) and reduction (%) for all wetlands in the Macquarie Marshes under natural and actual conditions. Month

Natural

Actual

Reduction

January February March April May June July August September October November December

5406 4697 4184 3955 3672 3683 8723 12,778 13,501 12,316 12,250 9054

3417 3194 1466 1308 1545 1726 2428 5941 7880 7857 7373 5502

36.8 32.0 65.0 66.9 57.9 53.1 72.2 53.5 41.6 36.2 39.8 39.2

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was the dramatic reduction in the high flows with the 1-day, 3-day, 7-day, 30-day, and 90-day maximum flows all being greatly reduced for wetlands across the Marshes. In addition, the duration of high pulses were dramatically reduced. The reductions in high flows resulted in the previously discussed much smaller inundated wetland area (Fig. 8). This is a probable cause of the reported widespread encroachment of terrestrial plants and the degradation of river red gum forests in the Marshes (Kingsford and Thomas, 1995; Bowen and Simpson, 2010). Several studies reported that water resource development increased the minimum flows in the streams (Magilligana and Nislow, 2006; Graf, 2006; Opperman et al., 2010). However, our modelling suggests that the alterations in low flows to wetlands were small (Table 9). The flow rising and falling rates were also dramatically reduced (Table 9), and this might have important implications for fish and other aquatic fauna (Suen and Herricks, 2009). The relatively stable hydrological condition favours the migration and colonisation of the exotic European carp (Cyprinus carpio L.) throughout Murray– Darling Basin (Jones and Stuart, 2008). The timing of high flows was shifted, in most cases, from September and October to February and March (Table 9). This may be due to the operation of upstream dams to meet the agricultural water demand, and is consistent with other Australian semi-arid regions (Wen et al., 2011). The shift in high flow seasonality may be contributing to the biodiversity loss in the Marshes as the evolution of life history and/or the behavioural mechanism of the native organisms is adapted to the natural flood timing (King et al., 2009). For example, the shifts in flow seasonality can lead to loss of cues for fish spawning and migration. The analysis revealed that changes in flow frequency were spatially inconsistent for both low and high flows. For example, the frequency of high flows for storage 1 was reduced while it changed little for storage 26 and increased for storage 16 (Table 9). The increase in high flow frequency in the middle of the Marshes may due to the return of irrigation water sourcing from other parts of the Marshes.

4. Conclusion This paper has reported our recent efforts to better represent the hydrological characteristics of the key wetlands in a large lowland floodplain – the Macquarie Marshes in northern Murray–Darling Basin, Australia. The hydrological representation is essential for the success of wetland restoration projects, including the derivation of water sharing rules, which often aim to mimic the natural hydrological variability such as inundation extent, frequency and duration, and water level fluctuation. Among other applications (e.g. simulating and evaluating the impacts of engineering works), the developed MIKE FLOOD hydrodynamic model for the Macquarie Marshes was able to describe the motion of water in the Macquarie floodplain, and provided the essential data required to build the IQQM floodplain hydrological model. The data acquired from the hydrodynamic simulation included hydraulic characteristics of each wetland, time series of influxes to and effluxes from the wetlands, and time series of fluxes at particular locations. The credibility of the IQQM floodplain wetlands hydrological model developed based on the hydrodynamic simulation is acceptable with NSE ranges from 0.61 to 0.95 and PBIAS ranges from 1.93 to 37.60 when compared with long-term gauge records. The modelled inundation extent was also comparable with remote sensing derived estimates of flood extent. The calibrated and validated IQQM model was used to simulate the wetland hydrology under natural, current water sharing plans, and actual conditions for the past 19 years (1991–2009). The analysis of the simulated results revealed that recent water management practices have induced dramatic changes in the wetland hydrology. The most noticeable and consistent changes include the dramatic reductions in high flows, inundation extent, and flow rising/falling rates. For other analysed flow indicators, the magnitude and direction of the change were spatially inconsistent. The results also highlighted that the impacts of water management practices on some of the flow variables for the wetlands such as low flow magnitude and

Table 9 Impacts of water resources development on selected wetland hydrological variables with important ecological influencesa (period of analysis: 19 years from 1991 to 2009). Flow indictors

Mean annual flow (m3 s1) 1-day minimum (m3 s1) 3-day minimum (m3 s1) 7-day minimum (m3 s1) 30-day minimum (m3 s1) 90-day minimum (m3 s1) 1-day maximum (m3 s1) 3-day maximum (m3 s1) 7-day maximum (m3 s1) 30-day maximum (m3 s1) 90-day maximum (m3 s1) Low pulse duration (days) High pulse countb High pulse duration (days) Extreme low peak (m3 s1) Extreme low duration (days) Extreme low timingc Extreme low frequency High flow peak (m3 s1) High flow duration (days) High flow timingc High flow frequency High flow rise rate (m3 s1 day1) High flow fall rate (m3 s1 day1)

Storage 1

Storage 16

Storage 26

Natural

Actual

Natural

Actual

Natural

Actual

2.69 0.00 0.00 0.00 0.00 0.00 74.19 52.80 31.54 10.56 4.20

1.20 0.00 0.00 0.00 0.00 0.00 16.89 14.03 8.90 3.07 1.02

6 5.5 0.00 22.3 235.5 6 11.96 5.50 286.0 5 3.68 2.78

3 5.0 0.00 40.0 65.0 3 0.25 3.25 88.5 3 0.10 0.09

4.07 0.01 0.01 0.01 0.02 0.05 43.02 41.06 40.14 23.08 10.10 22.0 2 17.0 0.85 29.0 115.0 1 12.57 14.50 308.0 2 1.71 0.57

1.77 0.01 0.01 0.01 0.02 0.06 20.51 15.53 8.13 2.23 0.90 11.0 4 6.0 0.65 8.3 114.5 2 0.41 4.50 199.5 3 0.08 0.06

5.33 0.01 0.01 0.01 0.02 0.05 23.70 23.56 21.40 18.83 9.61 35.0 3 17.0 0.34 41.8 167.0 1 11.15 9.00 328.0 2 1.11 1.05

3.06 0.01 0.01 0.01 0.01 0.05 16.90 16.17 13.96 9.63 4.30 25.5 3 13.5 0.51 12.0 144.0 1 1.65 10.25 54.0 2 0.09 0.06

Bold letters indicate small or no change. a Indicators adopted from The Nature Conservancy (2009) and calculated using IHA 7.1. b Times per year. c Julian date of a year.

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duration were inconsistent with those for in-stream flow. For example, we did not find any evident alterations in the low flows. The IQQM model can provide the ‘hydrological foundation’ within the Macquarie Marshes floodplain environments for ecosystem response modelling to investigate options for wetland restoration. In particular, the model has the potential to enable researchers and water and wetland managers to: 1. Gain better understanding of the flow alteration and the ecological response relationships by linking ecological information collected in the Marshes, such as waterbird population dynamics and vegetation indices (Wen et al., 2012). 2. Understand the cumulative impacts of hydrological alteration that have already taken place, so that those management actions can be linked to observed changes in ecological conditions and ecosystem services as a basis for forecasting future ecological change in the context of water management policy. 3. Generate landscape characterisations of the river system by combining with other catchment scale environmental information, for example, landuse data. 4. Investigate the impacts of, and the limitations of adaptation to, future climate change. Downscaled regional future climate projections can be incorporated into the updated IQQM model to investigate the potential impacts on ecologically important hydrological variables, such as inter-flood periods.

Acknowledgements This project is funded through the Rivers Environmental Restoration Program (RERP) which is supported by the NSW Government and the Australian Government’s Water for the Future – Water Smart Australia Program. RERP aims to arrest the decline of wetlands through water recovery, effective management of environmental water and the sustainable management of our wetlands. We thank Daniel Large (NSW Department of Environment Climate Change and Water) and Daren Barma (Barma Water Resources) for important insights provided during the conception phase of these models. Dr. Peter Bauer and three anonymous reviewers made critical comments which greatly improved the early vision of the manuscript.

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