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Holistic Estimation of the Environmental Risk of a River
Basin
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Kilisimasi (Kris) Latu1,2*, Justin F Costelloe1, Tim Peterson1 and Hector M Malano1,2
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Department of Infrastructure Engineering, The University of Melbourne, Vic 3010, Australia
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CRC for Irrigation Futures, P.O. Box 56, Darling Downs, Qld 4350, Australia
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*Corresponding Author
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Kilisimasi (Kris) Latu
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Department of Infrastructure Engineering
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University of Melbourne
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Victoria Australia 3010
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Tel: 61 3 8344 7237
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Fax: 61 3 8344 4616
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Email: latuk@unimelb.edu.au; kris.latu@bigpond.com
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Sponsor: The study is funded by the Cooperative Research Centre for the Irrigation Future, Australia.
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Abstract
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The conventional approaches to estimating environmental risk in a river basin commonly do not consider
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the entire flow regime or all the environmental assets within a river system. These approaches are
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restricted because they represent environmental risk only with limited ecological risk (adequate for
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certain type of flows) and such risk alone cannot be used as a driving factor for water allocation.
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Therefore, a new approach for estimating environmental risk is required that considers the entire flow
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regime. We propose a holistic, dynamic and robust approach that is based on a statistical analysis of the
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entire flow regime and accounts for flow stress indicators to produce an environmental risk profile based
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on both frequency and consequence of occurrence of a given flow range. When applied to river reaches
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(catchments), the model produced a dynamic and robust environmental risk profile that clearly showed
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that when the current flow is drawn away from the optimum range of environmental flow demand, the
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environmental risk increased. An environmental risk curve produced from the environmental risk profiles
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provides guidelines for meeting environmental flow demands.
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KEY WORDS: environmental flow demand; environmental risk model; environmental risk profile;
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hydrological flow index; environmental risk curve.
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Glossary
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EFD
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Environmental Flow Demand
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RF
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Regulated Flow
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NF
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Natural Flow
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ERM
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Environmental Risk Model
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HF
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High Flow Index
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LF
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Low Flow Index
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ZF
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Zero Flow Index
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CV
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Coefficient of Variation Index
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VI
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Vulnerability Index
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1 Introduction
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Water scarcity in many parts of the world, including Australia, combined with recent severe droughts and
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increasing impacts of climate change are creating great pressure on the amount of water available for
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water consumptions, such as irrigation and environmental water demands (Morrison et al., 2009). This
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presents a challenge in managing of a water resource (river) to satisfy two or more competing water
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demands. Usually, a river would supply water for urban, irrigation and environmental flow demands.
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When water demands are not satisfied, there may be risk existed. In a river basin, when the environmental
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flow demands are not met, risk to river system may increase. On this paper, we provide a novel approach
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for estimating the environmental risk of a river system.
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1.1
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Risk is the chance that an adverse event with specific consequences will occur within a certain timeframe
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(Hart et al., 2005). Environmental risk can be defined as a deviation from natural conditions (Horne et al.,
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2009). Hydrological change will be detected at the scale of the flow regime (changes in variability,
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predictability) with responses at ecosystem level (species changes, adaptations) (Sheldon et al., 2000).
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Thus, the impacts on a flood pulse will expand until they affect the flow history and then the habitat and
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life-history cues. Over time the continued hydrological change, there will be overriding disturbances to
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the flow regime, and consequent distal loss of species (Sheldon et al., 2000). These kinds of disturbances
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have been experience in Australia due to prolong drought recently. Hence, determining environmental
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risk based on changes to flow would adequately account for the impacts on all aspects of a river system.
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Environmental risk has been estimated using a number of different approaches; which mostly used
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ecological risk to represent environmental risk (Cottingham et al., 2001; Horne et al., 2009). The problem
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with this approach is that ecological risk can represent only part of the environmental risk, due to the
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inability of ecological indicators to account for a wider range of environmental assets within a river basin,
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or to be applied across the entire flow range. For example, Horne et al. (2009) used a single ecological
Environmental Risk
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parameter (habitat rating) over the low flow range to define an environmental response curve that yields
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environmental risk. Cottingham et al. (2001) estimated risk focusing on key individual stressors, such as
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algae blooms in a river. Chee et al. (2005) focused on summer flow components (cease to flow, low flow,
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high flow and summer freshes) leaving out the winter flow components. Arguably these studies have
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limited application for estimating environmental risk for a water allocation model, which must consider
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the entire flow regime of a river basin (i.e. from cease to flow to overbank flow) (Ladson et al., 1999;
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SKM, 2009). Furthermore, the cumulative effects of environmental risk over the past years are often
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ignored. Therefore, what is a better method for estimating environmental risk?
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The process of risk assessment is based on quantifying the probability of an uncertain or undesirable
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event (with a given consequence), occurring either now or in the future (Cottingham et al., 2001; Tarek et
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al., 2002; Hart et al., 2005). In the context of the flow regime aspects of river health, we define
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consequence as any negative effects that may occur due to river flow being outside the optimum range of
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the EFD. While river health has a number of other aspects outside river flow, the use of river flow to
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estimate consequences of water allocation allows the direct linkage between river flow and negative
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effects of allocation. Therefore, this study focuses on estimating risk due to the level of departure of
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regulated flow from the natural flow of a river system.
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Risk assessment can be an essential tool that facilitates and informs decision-making in different fields,
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including water resource management, particularly with regards water allocation. Within a river basin,
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conjunctive water demands (including domestic, irrigation and other demands) and EFD (which is the
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amount of water required by a river system to sustain its health) are supplied from a combined storage
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(i.e. river and groundwater supplies). Competition between conjunctive water demand and EFD,
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particularly in the context of an inadequate water allocation policy, may increase risk to the environment
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and conjunctive water supply.
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There are many parameters which can be used to estimate environmental risk. However, estimating
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environmental risk based on individual ecological or geomorphologic parameters may not capture the full
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range of risk. Therefore, the parameters or factors used for estimating environmental risk must account
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for as much of the entire environmental asset as is practicable. We propose the use of the flow regime as
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an appropriate proxy based on the assumption that when the regulated flow (RF) is closer to natural flow
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of a river system, the EFD would be satisfied for most environmental assets (Neal et al., 2005).
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Environmental flow demand can be defined as that water demand that may occur in a natural flow
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condition (Neal et al., 2005; Arthington et al., 2006).
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In this paper, we define a new approach using statistical analysis to estimate environmental risk and apply
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it to the Campaspe basin in north-central Victoria, Australia. The approach estimates environmental risk
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by investigating the probability of the regulated flow occurring outside a pre-defined optimum range of
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the EFD using both parametric and non-parametric approaches. Then we define and test a method for
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estimating flow-related consequences based on the hydrological indices from the Index of Stream
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Condition (Ladson et al., 1999) and Tarek et al. (2002) for estimating risk.
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2 Study Area
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The study area is the lower reaches of the Campaspe basin, southern Australia (Figure 1). The study area
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has a total catchment area of 2,124 km2 and extends from the downstream end of Lake Eppalock (the
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main storage in the catchment) to the junction with the Murray River at Echuca. The catchment is
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relatively flat in the downstream northern half with increased higher terrain towards Lake Eppalock
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(Chiew et al., 1995). The climate is fairly uniform, with hot summers experienced, particularly in the
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north. The annual average rainfall is 450 mm (Chiew et al., 1995) and 69 mm average annual runoff
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(CSIRO, 2008). The rainfall occurs throughout the year, with the winter and early spring being the wettest
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period and January – February being the driest months (Chiew et al., 1995). The areal potential
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evapotranspiration is estimated to be around 525 mm (CSIRO, 2008). The native vegetation of the basin
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has been largely cleared since the settlement of Europeans in the region since the 1800s. This has resulted
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in the replacement of deep rooted native plants with short rooted exotic plants resulting in a rising of the
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water table in the region (Chiew and McMahon, 1991; Macumber, 1991; Chiew et al., 1995; CSIRO,
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2008).
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The locations of major demand centres are also shown in Figure 1 and the study area has been divided
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into three reaches based on four major hydrological structures; Lake Eppalock, Campaspe Weir,
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Campaspe Siphon and the outlet to Murray River in Echuca. In Reach 1, the major demand is from
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private diversion with very little irrigation compared to Reach 2 and 3. In Reach 2, demand consists of the
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Campaspe Irrigation Areas (East and West). In Reach 3, the Rochester Irrigation Areas (East and West)
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are located but both divert water from the Waranga Western Channel (mark by the boundary of Reach 2
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and 3), as water users buy water from the Goulburn River System (neighbouring catchment to the east).
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Within Reach 3, the main demand from the river is by private diversions. Each reach contains a gauge
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station at the downstream end where the observed flows were obtained; Reach 1 – 406201, Reach 2 -
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406202 and Reach 3 – 406265. Because the levels of water demands are different for each reach, the
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environmental risk must be individually determined for each reach.
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Figure 1 about here.
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3 Method
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The estimation of environmental risk profile is based on two key objectives. Firstly, the approach should
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produce dynamic and robust environmental risk profiles that take into account the cumulative effects of
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past environmental risk. Secondly, it must account for risks that arise over the entire flow regime (cease
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to flow to overbank flow), thus accounting for most environmental flow related consequences. In order to
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satisfy these two objectives, we propose a method that involves five key steps, as summarised in Figure 2.
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Figure 2 about here.
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3.1
Estimation of the EFD from Natural Flow
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The environmental flow demand is the specified flow magnitude required for the river, as recommended
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by ecologists and expert panels, based on a ‘flow method’ assessment (SKM, 2006). However, these
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recommended values are in aggregated form and the flow demand does not specify the specific time when
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the flow should be applied in relation to the level of the RF. Instead, only the frequency, duration and
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magnitude of flow required within a time frame is defined, without a clear link to the level of the RF in
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the river. For this reason, this flow requirement must be disaggregated into a time series of EFD to enable
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water authorities to adequately provide environmental flow requirement to the river. This disaggregation
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can be done using a method developed by Neal et al. (2005). Measures of flow magnitude (annual,
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seasonal, monthly, daily), the frequency, timing, duration and predictability of flow events (floods and
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droughts), rate of change from one flow condition to another (rate of rise and fall of flood hydrographs),
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and the temporal sequencing of flow conditions should be included as they influence many aspects of
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river ecosystems (Arthington et al., 2006). The model developed by Neal et al. (2005) involves the
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derivation of environmental flow requirement (SKM, 2006) from modelled natural daily flow at a site.
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The modelled natural daily flow data were obtained from the Department of Sustainability and
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Environment (DSE). This approach assumes that most environmental flows are only provided if these
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flows would naturally have occurred (Neal et al., 2005) It allows automated decision making for the
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provision or otherwise of seasonal flows of a given magnitude and a given annual frequency,
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independence between events, and rates of rising and falling hydrograph limbs. Each component of the
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recommended flow is progressively added until a time series of EFD is constructed over the period of
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modelling.
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The method estimates the EFD for the Campaspe Basin from daily modelled Natural Flow (NF) data of
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the Campaspe River and the environmental flow requirements recommended by SKM (2006). The daily
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NF data are required as a reference series against which to decide whether to provide environmental flows
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(Neal et al., 2005). The emphasis of this paper is on how an ER profile is developed rather than how the
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EFD is determined, which also can be produced with any of the methods discussed by Arthington et al.
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(2006) or others (Marsh and Pickett, 2009). Because the model developed by Neal et al. (2005) included
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the key hydrological factors, such as frequency and duration across the natural flow range, we considered
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this method appropriate to provide an acceptable EFD as shown in Figure 3. However, relying on
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estimated daily natural flow to produce monthly EFD may introduce uncertainty due to assumptions used
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in estimating of the natural flow, which may affect the EFD and subsequently the environmental risk.
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Figure 3 about here
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3.2
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The optimum risk profile for an EFD is that where the regulated monthly flow satisfies the EFD, and
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therefore would present minimum risk. The monthly median, 25th and 75th percentiles are used to define
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the optimum range. The selection of 25th and 75th percentiles was based on the assumption that if a
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monthly regulated flow falls within this range than EFD is satisfied. The values are changeable thus
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narrowing or expanding the risk band to investigate their effects of risk. In addition, it is a simplification
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to assume that the percentiles are time invariant. The use of percentiles avoids making assumption that the
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data are normally distributed from one month to another. Therefore, if a river flow magnitude is above or
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below this optimum range then there will be environmental risk whilst acknowledging that the percentile
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thresholds are subject to variation when the risk is assessed.
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3.3
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The next step is to estimate the probability of the RF occurring. Both parametric and non-parametric
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statistical methods were trialled in estimating the probability. Parametric methods assume that the data
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fall into a known distribution. Such methods are reasonably robust but can be affected by outliers and
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large departures from normality (Wild and Seber, 1995). Normal distribution was selected for this study
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over the other distributions, such as Weibull, Gamma and Lognormal. The selection was based upon
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testing the “goodness of fit” using three methods; Anderson-Darling, Kolmogorov-Smirnov and Chi-
EFD - Optimum Range
Regulated Flow Exceedence Probability
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Squared tests (Latu et al., in press). The estimation uses the “normdist” function (excel), which is based
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on the normal curve equation of Wild and Seber (1995). For this study, we determine the probability of
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the investigated value based on its deviation from the mean.
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3.4
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Herein, consequence is defined as any negative effect that may occur due to river flow being outside the
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optimum range of the EFD; that is the difference between EFD and regulated flow. As this difference
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increases, the consequence effects also increase. For instance, if during summer, the river flow is above
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the EFD band, this may cause both geomorphological and ecological consequences, such as erosion and
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habitat disturbance (Sheldon et al., 2000; Horne et al., 2010). On the other hand, if the flow is too low
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during the winter season, then ecological problems may also arise as found by Horne et al (2010). Such a
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change flow regime will affect responses at the organism level (individual survival, spawning success,
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recruitment success). Therefore, there is a need to adequately account for the asymmetric consequences
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for flow in different seasons and within seasons (e.g. when there is low flow occurring in winter when
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high flow should be occurring). There is great difficulty in relating flow to a consequence score with a
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single indicator of an aspect of river health. This is simply caused by the inability of a single indicator to
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represent the effects that may arise when the EFD are not satisfied over the entire flow regime. Therefore,
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a suite of indicators are required that cover all aspects of the flow regime.
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The Index of Stream Condition (ISC), developed for southern Australian rivers, provides a set of multiple
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indicators to estimate consequence (Ladson et al., 1999). The ISC is a holistic approach that provides an
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integrated measure of river health and benchmarks the condition of a stream. It looks at the long-term
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assessment of a river and is measured every 5 years, or after a significant flood event (Ladson et al.,
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1999). The above requirements and single output snapshot of the ICS will not satisfy a monthly water
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allocation model that requires risk as its main driver on a monthly basis. However, aspects of the ISC
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have been adapted to produce consequence scores that are flow related (Ladson et al., 1999). Out of the
Estimation of the Environmental Consequences
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five sub-indices of ISC, some of the measures of the hydrology index are used to relate consequence to a
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flow magnitude. The river flow data are readily available, in contrast to the data for other indices, such as
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the physical form index (see Ladson et al., 1999). The components of the hydrological index of the ISC
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allow a comparison between the RF and EFD, where the EFD is derived from modelled NF data. These
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two reasons satisfy the definition of ER previously described. The Consequence Index (C) for this study
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consists of five sub-indices; low flow index (LF), high flow index (HF), zero flow index (ZF), coefficient
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of variation index (CV) and vulnerability index (VI). Each is outlined below.
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These five indices cover the entire flow range and therefore, account for the asymmetric nature of
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consequences when regulated flows occur outside their natural pattern. For instance, high flow occurring
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during a summer period will generate higher consequences compared to similar magnitude flows
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occurring during a winter period. The indices also allow for the cumulative effects of not satisfying the
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past EFD to be taken into account using a cumulative period (CP). The cumulative period is not only a
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vital part of the calculation of consequences but also allows the accumulation of consequences to be
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included in the risk calculation. This has been generally neglected in previous studies.
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Alteration of the magnitude of low flows changes the availability of in-stream habitats, which can lead to
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a long-term reduction in the viability of the populations of flora and fauna (Sheldon et al., 2000; SKM,
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2009). A low flow index has been adopted (LF, Equation 1) that measures the changes in low flow
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magnitude under the RF and the optimum range of EFD. A review by SKM (2009) found that the low
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flow requirements often correspond to the daily 90% exceedence flow. However, at a monthly time step,
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the index is calculated using two flow thresholds: one based on the flow exceeded 91.7% of the time (i.e.
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11 months out of 12) and flow exceeded 83.3% of the time (10 months out of 12) (SKM, 2009).
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LF91.7, j   P  Q91.7 EFD  P  Q91.7 R 
j 1
(1)
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where: LF91.7,j is the range-standardised low flow index based on the 91.7% exceedence flow for specified
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CP of years (j) (e.g. 1 - 10 years). The variable j is the number of years counting back from the
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investigated month. It represents the period of data used to determine the PEFD and PR. P(Q91.7)R is the
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proportion of months that the 91.7th percentile flow of RF (Q91.7)R is exceeded by the annual 91.7th
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percentile EFD flow (Q91.7)EFD. P(Q91.7)EFD is the proportion of years of of the EFD that the 91.7th
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percentile flow over j years is exceeded by the annual 91.7th percentile of the EFD. The above process is
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repeated for the 83.3% exceedence flow. Then the range-standardised low flow index (LFt) is calculated
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as the average of (LF91.7) and (LF83.3). The advantage of using two range-standardised low flow indices to
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calculate the LFt, is that it reduces the level of bias and dominance of either the RF or EFD in determining
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LFt.
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High flows act as a natural disturbance in river systems, as they can remove vegetation and organic
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material while resetting successional processes, especially during winter periods (Sheldon et al., 2000;
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SKM, 2009). However, it may result in adverse consequences if it occurs during the summer period. The
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high flow index (HF) measures the change in high flows under RF and EFD conditions. A similar
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approach used for LFt is applied for this index; however, it is calculated using the 8.3% and 16.7%
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exceedence values. During high flow seasons (winter) the difference between EFD and RF should be low
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as both values may be very similar, producing a low value of HF. However, if a high flow event occurs
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during summer period, the difference between EFD and RF would be high causing a high value of HF.
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This means that that both HF and LF produce asymmetric responses to the difference between EFD and
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RF during different seasons.
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The zero flow index compares the proportion of zero flow spells occurring under EFD and RF conditions
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as shown in Equation 3. If the number and length of cease to flow spells is unchanged between EFD and
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current flow conditions, then the value of the index is 0, indicating no consequence.
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ZFt   max  PZ EFD , PZ R   min  PZ EFD , PZ R  j ,t 1
(2)
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where: ZFt is the zero flow index for month t and PZEFD is the proportion of zero flow over the whole
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monthly data set under EFD condition. PZR is the proportion of zero flow over j for the regulated flow.
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The max and min ensure that the higher value between PZEFD and PZR is the value where the lesser of the
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two is deducted from.
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The coefficient of variation (CVt) of monthly flows between the RF (CVR) and EFD (CVEFD) for the CP of
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j measures the variability between the two flows. Seasonal variation in flow is relatively predictable and it
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acts as an important hydrological driver of aquatic ecosystems (Sheldon et al., 2000). This index should
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reflect the regulated nature of the Campaspe River due to irrigation and private diversions. That is,
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regulated systems have lower CV values than unregulated rivers with natural flows.
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CVt  CVEFD / CVR  j ,t 1
(3)
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During the high flow season, the regulated river flow being higher than the EFD is a natural phenomenon,
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although it may still cause negative effects to the river system. The concern here is to ensure that when
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this occurs, that ER should not be higher due to a significant difference between RF and EFD. Because of
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this reason, an index is required to make sure that when such circumstances occur, ER is appropriately
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estimated. Therefore, we propose a new index called the vulnerability index (VI) as shown by Equation 4.
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The index is based on a vulnerability criterion used to quantify the severity of failure in reservoir
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operation and to estimate the risk in water supply and demand deficit (Tarek et al., 2002). For this study,
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we refined the approach of Tarek et al. (2002) by using the proportion of time when the two flow
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magnitudes are different. The advantage of using proportion of time is that each month represents a
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magnitude of one, regardless of its flow magnitude. On the other hand, if the flow magnitude is used, then
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a spike in RF may cause very high or low consequence.
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

T
 
 
T

 j
VI t     PEFD  PR t  /   PEFD  
t 1
t 1
t
(4)
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where; VIt is the Vulnerability Index at month (t) using a CP (j), PEFD is the proportion of time the RF falls
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inside the EFD range, while PR is the proportion of time the RF occurs outside the EFD range.
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3.5
Risk
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Each of the five indices values range between 0 (no consequence) and 1 (negative consequence). The total
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consequence score is calculated out of 1 (Equation 6) based on a uniform weighting of the individual
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indices. The weighting, and indeed the use of other or additional flow indices, can be easily modified
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according to the characteristics of the catchment under consideration. The calculation of the consequence
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uses different CPs from 1 to 10 years, which is the length of data used to estimate the consequence of the
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investigated month. The risk is then calculated by Equation 6, where P is the probability and C is the
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consequence. The model is simulated using code written in Matlab and we refer to it as the Environmental
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Risk Model (ERM).
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Ct  0.2  LF  HF  ZF  V  VI t 1
(5)
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ERt  Pt 1  Ct 1
(6)
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At the completion of the risk calculation, the risk values are used to develop an environmental risk curve
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for each reach. The purpose of the risk curve is to provide a relationship between RF and risk values. That
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is, if a certain amount of flow is in the river, what is the expected risk value? As the risk range is related
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to the actual RF in any point of time, decision makers have a guideline through the environmental risk
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curve in making water allocation decisions that would affect the amount of water that should remain in
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the river or be released into the river in the coming months.
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The environmental risk curve is formed by compiling each month’s regulated flow and risk values sorted
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in descending order according to the flow values. The risk values are then plotted against the log10 values
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of the regulated flow values before zones are assigned. The use of log10 values rather than the actual
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regulated flow values allow better scaling of the flow values when they are plotted. Risk zones are
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allocated between 0 and 1. For instance, zone 1 will have a risk range between 0 and 0.2, meaning very
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low risk.
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4 Results
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We present results from Reach 1 to illustrate the capability of the ERM model to produce ER profiles. We
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also included the risk graphs for both Reach 2 and 3 to further demonstrate the capability of the model in
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different reaches. We first discuss the EFD and consequence indices results. Then we look at the
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environmental risk profiles produced by ERM. Lastly, we discuss the environmental risk curves.
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4.1
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The optimum range of the monthly EFD (Figure 4) shows higher flow during winter months (July –
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October) and lower during summer months (December to March), reflecting the natural flow pattern of
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the system. In contrast to the regulated flow which shows higher flows during summer months and lower
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flows during winter months. Hence, it illustrates that the river flow has been reversed due to diversions
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from the river to satisfied water demands such as irrigations. Similar results were observed on Reach 2
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and 3.
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Figure 4 about here.
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4.2
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We found that all indices made significant contributions to the ER profile (Figure 5) except for the ZF,
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which is probably caused by the model operating on a monthly basis, which may not appropriately
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account for zero flow values. Although, different low values were trialled to represent zero flow values,
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its contributions were still very low in all reaches. For Figure 5, it was simulated with 100 ML/Mon.
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Therefore, the ZF was omitted from the consequence estimation and was not included in risk estimation.
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For all reaches, LF and HF had the highest contribution, followed by CV and then VI as shown in Figure
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5. This indicates that the components of LF and HF are not often met due to low flows caused by low
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precipitation and the effect of the river being regulated for irrigation water transfers and diversions.
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Because the available duration of the RFs time-series are relatively short in comparison with the EFD’s
Environmental flow demand
Indices
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data length, we could only model up to 10 years CP. That is, the EFD data starts from 1900 while RF only
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starts from 1977. However, the model is capable of simulating longer data lengths.
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Figure 5a and 5b about here.
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The seasonal variations of consequences were also assessed using the mean monthly values from a 1 yr
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CP (calculated over the entire time series) as shown in Figure 5a. It appears that the consequences are
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higher during summer periods compared to winter periods. When compared to values from a 10 yrs CP,
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the values are reduced, thus reflecting the smoothing of the effects over a 10 years period as shown in
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Figure 5b. We note, that the used of mean average thus reduced the effects of consequence. However, it is
352
important to note that if an individual year is assessed that the cumulative nature of consequence
353
estimation is taking into account. For example, the consequence for Jan 2000 is calculated using a 1 CP
354
(from Feb 1999 to Jan 2000). This means that a direct comparison of the difference between EFD and RF
355
for a particular month using the consequence values must be done with great care.
356
4.3
357
The ER profiles (time series of risk) produced by the ERM are shown in Figure 6. The profiles show
358
expected patterns (i.e. risk is higher when RF is outside of the optimum range of EFD). Figure shows that
359
the model can also produce a very dynamic ER profile reflecting the differences between RF and EFD. As
360
per the main assumption of this approach, if the RF is within the optimum range, then there is minimum
361
risk, rather than no risk, due to the ability of the model to account for the cumulative risk effects of
362
unsatisfied environmental water demand in previous years. When RF is outside of the optimum range of
363
EFD, the risk is higher. For instance when a high flow occurs in summer rather than winter, high risk is
364
simulated. This means that the model accounts for time, duration and frequency of a certain flow that
365
should be in the river at any particular time of the year. However, by taking into account that a
366
consequence value of a particular month is calculated from a past year for a 1 CP, for instance, the direct
367
comparison of RF, EFD and produced risk must be done with care. Because, the difference between the
Calculation of risk
17
368
EFD and RF on that particular month does not solely responsible for estimating its consequence value
369
rather is the entire length of CP.
370
Figure 6 about here.
371
The model provides a direct relationship between RF and EFD and also indirectly provides a relationship
372
between RF and modelled natural flow, because the EFD data is driven from natural flow. This means
373
that the effects of climate change can be included in estimating risk by use of an appropriate modelled
374
flow series incorporating possible climate change scenarios. As shown by Figure 7, the high flow event
375
on 1998 is followed by higher risk value on the 1yr CP. This means that the model in some degree can
376
account for the climate change. However, further test is required to ensure that the model can accurately
377
account for climate change.
378
The effects of using different CPs are demonstrated on Figure 7 and Figure 8. The consequence is
379
smoothed out when the CP is longer. For instance, a 10 year CP uses the entire value within that 10 years
380
period to calculate a single value of consequence for the corresponding month. Although the consequence
381
is smoothed, it allows the effect of previous years to be included in the consequence of the current year.
382
For instance, the effect of not satisfying demand in a previous year is included in the current year but at
383
the same it does not dominate the consequence caused by unsatisfied water demand in the current year.
384
The risks as shown in Figure 8 show little difference from when the CP is short compared to when it is
385
long. In contrast, the consequence shows significant variance between a 1 and 10 years CP, indicating that
386
the probability heavily influences the risk calculation.
387
Figure 7 & 8 about here.
388
As the CP increases, the mean risks differed from one reach to another (Figure 9). For Reach 1 and 2, the
389
risks are higher during the summer months than winter periods. This reflects the highly regulated nature
390
of the system due to transferring water during summer periods to satisfy demands, which indicates that
391
the system has significantly departed from its natural flow status. In contrast, Reach 3 has lower risks
18
392
during summer months and higher risk during winter months. This means that the EFD is satisfied during
393
summer while the high flows during winter are not met. This reflects the nature of the system where
394
irrigation demand at Reach 3 is satisfied from the neighbouring Goulburn System and the main demand
395
from the river are only the private diversions. In addition, the water transfers through the Campaspe River
396
from the Goulburn catchment to the Murray River satisfy the EFD within the Campaspe River. Hence,
397
Reach 3 has conditions that are closer to the natural system. Consequently, the consequences are lower
398
which drive the risks lower. However, its higher risk values during winter indicate that regulated flow do
399
not meeting the optimum range of the EFD. This means that the higher flows on regulated flow
400
conditions are lower than those in natural flow.
401
Figure 9 about here.
402
4.4
403
The environmental risk curves are shown in Figure 10 (a, b and c) which were compiled from the values
404
calculated for Reach 1. Figure 10a shows that each month has an optimum regulated flow range where the
405
risks are lower. The optimum range of one month differs from the others as further illustrated by Figure
406
10b. Outside of a monthly regulated flow optimum range, the risks are increasingly higher. This means
407
that the environmental risk can be minimised by maintaining regulated flow within each monthly
408
optimum range. By combining each month’s individual risk curve, an overall environmental risk curve
409
can be formed (Figure 10c).
410
The overall risk curves for each reach were assigned to different zones (Figure 10c). The area below the
411
optimum regulated flow is allocated to different zones to represent the summer flow ranges and area
412
above is allocated to different zones that represent the winter flow ranges. The risk zone allocations
413
provide guidelines for decision makers in keeping regulated flow to levels that minimise environmental
414
risk. The allocation for risk zones provides a direct relationship between risk and the regulated flow. Note
415
that this method allows to manage flows for individual months and the risk zone allocations will be differ
Environmental Risk Curve
19
416
from one month to another. Having an environmental risk curve for each month would provide adequate
417
information that includes seasonal effects of water allocation on a monthly basis which represents by risk.
418
The definition of each risk zones are classified in Table 1, which shows the flow range in relation to risk
419
zones.
420
Figure 10 about here.
421
5 Discussion
422
The production of a time series of risk enables it to be used as a major driver for a water allocation model
423
that ensures that EFD is satisfied while taking into account competition with other demands within a river
424
basin. The effect of competition between different water demands on environmental risk will be
425
addressed in other part of this study. This is an advantage over other approaches that produce single risk
426
values for an entire stream (Arthington et al., 2006). The ERM addresses the entire flow regime (low to
427
overbank flow) in determining the consequence index and this enables it to account for most
428
environmental assets of a river. This is an improvement on approaches that focus on a single ecological
429
parameter, such as habitat ratings (Horne et al., 2010) and algal blooms (Cottingham et al., 2001), or only
430
considering a part of the entire flow spectrum (Chee et al., 2006). This means that when the predefined
431
EFD for a specific period is met, the overall environmental flow objectives are satisfied rather than
432
focusing of satisfying the water demands of a single parameter at the expense of others. Hence,
433
mimicking the natural flow of the system would reduce the negative effects on river system (Nott et al.,
434
1998).
435
Only four consequence indices were used to calculate risk for this model. However, ERM has the ability
436
to add more consequence indices and change the probability estimation from normal distribution to other
437
methods if necessary to reflect the true nature of the flow data. The high flow and low flow indices were
438
dominant in lower CP, thus reflecting the differences between EFD and RF. On larger CPs, the coefficient
439
of variation index becomes the dominant index, reflecting larger variation in data due to longer data series
20
440
being used to estimate consequences. However, when the risk was assessed between 1 and 10 year CPs,
441
we found that the risk is only marginally increased with the increased of CP. We found that this is caused
442
by the probability of flow occurrence. Therefore, using of a 1 year CP will be sufficient to estimate the
443
environmental risk for the Campaspe Basin.
444
The use of different CP has a number of significant advantages; firstly, it captures the effects of the
445
historical variation in climate. The sequences of dry or wet years and their cumulative effects are
446
considered. When a dry year is followed by a wet year, the model still accounts for the cumulative risk
447
effect caused by the dry year through the calculation of consequences. Secondly, it enables the model to
448
holistically estimate risk rather than individually assessing risk for different climatic scenarios, thus
449
including the cumulative nature of risk. This is a significant advantage over other approaches that may
450
provide only a single risk value and ignore the significance of cumulative risk on the environment. Thus,
451
it enables the decision makers to provide better allocation decisions by considering previous risk within
452
the river system and can be cautious with how the effects of their decisions may influence future risks.
453
Furthermore, it gives the water managers an indication and ability to recover and restore damaged
454
environmental system by minimising risk. This can be done by forcing allocations to provide more water
455
for the environment by controlling the amount that goes to other forms of water demands within a river
456
catchment.
457
Producing an ER profile of a reach allows the visualisation of how the system performs, it allows decision
458
makers to see what should be the actual monthly value to be supplied instead of guessing values based on
459
aggregated volume, as supplied in many environmental flow requirements (SKM, 2006). We note that the
460
model cannot be calibrated or evaluated as there is no observed risk data to compare against. However,
461
we can judge the accuracy of the model by how its outcomes follow our expectations. We expect the risk
462
to increase when the EFDs are not being satisfied and the model results demonstrate that the model
463
achieves this expectation. In addition, the ability of the model to mimic the physical aspects of water
464
allocation within the Campaspe basin also demonstrated that the model is producing reasonable results.
21
465
For example, the differences in annual risk profiles between each reach, indicates that the regulated flow
466
regime within Reach1 and 2 are reversed, as stated in literature (SKM, 2006; SKM, 2009). In comparison,
467
the model shows that in Reach 3 the current flow regime is closer to the natural flow regime although the
468
flow magnitudes are significantly lower.
469
The model was tested with regulated flow within the river. The next phase of the study will test the model
470
with how much water would remain in the river as result of water allocation. It is plausible that the model
471
can perform under extreme conditions such as droughts or flood conditions due to the following reasons.
472
Because the consequences are estimated based on the river flow and each consequence index accounts for
473
certain part of the flow regime, hence, the model can account for extreme conditions. For instance, when
474
an extreme low flow condition is occurring during summer, low flow index would provide high
475
consequence. On the other hand, if during winter, an extreme high flow event would cause the high flow
476
index to have high consequence. When the extreme condition is occurring outside of its natural season,
477
high consequences will be the result. For example, high consequence caused higher risk during summer
478
of 2001 due higher flow in summer rather than during winter as shown in Figure 6a.
479
Further test is currently being investigated to test the robustness of the model and whether it can
480
appropriately account for the effect of climate change. That is, we need to test the model with different
481
scenarios such as dry and wet year periods alone and assess the estimated consequences.
482
6 Conclusion
483
The Environmental Risk Model is capable of producing robust and dynamic environmental risk profiles
484
on a monthly basis. Environmental risk curves produced by this study provide a guideline that links the
485
regulated flows to environmental risk zones. This means that if the regulated flows are within the
486
optimum range as shown in Figure 10, then the mean EFD will be met. Operating the river from a higher
487
risk zone will mean that the EFD will not be met to a certain degree. If such an approach is ongoing then
488
negative effects on the river will increase. In addition, the curve provides decision makers with
22
489
indications that may allow them to operate the river in intermediate risk zones, hence compromising the
490
EFD but allowing other water demands to be satisfied to a certain degree during dry year periods. This
491
may allow maximising agricultural and industrial productivity while still maintaining healthy river flows.
492
7 Acknowledgement
493
We appreciated the financial supports given by the Cooperative Research Centre for Irrigation Futures to
494
fund this research.
495
Reference
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
Arthington, A.H., Bunn, S.E., Poff, N.L., Naiman, R.J., 2006. The challenge of providing environmental
flow rules to sustain river ecosystems. Ecological Applications, 16(4): 1311-1318.
Chee, Y.E.C., Webb, A., Stewardson, M., Cottingham, P., 2006. Victorian environmental flows monitoring
and assessment program. The University of Melbourne and eWater CRC.
Chiew, F.H.S., McMahon, T.A., 1991. Groundwater recharge from rainfall and irrigation in the campaspe
river basin. Australian Journal of Soil Research, 29(5): 651-670.
Chiew, F.H.S., McMahon, T.A., Dudding, M., Brinkley, A.J., 1995. Technical and economic evaluation of
the conjunctive use of surface and groundwater in the Campaspe Valley, north-central Victoria,
Australia. Water Resources Management, 9(4): 251-275.
Cottingham, P. et al., 2001. Assessment of Ecological Risk Associated with Irrigation Systems in the
Goulburn Broken Catchment, Cooperative Research Centre for Freshwater Ecology, University of
Canberra, ACT 2601, Australia.
CSIRO, 2008. Water availability in the Campaspe. A report to the Australian Government from the CSIRO
Murray-Darling Basin Sustainable Yields Project, CSIRO, Australia.
Hart, B. et al., 2005. Ecological risk management framework for the irrigation industry, Report to the
National Program for Sustainable Irrigation by the Water Studies Centre, Monash University,
Clayton, Australia.
Horne, A., Stewardson, M., Freebairn, J., McMahon, T.A., 2009. Environmental Response Curves: A
method to represent environmental water needs in an economic framework. River Research
and Applications, Draft.
Horne, A., Stewardson, M., Freebairn, J., McMahon, T.A., 2010. Environmental Response Curves: A
method to represent environmental water needs in an economic framework. River Research
and Applications, (in press).
Ladson, A.R. et al., 1999. Development and testing of an Index of Stream Condition for waterway
management in Australia. Freshwater Biology, 41(2): 453-468.
Latu, K., Costelloe, J.F., Malano, H.M., in press. New Approach for Estimating Environmental Risk of a
River Basin, 34th IAHR World Congress, Brisbane Convention Centre, Brisbane, Australia.
Macumber, P.G., 1991. Interaction between groundwater and surface systems in northern Victoria.
Dept. of Conservation and Environment, East Melbourne, xvi, 345 p. pp.
Marsh, N., Pickett, T., 2009. Quantifying environmental water demand to inform environmental flow
studies, 18th World IMACS / MODSIM Congress, Cairns, Australia.
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531
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537
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Morrison, J., Morikawa, M., Murphy, M., Schulte, P., 2009. Water Scarcity & Climate Change: Growing
Risks for Businesses & Investors. In: Institute, C.P. (Ed.). Ceres & Pacific Institute.
Neal, B., Sheedy, T., Hansen, B., Godoy, W., 2005. Modelling of complex daily environmental flow
recommendations with a monthly resource allocation. In: Australia, E. (Ed.), 298th Hydrology
and Water Resources Symposium, Canberra, Australia.
Nott, M.P. et al., 1998. Water levels, rapid vegetational changes, and the endangered Cape Sable
seaside-sparrow. Animal Conservation, 1(1): 23-32.
Sheldon, F., Thoms, M., C, Berry, O., Puckridge, J., 2000. Using disaster to prevent catastrophe:
referencing the impacts of flow changes in large dryland rivers. Regulated Rivers: Research &
Management, 16(5): 403-420.
SKM, 2006. Campaspe River Environmental FLOWS Assessment. Sinclair Knight Merz, Malvern, VIC 3144,
Australia.
SKM, 2009. Development and Application of a Flow Stressed Ranking Procedure, Sinclair Knight Merz,
Victoria, Australia.
Tarek, M., Akira, K., Kenji, J., Jonas, O., 2002. Risk assessment for optimal drought management of an
integrated water resources system using a genetic algorithm. Hydrological Processes, 16(11):
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Wild, C.J., Seber, G.A.F., 1995. Introduction to Probability and Statistics. Department of Statistics,
University of Auckland, New Zealand.
24
LOWER CAMPASPE VALLEY
406265
RIA (West)
# +$ ECHUCA
Legend
Reach3
Campaspe
P.D
Campaspe River
#
³
River Stations
Study Area
#
Campaspe Siphon
406202
RIA (East)
$+ ROCHESTER
CIA (West)
Reach2
Campaspe Weir
$+
ELMORE
CIA (East)
Reach1
#
406201
Mount
Pleasant
Ck
Campaspe R
Campaspe Private
Diversion (P.D)
Axe Ck
Forest Ck
Campaspe Private
Diversion (P.D)
STRATHFIELDSAYE
+
$
Sheepwash Ck
Axe Ck
km
0
547
2.5
5
7.5 10
25
548
549
Figure 1: Presents the Lower Campaspe Valley as the study area and the demand nodes in relation
to the Campaspe River and locations of the three reaches.
550
Step 1
Step 2
Env Flow
Requirements
Daily Natural
Flow
EFD Estimation
Observed Regulated
River Flow
Flow Band Specification
EFD Upper
Limit
EFD Optimum
Range
EFD Lower
Limit
Step 4
High Flow Index
Low Flow Index
Non Parametric &
Parametric Method
Selection
Zero Flow Index
Variation Index
Estimate Probability (P)
Step 3
P
Vulnerability Index
ER = P x C
Total Consequence
(C)/5
C
Step 5
Environmental Risk Curve
551
552
553
554
555
556
Figure 2: Five key steps of estimating the environmental risk profile of a river basin.
26
Environmental Flow Demand
Natural Flow
20000
Environmental Flow Demand
Flow (ML/Mth)
Regulated Flow
15000
10000
5000
0
Jan-02 Feb-02 Mar-02 Apr-02 May-02 Jun-02 Jul-02 Aug-02 Sep-02 Oct-02 Nov-02 Dec-02
Month
557
558
559
560
561
Figure 3: Comparison of the environmental flow demand, natural flow data and the regulated river
flow of Reach 1 for 2002. It’s clearly shown that the seasonal flow has been changed due to
diversion for irrigation.
27
R1 EFD Optimum Range
18000
25 percentile
16000
50 percentile
75 percentile
Flow (ML/Month)
14000
12000
10000
8000
6000
4000
2000
0
Jan
562
563
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Month
Figure 4: The annual optimum range of the EFD for Reach1.
Dec
28
Consequence Indices
1.00
0.90
0.80
Consequence
0.70
0.60
0.50
0.40
LF
HF
0.30
CV
0.20
VI
ZF
0.10
0.00
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
a
Month
564
Consequence Indices
1.00
0.90
0.80
Consequence
0.70
0.60
0.50
0.40
LF
HF
0.30
CV
VI
0.20
ZF
0.10
0.00
Jan
565
566
567
568
Feb
Mar
Apr
May
Jun
Jul
Month
Aug
Sep
Oct
Nov
Dec
b
Figure 5(a): Shows the monthly average (over the entire time series) of consequence indices for
Reach 1 on a 1 year CP while (b) shows the consequences are being smoothed due the use of 10
years CP
29
Reach 1 - Risk Profile
1000000
0.9
0.8
100000
0.7
10000
0.6
Risk
Flow (ML/M)
0.5
1000
0.4
100
0.3
0.2
10
0.1
Jul-05
Jan-05
Jul-04
Jan-04
Jul-03
Jan-03
Jul-02
Jul-01
Jan-02
Jul-00
Jan-01
Jan-00
Jul-99
Jan-99
Jul-98
Jan-98
Jul-97
Jan-97
Jul-96
Jul-95
Jan-96
0.0
Jan-95
1
Month
569
EFD-Lower Limit
(a)
EFD-Upper Limit
Reg Flow
Risk
Reach 2 - Risk Profile
1000000
0.9
0.8
100000
0.7
10000
0.6
Flow (ML/M)
0.4
100
Risk
0.5
1000
0.3
0.2
10
0.1
Jul-05
Jan-05
Jul-04
Jan-04
Jul-03
Jan-03
Jul-02
Jan-02
Jul-01
Jul-00
Jan-01
Jan-00
Jul-99
Jan-99
Jul-98
Jan-98
Jul-97
Jan-97
Jul-96
Jul-95
Jan-96
0.0
Jan-95
1
Month
Reg Flow
570
EFD-Lower Limit
(b)
EFD-Upper Limit
Risk
Reach 3 Risk Profile
100000
1.2
1.0
10000
0.8
0.6
100
0.4
10
0.2
Jul-05
Jan-05
Jul-04
Jan-04
Jul-03
Jan-03
Jul-02
Jan-02
Jul-01
Jul-00
Jan-01
Jan-00
Jul-99
Jan-99
Jul-98
Jan-98
Jul-97
Jan-97
Jul-96
Jul-95
Jan-96
0.0
Jan-95
1
Month
571
572
Risk
Flow (ML/M)
1000
Reg Flow
EFD-Lower Limit
EFD-Upper Limit
Risk
Figure 6: Environmental risk profiles for all reaches over a 1 year CP.
(c)
30
Consequences for Different CPs
0.80
1yr
3yrs
5yrs
10yrs
RF
140000
0.75
120000
0.70
Consequence
100000
80000
0.65
60000
0.60
40000
0.55
20000
0.50
Jan-95
0
Jan-96
Jan-97
Jan-98
Jan-99
Jan-00
Month
573
574
575
576
Figure 7: Comparison of different consequences produced by ERM for different CP.
31
Risks for Different CPs
0.80
Risk
0.60
0.40
1yr
0.20
5yrs
10yrs
0.00
577
578
579
Month
Figure 8: Comparison of risk profiles produced by ERF for different CPs.
32
Environmental Risk Profiles
0.6
Winter Months
Summer Months
0.5
Risk
0.4
0.3
0.2
0.1
R1 - 1yr
R1 - 5yr
R1 - 10yr
R2 - 1yr
R2 - 10yr
R3 - 1yr
R3 - 5yr
R3 - 10yr
R2 - 5yr
0.0
Jan
580
581
582
583
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Month
Figure 9: Annual risk profiles for different CPs.
Oct
Nov
Dec
33
Monthly Risks
1.0
0.9
0.8
Risk Jan
0.7
Risk Feb
Risk
0.6
Risk Mar
Risk Apr
0.5
Risk May
Risk Jun
0.4
Risk Jul
Risk Aug
0.3
Risk Sep
Risk Oct
0.2
Risk Nov
Risk Dec
0.1
4.67
4.51
4.18
4.10
4.09
4.07
4.06
4.05
4.04
4.03
4.02
4.02
3.99
3.94
3.93
3.93
3.91
3.89
3.89
3.86
3.77
3.75
3.51
3.45
0.0
(a)
Flow (log10)
584
January Environmental Risk Curve
1.0
0.9
y = 0.0051x2 - 0.134x + 1
R² = 0.8749
0.8
Risk Jan
0.7
Poly. (Risk Jan)
0.6
Risk 0.5
0.4
0.3
0.2
0.1
4.32
4.21
4.20
4.12
4.11
4.11
4.09
4.08
4.07
4.05
4.05
4.03
4.02
4.00
3.97
3.95
3.92
3.91
3.85
3.82
3.80
3.75
3.56
3.52
0.0
(b)
Flow (Log10)
585
Environmental Risk Curve
1.00
Risk
Poly. (Risk)
0.90
0.80
0.70
Risk
0.60
0.50
0.40
Low
Flow
Zone3
0.30
0.20
Low
Flow
Zone2
Low
Flow
Zone1
High
Flow
Zone1
High
Flow
Zone2
High
Flow
Zone2
0.10
Preferred Flow Range in the River
586
587
588
589
Flow (Log10)
4.83
4.71
4.56
4.42
4.40
4.35
4.20
4.11
4.04
4.01
3.97
3.89
3.85
3.82
3.78
3.75
3.69
3.64
3.60
3.56
3.48
3.38
3.31
3.26
0.00
(c)
Figure 10: (a) shows the environmental risk curves for all months, (b) environmental risk curve for
January and (c) presents the environmental risk curve for the entire Reach 1.
34
Table 1: Definition of the environmental risk in relation to flows
590
591
592
Risk Scale (%)
Risk Class
Definition
R ≤ 20
Insignificant risk
20 < R ≤ 40
Low Risk
40 < R ≤ 60
Moderate Risk
The flow objectives are not satisfied due to current flow
not being able to satisfy the EFDs
60 < R ≤ 80
Significant Risk
Significant portion of the EFDs are not being satisfied
80 < R ≤ 100
Very High Risk
The EFDs are totally not being met and resulted in flow
objectives being unsatisfied
The EFDs are mostly often met and therefore flow
objectives are being satisfied
The EFD are not fully satisfied resulted in some of the
flow objectives are not being fully met