1 1 2 3 4 Holistic Estimation of the Environmental Risk of a River Basin 5 6 7 8 9 Kilisimasi (Kris) Latu1,2*, Justin F Costelloe1, Tim Peterson1 and Hector M Malano1,2 10 11 1 Department of Infrastructure Engineering, The University of Melbourne, Vic 3010, Australia 12 2 CRC for Irrigation Futures, P.O. Box 56, Darling Downs, Qld 4350, Australia 13 14 *Corresponding Author 15 Kilisimasi (Kris) Latu 16 Department of Infrastructure Engineering 17 University of Melbourne 18 Victoria Australia 3010 19 Tel: 61 3 8344 7237 20 Fax: 61 3 8344 4616 21 Email: latuk@unimelb.edu.au; kris.latu@bigpond.com 22 23 24 25 26 Sponsor: The study is funded by the Cooperative Research Centre for the Irrigation Future, Australia. 2 27 Abstract 28 The conventional approaches to estimating environmental risk in a river basin commonly do not consider 29 the entire flow regime or all the environmental assets within a river system. These approaches are 30 restricted because they represent environmental risk only with limited ecological risk (adequate for 31 certain type of flows) and such risk alone cannot be used as a driving factor for water allocation. 32 Therefore, a new approach for estimating environmental risk is required that considers the entire flow 33 regime. We propose a holistic, dynamic and robust approach that is based on a statistical analysis of the 34 entire flow regime and accounts for flow stress indicators to produce an environmental risk profile based 35 on both frequency and consequence of occurrence of a given flow range. When applied to river reaches 36 (catchments), the model produced a dynamic and robust environmental risk profile that clearly showed 37 that when the current flow is drawn away from the optimum range of environmental flow demand, the 38 environmental risk increased. An environmental risk curve produced from the environmental risk profiles 39 provides guidelines for meeting environmental flow demands. 40 41 42 43 44 45 46 47 48 KEY WORDS: environmental flow demand; environmental risk model; environmental risk profile; 49 hydrological flow index; environmental risk curve. 3 50 Glossary 51 EFD = Environmental Flow Demand 52 RF = Regulated Flow 53 NF = Natural Flow 54 ERM = Environmental Risk Model 55 HF = High Flow Index 56 LF = Low Flow Index 57 ZF = Zero Flow Index 58 CV = Coefficient of Variation Index 59 VI = Vulnerability Index 60 4 61 1 Introduction 62 Water scarcity in many parts of the world, including Australia, combined with recent severe droughts and 63 increasing impacts of climate change are creating great pressure on the amount of water available for 64 water consumptions, such as irrigation and environmental water demands (Morrison et al., 2009). This 65 presents a challenge in managing of a water resource (river) to satisfy two or more competing water 66 demands. Usually, a river would supply water for urban, irrigation and environmental flow demands. 67 When water demands are not satisfied, there may be risk existed. In a river basin, when the environmental 68 flow demands are not met, risk to river system may increase. On this paper, we provide a novel approach 69 for estimating the environmental risk of a river system. 70 1.1 71 Risk is the chance that an adverse event with specific consequences will occur within a certain timeframe 72 (Hart et al., 2005). Environmental risk can be defined as a deviation from natural conditions (Horne et al., 73 2009). Hydrological change will be detected at the scale of the flow regime (changes in variability, 74 predictability) with responses at ecosystem level (species changes, adaptations) (Sheldon et al., 2000). 75 Thus, the impacts on a flood pulse will expand until they affect the flow history and then the habitat and 76 life-history cues. Over time the continued hydrological change, there will be overriding disturbances to 77 the flow regime, and consequent distal loss of species (Sheldon et al., 2000). These kinds of disturbances 78 have been experience in Australia due to prolong drought recently. Hence, determining environmental 79 risk based on changes to flow would adequately account for the impacts on all aspects of a river system. 80 Environmental risk has been estimated using a number of different approaches; which mostly used 81 ecological risk to represent environmental risk (Cottingham et al., 2001; Horne et al., 2009). The problem 82 with this approach is that ecological risk can represent only part of the environmental risk, due to the 83 inability of ecological indicators to account for a wider range of environmental assets within a river basin, 84 or to be applied across the entire flow range. For example, Horne et al. (2009) used a single ecological Environmental Risk 5 85 parameter (habitat rating) over the low flow range to define an environmental response curve that yields 86 environmental risk. Cottingham et al. (2001) estimated risk focusing on key individual stressors, such as 87 algae blooms in a river. Chee et al. (2005) focused on summer flow components (cease to flow, low flow, 88 high flow and summer freshes) leaving out the winter flow components. Arguably these studies have 89 limited application for estimating environmental risk for a water allocation model, which must consider 90 the entire flow regime of a river basin (i.e. from cease to flow to overbank flow) (Ladson et al., 1999; 91 SKM, 2009). Furthermore, the cumulative effects of environmental risk over the past years are often 92 ignored. Therefore, what is a better method for estimating environmental risk? 93 The process of risk assessment is based on quantifying the probability of an uncertain or undesirable 94 event (with a given consequence), occurring either now or in the future (Cottingham et al., 2001; Tarek et 95 al., 2002; Hart et al., 2005). In the context of the flow regime aspects of river health, we define 96 consequence as any negative effects that may occur due to river flow being outside the optimum range of 97 the EFD. While river health has a number of other aspects outside river flow, the use of river flow to 98 estimate consequences of water allocation allows the direct linkage between river flow and negative 99 effects of allocation. Therefore, this study focuses on estimating risk due to the level of departure of 100 regulated flow from the natural flow of a river system. 101 Risk assessment can be an essential tool that facilitates and informs decision-making in different fields, 102 including water resource management, particularly with regards water allocation. Within a river basin, 103 conjunctive water demands (including domestic, irrigation and other demands) and EFD (which is the 104 amount of water required by a river system to sustain its health) are supplied from a combined storage 105 (i.e. river and groundwater supplies). Competition between conjunctive water demand and EFD, 106 particularly in the context of an inadequate water allocation policy, may increase risk to the environment 107 and conjunctive water supply. 108 There are many parameters which can be used to estimate environmental risk. However, estimating 109 environmental risk based on individual ecological or geomorphologic parameters may not capture the full 6 110 range of risk. Therefore, the parameters or factors used for estimating environmental risk must account 111 for as much of the entire environmental asset as is practicable. We propose the use of the flow regime as 112 an appropriate proxy based on the assumption that when the regulated flow (RF) is closer to natural flow 113 of a river system, the EFD would be satisfied for most environmental assets (Neal et al., 2005). 114 Environmental flow demand can be defined as that water demand that may occur in a natural flow 115 condition (Neal et al., 2005; Arthington et al., 2006). 116 In this paper, we define a new approach using statistical analysis to estimate environmental risk and apply 117 it to the Campaspe basin in north-central Victoria, Australia. The approach estimates environmental risk 118 by investigating the probability of the regulated flow occurring outside a pre-defined optimum range of 119 the EFD using both parametric and non-parametric approaches. Then we define and test a method for 120 estimating flow-related consequences based on the hydrological indices from the Index of Stream 121 Condition (Ladson et al., 1999) and Tarek et al. (2002) for estimating risk. 122 2 Study Area 123 The study area is the lower reaches of the Campaspe basin, southern Australia (Figure 1). The study area 124 has a total catchment area of 2,124 km2 and extends from the downstream end of Lake Eppalock (the 125 main storage in the catchment) to the junction with the Murray River at Echuca. The catchment is 126 relatively flat in the downstream northern half with increased higher terrain towards Lake Eppalock 127 (Chiew et al., 1995). The climate is fairly uniform, with hot summers experienced, particularly in the 128 north. The annual average rainfall is 450 mm (Chiew et al., 1995) and 69 mm average annual runoff 129 (CSIRO, 2008). The rainfall occurs throughout the year, with the winter and early spring being the wettest 130 period and January – February being the driest months (Chiew et al., 1995). The areal potential 131 evapotranspiration is estimated to be around 525 mm (CSIRO, 2008). The native vegetation of the basin 132 has been largely cleared since the settlement of Europeans in the region since the 1800s. This has resulted 133 in the replacement of deep rooted native plants with short rooted exotic plants resulting in a rising of the 7 134 water table in the region (Chiew and McMahon, 1991; Macumber, 1991; Chiew et al., 1995; CSIRO, 135 2008). 136 The locations of major demand centres are also shown in Figure 1 and the study area has been divided 137 into three reaches based on four major hydrological structures; Lake Eppalock, Campaspe Weir, 138 Campaspe Siphon and the outlet to Murray River in Echuca. In Reach 1, the major demand is from 139 private diversion with very little irrigation compared to Reach 2 and 3. In Reach 2, demand consists of the 140 Campaspe Irrigation Areas (East and West). In Reach 3, the Rochester Irrigation Areas (East and West) 141 are located but both divert water from the Waranga Western Channel (mark by the boundary of Reach 2 142 and 3), as water users buy water from the Goulburn River System (neighbouring catchment to the east). 143 Within Reach 3, the main demand from the river is by private diversions. Each reach contains a gauge 144 station at the downstream end where the observed flows were obtained; Reach 1 – 406201, Reach 2 - 145 406202 and Reach 3 – 406265. Because the levels of water demands are different for each reach, the 146 environmental risk must be individually determined for each reach. 147 Figure 1 about here. 148 3 Method 149 The estimation of environmental risk profile is based on two key objectives. Firstly, the approach should 150 produce dynamic and robust environmental risk profiles that take into account the cumulative effects of 151 past environmental risk. Secondly, it must account for risks that arise over the entire flow regime (cease 152 to flow to overbank flow), thus accounting for most environmental flow related consequences. In order to 153 satisfy these two objectives, we propose a method that involves five key steps, as summarised in Figure 2. 154 Figure 2 about here. 8 155 3.1 Estimation of the EFD from Natural Flow 156 The environmental flow demand is the specified flow magnitude required for the river, as recommended 157 by ecologists and expert panels, based on a ‘flow method’ assessment (SKM, 2006). However, these 158 recommended values are in aggregated form and the flow demand does not specify the specific time when 159 the flow should be applied in relation to the level of the RF. Instead, only the frequency, duration and 160 magnitude of flow required within a time frame is defined, without a clear link to the level of the RF in 161 the river. For this reason, this flow requirement must be disaggregated into a time series of EFD to enable 162 water authorities to adequately provide environmental flow requirement to the river. This disaggregation 163 can be done using a method developed by Neal et al. (2005). Measures of flow magnitude (annual, 164 seasonal, monthly, daily), the frequency, timing, duration and predictability of flow events (floods and 165 droughts), rate of change from one flow condition to another (rate of rise and fall of flood hydrographs), 166 and the temporal sequencing of flow conditions should be included as they influence many aspects of 167 river ecosystems (Arthington et al., 2006). The model developed by Neal et al. (2005) involves the 168 derivation of environmental flow requirement (SKM, 2006) from modelled natural daily flow at a site. 169 The modelled natural daily flow data were obtained from the Department of Sustainability and 170 Environment (DSE). This approach assumes that most environmental flows are only provided if these 171 flows would naturally have occurred (Neal et al., 2005) It allows automated decision making for the 172 provision or otherwise of seasonal flows of a given magnitude and a given annual frequency, 173 independence between events, and rates of rising and falling hydrograph limbs. Each component of the 174 recommended flow is progressively added until a time series of EFD is constructed over the period of 175 modelling. 176 The method estimates the EFD for the Campaspe Basin from daily modelled Natural Flow (NF) data of 177 the Campaspe River and the environmental flow requirements recommended by SKM (2006). The daily 178 NF data are required as a reference series against which to decide whether to provide environmental flows 179 (Neal et al., 2005). The emphasis of this paper is on how an ER profile is developed rather than how the 9 180 EFD is determined, which also can be produced with any of the methods discussed by Arthington et al. 181 (2006) or others (Marsh and Pickett, 2009). Because the model developed by Neal et al. (2005) included 182 the key hydrological factors, such as frequency and duration across the natural flow range, we considered 183 this method appropriate to provide an acceptable EFD as shown in Figure 3. However, relying on 184 estimated daily natural flow to produce monthly EFD may introduce uncertainty due to assumptions used 185 in estimating of the natural flow, which may affect the EFD and subsequently the environmental risk. 186 Figure 3 about here 187 3.2 188 The optimum risk profile for an EFD is that where the regulated monthly flow satisfies the EFD, and 189 therefore would present minimum risk. The monthly median, 25th and 75th percentiles are used to define 190 the optimum range. The selection of 25th and 75th percentiles was based on the assumption that if a 191 monthly regulated flow falls within this range than EFD is satisfied. The values are changeable thus 192 narrowing or expanding the risk band to investigate their effects of risk. In addition, it is a simplification 193 to assume that the percentiles are time invariant. The use of percentiles avoids making assumption that the 194 data are normally distributed from one month to another. Therefore, if a river flow magnitude is above or 195 below this optimum range then there will be environmental risk whilst acknowledging that the percentile 196 thresholds are subject to variation when the risk is assessed. 197 3.3 198 The next step is to estimate the probability of the RF occurring. Both parametric and non-parametric 199 statistical methods were trialled in estimating the probability. Parametric methods assume that the data 200 fall into a known distribution. Such methods are reasonably robust but can be affected by outliers and 201 large departures from normality (Wild and Seber, 1995). Normal distribution was selected for this study 202 over the other distributions, such as Weibull, Gamma and Lognormal. The selection was based upon 203 testing the “goodness of fit” using three methods; Anderson-Darling, Kolmogorov-Smirnov and Chi- EFD - Optimum Range Regulated Flow Exceedence Probability 10 204 Squared tests (Latu et al., in press). The estimation uses the “normdist” function (excel), which is based 205 on the normal curve equation of Wild and Seber (1995). For this study, we determine the probability of 206 the investigated value based on its deviation from the mean. 207 3.4 208 Herein, consequence is defined as any negative effect that may occur due to river flow being outside the 209 optimum range of the EFD; that is the difference between EFD and regulated flow. As this difference 210 increases, the consequence effects also increase. For instance, if during summer, the river flow is above 211 the EFD band, this may cause both geomorphological and ecological consequences, such as erosion and 212 habitat disturbance (Sheldon et al., 2000; Horne et al., 2010). On the other hand, if the flow is too low 213 during the winter season, then ecological problems may also arise as found by Horne et al (2010). Such a 214 change flow regime will affect responses at the organism level (individual survival, spawning success, 215 recruitment success). Therefore, there is a need to adequately account for the asymmetric consequences 216 for flow in different seasons and within seasons (e.g. when there is low flow occurring in winter when 217 high flow should be occurring). There is great difficulty in relating flow to a consequence score with a 218 single indicator of an aspect of river health. This is simply caused by the inability of a single indicator to 219 represent the effects that may arise when the EFD are not satisfied over the entire flow regime. Therefore, 220 a suite of indicators are required that cover all aspects of the flow regime. 221 The Index of Stream Condition (ISC), developed for southern Australian rivers, provides a set of multiple 222 indicators to estimate consequence (Ladson et al., 1999). The ISC is a holistic approach that provides an 223 integrated measure of river health and benchmarks the condition of a stream. It looks at the long-term 224 assessment of a river and is measured every 5 years, or after a significant flood event (Ladson et al., 225 1999). The above requirements and single output snapshot of the ICS will not satisfy a monthly water 226 allocation model that requires risk as its main driver on a monthly basis. However, aspects of the ISC 227 have been adapted to produce consequence scores that are flow related (Ladson et al., 1999). Out of the Estimation of the Environmental Consequences 11 228 five sub-indices of ISC, some of the measures of the hydrology index are used to relate consequence to a 229 flow magnitude. The river flow data are readily available, in contrast to the data for other indices, such as 230 the physical form index (see Ladson et al., 1999). The components of the hydrological index of the ISC 231 allow a comparison between the RF and EFD, where the EFD is derived from modelled NF data. These 232 two reasons satisfy the definition of ER previously described. The Consequence Index (C) for this study 233 consists of five sub-indices; low flow index (LF), high flow index (HF), zero flow index (ZF), coefficient 234 of variation index (CV) and vulnerability index (VI). Each is outlined below. 235 These five indices cover the entire flow range and therefore, account for the asymmetric nature of 236 consequences when regulated flows occur outside their natural pattern. For instance, high flow occurring 237 during a summer period will generate higher consequences compared to similar magnitude flows 238 occurring during a winter period. The indices also allow for the cumulative effects of not satisfying the 239 past EFD to be taken into account using a cumulative period (CP). The cumulative period is not only a 240 vital part of the calculation of consequences but also allows the accumulation of consequences to be 241 included in the risk calculation. This has been generally neglected in previous studies. 242 Alteration of the magnitude of low flows changes the availability of in-stream habitats, which can lead to 243 a long-term reduction in the viability of the populations of flora and fauna (Sheldon et al., 2000; SKM, 244 2009). A low flow index has been adopted (LF, Equation 1) that measures the changes in low flow 245 magnitude under the RF and the optimum range of EFD. A review by SKM (2009) found that the low 246 flow requirements often correspond to the daily 90% exceedence flow. However, at a monthly time step, 247 the index is calculated using two flow thresholds: one based on the flow exceeded 91.7% of the time (i.e. 248 11 months out of 12) and flow exceeded 83.3% of the time (10 months out of 12) (SKM, 2009). 249 LF91.7, j P Q91.7 EFD P Q91.7 R j 1 (1) 250 where: LF91.7,j is the range-standardised low flow index based on the 91.7% exceedence flow for specified 251 CP of years (j) (e.g. 1 - 10 years). The variable j is the number of years counting back from the 12 252 investigated month. It represents the period of data used to determine the PEFD and PR. P(Q91.7)R is the 253 proportion of months that the 91.7th percentile flow of RF (Q91.7)R is exceeded by the annual 91.7th 254 percentile EFD flow (Q91.7)EFD. P(Q91.7)EFD is the proportion of years of of the EFD that the 91.7th 255 percentile flow over j years is exceeded by the annual 91.7th percentile of the EFD. The above process is 256 repeated for the 83.3% exceedence flow. Then the range-standardised low flow index (LFt) is calculated 257 as the average of (LF91.7) and (LF83.3). The advantage of using two range-standardised low flow indices to 258 calculate the LFt, is that it reduces the level of bias and dominance of either the RF or EFD in determining 259 LFt. 260 High flows act as a natural disturbance in river systems, as they can remove vegetation and organic 261 material while resetting successional processes, especially during winter periods (Sheldon et al., 2000; 262 SKM, 2009). However, it may result in adverse consequences if it occurs during the summer period. The 263 high flow index (HF) measures the change in high flows under RF and EFD conditions. A similar 264 approach used for LFt is applied for this index; however, it is calculated using the 8.3% and 16.7% 265 exceedence values. During high flow seasons (winter) the difference between EFD and RF should be low 266 as both values may be very similar, producing a low value of HF. However, if a high flow event occurs 267 during summer period, the difference between EFD and RF would be high causing a high value of HF. 268 This means that that both HF and LF produce asymmetric responses to the difference between EFD and 269 RF during different seasons. 270 The zero flow index compares the proportion of zero flow spells occurring under EFD and RF conditions 271 as shown in Equation 3. If the number and length of cease to flow spells is unchanged between EFD and 272 current flow conditions, then the value of the index is 0, indicating no consequence. 273 ZFt max PZ EFD , PZ R min PZ EFD , PZ R j ,t 1 (2) 274 where: ZFt is the zero flow index for month t and PZEFD is the proportion of zero flow over the whole 275 monthly data set under EFD condition. PZR is the proportion of zero flow over j for the regulated flow. 13 276 The max and min ensure that the higher value between PZEFD and PZR is the value where the lesser of the 277 two is deducted from. 278 The coefficient of variation (CVt) of monthly flows between the RF (CVR) and EFD (CVEFD) for the CP of 279 j measures the variability between the two flows. Seasonal variation in flow is relatively predictable and it 280 acts as an important hydrological driver of aquatic ecosystems (Sheldon et al., 2000). This index should 281 reflect the regulated nature of the Campaspe River due to irrigation and private diversions. That is, 282 regulated systems have lower CV values than unregulated rivers with natural flows. 283 CVt CVEFD / CVR j ,t 1 (3) 284 During the high flow season, the regulated river flow being higher than the EFD is a natural phenomenon, 285 although it may still cause negative effects to the river system. The concern here is to ensure that when 286 this occurs, that ER should not be higher due to a significant difference between RF and EFD. Because of 287 this reason, an index is required to make sure that when such circumstances occur, ER is appropriately 288 estimated. Therefore, we propose a new index called the vulnerability index (VI) as shown by Equation 4. 289 The index is based on a vulnerability criterion used to quantify the severity of failure in reservoir 290 operation and to estimate the risk in water supply and demand deficit (Tarek et al., 2002). For this study, 291 we refined the approach of Tarek et al. (2002) by using the proportion of time when the two flow 292 magnitudes are different. The advantage of using proportion of time is that each month represents a 293 magnitude of one, regardless of its flow magnitude. On the other hand, if the flow magnitude is used, then 294 a spike in RF may cause very high or low consequence. 295 T T j VI t PEFD PR t / PEFD t 1 t 1 t (4) 296 where; VIt is the Vulnerability Index at month (t) using a CP (j), PEFD is the proportion of time the RF falls 297 inside the EFD range, while PR is the proportion of time the RF occurs outside the EFD range. 14 298 3.5 Risk 299 Each of the five indices values range between 0 (no consequence) and 1 (negative consequence). The total 300 consequence score is calculated out of 1 (Equation 6) based on a uniform weighting of the individual 301 indices. The weighting, and indeed the use of other or additional flow indices, can be easily modified 302 according to the characteristics of the catchment under consideration. The calculation of the consequence 303 uses different CPs from 1 to 10 years, which is the length of data used to estimate the consequence of the 304 investigated month. The risk is then calculated by Equation 6, where P is the probability and C is the 305 consequence. The model is simulated using code written in Matlab and we refer to it as the Environmental 306 Risk Model (ERM). 307 Ct 0.2 LF HF ZF V VI t 1 (5) 308 ERt Pt 1 Ct 1 (6) 309 At the completion of the risk calculation, the risk values are used to develop an environmental risk curve 310 for each reach. The purpose of the risk curve is to provide a relationship between RF and risk values. That 311 is, if a certain amount of flow is in the river, what is the expected risk value? As the risk range is related 312 to the actual RF in any point of time, decision makers have a guideline through the environmental risk 313 curve in making water allocation decisions that would affect the amount of water that should remain in 314 the river or be released into the river in the coming months. 315 The environmental risk curve is formed by compiling each month’s regulated flow and risk values sorted 316 in descending order according to the flow values. The risk values are then plotted against the log10 values 317 of the regulated flow values before zones are assigned. The use of log10 values rather than the actual 318 regulated flow values allow better scaling of the flow values when they are plotted. Risk zones are 319 allocated between 0 and 1. For instance, zone 1 will have a risk range between 0 and 0.2, meaning very 320 low risk. 15 321 4 Results 322 We present results from Reach 1 to illustrate the capability of the ERM model to produce ER profiles. We 323 also included the risk graphs for both Reach 2 and 3 to further demonstrate the capability of the model in 324 different reaches. We first discuss the EFD and consequence indices results. Then we look at the 325 environmental risk profiles produced by ERM. Lastly, we discuss the environmental risk curves. 326 4.1 327 The optimum range of the monthly EFD (Figure 4) shows higher flow during winter months (July – 328 October) and lower during summer months (December to March), reflecting the natural flow pattern of 329 the system. In contrast to the regulated flow which shows higher flows during summer months and lower 330 flows during winter months. Hence, it illustrates that the river flow has been reversed due to diversions 331 from the river to satisfied water demands such as irrigations. Similar results were observed on Reach 2 332 and 3. 333 Figure 4 about here. 334 4.2 335 We found that all indices made significant contributions to the ER profile (Figure 5) except for the ZF, 336 which is probably caused by the model operating on a monthly basis, which may not appropriately 337 account for zero flow values. Although, different low values were trialled to represent zero flow values, 338 its contributions were still very low in all reaches. For Figure 5, it was simulated with 100 ML/Mon. 339 Therefore, the ZF was omitted from the consequence estimation and was not included in risk estimation. 340 For all reaches, LF and HF had the highest contribution, followed by CV and then VI as shown in Figure 341 5. This indicates that the components of LF and HF are not often met due to low flows caused by low 342 precipitation and the effect of the river being regulated for irrigation water transfers and diversions. 343 Because the available duration of the RFs time-series are relatively short in comparison with the EFD’s Environmental flow demand Indices 16 344 data length, we could only model up to 10 years CP. That is, the EFD data starts from 1900 while RF only 345 starts from 1977. However, the model is capable of simulating longer data lengths. 346 Figure 5a and 5b about here. 347 The seasonal variations of consequences were also assessed using the mean monthly values from a 1 yr 348 CP (calculated over the entire time series) as shown in Figure 5a. It appears that the consequences are 349 higher during summer periods compared to winter periods. When compared to values from a 10 yrs CP, 350 the values are reduced, thus reflecting the smoothing of the effects over a 10 years period as shown in 351 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. 23 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 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): 2189-2208. 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