Abstract
Hedging is universally recognized as a useful operational practice in surface water reservoirs to temporally redistribute water supplies and thereby avoid large, crippling water shortages. When based on the zones of available water in storage, hedging has traditionally involved a static rationing (i.e. supply to demand) ratio. However, given the usual seasonality of reservoir inflows, it is also possible that hedging could be dynamic with seasonally varying rationing ratios. This study examined the effect of static and dynamic hedging policies on the performance of the Pong reservoir in India during a period of climate change. The results show that the reservoir vulnerability was unacceptably high (≥60%) without hedging and that this vulnerability further deteriorated as the catchment became drier due to projected climate change. The time- and volume-based reliabilities were acceptable. The introduction of static hedging drastically reduced the vulnerability to <25%, although the hedging reduction in the water supplied during normal operational conditions was only 17%. Further analyses with dynamic hedging provided only modest improvements in vulnerability. The significance of this study is its demonstration of the effectiveness of hedging in offsetting the impact of water shortages caused by climate change and the fact that static hedging can match more complex dynamic hedging policies.
The purpose of a surface water storage reservoir as a component of a water supply system is to regulate natural fluctuations in river flow by storing excess water for later release when the flow is insufficient to meet the domestic, industrial, agricultural and other demands served by the system. The planning of reservoirs using historical runoff data observed at the reservoir site is the best option available to the analyst, but is flawed in that an unsatisfactory performance of the reservoir during operation will result if the operational runoff situation of the reservoir is radically different from the planning situation. Such an unsatisfactory situation will be exacerbated by predicted climate change, which may further reduce the amount, and increase the variability, of reservoir inflows in various regions of the world (IPCC 2007).
The realization that climate change will affect future inflow series and hence the performance of reservoirs to meet their obligations has led to the intensification of research to assess these impacts as a precursor to the development of effective mitigation and adaptation strategies (e.g. Fowler et al. 2003; Nawaz & Adeloye 2006; Li et al. 2009; Adeloye et al. 2013). In general, most of these studies have reported a deteriorating performance with climate change, such as lower reliability and an increasing frequency and/or magnitude of water shortages, which will require either the expansion of facilities (e.g. building new reservoirs, the development of other new sources, such as groundwater) or the development of improved operational practices for existing facilities. The former takes time and can have unwanted socio-environmental consequences; the latter is much quicker and can be effective in moderating the vulnerability of systems (Adeloye et al. 2016).
Most reservoirs are operated using rule curves, which guide the operator's decision on the amount of water to release based on the total amount of water available at the beginning of each month (Yin et al. 2015). Rule curves show the target storage levels to be maintained in the reservoir if it is to be able to successfully meet demand. They are normally determined using any of the available reservoir planning analysis tools, such as the sequent peak algorithm (SPA) (McMahon & Adeloye 2005), forced with the historical runoff data recorded at the reservoir site. Once determined, they remain fixed and are used to guide the operation of the reservoir indefinitely.
A schematic illustration of basic rule curves is shown in Figure 1a, in which the operator will attempt to meet the full monthly demand whenever the total amount of water available (i.e. the starting storage level plus the expected inflow during the month) is in the interval [LRCm, URCm] for the month m under consideration, where LRCm and URCm are, respectively, the ordinates of the lower and upper rule curves for month m. No water is supplied in a given month if the water available is less than LRCm. A fundamental assumption here is that the anticipated inflow during the month is known at the start and is equal to the historical monthly flow.
Schematic illustration of zone-based operating rules for (a) no hedging, (b) static hedging and (c) dynamic hedging. See text for definitons of variables.
Rule curves are easy to deploy, but may result in high vulnerability or large single-period shortages, a problem that will be exacerbated by projected climate change. For example, Chiamsathit et al. (2014) reported vulnerabilities (or maximum single-period shortages) of 67 and 88% for domestic and industrial water allocations, respectively, at the Ubonratana reservoir in northeastern Thailand when operated with basic rule curves such as those illustrated in Figure 1a. To improve the effectiveness of rule curves in curbing excessive vulnerability, water rationing (or hedging) during normal operational periods is often carried out (Tu et al. 2008; Eum et al. 2011). A normal operational period is defined as when there is sufficient water in storage to meet the full demand. If the cut-back during hedging is moderate, i.e. ≤25% of the requirement, the impact on the socio-economic wellbeing of water users will be minimal (Fiering 1982).
A single-stage, static hedging policy is illustrated in Figure 1b. It shows the critical curve that triggers the onset of hedging (once the available water level falls below it), with α representing the static (or constant) fraction of the demand to supply. Thus, compared with the no hedging rule curve illustrated in Figure 1a, normal operation in which full demand is satisfied only occurs when the amount of available water is above the critical rule curve. Whenever the amount of available water is below the critical rule curve, the water supply is rationed by delivering at most only a fraction α of the full demand, i.e. Dt′ ≤ αDt, where Dt′ is the supply, Dt is the demand and α (0 ≤ α ≤ 1) is the static rationing ratio.
The difference between the dynamic case in Figure 1c and the static case in Figure 1b is that the rationing ratio is not constant in the dynamic case, but varies from one month to the next, i.e. Dt′ = αm Dt, where m = 1, 2,…, 12 and (0 ≤ αm ≤ 1) is the rationing ratio for month m. The rationale for a dynamic hedging policy is that hedging works better if it reflects the inflow situation, such that a larger proportion of the water demand can be supplied during the wetter seasons, but less so during drier seasons. Dynamic hedging schemes (i.e. in which water rationing varies with time) have received little attention and a possible reason for this is the increased number of decision variables required in the associated optimization formulations. Shiau (2009) attempted three different dynamic hedging schemes – namely, semi-annually, quarterly and monthly varying hedging – which were then compared with static hedging. The results showed that dynamic hedging improves system performance during shortages compared with static hedging. However, Shiau (2009) used the standard operating policy as the basis for developing the hedging policies. As noted by Adeloye et al. (2016), some of the hedging paradigms based on the standard operating policy, e.g. the so-called one-point hedging (see Peng et al. 2015), start hedging in the region where the availability of water is already very poor, which runs counter to the notion that, to be effective, hedging should be saving water during normal operational periods, not aggravating water stress. The few previous studies on dynamic hedging have not attempted to study the performance of such hedging policies within the context of climatic change.
We report here an attempt to bridge this knowledge gap in reservoir operation. The determination of the critical curve that triggers the hedging (CRC) and the associated hedging factors α and αm is achieved using genetic algorithm optimization. The aim of this work is to explore the effect of hedging-integrated, zone-based rule curves on reservoir performance during climate change perturbations. In particular, given the high vulnerability associated with zone-based operating rule curves, we investigated the extent to which hedging can moderate vulnerability and thus help reservoir operators to adapt to water shortages induced by climate change. The work was based on the Pong reservoir, a dual-purpose (irrigation and hydropower generation) facility on the Beas River in Himachal Pradesh, India.
Methodology
The work involved five main stages (see Fig. 2):
the development of basic zone-based rule curves using genetic algorithm optimization;
the development of hedging-integrated zone-based rule curves using genetic algorithm optimization (static situation);
the development of hedging-integrated zone-based rule curves using genetic algorithm optimization (dynamic situation);
the development of alternative, reservoir inflow scenarios affected by climate change;
reservoir simulations to assess performance under alternative inflow scenarios and operational policies.
Tasks 1–3 were accomplished using genetic algorithm optimization, whereas task 4 was accomplished using plausible delta perturbations of precipitation and temperature to force a calibrated HYSIM conceptual catchment hydrological model (Pilling & Jones 1999; Manley & WRA 2006). HYSIM takes precipitation, temperature and potential evaporation as inputs and simulates the hydrology of the catchment with the help of seven stores representative of land use and soil type, while the hydraulic sub-routine is conducted using the kinematic routing of flows. The full structure of the model is shown schematically in Manley & WRA (2006). The seven natural stores into which the hydrology routine has been conceptualized are: interception storage; upper soil horizon; lower soil horizon; transitional groundwater store; groundwater store; snow storage; and minor channel storage. The associated hydrological parameters are detailed by Pilling & Jones (1999).
Methodology flow chart.
The final reservoir simulations (task 5) were based on behavioural analyses (McMahon & Adeloye 2005). Further details relating to the implementation of these tasks have been reported elsewhere (Adeloye et al. 2016; Soundharajan et al. 2016) and only brief summaries are provided here.
Genetic algorithm for rule curve optimization
A genetic algorithm is a random search optimization algorithm inspired by biological evolution and provides a robust method for searching for the optimum solution to complex problems (Michalewicz 1992). In a genetic algorithm, the solution set is represented by a population of strings, which consists of a number of blocks, each representing the individual decision variable of the problem. The strings are processed and combined according to their fitness (an objective function value evaluated using the components in the string) to generate new strings that have the best features of the two parent strings. The strings with the best fitness have the greatest chance of future generations, similar to the process of natural selection.
To start the genetic algorithm optimizer, the initial solutions (or strings) are randomly generated. Three fundamental operations are involved in manipulating the strings and moving to a new generation: selection, cross-over and mutation. The selection operation helps to identify the strings, which are included in the reproduction process to develop the next generation of strings. There are a number of approaches for selection, all of which determine the probability of selection as a function of fitness. Because the initial solutions are randomly generated and hence may not be reproduced exactly if repeated, the genetic algorithm is usually repeated over several randomly generated initial solutions and either the mean of the best solutions or the most superior best solution is taken as the final solution. A detailed description of the genetic algorithm will not be attempted in this paper; interested readers are referred to the fundamental treatise on the subject by Michalewicz (1992).
Despite the existence of a large number of traditional non-linear programming techniques for solving this kind of constrained optimization problem, a search-based optimizer such as the genetic algorithm is preferred as a result of its ability to search for the solution from a population of points (not a single point), its use of the objective function information itself, rather that the derivatives of the function, and its use of probabilistic (as opposed to deterministic) transitions rules. Genetic algorithms have been widely applied in reservoir operational studies to develop optimum rule curves, operating policies and hedging (Oliveira & Loucks 1997; Wardlaw & Sharif 1999; Sharif & Wardlaw 2000; Ahmed & Sarma 2005; Jothiprakash & Shanthi 2006; Kim et al. 2006; Reddy & Kumar 2006; Kangrang et al. 2008).
The ordinates of the CRC for each month and the rationing ratios (static and dynamic) were optimized using the following objective function and constraints.
The objective function is the minimization of the sum of squares of the water deficit
The constraints are as follows:
The decision variables for the optimization are the CRCm; m = 1, 2,… 12 ordinates for each month m of the year, giving a total of 12 variables (i.e. 12 values representing the 12 monthly ordinates) and α (the static rationing ratio) or αm; m = 1, 2,… 12 (dynamic rationing ratios). The number of decision variables is therefore 13 in the case of static hedging and 24 in the case of dynamic hedging. The genetic algorithm optimization involved the usual selection, cross-over and mutation operations (Wardlaw & Sharif 1999; Rani & Moreira 2010; Hossain & El-Shafie 2013). A real-value coding was used with the following parameters: cross-over fraction = 0.8; mutation rate = 0.01; and number of elite children = 2. The genetic operations were repeated for 500 generations.
Impacts of climate on reservoir inflows
The assessment of the effects of projected climate change on reservoir inflows used the delta perturbation approach (Vicuna et al. 2012) to avoid the notorious uncertainties associated with global climate model projections and their downscaling. In this approach, plausible changes (or delta perturbations) in both temperature and precipitation were arrived at by analysing the Coupled Model Intercomparison Project Phase 5 (CMIP5) projections until the year 2100 for the Beas basin for four representative concentration pathways: RCP2.6, RCP4.5, RCP6.0 and RCP8.5 (Taylor et al. 2012; IPCC 2013). Details of how these delta perturbations were objectively arrived at were presented by Soundharajan et al. (2016) and essentially involved constructing the 95% confidence interval based on the mean and standard deviation of the temperature and precipitation projections for each of the RCPs. With these, the resulting delta perturbations in temperature ΔT (°C) were [0, 5; 1], whereas the corresponding perturbations in precipitation ΔP (%) were [−10, 10; 5].
These delta perturbations were applied to historical precipitation and temperature data for the Beas basin and the perturbed series were used to force a calibrated HYSIM rainfall–runoff model for the catchment to determine an alternative reservoir inflow series resulting from projected climate change.
Simulation of reservoir behaviour and performance indices
Reservoir simulation used the mass balance equation of the inflows and outflows for the reservoir as follows:
Reliability:
Resilience, φ:
Vulnerability, η:
Sustainability, λ:
Case study and data
The Pong dam (and its reservoir) is located on the Beas River, which is one of the five major rivers of the Indus basin, India (Fig. 3). Located at longitude 76° 05′ E and latitude 32° 01′ N, the reservoir drains a catchment area of 12 561 km2, out of which the permanent snow catchment is 780 km2 (Jain et al. 2007). The active storage capacity of the reservoir is 7051 Mm3. Apart from its use for generating hydropower, the Pong reservoir meets irrigation water demands of c. 7913 Mm3 annually, which, as illustrated in Figure 4, is spread relatively uniformly throughout the year. The total irrigated area is 1.6 Mha, with rice, wheat and cotton being the major crops sown.
Map of India and the Beas River catchment.
Average monthly inflow and irrigation demands from the Pong reservoir in the time period 2000–2008.
The monsoon rainfall between July and September is a major source of water inflow into the reservoir, in addition to snow and glacier melt. Snow and glacier melt runoff in the Beas catchment was studied from 1990 to 2004 by Kumar et al. (2007), from which it is known that its contribution is c. 35% of the annual flow of the Beas River at Pondoh dam (upstream of Pong dam). Consequently, the ability of the Pong reservoir to satisfactorily perform its functions is susceptible to possible disturbances of these water sources due to climate change. For a system that is inextricably linked to the socio-economic wellbeing of its region, any significant deterioration in performance of the Pong reservoir or its ability to meet demands will have far-reaching consequences. This is why it is important to carry out a systematic assessment of the performance of the reservoir operation and to use the outcome to inform the development of appropriate decision making.
The monthly reservoir inflow and irrigation demands from January 2000 to December 2008 (nine years) were available for study. The historical mean annual runoff at the dam site is 8485 Mm3 (annual coefficient of variation 0.225) and the seasonal distribution of the annual runoff is shown in Figure 4. The inflow into the Pong reservoir is highly seasonal, with the bulk of the inflow occurring during the monsoon season. Another feature of the hydrology of the Pong reservoir is that, except for the monsoon season when the natural river flow exceeds the irrigation demand, there are irrigation supply–demand deficits for much of the year, which must be met by releases from the reservoir.
The Pong reservoir, like most reservoirs, is operated using rule curves, which guide the operator to decide how much water to release based on the water available in the reservoir at the beginning of a month. Because the currently used curves were not made available to us, new rule curves were derived based on the historical runoff and demand using the SPA (McMahon & Adeloye 2005). Further refinement of the SPA-derived rule curves was achieved through genetic algorithm optimization, with the SPA-derived curves defining the limits for the random sampling of the initial solution population of the genetic algorithm. The rule curves derived via the coupled SPA–genetic algorithm approach (Adeloye et al. 2016) then formed the basis of this study to derive the hedging policy.
Results and discussion
HYSIM calibration
Detailed information relating to the parametrization, calibration and validation of the HYSIM hydrological model is provided by Adeloye et al. (2016) and therefore only a brief summary is provided here. To accommodate the spatial variability within the catchment, the Beas catchment was divided into three sub-basins based on considerations of the altitude, spatial differences and available meteorological data (Adeloye et al. 2016). HYSIM hydrological parameters were initialized with the help of the analysis of the Harmonized World Soil Database and the area of each soil type of the catchment was taken into account to obtain an average value of the hydrological parameters. These parameters were then modified during the calibration of the model. To accommodate parameter uncertainty, a Monte Carlo approach involving the stochastic generation of 100 parameter sets for each sub-catchment during the calibration was used and the parameter set corresponding to the best-behaved simulation was selected.
The graphical illustration of the performance of the model in reproducing the observed runoff during calibration and validation was presented by Adeloye et al. (2016) and is not repeated here, although the performance was very good. Another reassuring aspect of the HYSIM performance, at least within the context of the water resources investigation, was its superior performance during low-flow periods relative to high-flow periods (Adeloye et al. 2016). The performance of reservoirs in meeting consumptive (i.e. water supply and irrigation) needs relies more on the low-flow regime and, as a consequence, the fact that the low-inflow sequences were better simulated by HYSIM is an indication that the reservoir performance was also adequately simulated. The R2 values recorded during calibration and validation were 0.92 and 0.83, respectively, and the corresponding Nash–Sutcliffe efficiency indices (Nash & Sutcliffe 1970) were 0.88 and 0.78, respectively.
Effects of climate change on reservoir inflows
Table 1 summarizes the effects of perturbations in temperature and precipitation on the inflows to the Pong reservoir. As expected, more inflows are recorded as the catchment precipitation increases, especially for no change in temperature. In terms of the seasonal patterns of the observed changes, the largest proportional increase in inflow occurred in the pre-monsoon period, which is not surprising because this is the driest season and hence is expected to profit the most from any increase in rainfall.
Changes (%) in the mean annual and seasonal runoff under different climate change scenarios
Table 1 also shows the effect that the runoff generated from melting ice and snow has on neutralizing the loss of water from the temperature-induced increase in evapotranspiration within the basin. The annual runoff increased for all increases in temperature, even when the precipitation remained unchanged. Even when the precipitation decreased for a 2°C increase in temperature, there was sufficient water from melting ice and snow to neutralize the reduction in runoff from the combined effects of lower rainfall and higher temperature (and hence evapotranspiration). This is a short-term advantage that will be impossible to maintain if the projected increase in temperature takes hold because such an increase in temperature would result in the gradual disappearance of the glacier and/or less precipitation falling as snow. These issues were not considered in detail in our simulation, which has assumed, albeit unrealistically, that the extent of the glacier and the seasonal snow accumulation will maintain the status quo.
Optimized rule curves
The optimized rule curves derived using the genetic algorithm are shown in Figure 5. Figure 5a is the basic set of rule curves in which no hedging is implemented. When operating the reservoir with the rule curves in Figure 5a, attempts will be made to meet the full demand for irrigation water whenever the available water lies within the confines of the upper rule curve and the lower rule curve. The space above the upper rule curve is meant for flood water and, as seen in Figure 5a, the upper rule curve deepens during the monsoon period to leave space for the increased inflow and thus reduces the potential for flooding. In the low-inflow winter and pre-monsoon seasons, the reservoir is kept as close as possible to the maximum, thus ensuring that more water is available to meet demand.
Genetic algorithm optimized rule curves. (a) No hedging; (b) single-stage constant hedging; (c) single-stage monthly varying hedging; and (d) single-stage seasonally varying hedging.
Figure 5b shows the single-stage, static rationing, hedging-integrated rule curves. The critical hedging curve that triggers water rationing lies everywhere between the upper rule curve and lower rule curve, as expected, and allows the full demand to be supplied over a wide range of water availabilities in the system during the high-flow season. The range of water availability in which the full supply can be attempted is much narrower for the drier, post-monsoon periods. When water rationing takes place, only 17% of the full demand is cut back, leaving 83% of the full demand being attempted. This optimized hedging shortfall is modest and less than the 25% tolerable shortage suggested by Fiering (1982). What is important, however, is its effect on the overall vulnerability of the reservoir system.
The dynamically varying hedging policies are shown in Figure 5c (monthly) and 5d (seasonally); their associated optimized supply limits (or rationing ratios) are shown in Tables 2 and 3, respectively. Unlike the situation in which the rationing ratio was static, dynamic rationing varies monthly (seasonally), reflecting the relative abundance of water in the various months (seasons). Thus, as seen in Figure 5c, the proportion of the demand supplied in the monsoon months was highest, approaching almost 100%. As the amount of available water reduced, e.g. during the winter and pre-monsoon seasons, the proportion of the demand supplied attained its least value of <80%. Although the dynamic situation seems more plausible in that it adjusts the hedging fraction to the reservoir inflow situation, it is a much more involved operating policy to develop and use than the simple static policy, with 24 (dynamic policy) variables and only 13 (static policy) variables.
Rationing ratios for monthly varying hedging
Rationing ratios for seasonally varying hedging
Another feature of the dynamic scheme is that the optimized critical storage curves that trigger hedging have also responded to the reservoir inflow situation. During the low-inflow winter season, the curves are below those for the dynamic case, ensuring that the water available to meet the full demand is greater and hence there are fewer occasions on which >20% reductions will be required. By contrast, during the high-inflow monsoon season, the critical curves for the dynamic policies are higher than those of static hedging, meaning that rationing will occur more frequently, although the cut-back amounts are small because the associated rationing ratios are close to unity.
This implies that the dynamic policies offer an improvement in performance over the static policy, but the question remains: by how much? The attractiveness of the dynamic policy stems from its effect on the performance of the system. A significant improvement in the performance of the dynamic policy over the static policy is required to justify its preference. Quantifying this improvement requires a simulation study of the reservoir.
Reservoir performance
Although all five performance indices defined in equations (3–7) were evaluated in the reservoir simulations forced alternatively with the simulated historical and climate change perturbed inflow sequences, only the results for the two reliability measures (time-based and volume-based) and the vulnerability are discussed. The vulnerability is important as a result of the acknowledged effect of hedging on the index. If hedging is to be adjudged to be effective, then it must significantly impact the vulnerability. In addition, the two reliability measures are routinely used in the evaluation of water resources systems and are therefore widely recognized. Consequently, their inclusion for discussion here not only recognizes their popularity, but also emphasizes the nature of the trade-offs between them. Adeloye et al. (2017) presented an approach that harmonized the two reliability measures, thus effectively removing the need for the trade-offs when using the reliability indices for the evaluation of reservoir performance. A further attraction of the two reliability measures stems from their efficiency. For example, the results of the extensive Monte Carlo simulations reported by Soundharajan et al. (2016) showed that the time-based and volume-based reliability have the least variability of all the indices of reservoir performance.
Figures 6, 7, 8 show the results of the performance evaluation using the time-based reliability (Fig. 6), the volume-based reliability (Fig. 7) and the vulnerability (Fig. 8). With no hedging, the time-based reliability is high under existing conditions, but decreases when climate change projects less water from precipitation and increases when the projected precipitation increases. The effect of an increase in temperature on the inflow contributions from melting ice and snow is manifested in the time reliability, with the projected 2°C increase in temperature resulting in the highest reliability. Hedging causes the time reliability to deteriorate significantly. This is to be expected given that hedging deliberately introduces additional failures (i.e. occasions of non-supply of the full demand). The greatest reduction in the time reliability occurred with the static hedging policy, where the time reliability reduced by almost 50% relative to the no hedging situation. When hedging was dynamically altered with regard to the seasons, the deterioration in the time reliability was reduced, but only marginally. Reference was made earlier regarding the position of the critical rule curve for the dynamic policies relative to that of the static policy and the fact that this was likely to result in improved performance of the reservoir. The current simulation result has shown this to be the case for the time reliability, although the recorded improvement was marginal.
Hedging effect on reservoir reliability (time-based). (a) No hedging; (b) single-stage constant hedging; (c) single-stage seasonally varying hedging; (d) single-stage monthly varying hedging.
Hedging effect on reservoir reliability (volume-based). (a) No hedging; (b) single-stage constant hedging; (c) single-stage seasonally varying hedging; (d) single-stage monthly varying hedging.
Hedging effect on reservoir vulnerability. (a) No hedging; (b) single-stage constant hedging; (c) single-stage seasonally varying hedging; (d) single-stage monthly varying hedging.
The volume reliability shown in Figure 7 has confirmed its generally higher value than its time-based counterpart in Figure 6. It is clear from the Figure 7 that, unlike the time-based reliability, the volume-based reliability is not as drastically affected by hedging. For example, for the simulated baseline reservoir inflow sequence, the volume-based reliability only changed from 92% (no hedging) to 89% (with static hedging). The Rv situation improved with the dynamic policies, but this improvement was too marginal to justify the complexity associated with the development and deployment of the dynamic (seasonally or monthly varying) policies. Thus, although hedging increased the number of occasions on which systems failure occurred, its effect on reducing the ability of the system to meet the total period demand was minimal. This is because the amount by which the water supply was cut back by hedging was optimized to be so small that, cumulatively over the entire period, the total water shortage was insignificant relative to the total period demand. The fact that the volume-based reliabilities for the different climate change temperature and precipitation perturbations considered were indistinguishable is an indication that hedging protects the performance of the system in meeting the total period demand, even with projected climate change. Thus hedging represents an effective adaptive measure for the effects of climate change on large-scale water resource facilities.
The vulnerability (or maximum single-period water shortage) is shown in Figure 8. The primary reason for deploying hedging is to limit single-period shortages and hence to temper the vulnerability of water resource systems. Also shown on the plots is the horizontal line for a vulnerability of 25% which, as remarked previously, represents the tolerable shortage limit for most water users. With no hedging, the vulnerability is high (c. 60%) under existing conditions and intensifies to c. 65% when the catchment becomes drier as a result of the projected reduction in precipitation caused by climate change. The vulnerability is tempered for wetter conditions, but even for the most benign of these, i.e. a projected 10% increase in precipitation, the recorded vulnerability was still >47%, much higher than the 25% tolerable vulnerability threshold suggested by Fiering (1982).
The dramatic effect of hedging on vulnerability can be seen in the evaluation of both the static and the dynamic policies. For example, for the static policy, the most fascinating aspect is the fact that hedging has virtually eliminated the huge vulnerability associated with the driest climate change scenario; indeed, for all the scenarios, the vulnerability has been reduced to below the 25% threshold from a peak of c. 65% when there was no hedging. This is remarkable given that only 17% of the demand was withheld during hedging. As was the case with the other performance indices, the vulnerability was slightly less when the dynamic (monthly and seasonally varying) policies were deployed. This reduction in vulnerability does not justify the extra efforts in developing and deploying the dynamic policies.
Conclusions
This study developed optimized static and dynamic zone-based hedging policies for the Pong reservoir in India and compared their performance with that of a basic, zone-based policy incorporating no hedging as a way of testing the adaptive and mitigating capacity of water hedging for water shortages induced by climate change. A genetic algorithm was used to optimize the decision variables for the policies, including the rationing ratio and the target storage values that trigger hedging in each month of the year. A simple delta perturbation approach was used to develop alternative reservoir inflow responses to plausible changes in temperature and precipitation. Subsequent reservoir simulations to test the effectiveness of the various operating policies showed that, without hedging, the performance of the reservoir (time- and volume-based reliability and vulnerability) deteriorated significantly with climate change scenarios that projected a drier future and vice versa. The vulnerability was particularly high, reaching >60%. However, as hedging was introduced, there was a significant reduction in the number of large single-period water shortages, resulting in reduced vulnerability for the Pong reservoir. The vulnerability was reduced to <25% for both the static and dynamic hedging policies, even for the worst (direst) climate change projections. Reducing the number of large shortages caused the total number of failure periods to increase, leading to a significant deterioration in the evaluated time-based reliability. However, because the amount of water shortages for most of these additional shortage periods was low to moderate, the overall volumetric reliability of the reservoir was practically unaffected. This is reassuring because what should matter most in the operation of reservoirs is not the number of failure occasions, but the deficit sustained during such failures. All this confirms that water resource systems have an inherent buffering capacity that, if well-harnessed through improved operating practices such as the hedging policies developed in this work, will offer effective and low-cost adaptation and mitigation strategies for climate change. In terms of the overall performance of the system, the dynamic hedging policies only marginally outperformed the static hedging policy.
This work has clearly demonstrated the importance of better operational practices in partially annulling the impacts of water shortages in projected climate change scenarios. The reliance of most regions on surface water reservoirs to meet domestic, industrial and agricultural water needs will be certainly threatened if future climate change results in reduced reservoir inflows, resulting in an inability of these reservoirs to meet the obligations placed upon them. As objectively demonstrated in this work, such catastrophes can be averted if changes are made in the operation of the reservoirs, e.g. simple static hedging. Given the complexity associated with the development and deployment of dynamic policies, the marginal improvement recorded here is not sufficient to prefer dynamic policies in reservoir operations. Although this study used the Pong reservoir in India as a case study, the approach is reasonably straightforward and could be implemented for other major surface water reservoirs in other parts of the world.
Funding
The work reported here was funded by the UK Natural Environment Research Council Project NE/N016394/1 – Sustaining Himalaya Water Resources in a Changing Climate (SusHi-Wat) – as part of the UK–India Newton–Bhabha Sustainable Water Resources thematic Programme.
- © 2018 The Author(s). Published by The Geological Society of London. All rights reserved
References
Effect of dynamically varying zone-based hedging policies on the operational performance of surface water reservoirs during climate change
