Reservoir Operation Policy Research Articles

Assessment of environmental flows using hydrological methods for Krishna River, India

Krishna River is significantly affected due to Srisailam dam from past 30 years. The impact of this hydraulic structure drastically reduced the minimum flow regime on the downstream, which made the river nearing to decaying stage. In the present paper, Environmental Flow called minimum flow values released for the dam are estimated with the help of three hydrological methods viz., Range of variability Approach (RVA), Desktop Reserve Model (DRM), and Global Environmental Flow Calculator (GEFC). DRM method suggested considering the intermediate values obtained from among the three methods to preserve the ecosystem on the downstream of the river, which amounts to an average annual allocation of 9378 Million Cubic Meter (MCM) which is equal to 23.11% of mean annual flow (MAF).In this regard GEFC and RVA methods accounted for 22 % and 31.04% of MAF respectively. The results indicate that current reservoir operation policy is causing a severe hydrological alteration in the high flow season especially in the month of July. The study concluded that in the case of non-availability of environmental information, hydrological indicators can be used to provide the basic assessment of environmental flow requirements. It is inferred from the results obtained from the study, that the new reservoir operations can fulfil human water needs without disturbing Environmental Flow Requirements.

Copula‐Based Chance‐Constrained Hydro‐Economic Optimization Model for Optimal Design of Reservoir‐Irrigation District Systems under Multiple Interdependent Sources of Uncertainty

AbstractThis paper presents a new explicit stochastic hydro‐economic optimization model for reservoir‐irrigation district systems design affected by multiple interdependent sources of uncertainties. The model solution determines both optimal design and long‐term operation policies of the systems while accounting for uncertainties of reservoir inflow, rainfall, crop yield, crop price, and production costs. The problem is formulated as a chance‐constrained program in which the dependence structure of discrete and continuous random coefficients is accounted for using copula. In addition to design variables such as the sizing of reservoir capacity and the irrigable area, optimal reservoir operation policies and irrigation management strategies are determined. The model performance and significance is tested in the case study of Chamshir hydrosystem in Iran, consisting of Chamshir Dam and downstream irrigation districts. Various scenarios assessing baseline conditions, risk attitude, and the impacts of change in climatic inputs and upstream conditions are also evaluated. The model results are used to quantify the interrelationships and trade‐offs among optimal values of design parameters, optimal irrigation policy, decision‐makers' risk‐attitude, uncertainty levels of agro‐economic factors, and target reliability levels of meeting crop yield and water requirement. The results demonstrate that smaller sizes of the reservoir and the irrigation district and stress‐avoidance irrigation policies, rather than deficit‐irrigation policies, are preferred when the decision maker is risk‐averse and uncertainties in agro‐economic factors and reliability levels are large. Additionally, we show how the optimal cropping pattern and irrigation strategies are affected by climate change‐induced rainfall variations and the alteration of conditions upstream of the project.

Many-Objective Multi-Scenario Algorithm for Optimal Reservoir Operation Under Future Uncertainties

An increase in greenhouse gases in future can exacerbate the climate change phenomenon and may have negative consequences on different elements of hydrologic system, including rainfall, temperature, and streamflow. Since the reservoir operation is highly dependent on the timing and magnitude of inflow, the impact of potential climate change on inflow sequences should be considered in deriving the system operation rule. Nevertheless, existing algorithms are only able to optimize the operation policy for a single predetermined climate scenario. Thus, the derived operation rule would not work well if the scenario changes. This paper proposes an algorithm which is able to handle simultaneously multiple scenarios in finding optimum system operation rule. Thus, it can overcome drawbacks caused by uncertainties in the occurrence of future scenarios. The proposed algorithm is used to optimize reservoir operation policy considering various climate change scenarios (RCPs). To evaluate the performance of the proposed algorithm, a five-reservoir system within Tehran region with several objectives including municipal, agricultural, environmental, and hydropower demands is employed as the case study. Results show that in all cases the multi-scenario rule derived by the proposed method performs as good as the operation rule derived for any specific scenario using a powerful optimization algorithm when evaluated for that scenario. While, in all other models as the future scenario changes to the one other than that used in deriving the operation rule, the model performance declines as compared to the proposed model.

Future changes in Yuan River ecohydrology: Individual and cumulative impacts of climates change and cascade hydropower development on runoff and aquatic habitat quality

The eco-hydrological system in southwestern China is undergoing great changes in recent decades owing to climate change and extensive cascading hydropower exploitation. With a growing recognition that multiple drivers often interact in complex and nonadditive ways, the purpose of this study is to predict the potential future changes in streamflow and fish habitat quality in the Yuan River and quantify the individual and cumulative effect of cascade damming and climate change. The bias corrected and spatial downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) General Circulation Model (GCM) projections are employed to drive the Soil and Water Assessment Tool (SWAT) hydrological model and to simulate and predict runoff responses under diverse scenarios. Physical habitat simulation model is established to quantify the relationship between river hydrology and fish habitat, and the relative change rate is used to assess the individual and combined effects of cascade damming and climate change. Mean annual temperature, precipitation and runoff in 2015–2100 show an increasing trend compared with that in 1951–2010, with a particularly pronounced difference between dry and wet years. The ecological habitat quality is improved under cascade hydropower development since that ecological requirement has been incorporated in the reservoir operation policy. As for middle reach, the runoff change from January to August is determined mainly by damming, and climate change influence becomes more pronounced in dry seasons from September to December. Cascade development has an effect on runoff of lower reach only in dry seasons due to the limited regulation capacity of reservoirs, and climate changes have an effect on runoff in wet seasons. Climate changes have a less significant effect on fish habitat quality in middle reach than damming, but a more significant effect in lower reach. In addition, the effect of climate changes on fish habitat quality in lower reach is high in dry seasons but low in flood seasons.

Balancing exploration, uncertainty and computational demands in many objective reservoir optimization

Abstract Reservoir operations are central to our ability to manage river basin systems serving conflicting multi-sectoral demands under increasingly uncertain futures. These challenges motivate the need for new solution strategies capable of effectively and efficiently discovering the multi-sectoral tradeoffs that are inherent to alternative reservoir operation policies. Evolutionary many-objective direct policy search (EMODPS) is gaining importance in this context due to its capability of addressing multiple objectives and its flexibility in incorporating multiple sources of uncertainties. This simulation-optimization framework has high potential for addressing the complexities of water resources management, and it can benefit from current advances in parallel computing and meta-heuristics. This study contributes a diagnostic assessment of state-of-the-art parallel strategies for the auto-adaptive Borg Multi Objective Evolutionary Algorithm (MOEA) to support EMODPS. Our analysis focuses on the Lower Susquehanna River Basin (LSRB) system where multiple sectoral demands from hydropower production, urban water supply, recreation and environmental flows need to be balanced. Using EMODPS with different parallel configurations of the Borg MOEA, we optimize operating policies over different size ensembles of synthetic streamflows and evaporation rates. As we increase the ensemble size, we increase the statistical fidelity of our objective function evaluations at the cost of higher computational demands. This study demonstrates how to overcome the mathematical and computational barriers associated with capturing uncertainties in stochastic multiobjective reservoir control optimization, where parallel algorithmic search serves to reduce the wall-clock time in discovering high quality representations of key operational tradeoffs. Our results show that emerging self-adaptive parallelization schemes exploiting cooperative search populations are crucial. Such strategies provide a promising new set of tools for effectively balancing exploration, uncertainty, and computational demands when using EMODPS.

Evaluation of Seasonal Autoregressive Integrated Moving Average Models for River Flow Forecasting

Reservoir operation policies cannot be functional in instant decision making without forecasting the future reservoir inflows. For forecasting inflows into reservoirs with only hydrological data is available like Koga irrigation dam, multivariate forecasting models cannot be used to generate accurate river flow information. As a result, an evaluation of univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) models was done for forecasting monthly Koga River flow with Gnu Regression, Econometrics and Time-series Library (GRETL) software. The stationarity of historical river flow sequence was checked by Augmented Dickey-Fuller (ADF) unit root analysis. Then, seasonality was removed from the river flow time series by seasonal differencing. Using seasonally differenced correlogram characteristics various SARIMA models were identified and evaluated, their parameters were optimized and diagnostic checks of forecasts were performed using residual correlograms and Ljung-Box tests. Finally, based on minimum Akaike Information criteria, SARIMA (1, 0, 1) (3, 1, 3)12 model was selected for Koga River flow forecasting. The stationarity test of the forecasted values of this model has proved the similarity of forecast values and patterns with those of the historical ones. Thus, irrigation managers could use this model and forecast information for optimal irrigation planning and development of reservoir operation strategies in order to protect farmers and downstream environment from water shortages. Moreover, the use of stationarity test of forecast flow patterns is useful and applicable in selecting best forecast model during forecasting of any river flows.

Reservoir operation using a robust evolutionary optimization algorithm

In this research, a significant improvement in reservoir operation was achieved using a state-of-the-art evolutionary algorithm named Borg MOEA. A real-world multipurpose dam was used to test the algorithm's performance, and the target of the reservoir operation policy was to fulfil downstream water demands in drought condition while maintaining a sustainable quantity of water in the reservoir for the next year. The reservoir's performance was improved by increasing the maximum reservoir storage by 14.83 million m3. Furthermore, sustainable water storage in the reservoir was achieved for the next year, for the simulated low flow condition considered, while the total annual imbalance between the monthly reservoir releases and water demands was reduced by 64.7%. The algorithm converged quickly and reliably, and consistently good results were obtained. The methodology and results will be useful to decision makers and water managers for setting the policy to manage the reservoir efficiently and sustainably.

Fuzzy Set–Based System Performance Evaluation of an Irrigation Reservoir System

This paper presents the development of fuzzy set-based performance measures for an irrigation reservoir system to answer questions on how likely a system is to fail (fuzzy reliability), how quickly it is likely to recover from failure (fuzzy resiliency), and how severe the consequences of failure are likely to be (fuzzy vulnerability). The failure/success state of the system is defined as a fuzzy event. A fuzzy set with an appropriate membership function is defined to describe both the working and failed states, to account for the system being in partly working and partly failed state. Failure of the irrigation reservoir system is related to the evapotranspiration deficit of the crops in a period. Application of the performance measures is demonstrated with the case study of the Bhadra Reservoir system in Karnataka, India. The reservoir operation is simulated using three different reservoir operating policy models: steady-state policy obtained using a fuzzy stochastic dynamic programming (FSDP) model, steady-state policy obtained using a classical stochastic dynamic programming (SDP) model, and yearly operating policy using a conceptual operating policy (COP) model. All reservoir operating policy models consider the integration of the reservoir operation policy at 10-day time periods with water allocation decisions on a daily basis by maintaining the storage continuity and soil moisture balance. Inclusion of fuzziness in the performance measure gives realistic solutions by capturing uncertainties in the reservoir operating policy models. (C) 2017 American Society of Civil Engineers.

Forecasting the Rainfall Pattern on Upstream of Hirakud Reservoir Using L-Moment for Accessing the Inflow

Changes in the rainfall pattern are a challenge for filling schedule of reservoir, when it is fulfilling various demands. In monsoon fed reservoirs, the target remains for attaining full reservoir capacity in order to meet various demands during non-monsoon period and the flood control. The planners always eye towards the inflow trend and perspective frequency of rainfall in order to counter the extreme events. In this study, the case of Hirakud reservoir of Mahanadi basin of India is considered as this reservoir meets various demands as well as controls devastating floods. The inflow trend has been detected by using Mann Kendall test. The frequency analysis of monthly rainfall is calculated using L-moment program for finalizing a regional distribution. The falling trend in inflow to reservoir is visualized in the month of July and August. The Wakeby distribution is found suitable for the monthly rainfall of July, September and October, where as in June and August, General Extreme Value (GEV), General Normal (GN) and Pearson Type-III (PT-III) distributions are found suitable. The regional growth factors for the 20, 40, 50 and 100-year return period rain-falls along with inflow to reservoir observed between 1958-2010 are calculated in this study as a referral for reservoir operation policy.

Towards best water management policies: how current irrigation reservoir operation practices compare with theory in Alberta

ABSTRACTImproved reservoir operation policies based on river basin management simulations have typically not been implemented in the field. This article investigates this disconnect between reservoir operations theory and practice. Interviews with water managers in southern Alberta (Canada) reveal that reservoir operations are based on basin-wide cooperation, a focus on the current state of the system without hedging rules, and early-season announcements of water rationing. Model development should focus on multiple time-step optimization approaches that can produce optimal release decisions for various levels of risk, provide better insight into effects of alternative releases and potentially reduce supply shortfalls.

Evaluating the impact and uncertainty of reservoir operation for malaria control as the climate changes in Ethiopia

Promising environmental mechanisms to control malaria are presently underutilized. Water level fluctuations to interrupt larval development have recently been studied and proposed as a low-impact malaria intervention in Ethiopia. One impediment to implementing such new environmental policies is the uncertain impact of climate change on water resources, which could upend reservoir operation policies. Here we quantified the potential impact of the malaria management under future climate states. Simulated time-series were constructed by resampling historical precipitation, temperature, and evaporation data (1994–2002), imposing a 2 °C temperature increase and precipitation changes with a range of ±20 %. Runoff was generated for each climate scenario using the model GR4J. The runoff was used as input into a calibrated HEC ResSim model of reservoir operations. The malaria operation management increased the baseline scenario median energy generation by 18.2 GWh y−1 and decreased the energy generation at the 0.5 percentile (during dry conditions) by 7.3 GWh y−1. In scenarios with −20 % precipitation, malaria control increased average annual energy generation by 1.3 GWh y−1 but only decreased the lowest 0.5 percentile of energy by 0.2 GWh y−1; the irrigation demand was not met on 8.5 more days, on average, per year. Applying the malaria control rule to scenarios with +20 % precipitation decreased the likelihood of flooding by an average of 1.0 day per year. While the malaria control would divert some water away from other reservoir operational goals, the intervention requires 3.3–3.7 % of the annual precipitation budget, which is much less than reduction from potential droughts.

Basin/Reservoir System Integration for Real Time Reservoir Operation

This paper demonstrates the basin/reservoir system integration as a decision support system for short term operation policy of a multipurpose dam. It is desired to re-evaluate and improve the current operational regulation of the reservoir with respect to water supply and flood control especially for real time operation. The most innovative part of this paper is the development of a decision support system (DSS) by the integration of a hydrological (HEC-HMS) and reservoir simulation model (HEC-ResSim) to guide the professional practitioners during the real time operation of a reservoir to meet water elevation and flood protection objectives. In this context, a hybrid operating strategy to retain maximum water elevation is built by shifting between daily and hourly decisions depending on real time runoff forecasts. First, a daily hydro-meteorological rule based reservoir simulation model (HRM) is developed for both water supply and flood control risk. Then, for the possibility of a flood occurrence, hourly flood control rule based reservoir simulation model (FRM) is used. The DSS is applied on Yuvacik Dam Basin which has a flood potential due to its steep topography, snow potential, mild and rainy climate in Turkey. Numerical weather prediction based runoff forecasts computed by a hydrological model together with developed reservoir operation policy are put into actual practice for real time operation of the reservoir for March – June, 2012. According to the evaluations, proposed DSS is found to be practical and valuable to overcome subjective decisions about reservoir storage.

Assessing flood risk using reservoir flood control rules

The application of conventional flood operation regulation is restricted due to insufficient description of flood control rules for the Pubugou Reservoir in southern China. Based on the requirements of different flood control objects, this paper proposes to optimize flood control rules with punishment mechanism by defining different parameters of flood control rules in response to flood inflow forecast and reservoir water level. A genetic algorithm is adopted for solving parameter optimization problem. The failure risk and overflow volume of the downstream insufficient flood control capacity are assessed through the reservoir operation policies. The results show that an optimised regulation can provide better performance than the current flood control rules.

Curses, Tradeoffs, and Scalable Management: Advancing Evolutionary Multiobjective Direct Policy Search to Improve Water Reservoir Operations

AbstractOptimal management policies for water reservoir operation are generally designed via stochastic dynamic programming (SDP). Yet, the adoption of SDP in complex real-world problems is challenged by the three curses of dimensionality, modeling, and multiple objectives. These three curses considerably limit SDP’s practical application. Alternatively, this study focuses on the use of evolutionary multiobjective direct policy search (EMODPS), a simulation-based optimization approach that combines direct policy search, nonlinear approximating networks, and multiobjective evolutionary algorithms to design Pareto-approximate closed-loop operating policies for multipurpose water reservoirs. This analysis explores the technical and practical implications of using EMODPS through a careful diagnostic assessment of the effectiveness and reliability of the overall EMODPS solution design as well as of the resulting Pareto-approximate operating policies. The EMODPS approach is evaluated using the multipurpose Hoa ...

A question driven socio-hydrological modeling process

Abstract. Human and hydrological systems are coupled: human activity impacts the hydrological cycle and hydrological conditions can, but do not always, trigger changes in human systems. Traditional modeling approaches with no feedback between hydrological and human systems typically cannot offer insight into how different patterns of natural variability or human-induced changes may propagate through this coupled system. Modeling of coupled human–hydrological systems, also called socio-hydrological systems, recognizes the potential for humans to transform hydrological systems and for hydrological conditions to influence human behavior. However, this coupling introduces new challenges and existing literature does not offer clear guidance regarding model conceptualization. There are no universally accepted laws of human behavior as there are for the physical systems; furthermore, a shared understanding of important processes within the field is often used to develop hydrological models, but there is no such consensus on the relevant processes in socio-hydrological systems. Here we present a question driven process to address these challenges. Such an approach allows modeling structure, scope and detail to remain contingent on and adaptive to the question context. We demonstrate the utility of this process by revisiting a classic question in water resources engineering on reservoir operation rules: what is the impact of reservoir operation policy on the reliability of water supply for a growing city? Our example model couples hydrological and human systems by linking the rate of demand decreases to the past reliability to compare standard operating policy (SOP) with hedging policy (HP). The model shows that reservoir storage acts both as a buffer for variability and as a delay triggering oscillations around a sustainable level of demand. HP reduces the threshold for action thereby decreasing the delay and the oscillation effect. As a result, per capita demand decreases during periods of water stress are more frequent but less drastic and the additive effect of small adjustments decreases the tendency of the system to overshoot available supplies. This distinction between the two policies was not apparent using a traditional noncoupled model.

OPTIMAL OPERATION OF MULTIPURPOSE RESERVOIR FOR IRRIGATION PLANNING WITH CONJUNCTIVE USE OF SURFACE AND GROUNDWATER USING FUZZY RESOURCES

A Fuzzy Linear programming (FLP) model has been developed with the objective of maximizing the net benefits accrued from crops in a year and subjected to constraints on availability of surface water, groundwater, culturable area, and other inputs such as reservoir storages, water requirement of crops, canal capacity and reservoir continuity. It is a conjunctive use model of surface water and ground water to obtain the optimal operating policy for a multipurpose single reservoir by considering the fuzziness involved in the resources i.e. availability of surface water, groundwater and the culturable area. The linear membership function is developed to fuzzify the resources. The developed model is applied to the case study of Jayakwadi reservoir stage-I, built across the river Godavari, Maharashtra state, India. The final model is solved for the maximized degree of satisfaction (λ). The λ obtained is equal to 0.51 and corresponding values of the objective is 3438.439 million rupees.

Integrating Environmental Flows into Multi-Objective Reservoir Management for a Transboundary, Water-Scarce River Basin: Rio Grande/Bravo

Integrated water management seeks to balance the interests of multiple stakeholders who desire many end-uses for water within the context of institutions and regulations. This problem is particularly complex in transboundary and water-scarce basins. In the Big Bend region of the Rio Grande/Bravo, an arid, monsoonal climate combines with multiple human and environmental water demands and established treaty requirements to stress available water resources. We analyzed reservoir operation strategies in the basin to integrate environmental flow (EF) considerations into existing management objectives using a linear programming model to assess reservoir operation policies. Five potential EF regimes are evaluated for improving aquatic and riparian habitat in the Big Bend region. The model uses the historical hydrologic record of river inflows, data for flood control and bi-national water allocation requirements, and parameters for human demands and infrastructure; to compare current and optimized operations of Luis L. Leon reservoir for multiple objectives. Results indicate that alternative operational policies for monthly reservoir storage (compared to historic values) can increase EF allocations without affecting water deliveries or treaty allocations. Some tradeoffs may exist, however, in managing reservoirs for both EFs and flood control. Our approach informs management strategies for the water-stressed basin that seek to incorporate environmental goals into existing infrastructure and operations.

Optimal Operation of Multipurpose Reservoir for Irrigation Planning with Conjunctive Use of Surface and Groundwater

In the present study, a Linear Programming (LP) model is developed for the conjunctive use of surface water and ground water to obtain the optimal operating policy for a multipurpose single reservoir. The objective of the present study is to maximize the net benefit from the command area under consideration. The constraints imposed on the objective function are maximum and minimum irrigation demands, reservoir storages and canal capacity. The model takes into account the continuity constraint which includes inflows in to the reservoir, releases for irrigation, releases for hydro-power generation, evaporation losses, feeder canal releases, initial and final storages in the reservoir in each time period. The developed model is applied to the case study of Jayakwadi reservoir stage-I, built across river Godavari, Maharashtra, India. Initially the model is solved for the availability of surface water which results in net benefit of 3373.45 million rupees with irrigation intensity is 57.07%. Next the model solved by considering the availability of surface water and available potential of groundwater in the area, which results in net benefits of 3590.02 million rupees with an intensity of irrigation 58.48%. The present model takes in to account the socio-economic requirement of growing the essential crops to meet the requirement of the society. The model has also generated the canal wise optimal releases for irrigation and power, monthly utilization of groundwater, storages in the reservoir at the end of every month and corresponding head over the turbine.

Optimizing Reservoir Operation Policy Using Chance Constraint Nonlinear Programming for Koga Irrigation Dam, Ethiopia

One of typical problems in water resources system modeling is derivation of optimal operating policy for reservoir to ensure water is used more efficiently. This paper introduces optimization analysis to determine monthly reservoir operating policies for five scenarios of predetermined cropping patterns for Koga irrigation scheme, Ethiopia. The objective function of the model was set to minimize the sum of squared deviation (SSD) from the desired targeted supply. Reservoir operation under different water availability and thresholds of irrigation demands has been analyzed by running a chance constraint nonlinear programming model based on uncertain inflow data. The model was optimized using Microsoft Excel Solver. The lowest SSD and vulnerability, and the highest volumetric reliability were gained at irrigation deficit thresholds of 20 % under scenario I, 30 % under scenario II, III and V, and at 40 % under scenario IV when compensation release is permitted for downstream environment. These thresholds of deficits could be reduced by 10 % for all scenarios if compensation release is not permitted. In conclusion the reservoir water is not sufficient enough to meet 100 % irrigation demand for design command areas of 7,000 ha. The developed model could be used for real time reservoir operation decision making for similar reservoir irrigation systems. In this specific case study system, attempt should be made to evaluate the technical performance of the scheme and introduce a regulated deficit irrigation application.

Optimized cascade reservoir operation considering ice flood control and power generation

Summary Ice flood control is an important objective for reservoir operation in cold regions. Maintaining the reservoir outflow in a certain range is considered an effective way to remediate ice flood damage. However, this strategy may decrease the socio-economic benefit of reservoirs, for example, reduction of hydropower production. These conflicting objectives cause a dilemma for water managers when defining reservoir operation policy. This study considers seven cascade reservoirs in the upstream Yellow River, and ice flood control storage is introduced to balance the hydropower generation and ice flood control. The relation between the ice flood control storage volume of the Liujiaxia reservoir and cascade power output is analyzed. An optimization model to explore the trade-offs between hydropower generation and ice flood control requirements is developed. The model takes into account ice flood control requirements. The optimization model compared to simulation model based on the reservoir operation rule curves. The results show that the optimal operation rules are far more efficient in balancing the benefits within the power generation and ice flood control. The cascade reservoirs operation strategies proposed in this study can be effectively and suitably used in reservoir operation systems with similar conditions.