Operation Of Water Resources Research Articles

Nonconvexity and computational effort in the problem of Hydro-Power spillage policy assessment

This paper aims to assess the spillage policies in the Day-Ahead Hydro-Power Plant operation. For this purpose, a combination technique is proposed to prevent erroneous spillage decisions. Given that the developed mathematical modeling lacks the convexity property, we examine the multiple solutions obtained from the Hydro-Power production optimization subject to different power output formulations. In addition, a nonlinear approach to consider friction losses in the modeling is introduced. The modeling is described by Mixed-Integer Linear (MILP) and Integer Nonlinear (MINLP) Programming models. The optimal solutions are provided by Outer Approximation (OA), Branch-and-Bound (BB), and Interior Point algorithms. The simulations show that the water is managed distinctly depending on the optimizer and solution algorithms. Finally, the findings of the spillage policy obtained by the Mixed-Integer Nonlinear model under the cubic function for power generation encourage and indicate its adequacy for a more realistic operation and management of the Mexican Hydro-Power Plant water resources.

Editorial to the Special Issue “Recent Advances in Hydrological Modeling”

Hydrological models play a crucial role as essential tools in the realms of water resources operations, planning, and management practices [...]

Development of an Integrated Observation Data and Global Numerical Weather Prediction System-Based Rainfall-Runoff Forecasting Model as PJT-I’s Effort in Water Resources Disaster Mitigation

Given the vital role of water resources and their vulnerability to climate-driven disasters, especially floods and droughts, the need for effective disaster mitigation strategies in water resource management is evident. PJT-I, a key player in the industry, grapples with this challenge. To address it, PJT-I has embraced the Smart Water Management System (SWMS) platform and innovative enhancements in rainfall-runoff modeling, showcasing their dedication to bolstering real-time monitoring and predictive capabilities. These advances hold the promise of strengthening operational water resource management, spanning allocation and infrastructure operations, and are particularly relevant in the face of escalating extreme weather events. The study’s focus on the Sengguruh reservoir catchment area serves as an illustration, highlighting the creation of two hydrometric-based models: the AWLR Gadang model and the Sengguruh reservoir model. Their effectiveness is validated through data calibration and integration of GFS forecast data, enabling 120-hour forecasting. Rigorous assessment involving correlation, efficiency, determination, and error metrics provides insights into the models’ predictive capacities and limitations amid the dynamic interplay of rainfall-runoff dynamics.

Analysis of Sediment Erosion in Pelton Nozzles and Needles Affected by Particle Size

The sediment erosion of Pelton turbine components is a major challenge in the operation and development of high-head water resources, especially in mountainous areas with high sediment yield. In this paper, a study using numerical simulation was conducted with different sediment particle sizes in the fine sand range. And the erosion mechanism of the Pelton turbine injector was analyzed. The Eulerian Lagrange method was adopted to simulate the gas–liquid–solid flow. The Mansouri’s model was applied to estimate the injector erosion. The predicted erosion results were in accord with field erosion photographs. In particular, the asymmetrical erosion distribution on the needle surface was physically reproduced. With the sediment particle size increasing from 0.05 mm, the needle erosion rate decreased, while the nozzle casing erosion rate increased dramatically. In order to clarify this tendency, the characteristics of the three-phase flow were analyzed. Interestingly, the results show that with the rise in particle size, the separation of particles and water streamlines became more serious in the contraction section of the nozzle mouth. Consequently, it caused the enhancement of erosion of the nozzle surfaces and weakened the erosion of the needle surfaces. Significant engineering insights may be provided for weakening Pelton injector erosion with needle guides in the current study.

Climate Change Impact on Water Resources using Hydrological Modelling

In order to understand the effects of continuous improvement techniques on water resources initiatives, process efficiency, equipment downtime, and hydrological modelling, this study synthesizes empirical data to examine the synchronized efficacy of these tactics. The results show that staff efficiency has increased, with an overall improvement of 10% and a significant 15% uptick in the Production division. Lean Water resources reduced waste by 12%, Six Sigma Implementation reduced defects by 18%, Predictive Maintenance reduced equipment downtime by 20%, and Supply Chain Optimization improved logistical efficiency by 10%; all of these initiatives demonstrated substantial positive outcomes as a result of continuous improvement. The efficacy of the techniques on reducing operational interruptions is shown by the equipment downtime log analysis, which shows a 25% decrease in downtime hours ascribed to Predictive Maintenance. Reductions of 15% in cycle times and 12% in defect rates are two examples of the impressive gains seen in process efficiency indicators. A complete optimization of water resources operations is shown by the cumulative percentage changes across projects and processes, which highlight the transforming power of strategic interventions. This research offers a path for businesses to create better productivity, resilience, and operational excellence by providing empirical insights into the interwoven symphony of continuous improvement.

Optimization Operation of Water Resources Using Game Theory and Marine Predator Algorithm

Optimization Operation of Water Resources Using Game Theory and Marine Predator Algorithm

Spatiotemporal Analysis of Extreme Rainfall and Meteorological Drought Events over the Angat Watershed, Philippines

Understanding the spatiotemporal distribution of extreme rainfall and meteorological drought on a watershed scale could be beneficial for local management of any water resources system that supports dam operation and river conservation. This study considered the watershed of Angat as a case, given its economic importance in the Philippines. A series of homogeneity tests were initially conducted on each rainfall dataset from monitoring stations in and near the watershed, followed by trend analysis to determine the rate and direction of change in the annual and seasonal rainfall extreme indices in terms of intensity, duration, and frequency. Three indices, using the rainfall deviation method (%DEV), percent of normal rainfall index (PNRI), and Standardized Precipitation Index (SPI), were also used to identify meteorological drought events. Generally, rainfall in the watershed has an increasing annual PCPTOT (4–32 mm/year), with increasing frequency and intensity in heavy rainfall and wet days. A significant increasing trend (α = 5%) in the seasonal PCPTOT (7–65 mm/year) and R10mm (1.7–10.0 days/decade) was particularly observed in all stations during the Amihan Monsoon Season (Dec–Feb). The observed increasing rainfall intensity and frequency, if it continues in the future, could have an implication both for the water resources operation to satisfy the multiple objectives of Angat Reservoir and for the flood operation that prevents damage in the downstream areas. The effect of each ENSO (El Niño- Southern Oscillation) phase on the rainfall is unique in magnitude, intensity, and duration. The seasonal reversal of the ENSO in the extreme rainfall and meteorological drought signals in Angat Watershed was also evident. The identified meteorological drought events in the watershed based on SPI-12 persisted up to 12–33 months, could reduce more than 60% (PNRI < 40%) of the normal rainfall. Insights from the study have implications for the hydrology of the watershed that should be considered for the water resources management of the Angat Reservoir.

Long-run forecasting surface and groundwater dynamics from intermittent observation data: An evaluation for 50 years

The accurate prediction of water dynamics is critical for operational water resource management. In this study, we propose a novel approach to perform long-term forecasts of daily water dynamics, including river levels, river discharges, and groundwater levels, with a lead time of 7–30 days. The approach is based on the state-of-the-art neural network, bidirectional long short-term memory (BiLSTM), to enhance the accuracy and consistency of dynamic predictions. The operation of this forecasting system relies on an in-situ database observed for over 50 years with records gauging in 19 rivers, the karst aquifer, the English Channel, and the meteorological network in Normandy, France. To address the problem of missing measurements and gauge installations over time, we developed an adaptive scheme in which the neural network is regularly adjusted and re-trained in response to changing inputs during a long operation. Advances in BiLSTM with extensive learning past-to-future and future-to-past further help to avoid time-lag calibration that simplifies data processing.The proposed approach provides high accuracy and consistent prediction for the three water dynamics within a similar accuracy range as an on-site observation, with approximately 3 % error in the measurement range for the 7 day-ahead predictions and 6 % error for the 30 d-ahead predictions. The system also effectively fills the gap in actual measurements and detects anomalies at gauges that can last for years. Working with multiple dynamics not only proves that the data-driven model is a unified approach but also reveals the impact of the physical background of the dynamics on the performance of their predictions. Groundwater undergoes a slow filtration process following a low-frequency fluctuation, favoring long-term prediction, which differs from other higher-frequency river dynamics. The physical nature drives the predictive performance even when using a data-driven model.

Enhanced rainfall prediction performance via hybrid empirical-singular-wavelet-fuzzy approaches

Rainfall is a vital process in the hydrological cycle of the globe. Accessing reliable and accurate rainfall data is crucial for water resources operation, flood control, drought warning, irrigation, and drainage. In the present study, the main objective is to develop a predictive model to enhance daily rainfall prediction accuracy with an extended time horizon. In the literature, various methods for the prediction of daily rainfall data for short lead times are presented. However, due to the complex and random nature of rainfall, in general, they yield inaccurate prediction results. Generically, rainfall predictive models require many physical meteorological variables and consist of challenging mathematical processes that require high computational power. Furthermore, due to the nonlinear and chaotic nature of rainfall, observed raw data typically has to be decomposed into its trend cycle, seasonality, and stochastic components before being fed into the predictive model. The present study proposes a novel singular spectrum analysis (SSA)-based approach for decomposing observed raw data into its hierarchically energetic pertinent features. To this end, in addition to the stand-alone fuzzy logic model, preprocessing methods SSA, empirical mode decomposition (EMD), and commonly used discrete wavelet transform (DWT) are incorporated into the fuzzy models which are named as hybrid SSA-fuzzy, EMD-fuzzy, W-fuzzy models, respectively. In this study, fuzzy, hybrid SSA-fuzzy, EMD-fuzzy, and W-fuzzy models are developed to enhance the daily rainfall prediction accuracy and improve the prediction time span up to 3 days via three (3) stations' data in Turkey. The proposed SSA-fuzzy model is compared with fuzzy, hybrid EMD-fuzzy, and widely used hybrid W-fuzzy models in predicting daily rainfall in three distinctive locations up to a 3-day time horizon. Improved accuracy in predicting daily rainfall is provided by the SSA-fuzzy, W-fuzzy, and EMD-fuzzy models compared to the stand-alone fuzzy model based on mean square error (MSE) and the Nash-Sutcliffe coefficient of efficiency (CE) model assessment metrics. Specifically, the advocated SSA-fuzzy model is found to be superior in accuracy to hybrid EMD-fuzzy and W-fuzzy models in predicting daily rainfall for all time spans. The results reveal that, with its easy-to-use features, the advocated SSA-fuzzy modeling tool in this study is a promising principled method for its possible future implementations not only in hydrological studies but in water resources and hydraulics engineering and all scientific disciplines where future state space prediction of a vague nature and stochastic dynamical system is important.

A Stacking Ensemble Model of Various Machine Learning Models for Daily Runoff Forecasting

Improving the accuracy and stability of daily runoff prediction is crucial for effective water resource management and flood control. This study proposed a novel stacking ensemble learning model based on attention mechanism for the daily runoff prediction. The proposed model has a two-layer structure with the base model and the meta model. Three machine learning models, namely random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGB) are used as the base models. The attention mechanism is used as the meta model to integrate the output of the base model to obtain predictions. The proposed model is applied to predict the daily inflow to Fuchun River Reservoir in the Qiantang River basin. The results show that the proposed model outperforms the base models and other ensemble models in terms of prediction accuracy. Compared with the XGB and weighted averaging ensemble (WAE) models, the proposed model has a 10.22% and 8.54% increase in Nash–Sutcliffe efficiency (NSE), an 18.52% and 16.38% reduction in root mean square error (RMSE), a 28.17% and 18.66% reduction in mean absolute error (MAE), and a 4.54% and 4.19% increase in correlation coefficient (r). The proposed model significantly outperforms the base model and simple stacking model indicated by both the Friedman test and the Nemenyi test. Thus, the proposed model can produce reasonable and accurate prediction of the reservoir inflow, which is of great strategic significance and application value in formulating the rational allocation and optimal operation of water resources and improving the breadth and depth of hydrological forecasting integrated services.

Regular and Equal Water Supply System

Water scarcity is a severe problem in many Indian villages and cities. The urban water supply system is under immense strain in order to provide enough drinkable water to India’s rapidly growing population. In order to achieve self-sufficiency, villages require a significant amount of pure or high-quality water delivery. For that we created water distribution systems that supply equal and of high-quality water. As a result, in our automated regular, equal, and quality water distribution unit, quality testing sensors and various pressure sensors are used. The water distribution system’s equitable and quality water supply automation is versatile. It may be deployed in different ways in villages and metropolitan areas, depending on customer needs. This automation is also achievable in residential, industrial, and commercial buildings and rural locations. It is an effective strategy for reducing water scarcity and preventing health problems because it monitors water quality. It is an interdisciplinary framework in which Electronic & Telecommunications (E&TC) and Information and Communications Technologies (ICT) are combined with the Internet of Things (IoT) to provide effective and useful operation of water resources, water distribution, and water quality.

ОБҐРУНТУВАННЯ ГЕОБЕЗПЕЧНИХ ТА ГЕОМЕХАНІЧНИХ ПАРАМЕТРІВ РОБО-ТИ ГІДРОТЕХНІЧНИХ СПОРУД, РЕГУЛЮВАННЯ СТОКУ ТА УПРАВЛІННЯ ВО-ДНИМИ РЕСУРСАМИ ШЛЯХОМ ГІДРОГЕОЛОГІЧНОГО МОДЕЛЮВАННЯ

Purpose. The purpose of this work is the development and verification of the adequacy of the methodology for assessing the hydrodynamic and geomechanical impact of various drainage and hydrotechnical structures on the environment over time, based on hydrogeological modeling, which allows to substantiate the geosafe parameters of their long-term operation and management of water resources. Methods. The proposed comprehensive approach includes: collection and analysis of geological and hydrogeological data on the research area; numerical modeling of hydrodynamic processes; solving inverse problems; execution of predictive calculations regarding changes in the level of groundwater under the influence of drainage and hydrotechnical structures; analytical calculations of subsidence of the earth’s surface caused by this. Findings. According to the results of solving the inverse problems, it was proved that the deviation of the calculated marks of the groundwater levels on the model from the actually established ones does not exceed 0.3 m with a value of the water balance imbalance of 0.001%, which confirms the adequacy of the model and the correctness of the task of filtration characteristics, external and internal hydrodynamic boundaries . The solution of model forecasting problems made it possible to establish that the construction of two ponds with an absolute water mark of 54.0 m and a horizontal drainage 600 m long with a level mark of 56-57 m in the studied area leads to a decrease in groundwater and the elimination of areas under -fillings with a depth of less than one meter. It has been established that with the lowering of the groundwater level caused by the work of hydraulic and drainage structures, the maximum possible subsidence of the soil is 1.5 cm, which does not exceed the allowable subsidence for all types of buildings. Originality. The methodology for assessing the hydrodynamic and geomechanical impact of drainage and hydrotechnical structures on the environment has been substantiated and verified. The proposed method makes it possible to quantitatively assess the hydrodynamic regime and geomechanical state of rocks during long-term operation of these structures in areas with a complex geological structure under the significant influence of natural and man-made factors. Practical implication. The proposed method can be used in the design of new hydrotechnical and drainage structures, as well as the evaluation of parameters of their geo-safe operation, which ensure flow regulation and prevention of flooding of territories.

Prediction of groundwater level fluctuations using artificial intelligence-based models and GMS

Groundwater level fluctuations are one of the main components of the hydrogeological cycle and one of the required variables for many water resources operation models. The numerical models can estimate groundwater level (GWL) based on extensive statistics and information and using complex equations in any area. But one of the most important challenges in analyzing and predicting groundwater depletion in water management is the lack of reliable and complete data. For this reason, the use of artificial intelligence models with high predictive accuracy and due to the need for less data is inevitable. In recent years, the use of different numerical models has been noticed as an efficient solution. These models are able to estimate groundwater levels in any region based on extensive statistics and information and also various field experiments such as pumping tests, geophysics, soil and land use maps, topography and slope data, different boundary conditions and complex equations. In the current research, first, by using available statistics, information and maps, the groundwater level fluctuations of the Sonqor plain are simulated by the GMS model, and the accuracy of the model is evaluated in two stages of calibration and validation. Then, due to the need for much less data volume in artificial intelligence-based methods, the GA-ANN and ICA-ANN hybrid methods and the ELM and ORELM models are utilized. The results display that the output of the ORELM model has the best fit with observed data with a correlation coefficient equal to 0.96, and it also has the best and closest scatter points around the 45 degrees line, and in this sense, it is considered as the most accurate model. To ensure the correct selection of the best model, the Taylor diagram is also used. The results demonstrate that the closest point to the reference point is related to the ORELM method. Therefore, to predict the groundwater level in the whole plain, instead of using the complex GMS model with a very large volume of data and also the very time-consuming process of calibration and verification, the ORELM model can be used with confidence. This approach greatly helps researchers to predict groundwater level variations in dry and wet years using artificial intelligence with high accuracy instead of numerical models with complex and time-consuming structures.

Towards Informed Water Resources Planning and Management

In Water Resources Planning and Management, decision makers, although unsure of future outcomes, must take the most reliable and assuring decisions. Deterministic and probabilistic prediction techniques, combined with optimization tools, have been widely used to meet the objective of improving planning as well as management. Bayesian decision approaches are available to link probabilistic predictions to optimized decision schemes, but scientists are not fully able to express themselves in a language familiar to decision makers, who fear basing their decisions on “uncertain” forecasts in the vain belief that deterministic forecasts are more informative and reliable. This situation is even worse in the case of climate change projections, which bring additional degrees of uncertainty into the picture. Therefore, a need emerges to create a common approach and means of communication between scientists, who deal with optimization tools, probabilistic predictions and long-term projections, and operational decision makers, who must be facilitated in understanding, accepting, and acknowledging the benefits arising from operational water resources management based on probabilistic predictions and projections. Our aim here was to formulate the terms of the problem and the rationale for explaining and involving decision makers with the final objective of using probabilistic predictions/projections in their decision-making processes.

Study of Water Resources Optimal Operation Model of Multireservoir: A Case Study of Kuitun River Basin in Northwestern China

Aiming at the problems that need to be solved urgently in the current operation of a multireservoir in Kuitun River Basin, such as the uneven distribution of water resources in time and space, the large workload of manual operation calculation, and low coordination level, the paper takes the optimal operation of water resources in the basin as the main goal and carries out the research on the optimal operation model of the multireservoir in combination with the complex characteristics of local water resources system. Firstly, based on the generalization of hydraulic engineering in Kuitun River Basin, a water resources optimal operation model of the multireservoir is established and is solved by the graph theory. Then, the actual data of typical years were selected to test the model. The test results show that, compared with the actual water distribution, the water shortage rate of 2015 and 2016 in high flow years decreased by 98.57% and 100%, respectively; the water shortage rate of 2013 and 2014 in normal flow years decreased by 92.65% and 96.38%, respectively; and the water shortage rate of 2009 in a low flow year decreased by 87.78%. The model can provide the optimal operation scheme for the optimal operation of the multireservoir in the basin. And it can solve the problems such as the uneven distribution of water resources and the large workload of manual operation calculation and can provide technical support for the optimal operation of water resources of the multireservoir in Kuitun River Basin in the future.

Assessment of Variation in Groundwater Quality and Level for Irrigation Using Long-Term Data

In Pakistan, the symbols of water stress are ubiquitous in the form of water resources depletion, water scarcity, quality, depletion of level and contamination. Water is a precious gift of God for all living and non-living things to complete the cycle of ecosystem. This research was conducted to check the trends groundwater level and its quality analysis using long term data of same study area. In this study area, groundwater quality and level data of 421 piezometric wells of 15 years were used to analyze through graphical analysis, from a region in Pakistan. The four major indicators for irrigation as recommended by Punjab Irrigation Department are; pH, EC, RSC and SAR. These parameters were used and appraisal for irrigation to fit or unfit the monitoring sites. The results indicate the significant increasing trend of level as well as quality of water. Average declining of groundwater table is 0.0963 meter per year in study area. As 20% of the groundwater depth is declining per year. Sites unfit ratio is 50% of the total monitoring sites being installed with respect to quality parameters. These trends indicate more water extraction in region that cause adverse effect on quality and level. This research is an excellent recommendation for water experts, to amend new law for operation of water resources and its extraction from groundwater through tubewells. The heavy metal concentration, hazardous waste to soil and sewerage of colonies should be supervised on daily basis.

Multi-objective Optimal Operation of Combined Cascade Reservoir and Hydrogen System

As a kind of zero-carbon and high-efficiency energy, hydrogen has attracted more attention with clean energy technology development. However, large-scale green hydrogen application is difficult to promote because of its high production cost. China is rich in water resources, but the utilization rate is low, which causes serious water abandonment problems. This article studies the optimal operation of combined cascade reservoir and hydrogen system to improve the utilization of water resources and the system's economic benefits. In this article, a multi-objective nonlinear model is established. An expanded progressive optimization algorithm is adopted to solve the problem more efficiently and accurately. The weights of objective functions are discussed with data from the Hongru River Basin in China. The rationality and effectiveness of combining cascade reservoir and hydrogen system are verified through the optimal operation of water resources and the optimal allocation of power and hydrogen. In addition, the overall economic benefits and reservoir operation efficiency are improved.

Filling and validating rainfall data based on statistical techniques and artificial intelligence

The study of the hydric regime of rainfall helps in management analysis and decision-making in hydrographic basins, but a fundamental condition is the need for continuous time series of data. Therefore, this study compared gap filling methods in precipitation data and validated them using robust statistical techniques. Precipitation data from the municipality of Itirapina, which has four monitoring stations, were used. Four gap filling techniques were used, namely: normal ratio method, inverse distance weighting, multiple regression and artificial neural networks, in the period from 1979 to 1989. For validation and performance evaluation, the coefficient of determination (R²), mean absolute error (MAE), mean squared error (RMSE), Nash-Sutcliffe coefficient (Nash), agreement index (D), confidence index were used (C) and through non-parametric techniques with Mann-Witney and Kruskal-Wallis test. Excellent performances of real data were verified in comparison with estimated data, with values above 0.8 of the coefficient of determination (R²) and of Nash. Kruskal-Wallis and Mann-Whitney tests were not significant in Stations C1 and C2, demonstrating that there is a difference between real and estimated data and between the proposed methods. It was concluded that the multiple regression and neural network methods showed the best performance. From this study, efficient tools were found to fill the gap, thus promoting better management and operation of water resources.
 Keywords: artificial neural networks, inverse distance weighting, multiple regression, normal ratio method.

SuperflexPy 1.3.0: an open-source Python framework for building, testing, and improving conceptual hydrological models

Abstract. Catchment-scale hydrological models are widely used to represent and improve our understanding of hydrological processes and to support operational water resource management. Conceptual models, which approximate catchment dynamics using relatively simple storage and routing elements, offer an attractive compromise in terms of predictive accuracy, computational demands, and amenability to interpretation. This paper introduces SuperflexPy, an open-source Python framework implementing the SUPERFLEX principles (Fenicia et al., 2011) for building conceptual hydrological models from generic components, with a high degree of control over all aspects of model specification. SuperflexPy can be used to build models of a wide range of spatial complexity, ranging from simple lumped models (e.g., a reservoir) to spatially distributed configurations (e.g., nested sub-catchments), with the ability to customize all individual model components. SuperflexPy is a Python package, enabling modelers to exploit the full potential of the framework without the need for separate software installations and making it easier to use and interface with existing Python code for model deployment. This paper presents the general architecture of SuperflexPy, discusses the software design and implementation choices, and illustrates its usage to build conceptual models of varying degrees of complexity. The illustration includes the usage of existing SuperflexPy model elements, as well as their extension to implement new functionality. Comprehensive documentation is available online and provided as a Supplement to this paper. SuperflexPy is available as open-source code and can be used by the hydrological community to investigate improved process representations for model comparison and for operational work.

WRSS: An Object-Oriented R Package for Large-Scale Water Resources Operation

Water resources systems, as facilities for storing water and supplying demands, have been critically important due to their operational requirements. This paper presents the applications of an R package in a large-scale water resources operation. The WRSS (Water Resources System Simulator) is an object-oriented open-source package for the modeling and simulation of water resources systems based on Standard Operation Policy (SOP). The package provides R users several functions and methods to build water supply and energy models, manipulate their components, create scenarios, and publish and visualize the results. WRSS is capable of incorporating various components of a complex supply–demand system, including numerous reservoirs, aquifers, diversions, rivers, junctions, and demand nodes, as well as hydropower analysis, which have not been presented in any other R packages. For the WRSS’s development, a novel coding system was devised, allowing the water resources components to interact with one another by transferring the mass in terms of seepage, leakage, spillage, and return-flow. With regard to the running time, as a key factor in complex models, WRSS outshone the existing commercial tools such as the Water Evaluation and Planning System (WEAP) significantly by reducing the processing time by 50 times for a single unit reservoir. Additionally, the WRSS was successfully applied to a large-scale water resources system comprising of 5 medium- to large-size dams with 11 demand nodes. The results suggested dams with larger capacity sizes may meet agriculture sector demand but smaller capacities to fulfill environmental water requirement. Additionally, large-scale approach modeling in the operation of one of the studied dams indicated its implication on the reservoirs supply resiliency by increasing 10 percent of inflow compared with single unit operation.