Application of the Colorado River Simulation System Model to Evaluate Water Shortage Conditions in the Central Arizona Project

Central Arizona Project (CAP) is currently enhancing its water resources modeling capabilities to improve water resources management and planning activities and to better understand the inherent complexities of the Colorado River Basin system. CAP modeling activities in the Colorado River Basin extensively utilize the Colorado River Simulation System (CRSS) model. CRSS is a sophisticated object-oriented surface water model developed under the RiverWare modeling environment and maintained by the United States Bureau of Reclamation (USBR). CRSS incorporates important aspects of the Colorado River Basin: main stem, reservoirs along the river, water inflows to the river, and points of water deliveries. By using the object-oriented and rule-based capabilities of RiverWare, CRSS has embedded the rules of the Law of the River. These set of rules guide the operation and management of the Colorado River Basin’s surface water supply. This analysis executes CRSS short term simulations to evaluate the vulnerability of water deliveries to CAP from the Colorado River under different extreme hydrological and policy conditions. In the future, this type of analysis will provide key input for other CAP models, aimed to improve a quantitative understanding of the impacts of different uncertain and complex scenarios: drought conditions, future user demand behavior, reservoir operation, and optimize water recovery as a part of Arizona Water Bank Authority (AWBA), among others.     

梯级水库集中排沙与应用

水库泥沙淤积问题已受到越来越多的电力生产和水库管理人员的关注.重点介绍了龚嘴水库来水、来沙特点,结合龚嘴梯级水库的具体情况,分析了梯级水库联合排沙的调度方法,着重介绍了基于水情预测的集中降水位排沙的调度方式在龚嘴梯级中的应用.     

Investigation of the Behavior of an Agricultural-Operated Dam Reservoir Under RCP Scenarios of AR5-IPCC

In regions where the Mediterranean climate prevails, the agricultural sector and agricultural-operated dam reservoirs are threatened by climate change. In this respect, the prediction of hydro-meteorological changes that may occur in surface water resources under climate change scenarios is essential to examine the sustainability of reservoirs. In this paper, Demirköprü reservoir in the Gediz Basin/Turkey, a reservoir operated for irrigation purposes, was analyzed against the RCP4.5 and RCP 8.5 scenarios specified in the AR5 report of the IPCC. Projection period was evaluated as 2016-2050 water year period. First, statistical downscaling, Bayesian model averaging and quantile delta mapping bias correction techniques were respectively applied to monthly total precipitation and monthly average temperatures of meteorological stations in the region using 12 GCMs. According to RCP4.5 and RCP8.5, negligible reductions in precipitation are foreseen, while significant increases of 1.3 and 1.8 °C, respectively, are projected for temperatures under the same scenarios. Following the calibration of rainfall-runoff models for the sub-basins feeding the reservoir, streamflow simulations were also performed with projected precipitation and temperatures. In particular, according to the RCP 8.5 scenario, reservoir inflows during the period 2016-2050 could be reduced by 21% compared to the reference scenario results. Finally, the projected crop water demands and hydro-meteorological changes are evaluated together and the reservoir performances are examined using various indices. Assuming that the performance of the past irrigation yields will not change in the future, it is foreseen that reservoir’s sustainability will decrease by 16% under the RCP8.5 scenario. Even if the irrigation efficiency is increased by 40%, the reservoir cannot reach past sustainability characteristics.     

Operating Rules Derivation of Jinsha Reservoirs System with Parameter Calibrated Support Vector Regression

The reservoir optimal operation depends on not only specific characteristics of reservoirs and hydropower stations but also stochastic inflows. The key issue of actual hydropower operation is to make an approximate optimal decision triggered by limited inflow forecasts. To implement actual optimal operation of hydropower system with limited inflows forecast, this paper makes use of Support Vector Regression (SVR) to derive optimal operating rules. To improve the performance of SVR, parameters in SVR model are calibrated with grid search and cross validation techniques. The trained SVR model describes the complex nonlinear relationships between reservoir operation decisions and factors by considering both generalization and regression performance, which overcomes local optimization and over fitting deficits. Hybrid programming platform is further developed to implement system simulation. This SVR model along with simulation platform is applied to the largest hydropower base in China – Jinsha system. Three scenarios are developed for comparison: deterministic optimal operation, SVR based simulation with calibrated parameters, SVR based simulation with default parameters. Comprehensive evaluation indicates that, operating rules derived from SVR presents a reliable performance in system power generation and output processes with respect to ideal deterministic results, especially when the parameters are calibrated. Hybrid programming technique provides a feasible and compatible platform for future research.     

Effect of Hedging on Over-Year Reservoir Performance

Based on a detailed Monte-Carlo simulation, the effects of hedging parameters namely, starting water availability (SWA), ending water availability (EWA) and hedging factor (HF) on reservoir storage performance indicators have been investigated within the storage-yield plane of over-year reservoirs. Also, trade-off relationships between the various storage performance indicators are developed and selection of reasonable compromising hedging policies based on performance criteria is attempted for over-year reservoirs. Regions within the storage-yield plane of over-year reservoirs where hedging would be effective are identified. This would help the reservoir managers in mitigating the severity during long stretched critical drought periods.     

A Modelling Approach to Forecast the Effect of Climate Change on the Tagus-Segura Interbasin Water Transfer

This study was conducted in the upper Tagus River basin (UTRB), whose available water resources are partially transferred from the Entrepeñas and Buendía reservoirs after local needs satisfaction to the Segura River basin using the Tagus-Segura water transfer (TSWT), the largest hydraulic infrastructure in Spain. This study evaluates the climate change impact on the TSWT by considering future evaporation rates and bathymetric changes in the Entrepeñas and Buendía reservoirs. The findings of this study indicate a consistent decline in precipitation and an increase in temperature and evaporation under all climate impact scenarios. Consequently, inflows to the reservoirs will decline by 19% (RCP 4.5) and 53% (RCP 8.5) for 2070–2099, which could reduce water volumes that could be transferred to the Segura basin by more than 60%. The simulation of the TSWT operation rules, taking into account the impact of future evaporation and bathymetric changes, demonstrates an additional increase in reductions of water transfer of around 4%, which reveals the need to consider these effects in hydrological planning.

     

水库群系统发电调度的合作博弈研究

水资源的竞争性和非排他性导致水库管理者基于个体利益进行发电调度,使得水库在满足个体利益的同时往往忽略了系统的整体效益。为了在保证个体利益的基础上实现系统总效益的最大化,建立了梯级水库群发电调度合作博弈模型;采用改进后的水循环算法对模型进行分层求解。以金沙江两库与三峡梯级构成的梯级水库群为研究对象,选取典型年进行实例计算。计算结果表明：梯级水库群发电调度的合作博弈模型在获得系统最大效益的同时使得个体利益达到Pareto最优状态,实现水库群总效益和单库个体效益的双赢,既优于联合优化调度模型又优于单库优化调度模型。该合作博弈模型及其新解法可为水库群调度决策分析开创一种新思路。     

Multivariate Bayesian Regression Approach to Forecast Releases from a System of Multiple Reservoirs

This research presents a model that simultaneously forecasts required water releases 1 and 2 days ahead from two reservoirs that are in series. In practice, multiple reservoir system operation is a difficult process that involves many decisions for real-time water resources management. The operator of the reservoirs has to release water from more than one reservoir taking into consideration different water requirements (irrigation, environmental issues, hydropower, recreation, etc.) in a timely manner. A model that forecasts the required real-time releases in advance from a multiple reservoir system could be an important tool to allow the operator of the reservoir system to make better-informed decisions for releases needed downstream. The model is developed in the form of a multivariate relevance vector machine (MVRVM) that is based on a sparse Bayesian regression model approach. With this Bayesian approach, a predictive confidence interval is obtained from the model that captures the uncertainty of both the model and the data. The model is applied to the multiple reservoir system located in the Lower Sevier River Basin near Delta, Utah. The results show that the model learns the input–output patterns with high accuracy. Computing multiple-time-ahead predictions in real-time would require a model which guarantees not only good prediction accuracy but also robustness with respect to future changes in the nature of the inputs data. A bootstrap analysis is used to guarantee good generalization ability and robustness of the MVRVM. Test results demonstrate good performance of predictions and statistics that indicate robust model generalization abilities. The MVRVM is compared in terms of performance and robustness with another multiple output model such as Artificial Neural Network (ANN).     

Integrated Methodological Framework for Assessing the Risk of Failure in Water Supply Incorporating Drought Forecasts. Case Study: Andean Regulated River Basin

Hydroclimatic drought conditions can affect the hydrological services offered by mountain river basins causing severe impacts on the population, becoming a challenge for water resource managers in Andean river basins. This study proposes an integrated methodological framework for assessing the risk of failure in water supply, incorporating probabilistic drought forecasts, which assists in making decisions regarding the satisfaction of consumptive, non-consumptive and environmental requirements under water scarcity conditions. Monte Carlo simulation was used to assess the risk of failure in multiple stochastic scenarios, which incorporate probabilistic forecasts of drought events based on a Markov chains (MC) model using a recently developed drought index (DI). This methodology was tested in the Machángara river basin located in the south of Ecuador. Results were grouped in integrated satisfaction indexes of the system (DSI_G). They demonstrated that the incorporation of probabilistic drought forecasts could better target the projections of simulation scenarios, with a view of obtaining realistic situations instead of optimistic projections that would lead to riskier decisions. Moreover, they contribute to more effective results in order to propose multiple alternatives for prevention and/or mitigation under drought conditions.     

Optimal Hydropower Generation Under Climate Change Conditions for a Northern Water Resources System

This paper examines climate change impacts on the water resources system of the Manicouagan River (Québec, Canada). The objective is to evaluate the performance of existing infrastructures under future climate projections and the associated uncertainties. The main purpose of the water resources system is hydropower production. A reservoir optimization algorithm, Sampling Stochastic Dynamic Programming (SSDP), was used to derive weekly operating decisions for the existing system subject to reservoir inflows reflecting future climate, for optimum hydropower production. These projections are simulations from the SWAT hydrologic model for climate change scenarios for the period from 2010 to 2099. Results show that the climate change will alter the hydrological regime of the study area: earlier timing of the spring flood, reduced spring peak flow, and increased annual inflows volume in the future compared to the historical climate. The SSDP optimization algorithm adapted the operating policy to the future hydrological regime by adjusting water reservoir levels in the winter and spring, and increasing the release through turbines, which in the end increased power generation. However, there could be more unproductive spills for some power plants, which would decrease the overall efficiency of the existing water resources system.     

Modeling the Yield–Evaporation–Spill in the Reservoir Storage Process: The Regulation Triangle Diagram

From the dimensionless reservoir water budget equation, a graphical method to model the yield–spill–evaporation loss trade-off in the reservoir storage process was built. The reservoir inflows were transformed into three parts that sum to the total mean inflow for long-term operation: evaporation, spill and yield. A regulation triangle diagram (RTD) has been proposed to provide a better understanding of the reservoir storage process as a function of reservoir capacity, hydrological river regime, evaporation and reservoir morphology. The inflows were assumed to be serially uncorrelated and to originate from a Gamma probability distribution function. The diagrams were developed using the Monte Carlo method, while the graphics were developed for intermittent rivers with a coefficient of variation of annual inflows that ranges from 0.6 to 1.6. In the model, the reservoir is a single over-year system, and the values are referenced to the steady state conditions.     

Multi-Reservoir Operation Planning Using Hybrid Genetic Algorithm and Linear Programming (GA-LP): An Alternative Stochastic Approach

Many models have been suggested to deal with the multi-reservoir operation planning stochastic optimization problem involving decisions on water releases from various reservoirs in different time periods of the year. A new approach using genetic algorithm (GA) and linear programming (LP) is proposed here to determine operational decisions for reservoirs of a hydro system throughout a planning period, with the possibility of considering a variety of equally likely hydrologic sequences representing inflows. This approach permits the evaluation of a reduced number of parameters by GA and operational variables by LP. The proposed algorithm is a stochastic approximation to the hydro system operation problem, with advantages such as simple implementation and the possibility of extracting useful parameters for future operational decisions. Implementation of the method is demonstrated through a small hypothetical hydrothermal system used in literature as an example for stochastic dual dynamic programming (SDDP) method of Pereira and Pinto (Pereira, M. V. F. and Pinto, L. M. V. G.: 1985, Water Res. Res. 21(6), 779–792). The proposed GA-LP approach performed equally well as compared to the SDDP method.     

Considering precipitation forecasts for real-time decision-making in hydropower operations

This paper presents a new decision-making strategy for hydropower operations to handle uncertainty of forecasting precipitation. This strategy takes into account three basic components: uncertainty of precipitation, operation policies and a risk-evaluation model. In real-time operation, precipitations with different probabilities at different forecasting levels are obtained, and these precipitations are applied to forecast inflows using a hydrological forecasting model. Based on the forecasting inflows, the operation policies and risks with different probabilities are obtained. This study implements China’s Huanren reservoir and medium-term precipitation forecasts from the Global Forecast System to study the efficiency and stability of this strategy.     

An Efficient Method to Correct Under-Dispersion in Ensemble Streamflow Prediction of Inflow Volumes for Reservoir Optimization

Ensemble streamflow prediction (ESP) has been widely used to gain insight on possible future inflows to hydropower reservoirs. However underestimation of climate, model structure and initial condition uncertainty often leads to under-dispersed ESP forecasts. In this paper, we present a novel approach called “Hindcast-mode Uncertainty Estimation” (HUE) to efficiently add variability in ESP forecasts to reduce their under-dispersion. The method was tested on a Canadian catchment used by Rio Tinto – Aluminium division to produce hydropower for their aluminium smelting plants. This project was focused on correcting long-term predictions of freshet runoff volumes to optimize drawdown volumes, with up to 6 months of lead time. It was found that by adding an error term to the hydrological model’s snow water equivalent (SWE) state variable at the time of forecast in hindcasting mode, the resulting simulation could be forced to perfectly reproduce the freshet runoff volume. This error term was computed for all years on record which enabled modeling of the error’s distribution. This distribution can then be sampled from to add noise to the model’s SWE at the start of a new ESP forecast. Results show that the current winter ESP forecasts are strongly under-dispersed for the freshet runoff volume estimation and that the proposed method is able to widen the ESPs to correct the under-dispersion problem. This was validated by using Talagrand diagrams which shifted from a U-shape (prior to HUE) to a uniform distribution (with HUE). The project objectives of correcting the ESP forecast’s under-dispersion in spring runoff estimations was thus attained with minimal effort, bypassing the need to perform more complex ensemble data assimilation techniques.     

Reservoir flood control operation model incorporating multiple uncontrolled water flows

This study presents a weighted pre‐emptive goal programming model formulation for coordinated reservoir operation, with easy inclusion of uncontrolled water flows. The model is combined with a multiple water inflows forecasting model, and can be used for real time reservoir operation. Water flow routing from various upstream sites is accounted by with a single compact equation. Integration of controlled and uncontrolled water flows in the optimization model simplifies the operation model, resulting in accurate computation of the downstream water flow. Multiple objectives with water storage and flow variables are used to derive optimal regulation for a reservoir system under flood conditions. For real time operations, the model can be used to determine optimal water release rates for a current period, on the basis of an optimal water release schedule for an operating horizon (T). The model is applied to the flood control operation of reservoirs in the Narmada River Basin (India), with three controlled and three uncontrolled water flows affecting the downstream flow at Hoshangabad. Reservoir water storage and downstream control point flows are zoned, with prioritized objectives used to derive the optimal water release rates. Model applications to the 1999 flood event in the Narmada River Basin with observed and forecasted inflows illustrates that, if water inflows were known through a forecasting technique well in advance, the coordinated operation of the reservoirs could substantially reduce the peak water flows at the control points. The study also indicates that uncontrolled channel flows at the damage site were sufficiently high to cause flooding at the damage site.     

Benin 2025—Balancing Future Water Availability and Demand Using the WEAP ‘Water Evaluation and Planning’ System

In Benin, annual water availability per capita far exceeds the critical threshold of about 1,700 m³, but during the dry season, water scarcity occurs at the local scale. By modeling the water balance of the Ouémé–Bonou catchment with WEAP (Water Evaluation and Planning System), this study aimed at analyzing Benin’s future water situation under different scenarios of socio-economic development and climate change until 2025. The results show that the pressure on Benin’s water resources will increase, leading to greater competition for surface water. Furthermore, financial and technological constraints hinder a satisfactory development, and exploration of groundwater and reservoir resources. However, improvements are most needed, especially in rural areas. Decreasing inflows and groundwater recharge due to climate change aggravate this situation. Even though there are uncertainties and constraints concerning the model and input data, this study shows that the WEAP results offer a solid basis to assist planners in developing recommendations for future water resource management by revealing hot spots of action.     

Reservoir Management Using Coupled Atmospheric and Hydrological Models: The Brazilian Semi-Arid Case

This study investigated the sensitivity of a dynamic downscaling atmospheric model system coupled with a rainfall-runoff model to hindcast an example of reservoir water management in the semi-arid region of Northeast Brazil (NEB). A regional atmospheric spectral model (RSM) is driven by the outputs of an atmospheric general circulation model (AGCM), itself forced by the observed sea surface temperature over the World Ocean. Daily precipitation simulated by the RSM was then used as the input to a hydrological rainfall-runoff model for the Upper Jaguaribe River Basin to estimate inflows at the Orós Reservoir in the state of Ceará. A hindcast analysis of precipitation was performed during the rainy season over NEB (January to June) from 1971 to 2000. The RSM captured the precipitation variability relatively well when a probability density function (PDF) was used to correct the numerical bias. Three hindcast series of inflow using (i) the observed rainfall, (ii) the simulated rainfall before the PDF correction, and (iii) the simulated rainfall after the PDF correction were performed during the study period and then compared to the series of observed inflow. The atmospheric-rainfall-runoff “cascade” model efficiency was evaluated by comparing the Orós Reservoir release decisions from different scenarios based on observed, simulated (RSM, RSM-PDF), and mean historical reservoir inflows. The cascade model has the potential, relatively well balanced during dry, normal or wet years, to be a useful tool to correctly forecast the decision managements of reservoirs in the semi-arid region of NEB. Additional progress in the numerical simulation is however necessary to improve the performance.     

Optimal Allocation of Flood Control Capacity for Multi-Reservoir Systems Using Multi-Objective Optimization Approach

Operation of multi-reservoir systems during flood periods is of great importance in the field of water resources management. This paper proposes a multi-objective optimization model with new formulation for optimal operation of multi-reservoir systems. In this model, the release rate and the flood control capacity of each reservoir is considered as decision variable and the resulting nonlinear non-convex multi-objective optimization problem is solved with ε-constraint method through the mixed integer linear programming (MILP). Objective functions of the model are minimizing the flood damage at downstream sites and the loss of hydropower generation. The developed model is used to determine optimal operating strategies for Karkheh multi-reservoir system in southwestern Iran. For this purpose, the model is executed in two scenarios based on “two-reservoir” and “six-reservoir” systems and for floods with return periods of 25 and 50 years. The results show that in two-reservoir system, flood damage is at least about 114 million dollars and cannot be mitigated any further no matter how hydropower generation is managed. But, in the case of developing all six reservoirs, optimal strategies of coordinated operation can mitigate and even fully prevent flood damage.     

Assessing the effects of non-stationarity on reservoir yield estimations: A case study of the Southern Okavango Integrated Water Development system in Botswana

Streamflow data used for water resources planning should ideally be stationary, and any non-stationary behaviour is taken into account. However, with limited time series data, the influence of non-stationarity is often hidden and can result in unreliable estimates. This paper examines the impact of non-stationarity on the Southern Okavango Integrated Water Development (SOIWD) project that was carried out with streamflow collected between 1969 and 1989 against an extended time series from 1990 to 2019. To achieve this, (a) the statistics of these inflows and (b) the reliability of satisfying water demands from the proposed reservoirs were tested based on the two periods (1969–1989 and 1990–2019). The results show that average monthly flows for July, August and September significantly change when peak outflows from the delta occur. Given the expected variability of the flow regime, an uncertainty approach utilising flow perturbation from ±5% to ±90% was also used to investigate the system's response to changes in the driving flow conditions. The increasing availability of flow data from 1990 to 2019 has shown that the SOIWD system would not have satisfied the water demand as it would not be operationally viable. This confirms the importance of accounting for non-stationarity in reservoir yield estimation and reemphasises its importance in hydrological studies.     

Simulation and Optimization of Multi-Reservoir Operation in Inter-Basin Water Transfer System

Inter-basin water transfer projects are usually considered as one of the most effective facilities to balance the non-uniform temporal and spatial distribution of water resources and water demands by diverting water from surplus to deficient area. However, the operation of these projects are always daunting, especially for projects with multi-donor reservoirs but only one recipient reservoir. In this study, a set of water transfer rule curves are firstly proposed to determine when, where and how much water should be diverted from each donor reservoir. In addition, a simulation-optimization model with the objective to minimize both water shortage risk and vulnerability is established to derive the optimal operation rule curves. Following that, the new transfer rules are applied to provide guidelines for the operation of a water transfer-supply project with two donor reservoirs in central China. The effects of water diversion on each reservoir are evaluated under different scenarios including no diversion, diversion from the donor reservoir with relatively sufficient water, diversion from the donor reservoir with relatively limited water, and diversion from both donor reservoirs. The results show the advantages of improving the performance of whole water diversion system and demonstrate the feasibility of the proposed approach.