Basin Scale Water Resources Systems Modeling Under Cascading Uncertainties

Global change in climate and consequent large impacts on regional hydrologic systems have, in recent years, motivated significant research efforts in water resources modeling under climate change. In an integrated future hydrologic scenario, it is likely that water availability and demands will change significantly due to modifications in hydro-climatic variables such as rainfall, reservoir inflows, temperature, net radiation, wind speed and humidity. An integrated regional water resources management model should capture the likely impacts of climate change on water demands and water availability along with uncertainties associated with climate change impacts and with management goals and objectives under non-stationary conditions. Uncertainties in an integrated regional water resources management model, accumulating from various stages of decision making include climate model and scenario uncertainty in the hydro-climatic impact assessment, uncertainty due to conflicting interests of the water users and uncertainty due to inherent variability of the reservoir inflows. This paper presents an integrated regional water resources management modeling approach considering uncertainties at various stages of decision making by an integration of a hydro-climatic variable projection model, a water demand quantification model, a water quantity management model and a water quality control model. Modeling tools of canonical correlation analysis, stochastic dynamic programming and fuzzy optimization are used in an integrated framework, in the approach presented here. The proposed modeling approach is demonstrated with the case study of the Bhadra Reservoir system in Karnataka, India.     

基于贝叶斯统计与MCMC思想的水库随机优化调度研究

针对马尔柯夫随机动态规划中的维数灾问题,提出一种改进马尔柯夫随机动态规划方法,基于贝叶斯统计原理,采用马尔柯夫链蒙特卡洛方法(Markov Chain Monte Carlo,MCMC)从数学角度出发,推求出一定预报级别下的实际来流概率密度函数,建立与预报级别相关的实际来流概率矩阵,在考虑预报误差发生的情况下进行不确定性优化调度,并且将该方法计算结果与有无预报时段相结合的马尔柯夫随机动态规划方法计算结果进行比较。结果表明,该方法所得到的结果比马尔柯夫随机动态规划结果更加贴近实际多年平均发电量,并且能够有效地减少计算量,缩短计算时间,从一定程度上解决了维数灾问题,本方法为不确定性优化调度提供重要理论参考。     

Reliability-Based Pipe Size Computation of On-Demand Irrigation Systems

The variability of flow regimes in on demand pressurized irrigation systems leads to uncertainty in head at the nodes affecting the system performance, and thus it should be considered when designing and/or rehabilitating water distribution systems. Based on these considerations a new approach for the optimization of on demand pressurized irrigation systems is presented combining the minimization of cost with the maximization of reliability taking into account the stochastic variability of the flows into each section of the network. The new model, Clément and the cumulated random generated discharges model (FAO model) were applied to three pressurized irrigation networks of different dimensions (large, medium and small) operating on demand in Southern Italy. The optimization algorithm used in all the cases is the Labye iterative discontinuous method, a formulation of the dynamic programming. The results of the different models were compared showing that the cost of the optimal network calculated using the new model was reduced by more than 20%, without any significant decrease of the system reliability or reduction of the network capacity.     

黄河三角洲1961—2000年水资源时空变化特征

采用降水量、径流量、地下水位及埋深4个指标对1961—2000年水资源的时空变化特征进行分析。结果表明:①降水量年内及年际变化均较大,年内降水量集中于夏季,年际呈下降趋势,空间分布呈自南向北逐渐下降的趋势。②黄河入境径流量年内及年际变化均较大,根据趋势分析1986年之前径流量变化较平缓,之后呈线性下降趋势。当地径流量年内变化较大,夏季径流量超过全年的80%;年际变化较小,呈峰-谷-峰的变化趋势。③东营市浅层地下水位年内变化较小,其中广饶县年内及年际变化均较大,自20世纪80年代至21世纪初平均下降了12.78m。④广饶县地下水埋深自70年代至90年代增幅达到14.70m。1975—2000年广饶县地下水埋深呈显著线性增加趋势(R2=0.93),年均增速达到0.72m/a。     

Optimization of Industrial Structure Considering the Uncertainty of Water Resources

This paper developed a stochastic linear fractional programming model for industry optimization allocation base on the uncertainty of water resources incorporating chance constrained programming and fractional programming. In this paper, the stochastic linear fractional programming is used in the real word. The development SLFP has the following advantages: (1) The model can compare the two aspects of the targets; (2) The model can reflect the system efficiency intuitively; (3) The model can deal with uncertain issues with probability distribution; (4) The model can give different optimal plans under different risk conditions. The model has a significant value for the industry optimization allocation under uncertainty in local and areas to achieve the maximum economic benefits and the full use of the water resources.     

Benefit and Risk Balance Optimization for Stochastic Hydropower Scheduling

Limited by inflow forecasting methods, the forecasting results are so unreliable that we have to take their uncertainty and risk into account when incorporating stochastic inflow into reservoir operation. Especially in the electricity market, punishment often happens when the hydropower station does not perform as planned. Therefore, focusing on the risk of power generation, a benefit and risk balance optimization model (BRM) which takes stochastic inflow as the major risk factor is proposed for stochastic hydropower scheduling. The mean-variance theory is firstly introduced into the optimal dispatching of hydropower station, and a variational risk coefficient is employed to give service to managers’ subjective preferences. Then, the multi-period stochastic inflow is simulated by multi-layer scenario tree. Moreover, a specific scenario reduction and reconstruction method is put forward to reduce branches and computing time accordingly. Finally, the proposed model is applied to the Three Gorges Reservoir (TGR) in China for constructing a weekly generation scheduling in falling stage. Compared to deterministic dynamic programming (DDP) and stochastic dynamic programming (SDP), BRM achieves more satisfactory performance. Moreover, the tradeoffs for risk-averse decision makers are discussed, and an efficient curve about benefit and risk is formed to help make decision.     

Incorporating Economy and Long-term Inflow Forecasting Uncertainty into Decision-making for Agricultural Water Allocation during Droughts

Optimizing water allocations for the agricultural sector, the main water consumer, at the beginning of a period of drought is essential. However, long-term inflow forecasting, with its high uncertainty, is a necessary component of the allocation process. This paper presents a methodology that combines this uncertainty with economic factors to determine water allocation. The following models were developed and linked: optimization of agricultural water allocation under water scarcity, long-term flow forecast and quantification of forecast uncertainties. The approach coordinates economic values of water with system operational requirements. The Zayandeh Rud dam and irrigation system was selected to explore the methodology of this research.     

抽水蓄能电站及其系统的概率模拟与运行优化

为满足实际应用的要求，在随机生产模拟中运用负荷曲线分解技术和动态规划，提出了抽水蓄能电站及其系统的概率模拟与运行优化模型。模型以系统发电运行成本与缺电损失最小为目标，满足电站水库蓄水及库容限制条件，满足日或周的抽水—发电循环电力电量平衡条件，并考虑了抽水蓄能电站在抽水和发电方式下随机停运的影响。可应用于电源规划、发电计划和系统运行优化，可以更准确地模拟、分析和优化抽水蓄能电站及其系统的运行状况。通过算例对模型和应用进行了说明。     

An Inexact Two-stage Fuzzy-stochastic Programming Model for Water Resources Management

An inexact two-stage fuzzy-stochastic programming (ITFSP) method is developed for water resources management under uncertainty. Fuzzy sets theory is introduced to represent various punishment policies under different water availability conditions. As an extension of conventional two-stage stochastic programming (TSP) method, two special characteristics of the proposed approach make it unique compared with existing approaches. One is it could handle flexible penalty rates, which are much reasonable for both of the authorities and users, and have seldom been considered in the TSP framework. The other is uncertain information expressed as discrete intervals and probability distribution functions can be effectively reflected in the optimization processes and solutions. After formulating the model, a hypothetical case is employed for demonstrating its applicability under two scenarios, where the inflow is divided into four and eight intervals, respectively. The results indicate that reasonable solutions have been obtained. They provide desired allocation patterns with maximized system benefit under two feasibility levels. The solutions present as stable intervals with different risk levels in violating the water demands, and can be used for generating decision alternatives. Comparisons of the solution from the ITFSP with that from the ITSP (inexact two-stage stochastic programming) and TSP approach are also undertaken. It shows that the ITFSP could produce more system benefit than existing methods and deal with flexible penalty policies for better water management and utilization.     

Model-based Regional Assessment of Water Use An Example for Semi-arid Northeastern Brazil

Abstract

Water use assessments are a necessary prerequisite for sustainable water resources management and planning in river basins, federal states, or countries. For reasons of transparency, flexibility, ease of update, and the possibility to generate scenarios of future water use, such assessments are best carried out by applying a water use model. To support water resources planning in two federal states of semi-arid Northeastern Brazil, Ceará and Piauí, the regional-scale water use model NoWUM was developed. It computes withdrawal and consumptive water use for each of 332 municipalities, distinguishing five water use sectors: irrigation, livestock, households, industry, and tourism. The model is suited to simulate the impact of global change and of management measures on water demand. Using NoWUM, the present-day water use situation in Ceará and Piauí is assessed. In addition, the impact of inter-annual climate variability and long-term climate change on irrigation requirements is considered. Scarce and uncertain input data lead to a high level of uncertainty in the model results. It is likely that water use in the most important sector, irrigation, is underestimated, while industrial water use is possibly overestimated. With some modifications, NoWUM has the potential to be applied for water use assessments in other data-poor regions of the globe.     

An Adaptive Metropolis-Hastings Optimization Algorithm of Bayesian Estimation in Non-Stationary Flood Frequency Analysis

Global climate changing and human activities have altered the assumption of stationarity, and intensified the variation of hydrological process in recent decades. It is essential to make progress in accommodating appropriate models to the changing environment where non-stationary models are taken into account. The developing adapted Bayesian inference offers an attractive framework to estimate non-stationary models, when compared with conventional maximum likelihood estimation (MLE). As the inseparable companions of Bayesian inference, an efficient MCMC sampler are expected to be built. However, proper tunings are needed for the sampler to improve the performance by integrating adaptive algorithm and optimization method. A Bayesian approach with the adaptive Metropolis-Hastings optimization (AM-HO) algorithm is adopted to estimate the parameters and quantify the uncertainty in a two-parameter non-stationary Lognormal distribution model. To verify the performance of the developed model, simulation experiments and practical applications are implemented to fit annual maximum flood series of two gauges in Hanjiang River basin. From the point view of parameters estimation, both Bayesian and MLE methods perform similarly. However, Bayesian method is more attractive and reliable than MLE on uncertainty quantification, which provides a relative narrow intervals to be beneficial for risk analysis and water resource management.     

Two-step daily reservoir inflow prediction using ARIMA-machine learning and ensemble models

The reservoirs play a crucial role in the development of civilisation as they facilitate the storage of water for multiple purposes like hydroelectric power generation, flood control, irrigation, and drinking water etc. In order to effectively meet these multiple purposes, the knowledge of the inflow in the reservoir is essential. Apart from the historical data, future prediction of the inflows is also necessary specially in context of climate change. A two-step algorithm for the prediction of reservoir inflow to enable meticulous planning and execution of daily reservoir operation keeping the historical variation of inflow in account has been proposed. The developed algorithm takes into account the patterns in the historic inflow data using the time series analysis along with the variability in the climatic patterns using the different predictors in the machine learning model. The first step uses time series model, ARIMA method to forecast the monthly inflows, which are then used as the targets in the second step for the month-wise daily forecasting of the inflows using the two types of ensemble models, namely, averaging and boosting models in machine learning. The test results show that for both the monthly models and daily models the NRMSE and NMAE values were low for the monsoon periods compared to the non-monsoon periods. The averaging ensemble models were found to perform better than the boosting ensemble models for maximum number of months. The yearly results show an error of less than 5% between actual and predicted values for all the test cases, showing the precision in the developed algorithm. Further, the uncertainty analysis shows that the prediction done using the weighted average of the different inflow scenarios performs better than the prediction against the single inflow scenario.     

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.     

Optimization of Nebhana Reservoir Water Allocation by Stochastic Dynamic Programming

This study is devoted to the identification of an optimal rule that would permit to improve the water resources management of dam in arid condition. The Nebhana dam is considered in this study as a representative of a set dams situated in such condition. The water storage is used for irrigation purpose. The identification of an optimal rule is based on two opposite objectives: the satisfaction of the irrigation water demand and the safeguard of a minimal water storage in the dam. By considering different weights for these objectives, the stochastic dynamic programming technique was lead to various optimal rules for the water resources management of the Nebhana dam. This technique takes into account the variability of the volume of water inflow to the dam on the basis of their occurrence probability; the water losses by means of forecasting models and the water resources goals using weight coefficients. The identified optimal rule would permit to estimate the necessary water release volume for irrigation by considering the water storage and the decision period.     

Optimal Long-term Operation of Reservoir-river Systems under Hydrologic Uncertainties: Application of Interval Programming

Long-term basin-wide reservoir-river operation optimization problems are usually complex and nonlinear especially when the water quality issues and hydrologic uncertainties are incorporated. It is due to non-convex functions in water quality modeling and a large number of computational iterations required by most of stochastic programming methods. The computational burden of uncertainty modeling can be reduced by a special combination of uncertainty modeling and interval programming, though the problem solution is still a challenge due to model nonlinearity. In this paper, an integrated water quantity-quality model is developed for optimal water allocation at river-basin scale. It considers water supply and quality targets as well as hydrologic, water quality and water demand uncertainties within the nonlinear interval programming (NIP) framework to minimize the slacks in water supply and quality targets during a long-term planning horizon. A fast iterative linear programming (ILP) method is developed to convert the NIP into a linear interval programming (LIP). The ILP resolves two challenges in NIP, first converting the large non-linear programming (NLP) into a linear programming (LP) with minimum approximation and second reducing the iterations needed in interval programming for NLP into just two iterations for the upper and lower limits of decision variables. This modeling approach is applied to the Zayandehrood river basin in Iran that has serious water supply and pollution problems. The results show that in this river basin at dry conditions when available surface water resources are below 85 % of normal hydrologic state and water demands are 115 % of current water demands, the total dissolved solids (TDS) concentration can be reduced by 50 % at the inlet of the Gavkhuni wetland located downstream of the river basin.     

明渠—前池—有压引水系统水力瞬变过程计算研究

针对水电站明渠—前池—有压引水系统水力瞬变过程中明渠段和有压段时间步长相差大、计算过程复杂等问题,提出了有限差分法和特征线法相结合的计算方法。对明渠和有压引水系统分别采用有限差分法与特征线法计算,把前池当集中元件处理,联合水轮机、调速器计算模型,采用统一的时间步长,在Simulink中采用模块化编程,进行过渡过程计算。将模型应用于MZD水电站水力瞬变过程计算,得到关键节点调压室的最高涌浪水位计算值为2 178.96 m,而物理试验值为2 178.57 m,二者非常接近。调压室水位波动过程线与试验结果亦吻合良好。研究表明,有限差分法和特征线法相结合,实现了明渠段和有压段时间步长的统一,降低了计算的复杂程度,可用于明渠—前池—有压引水系统设计。     

漳卫南灌区农业水资源优化配置研究

考虑到不确定条件下漳卫南灌区农业水资源管理的复杂性,为了解决当灌区水资源用户供水目标不能满足需求时的水资源优化配置问题,结合LFP模型与TSP模型的优点,开发了一种分式两阶段随机规划模型(FTSP)。选择漳卫南灌区最大控制性工程岳城水库的两个大型供水灌区作为验证实例,模型应用结果表明,不同决策情景所对应的经济效益和缺水风险不同,最优决策实现了经济效益和缺水风险之间的平衡;不同径流水平下,各用户的正常灌溉面积会发生相应变化,高径流水平时所有用户均能得到正常灌溉。     

Monthly Joint Operations for the Nakdong Multi-reservoir System in Korea

Abstract

This study applies a state-of-art optimization technique, SSDP/ESP (Sampling Stochastic Dynamic Programming with Ensemble Streamflow Prediction), to derive a monthly joint operating policy for the Nakdong multi-reservoir system in Korea. A rainfall-runoff model, SSARR (Streamflow Synthesis And Reservoir Regulation), is linked to the SSDP/ESP model to provide ESP scenarios for runoff during the next month in the Nakdong River basin. The primary advantage of the SSDP/ESP is that it updates the derived operating policy as new ESP forecasts become available. Another SSDP model that employs historical runoff scenarios (SSDP/Hist) is also developed. The main difference between the two SSDP models is that SSDP/Hist is an off-line model whereas the SSDP/ESP is on-line. The developed operating policies are tested with a simulation model using an object-oriented simulation software, STELLA. The simulation results show that SSDP/ESP is superior to SSDP/Hist with respect to the water supply criterion, although both models perform similarly with respect to the hydroelectric energy production criterion.     

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.     

石家庄平原区浅层地下水位变化研究

通过对石家庄平原区浅层地下水位动态特征分析,揭示了地下水位年际及年内变化规律及其主要影响因素(开采和降水)。结果表明,石家庄平原区地下水位年际下降明显,但不同月份的地下水位年际下降速率不等,雨季前的1月-5月年际平均下降幅度比雨季及其之后的6月-12月年际平均下降幅度大。地下水位的变化主要是受到开采量和降水量的共同作用。在年际变化上,开采量增大导致地下水位下降;而降水量对地下水位的年际影响主要表现在丰水年时地下水位出现回升或下降速度减缓,枯水年时地下水位下降速度增加。在年内变化上,3月份之后开采量增加和降水量较小导致地下水位下降较快,而从6月份开始降水量增大和开采量减小导致地下水位开始回升。