Evaluating the Impacts of a Large-Scale Multi-Reservoir System on Flooding: Case of the Huai River in China

The extensive constructions of reservoirs change the hydrologic characteristics of the associated watersheds, which increases the complexity of watershed flood control decisions. By evaluating the impacts of the multi-reservoir system on the flood hydrograph, it becomes possible to improve the effectiveness of the flood control decisions. This study compares the non-reservoir flood hydrograph with the actual observed flood hydrograph using the Lutaizi upstream of Huai River in East China as a representative case, where 20 large-scale/large-sized reservoirs have been built. Based on the total impact of the multi-reservoir system, a novel strategy is presented to evaluate the contribution of each reservoir to the total impact. According their contributions, the “highly effective” reservoirs for watershed flood control are identified via hierarchical clustering. Moreover, the degree of impact of the reservoir operation rules on the flood hydrograph are estimated. We find the multi-reservoir system of Huai River has a significant impact on flooding at the Lutaizi section, on average reducing the flood volume and peak discharge by 13.92 × 10⁸ m³ and 18.7% respectively. Under the current operation rules, the volume and peak discharge of flooding at the Lutaizi section are reduced by 13.69 × 10⁸ m³ and 1429 m³/s respectively. Each reservoir has a different impact on the flood hydrograph at the Lutaizi section. In particular, the Meishan, Xianghongdian, Suyahu, Nanwan, Nianyushan and Foziling reservoirs exert a strong influence on the flood hydrograph, and are therefore important for flood control on the Huai River.     

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.     

梯级水电站群中长期优化调度的离散梯度逐步优化算法

充分利用现有水电资源,进行库群中长期优化调度是构建清洁低碳、安全高效的现代能源体系的重要措施。逐步优化算法(POA)将多阶段问题转化为多个两阶段子优化问题,是求解中长期库群优化调度较为广泛且有效的一种方法。但随着水库数目的增加,POA仍会面临严重的"维数灾"问题。本文以梯度下降法为基础,提出离散梯度的概念及离散梯度逐步优化算法(DGPOA),该方法在不直接求导的情况下充分利用局部离散梯度信息确定最优搜索方向,可以快速获得优化结果。最后将该算法应用到澜沧江流域五水库梯级系统中,在不同离散精度和来水条件下,利用POA、POA-DPSA和DGPOA算法对梯级水库进行优化计算。结果表明,在不显著降低全局搜索能力的情况下,DGPOA的计算速度分别达到了POA-DPSA算法的8~12倍,POA算法的50~250倍,是一种解决梯级水库站群中长期优化调度中"维数灾"问题的有效方法。     

Investigation the Effect of Using Variable Values for the Parameters of the Linear Muskingum Method Using the Particle Swarm Algorithm (PSO)

Flood routing is a technique to determine the flood hydrograph at a point of downstream where is of great importance and flood-induced risks can cause irreparable damages. Routing methods can be classified into two categories: hydraulic routing and hydrologic routing. Hydrologic methods are less accurate than hydraulic methods but they are widely used for engineering of rivers due to simplicity and being acceptable. Muskingum is a simple, widely used hydrologic method in the flood routing. In present study, accuracy of the linear Muskingum method has been evaluated using the Particle Swarm Optimization (PSO) algorithm in a Karun River reach bounded to the Mollasani hydrometric station and Ahwaz station upstream and downstream of the river, respectively. The results suggest that if three distinct values rather than constant values are used for X, K, ?? parameters in the Muskingum method, the accuracy of computed outflow will be increased particularly in the peak section of hydrograph so that the Mean Relative Error (MRE) of the peak hydrograph section was 2.44% when constants were. However, in the case of using three different values for these parameters, the error value reached 0.89%.     

复杂防洪系统优化调度的三层并行逐步优化算法

逐步优化算法（POA）在求解水库群防洪优化调度问题方面,应用广泛且较为有效。但是随着水库数量的增加和计及洪水演进、干支流汇入等因素,该算法求解复杂防洪系统调度的效率降低。本文在POA算法基础上,引入莱维飞行更新策略、模式搜索法以及并行技术,提出了三层并行POA算法（TPPOA）,并应用于赣江中下游复杂防洪系统的防洪优化调度中。结果表明,相较常规防洪调度规则和POA算法,TPPOA获得的调度策略可以有效降低下游防洪控制点洪峰流量,减轻下游防洪压力。此外,在6核心CPU计算机下,TPPOA计算速度约为POA算法的3~7倍,计算速度更快。本文为复杂防洪系统的优化调度提供了一种新的方法。     

马斯京根分段演算与水箱模型组合在区间洪水过程预报中的应用

介绍了马斯京根流量演算方法和水箱模型,提出了将马斯京根分段分步流量演算方法与水箱模型相结合进行区间洪水预报的方法,并将该方法应用于渭河林家村站、汉江安康水库站的洪水预报的研究中,实践证明该方法合理可行,可以推广应用到其它的区间洪水预报中。     

水库群防洪系统优化调度模型及应用

本文以澧水流域中上游江垭、皂市及宜冲桥3个水库及其下游河道防洪系统联合优化调度问题为背景，建立了基于河道洪水演进方程与多目标离散微分动态规划的水库群防洪系统多目标优化调度模型，给出了一种离散微分动态规划与马斯京根洪水演进相结合多目标优化算法.经计算分析，结果满意.     

A coupled rainfall-runoff and runoff-routing model for adaptive real-time flood forecasting

This paper describes an adaptive hydrologic modelling technique for real-time flood forecasting. The modelling approach is based on a linear stochastic time-varying representation of the rainfall-runoff process and on the Muskingum routing method formulated as an optimal linear filtering problem. The most general stochastic rainfall-runoff model used for linear forecasting is known as the transfer function noise model. An on-line identification procedure based on an extension of the recursive Instrumental Variable estimator is discussed. The routing procedure, based on the Muskingum method, is written in a state-space representation. This allows real-time updating of the state and the system parameters by means of Kalman filtering. The described method is used to forecast extreme flood events for the River Ourthe (drainage basin: approx 3626 km²), one of the main tributaries of the River Meuse, Belgium. The method is compared with stationary modelling procedures and its superiority based on objective forecasting criteria is demonstrated.     

基于马斯京根法的嫩江江桥-大赉段洪水演算

针对嫩江江桥-大赉河段洪水演进不平衡问题,首先分析了1998年前后江桥和大赉两个水文站的径流变化规律;然后引入河道洪水演进损失系数,建立了基于分段马斯京根方法的江桥-大赉河段的洪水演算模型;最后利用1998年后洪水资料,采用粒子群优化算法率定了河道洪水演进参数和洪水演进损失系数,并分析了参数和损失系数的合理性。结果表明,由于受1998年大洪水的冲刷以及河道两岸冲毁的影响,嫩江江桥-大赉段河道洪水传播速度加快,同时在演进过程中洪水向河道外满溢,导致上下游水量不平衡。     

基于区间入流预报的右江河道洪水演进

广西右江如果发生大洪水,将对郁江和南宁市的防洪产生严重威胁,因此右江洪水演进分析对南宁市的防洪具有重要意义。选出百色水文站洪峰流量依次增大的5场洪水,用右江百色-田东河段区间内的小流域作为代表性流域率定新安江模型参数,对区间入流洪水进行了预报。根据预报结果采用经参数试错法率定后的马斯京根模型对5场洪水进行了演进,计算了田东水文站断面的流量。结果表明:计算结果与实测流量相对误差较小,相关性系数较大,说明试错法对于马斯京根模型参数求解精度较高。     

Parameter Identification of Nonlinear Muskingum Model with Backtracking Search Algorithm

Nonlinear Muskingum model is a popular approach widely used for flood routing in hydraulic engineering. An improved backtracking search algorithm (BSA) is proposed to estimate the parameters of nonlinear Muskingum model. The orthogonal designed initialization population strategy and chaotic sequences are introduced to improve the exploration and exploitation ability of BSA. At the same time, a selection strategy based individual feasibility violation is developed to ensure that the computed outflows are non-negative in the evolutionary process. Finally, three examples are employed to demonstrate the performance of the improved BSA. The comparison between the results of routing outflows and those of Wilcoxon signed ranks test shows that the improved BSA outperforms particle swarm optimization, genetic algorithm, differential evolution and other algorithms reported in the literature in terms of solution quality. Therefore, it is reasonable to draw the conclusion that the proposed BSA is a satisfactory and efficient choice for parameter estimation of nonlinear Muskingum model.     

基于长办汇流曲线的多模型联合洪水演算研究

传统长办汇流曲线采用试算法确定稳定流的河段传播时间K值时,计算过程繁琐且不一定能得到最优解。基于此,提出利用遗传算法求解马斯京根模型的河段传播时间,进而优化长办汇流曲线模型的参数K。该方法既充分利用了长办汇流曲线模型中的经验性汇流系数,又融入了改进马斯京根法,可以保证参数全局最优的特点,有效提高了河道洪水演算精度,为河道洪水演算研究提供了一种多模型联合求解的新思路。     

长江上游控制性水库群联合调度及水资源影响分析

针对长江上游控制性水库群联合调度问题,建立了大规模混联水库群联合优化调度模型,并提出离散微分动态规划(DDDP)和逐步优化算法(POA)相结合的混合方法,实现大规模混联梯级水库群联合优化调度问题的高效求解。在此基础上,结合流域长系列历史径流资料,进行了长江上游控制性梯级水库群调度模拟,分析了联合调度的发电效益;并在此基础上,结合相关研究成果,探究并分析了梯级水库群建成投运后,联合调度对流域水资源的影响。成果表明,梯级水库群的建成及联合调蓄对于长江中下游枯水期的流量补偿效益十分明显,供水、航运以及压咸补淡等综合效益十分显著。     

A Triple-stage Operation Method for Deriving Operation Rules for Cascade Reservoirs during Catastrophic Flood Events

Alleviating flood stress and reducing flood disaster losses are vital issues when operating flood control systems. To address catastrophic floods, this study establishes an optimal flood control operation model for minimizing the excess flood volume of downstream flood control points. A triple-stage operation method (TSOM) is proposed to solve the model by dividing flood control operation processes into three stages. The constructed model is applied to the operation of a flood control system located in the middle and lower reaches of the Ganjiang River, China. The results demonstrate that the TSOM has superiority in solving efficiency, reducing excess flood volume by more than 30–230 million cubic meters compared with the progressive optimality algorithm (POA) for most designed floods with 500-year return periods. Furthermore, the discharge and water level processes created by the TSOM fluctuate less than those created by the POA, which meets the requirements of practical flood control operations.

     

Discussion of Muskingum method parameter X

The parameter X of the Muskingum method is a physical parameter that reflects the flood peak attenuation and hydrograph shape flattening of a diffusion wave in motion. In this paper, the historic process that hydrologists have undergone to find a physical explanation of this parameter is briefly discussed. Based on the fact that the Muskingum method is the second-order accuracy difference solution to the diffusion wave equation, its numerical stability condition is analyzed, and a conclusion is drawn： X ≤ 0.5 is the uniform condition satisfying the demands for its physical meaning and numerical stability. It is also pointed out that the methods that regard the sum of squares of differences between the calculated and observed discharges or stages as the objective function and the routing coefficients C0, C1 and C2 of the Muskingum method as the optimization parameters cannot guarantee the physical meaning of X.     

Parameter Estimation of Two Improved Nonlinear Muskingum Models Considering the Lateral Flow Using a Hybrid Algorithm

The Muskingum model was one of the most popular methods for flood routing in water resources engineering, many researchers had presented various versions of Muskingum model so as to enhance the precision of the Muskingum model in their papers. Similarly, two new nonlinear Muskingum models were presented in this paper. One considered the lateral flow, and the other considered the lateral flow and a variable exponent parameter, simultaneously. Minimizing the sum of the squared (SSQ) deviations between the observed and routed outflows was considered as the objective, and then three benchmark examples and a real example in Iran were applied to verify performances of two proposed models. A hybrid algorithm, which combined the improved real-coded adaptive genetic algorithm and the Nelder-Mead simplex algorithm, was utilized for parameter estimation of two proposed models. Comparisons of the optimal results for four examples by different models showed that two proposed models can produce more accurate fit to observed outflows, and the proposed model, which simultaneously considered a variable exponent parameter and the lateral flow, reduced the SSQ obviously.     

A Study on the Large-scale System Decomposition—Coordination Method Used in Optimal Operation of a Hydroelectric System

Abstract

The optimal operation problem of multiple hydroelectric reservoir systems is very complex because of the large dimensions. Large-scale system decomposition-coordination methods, which can simplify complex problems into several interrelated sub-problems to avoid the “curse of dimensionality” and to obtain the global optimum on the global through coordination among sub-systems, is particularly well suited for optimizing large-scale, multi-reservoir systems. Applying this kind of theory and method, this paper studies and analyzes the problems of optimal operation of multiple hydroelectric reservoir systems in series, and sets up the optimal operation model of hydroelectric reservoir systems in series. On this basis, a practical example of two hydroelectric reservoirs in series on the upper reaches of the Yellow River in China is calculated and analyzed and the results are satisfactory. It is believed that applying this model can cut down the dimensions of the problem notably and that the theory and method are effective for real time operation.     

A RIVER FLOW ROUTING MODEL BASED ON DIGITAL DRAINAGE NETWORK

On the basis of Digital Elevation Model （DEM） data, watershed delineation and spatial topological relationship were proposed by the Digital Elevation Drainage Network Model （DEDNM） for the area upstream of the Hanzhong Hydrological Station in the Hanjiang River in China. Then, the Muskingum-Cunge method considering lateral flow into the river was applied to flood routing on the platform of digital basin derived from DEDNM. Because of considering lateral flow into the river, the Muskingum-Cunge method performs better than the Muskingum method in terms of the Nash-Sutcliffe model efficiency coefficient and the relative error of flood discharge peak value. With a routing-after-superposition algorithm, the Muskingum-Cunge method performs better than the Muskingum method in terms of the Nash-Sutcliffe model efficiency coefficient and the relative error of flood discharge peak value. As a result, the digital basin coupled with the Muskingum-Cunge method provides a better platform for water resources management and flood control.     

基于多重迭代算法的梯级水库群调度图优化方法

基于梯级水库群中各水库的常规调度图,建立了以梯级水库群多年平均发电量最大为目标的梯级水库群调度图优化数学模型,提出了基于多重迭代实现高维模型降维的求解算法,即以轮库迭代法实现空间多水库降维,以轮线迭代法实现单一水库多调度线降维,以逐次优化算法实现调度线状态降维。万安溪-白沙梯级水库群的算例计算结果表明,该优化方法能获得形态合理的优化调度图,并能有效提高水库群发电量。     

Application of New Hybrid Optimization Technique for Parameter Estimation of New Improved Version of Muskingum Model

The Muskingum method is one of the most utilized lumped flood routing model in which calibration of its parameters provides an active area of research in water resources engineering. Although various techniques and versions of Muskingum model have been presented to estimate the parameters of different versions of Muskingum model, more rigorous approaches and models are still required to improve the computational precision of calibration process. In this study, a new hybrid technique was proposed for Muskingum parameter estimation which combines the Modified Honey Bee Mating Optimization (MHBMO) and Generalized Reduced Gradient (GRG) algorithms. According to the conducted literature-review on the improvement of Muskingum flood routing models, a new six-parameter Muskingum model was proposed. The hybrid technique was successfully applied for parameter estimation of this new version of Muskingum model for three case studies selected from literature. The obtained results were compared with those of other methods using several common performance evaluation criteria. The new hybrid method with the new proposed Muskingum model perform the best among all the considered approaches based on most of utilized criteria. The new Muskingum model significantly reduces the SSQ value for the double-peak case study. Finally, the achieved results demonstrate that not only the hybrid MHBMO-GRG algorithm overcomes the shortcomings of both phenomenon-mimicking and mathematical optimization techniques, but also the presented Muskingum model is appeared to be the most reliable version of Muskingum model comparing with other considered models in this research.