Optimized cascade reservoir operation considering ice flood control and power generation

Chang, Jianxia; Wang, Zongzhi; Wang, Xuebin; Huang, Qiang

doi:10.1016/j.jhydrol.2014.08.036

Cited by 57 publications

(27 citation statements)

References 39 publications

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“…The null hypothesis that there is no monotonic trend in the series is rejected by the H min, fre , V max , H ice , and T cum series for the Toudaoguai station and the H max, bre , H max, fre , V max , H ice , and T cum series for the Sanhuhekou station at the 10% significance level. The nonstationarities of H ice and T cum for both stations are the result of climate change and reservoir regulation (Chang et al, ). The joint operation of the Liujiaxia and Longyangxia reservoirs also influences the river channel erosion and deposition process by modifying the hydrograph (Ran, Wang, & Fan, ); thus, the change in river morphology leads to the nonstationarity of V max , H max, bre , H max, fre , and H min, fre in the Inner Mongolia Reach of the Yellow River.…”

Section: Resultsmentioning

confidence: 99%

“…The Inner Mongolia Reach of the upstream Yellow River is categorized as arid, semiarid desert, and desert steppe zones, and the reach is wide with gentle slopes, meandering twists and turns. The damage caused by ice flooding in the Ningxia‐Inner Mongolia Reach of the Yellow River, which consists of the Ningxia Reach (397 km) and the Inner Mongolia Reach (830 km), is the most serious in China (Chang, Meng, Wang, Wang, & Huang, ; Fu, Popescu, Wang, Mynett, & Zhang, ). According to statistics for ice flood disasters on the Ningxia‐Inner Mongolia Reach, there were 13 ice flood events from 1901 to 1949, and there have been 29 ice flood hazards since 1949, nine of them causing the levee to breach (Fu et al, ; Wang, Dong, & Li, ).…”

Section: Study Area and Datamentioning

confidence: 99%

“…There are seven cascade reservoirs, that is, Longyangxia (1986), Lijiaxia (2001), Liujiaxia (1968), Yanguoxia (1961), Bapanxia (1974), Daxia (1999), and Qingtongxia (1978), in the upstream region of the Yellow River; the number after the reservoir is the year when they became operative, and the locations of the seven mentioned reservoirs are shown in Figure . Among the seven reservoirs, Longyangxia is a multiyear reservoir, and Liujiaxia is a one‐year reservoir, whereas the others are run‐off reservoirs; thus, the reservoirs closely related to Inner Mongolia Reach ice control are primarily the Longyangxia and Liujiaxia reservoirs (Chang et al, ). The Liujiaxia reservoir became operational in October 1968, and joint operation of the Liujiaxia and Longyangxia reservoirs began in 1986.…”

Section: Study Area and Datamentioning

confidence: 99%

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Probability prediction of peak break‐up water level through vine copulas

Zhang

et al. 2019

Hydrological Processes

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Prediction of the peak break‐up water level, which is the maximum instantaneous stage during ice break‐up, is desirable to allow effective ice flood mitigation, but traditional hydrologic flood routing techniques are not efficient in addressing the large uncertainties caused by numerous factors driving the peak break‐up water level. This research provides a probability prediction framework based on vine copulas. The predictor variables of the peak break‐up water level are first chosen, the pair copula structure is then constructed by using vine copulas, the conditional density distribution function is derived to perform a probability prediction, and the peak break‐up water level value can then be estimated from the conditional density distribution function given the conditional probability and fixed values of the predictor variables. This approach is exemplified using data from 1957 to 2005 for the Toudaoguai and Sanhuhekou stations, which are located in the Inner Mongolia Reach of the Yellow River, and the calibration and validation periods are divided at 1986. The mean curve of the peak break‐up water level estimated from the conditional distribution function can capture the tendency of the observed series at both the Toudaoguai and Sanhuhekou stations, and more than 90% of the observed values fall within the 90% prediction uncertainty bands, which are approximately twice the standard deviation of the observed series. The probability prediction results for the validation period are consistent with those for the calibration period when the nonstationarity of the marginal distributions for the Sanhuhekou station are considered. Compared with multiple linear regression results, the uncertainty bands from the conditional distribution function are much narrower; moreover, the conditional distribution function is more capable of addressing the nonstationarity of predictor variables, and the conclusions are confirmed by jackknife analysis. Scenario predictions for cases in which the peak break‐up water level is likely to be higher than the bankfull water level can also be conducted based on the conditional distribution function, with good performance for the two stations.

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Section: Resultsmentioning

confidence: 99%

Section: Study Area and Datamentioning

confidence: 99%

Section: Study Area and Datamentioning

confidence: 99%

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Probability prediction of peak break‐up water level through vine copulas

Zhang

et al. 2019

Hydrological Processes

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“…In many northern communities, river flooding risks in spring are caused by ice‐related events (Chang, Meng, Wang, Wang, & Huang, ; Chokmani, Ouarda, Hamilton, Ghedira, & Gingras, ; Turcotte & Morse, ). Rising temperatures in spring result in ice cover breakup, and then the broken ice may further cause ice jams against intact ice further downstream (Fu, Popescu, Wang, Mynett, & Zhang, ; Lesack, Marsh, Hicks, & Forbes, , ).…”

Section: Introductionmentioning

confidence: 99%

Multiple model combination methods for annual maximum water level prediction during river ice breakup

Sun

Trevor

2018

Hydrological Processes

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The Athabasca River is the largest unregulated river in Alberta, Canada, with ice jams frequently occurring in the vicinity of Fort McMurray. Modelling tools are desired to forecast ice‐related flood events. Multiple model combination methods can often obtain better predictive performances than any member models due to possible variance reduction of forecast errors or correction of biases. However, few applications of this method to river ice forecasting are reported. Thus, a framework of multiple model combination methods for maximum breakup water level (MBWL) Prediction during river ice breakup is proposed. Within the framework, the member models describe the relations between the MBWL (predicted variable) and their corresponding indicators (predictor variables); the combining models link the relations between the predicted MBWL by each member model and the observed MBWL. Especially, adaptive neuro‐fuzzy inference systems, artificial neural networks, and multiple linear regression are not only employed as member models but also as combining models. Simple average methods (SAM) are selected as the basic combining model due to simple calculations. In the SAM, an equal weight (1/n) is assigned to n member models. The historical breakup data of the Athabasca River at Fort McMurray for the past 36 years (1980 to 2015) are collected to facilitate the comparison of models. These models are examined using the leave‐one‐out cross validation and the holdout validation methods. A SAM, which is the average output from three optimal member models, is selected as the best model as it has the optimal validation performance (lowest average squared errors). In terms of lowest average squared errors, the SAM improves upon the optimal artificial neural networks, adaptive neuro‐fuzzy inference systems, and multiple linear regression member models by 21.95%, 30.97%, and 24.03%, respectively. This result sheds light on the effectiveness of combining different forecasting models when a scarce river ice data set is investigated. The indicators included in the SAM may indicate that the MBWL is affected by water flow conditions just after freeze‐up, overall freezing conditions during winter, and snowpack conditions before breakup.

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“…However, the classic methods always perform poorly in solving complex problems, which makes the evolutionary methods develop rapidly [7]. Therefore, evolutionary methods are frequently used in water management problems, such as particle swarm optimization (PSO) [8-10], genetic algorithm (GA) [11][12][13], and so on [14][15][16]. With the increase in the pursuit of multi-objective benefits by decision makers, the multi-objective optimization algorithms (MOEAs) have received more attention and been improved a lot, such as gravity search algorithm (GSA) [7], strength Pareto evolutionary algorithm (SPEA) [17], non-dominated sorting genetic algorithm-III (NSGA-III) [18], and so on.…”

mentioning

confidence: 99%

Multi-Objective Joint Optimal Operation of Reservoir System and Analysis of Objectives Competition Mechanism: A Case Study in the Upper Reach of the Yangtze River

Chen

Dong

Jia

et al. 2019

Water

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The multi-objective optimal operation and the joint scheduling of giant-scale reservoir systems are of great significance for water resource management; the interactions and mechanisms between the objectives are the key points. Taking the reservoir system composed of 30 reservoirs in the upper reaches of the Yangtze River as the research object, this paper constructs a multi-objective optimal operation model integrating four objectives of power generation, ecology, water supply, and shipping under the constraints of flood control to analyze the inside interaction mechanisms among the objectives. The results are as follows. (1) Compared with single power generation optimization, multi-objective optimization improves the benefits of the system. The total power generation is reduced by only 4.09% at most, but the water supply, ecology, and shipping targets are increased by 98.52%, 35.09%, and 100% at most under different inflow conditions, respectively. (2) The competition between power generation and the other targets is the most obvious; the relationship between water supply and ecology depends on the magnitude of flow required by the control section for both targets, and the restriction effect of the shipping target is limited. (3) Joint operation has greatly increased the overall benefits. Compared with the separate operation of each basin, the benefits of power generation, water supply, ecology, and shipping increased by 5.50%, 45.99%, 98.49%, and 100.00% respectively in the equilibrium scheme. This study provides a widely used method to analyze the multi-objective relationship mechanism, and can be used to guide the actual scheduling rules.2 of 23 requirements for the water head and flow of hydropower stations between power generation and other benefit objectives, there exists mutual influence and interdependence among these benefit objectives [5]. Therefore, how to deal with the impact between these objectives and maximize the benefit of limited water resources is the focus and difficult point of current research. Many scholars have focused on the optimal operation of reservoirs and reservoir groups in specific areas.Many algorithms can be used to solve reservoir optimal operation and water management problems with the development of computing ability; such algorithms can be classified as classic or evolutionary methods [6]. However, the classic methods always perform poorly in solving complex problems, which makes the evolutionary methods develop rapidly [7]. Therefore, evolutionary methods are frequently used in water management problems, such as particle swarm optimization (PSO) [8-10], genetic algorithm (GA) [11][12][13], and so on [14][15][16]. With the increase in the pursuit of multi-objective benefits by decision makers, the multi-objective optimization algorithms (MOEAs) have received more attention and been improved a lot, such as gravity search algorithm (GSA) [7], strength Pareto evolutionary algorithm (SPEA) [17], non-dominated sorting genetic algorithm-III (NSGA-III) [18], and so on. With the de...

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Optimized cascade reservoir operation considering ice flood control and power generation

Cited by 57 publications

References 39 publications

Probability prediction of peak break‐up water level through vine copulas

Probability prediction of peak break‐up water level through vine copulas

Multiple model combination methods for annual maximum water level prediction during river ice breakup

Multi-Objective Joint Optimal Operation of Reservoir System and Analysis of Objectives Competition Mechanism: A Case Study in the Upper Reach of the Yangtze River

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