Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL) optimization framework

Naeini, Matin Rahnamay; Yang, Tiantian; Sadegh, Mojtaba; AghaKouchak, Amir; Hsu, Kuolin; Sorooshian, Soroosh; Duan, Qingyun; Lei, Xiaohui

doi:10.1016/j.envsoft.2018.03.019

Cited by 33 publications

(14 citation statements)

References 66 publications

(81 reference statements)

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“…The nonlinearity of the reservoir systems, along with the existing constraints, require an effective optimization algorithm to solve these types of problems [41]. Here, we employ the non-dominated sorting genetic algorithm-II (NSGA-II), which is a robust multi-objective optimization algorithm [36], to derive the parameters of the rule curves.…”

Section: Nsga-ii Optimization Algorithmmentioning

confidence: 99%

Evaluation of GloFAS-Seasonal Forecasts for Cascade Reservoir Impoundment Operation in the Upper Yangtze River

Chen

Guo

Wang

et al. 2019

Water

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Standard impoundment operation rules (SIOR) are pre-defined guidelines for refilling reservoirs before the end of the wet season. The advancement and availability of the seasonal flow forecasts provide the opportunity for reservoir operators to use flexible and early impoundment operation rules (EIOR). These flexible impoundment rules can significantly improve water conservation, particularly during dry years. In this study, we investigate the potential application of seasonal streamflow forecasts for employing EIOR in the upper Yangtze River basin. We first define thresholds to determine the streamflow condition in September, which is an important period for decision-making in the basin, and then select the most suitable impoundment operation rules accordingly. The thresholds are used in a simulation-optimization model to evaluate different scenarios for EIOR and SIOR by multiple objectives. We measure the skill of the GloFAS-Seasonal forecast, an operational global seasonal river flow forecasting system, to predict streamflow condition according to the selected thresholds. The results show that: (1) the 20th and 30th percentiles of the historical September flow are suitable thresholds for evaluating the possibility of employing EIOR;(2) compared to climatological forecasts, GloFAS-Seasonal forecasts are skillful for predicting the streamflow condition according to the selected 20th and 30th percentile thresholds; and (3) during dry years, EIOR could improve the fullness storage rate by 5.63% and the annual average hydropower generation by 4.02%, without increasing the risk of flooding. GloFAS-Seasonal forecasts and early reservoir impoundment have the potential to enhance hydropower generation and water utilization. events [3,4]. Conventionally, during wet season, reservoir operators release water preferentially for flood control [5,6] while storing water before the end of wet season to meet the demand for hydropower generation, navigation, and water supply. For full replenishment of storage, reservoir operators and academic researchers have highlighted the importance of reservoir impoundment operation and impoundment rules in several studies [7][8][9][10].Reservoir operation rules are often used to provide guidelines for reservoir operators to determine the amount of controlled discharge. Among these rules, the reservoir impoundment rules are designed to refill the reservoir and raise the storage water level. The New York City rule [11] is among the early guidelines for reservoir impoundment and provides the probability of spills rather than the amount of spill to minimize the water shortage [12]. Since the development of the New York City rule, different types of reservoir impoundment operation rules have been developed and employed for various reservoir systems [7,[13][14][15][16][17]. In the Yangtze River, which is one of the largest rivers in the world by discharge volume with huge reservoir storage capacity, the impoundment operation is complex and challenging. Water managers and stakeholders employ prede...

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Section: Nsga-ii Optimization Algorithmmentioning

confidence: 99%

Evaluation of GloFAS-Seasonal Forecasts for Cascade Reservoir Impoundment Operation in the Upper Yangtze River

Chen

Guo

Wang

et al. 2019

Water

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“…where µ is the location parameter, σ is the scale parameter and ξ is the shape parameter. To estimate these parameters, we used the maximum likelihood method (Coles 2001;Rahnamay Naeini et al 2018). This statistical model has been used in many hydrological studies to characterize the behavior of extreme events (Katz et al 2002, Aghakouchak 2013, Cheng et al 2014.…”

Section: Methodsmentioning

confidence: 99%

Changes in the exposure of California’s levee-protected critical infrastructure to flooding hazard in a warming climate

Mallakpour

Sadegh

AghaKouchak

2020

Environ. Res. Lett.

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Levee systems are an important part of California's water infrastructure, engineered to provide resilience against flooding and reduce flood losses. The growth in California is partly associated with costly infrastructure developments that led to population expansion in the levee protected areas. Therefore, potential changes in the flood hazard could have significant socioeconomic consequences over levee protected areas, especially in the face of a changing climate. In this study, we examine the possible impacts of a warming climate on flood hazard over levee protected land in California. We use gridded maximum daily runoff from global circulation models (GCMs) that represent a wide range of variability among the climate projections, and are recommended by the California's Fourth Climate Change Assessment Report, to investigate possible climate-induced changes. We also quantify the exposure of several critical infrastructure protected by the levee systems (e.g. roads, electric power transmission lines, natural gas pipelines, petroleum pipelines, and railroads) to flooding. Our results provide a detailed picture of change in flood risk for different levees and the potential societal consequences (e.g. exposure of people and critical infrastructure). Levee systems in the northern part of the Central Valley and coastal counties of Southern California are likely to observe the highest increase in flood hazard relative to the past. The most evident change is projected for the northern region of the Central Valley, including Butte, Glenn, Yuba, Sutter, Sacramento, and San Joaquin counties. In the leveed regions of these counties, based on the model simulations of the future, the historical 100-year runoff can potentially increase up to threefold under RCP8.5. We argue that levee operation and maintenance along with emergency preparation plans should take into account the changes in frequencies and intensities of flood hazard in a changing climate to ensure safety of levee systems and their protected infrastructure.

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“…Unfortunately, the paucity of progress in model development is partly due to structural inadequacy. For example, dynamic components in hydrological models are oversimplified due to a poor understanding of their physical mechanisms (Xiong et al, 2019;Deng et al, 2016Deng et al, , 2018Dakhlaoui et al, 2017;Sarhadi et al, 2016;Pathiraja et al, 2016;Ouyang et al, 2016). Previous studies have demonstrated that the assumption of time-invariant parameters is usually inappropriate.…”

Section: Introductionmentioning

confidence: 99%

“…To investigate the problems caused by time-invariant parameters, a control scheme, i.e., scheme 1, T. Lan et al: Dynamics of hydrological-model parameters: mechanisms, problems and solutions is designed and assessed in this study. In this regard, the dynamics of the hydrological-model parameters may be a type of compensation for models that are missing key processes such as climate-and land-surface-related changes (Xiong et al, 2019;Deng et al, 2016Deng et al, , 2018Wang et al, 2017b;Dakhlaoui et al, 2017;Sarhadi et al, 2016;Pathiraja et al, 2016;Ouyang et al, 2016;Todorovic and Plavsic, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…In view of the compensation between parameters, the most common approach for assessing the dynamics of the hydrological-model parameters is that the calibration period is partitioned into different sub-periods based on the temporal dynamic catchment characteristics (Sarhadi et al, 2016;Merz et al, 2011;Lan et al, 2018;Xiong et al, 2019;Motavita et al, 2019;Deng et al, 2016Deng et al, , 2018Dakhlaoui et al, 2017;Choi and Beven, 2007;Brigode et al, 2013;Kim et al, 2015;Kim and Han, 2017;Zhao et al, 2017;Pfannerstill et al, 2015;Me et al, 2015;Coron et al, 2014;Vormoor et al, 2018;Luo et al, 2012;Guse et al, 2016;Zhang et al, 2011Zhang et al, , 2015Ouyang et al, 2016). The parameter set in each sub-period is optimized to obtain the dynamic parameter team.…”

Section: Introductionmentioning

confidence: 99%

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Dynamics of hydrological-model parameters: mechanisms, problems and solutions

Lan

Lin

et al. 2020

Hydrol. Earth Syst. Sci.

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Abstract. It has been demonstrated that the application of time-varying hydrological-model parameters based on dynamic catchment behavior significantly improves the accuracy and robustness of conventional models. However, the fundamental problems for calibrating dynamic parameters still need to be addressed. In this study, five calibration schemes for dynamic parameters in hydrological models were designed to investigate the underlying causes of poor model performance. The five schemes were assessed with respect to the model performance in different flow phases, the transferability of the dynamic parameters to different time periods, the state variables and fluxes time series, and the response of the dynamic parameter set to the dynamic catchment characteristics. Furthermore, the potential reasons for the poor response of the dynamic parameter set to the catchment dynamics were investigated. The results showed that the underlying causes of poor model performance included time-invariant parameters, “compensation” among parameters, high dimensionality and abrupt shifts in the parameters. The recommended calibration scheme exhibited good performance and overcame these problems by characterizing the dynamic behavior of the catchments. The main reason for the poor response of the dynamic parameter set to the catchment dynamics may be the poor convergence performance of the parameters. In addition, the assessment results of the state variables and fluxes and the convergence performance of the parameters provided robust indications of the dominant response modes of the hydrological models in different sub-periods or catchments with distinguishing catchment characteristics.

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Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL) optimization framework

Cited by 33 publications

References 66 publications

Evaluation of GloFAS-Seasonal Forecasts for Cascade Reservoir Impoundment Operation in the Upper Yangtze River

Evaluation of GloFAS-Seasonal Forecasts for Cascade Reservoir Impoundment Operation in the Upper Yangtze River

Changes in the exposure of California’s levee-protected critical infrastructure to flooding hazard in a warming climate

Dynamics of hydrological-model parameters: mechanisms, problems and solutions

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