Copula-based method for multisite monthly and daily streamflow simulation

•We proposed a method for multisite monthly and daily streamflow simulation.•The Colorado River and the upper Yangtze River were selected as case studies.•The generated data can capture the properties of the single site.•The generated data can preserve the spatial correlation at different locations....

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Veröffentlicht in:Journal of hydrology (Amsterdam) 2015-09, Vol.528, p.369-384
Hauptverfasser: Chen, Lu, Singh, Vijay P., Guo, Shenglian, Zhou, Jianzhong, Zhang, Junhong
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container_end_page 384
container_issue
container_start_page 369
container_title Journal of hydrology (Amsterdam)
container_volume 528
creator Chen, Lu
Singh, Vijay P.
Guo, Shenglian
Zhou, Jianzhong
Zhang, Junhong
description •We proposed a method for multisite monthly and daily streamflow simulation.•The Colorado River and the upper Yangtze River were selected as case studies.•The generated data can capture the properties of the single site.•The generated data can preserve the spatial correlation at different locations. Multisite stochastic simulation of streamflow sequences is needed for water resources planning and management. In this study, a new copula-based method is proposed for generating long-term multisite monthly and daily streamflow data. A multivariate copula, which is established based on bivariate copulas and conditional probability distributions, is employed to describe temporal dependences (single site) and spatial dependences (between sites). Monthly or daily streamflows at multiple sites are then generated by sampling from the conditional copula. Three tributaries of Colorado River and the upper Yangtze River are selected to evaluate the proposed methodology. Results show that the generated data at both higher and lower time scales can capture the distribution properties of the single site and preserve the spatial correlation of streamflows at different locations. The main advantage of the method is that the trivairate copula can be established using three bivariate copulas and the model parameters can be easily estimated using the Kendall tau rank correlation coefficient, which makes it possible to generate daily streamflow data. The method provides a new tool for multisite stochastic simulation.
doi_str_mv 10.1016/j.jhydrol.2015.05.018
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Multisite stochastic simulation of streamflow sequences is needed for water resources planning and management. In this study, a new copula-based method is proposed for generating long-term multisite monthly and daily streamflow data. A multivariate copula, which is established based on bivariate copulas and conditional probability distributions, is employed to describe temporal dependences (single site) and spatial dependences (between sites). Monthly or daily streamflows at multiple sites are then generated by sampling from the conditional copula. Three tributaries of Colorado River and the upper Yangtze River are selected to evaluate the proposed methodology. Results show that the generated data at both higher and lower time scales can capture the distribution properties of the single site and preserve the spatial correlation of streamflows at different locations. The main advantage of the method is that the trivairate copula can be established using three bivariate copulas and the model parameters can be easily estimated using the Kendall tau rank correlation coefficient, which makes it possible to generate daily streamflow data. 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Multisite stochastic simulation of streamflow sequences is needed for water resources planning and management. In this study, a new copula-based method is proposed for generating long-term multisite monthly and daily streamflow data. A multivariate copula, which is established based on bivariate copulas and conditional probability distributions, is employed to describe temporal dependences (single site) and spatial dependences (between sites). Monthly or daily streamflows at multiple sites are then generated by sampling from the conditional copula. Three tributaries of Colorado River and the upper Yangtze River are selected to evaluate the proposed methodology. Results show that the generated data at both higher and lower time scales can capture the distribution properties of the single site and preserve the spatial correlation of streamflows at different locations. The main advantage of the method is that the trivairate copula can be established using three bivariate copulas and the model parameters can be easily estimated using the Kendall tau rank correlation coefficient, which makes it possible to generate daily streamflow data. 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Multisite stochastic simulation of streamflow sequences is needed for water resources planning and management. In this study, a new copula-based method is proposed for generating long-term multisite monthly and daily streamflow data. A multivariate copula, which is established based on bivariate copulas and conditional probability distributions, is employed to describe temporal dependences (single site) and spatial dependences (between sites). Monthly or daily streamflows at multiple sites are then generated by sampling from the conditional copula. Three tributaries of Colorado River and the upper Yangtze River are selected to evaluate the proposed methodology. Results show that the generated data at both higher and lower time scales can capture the distribution properties of the single site and preserve the spatial correlation of streamflows at different locations. 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subjects Copula function
Daily streamflow
Hydrology
Monthly streamflow
Multisite stochastic simulation
Preserves
Resource management
Sampling
Simulation
Stochasticity
Water runoff
title Copula-based method for multisite monthly and daily streamflow simulation
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