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    Managing Hydrological Risks with Extreme Modeling: Application of Peaks over Threshold Model to the Loukkos Watershed, Morocco

    Source: Journal of Hydrologic Engineering:;2014:;Volume ( 019 ):;issue: 009
    Author:
    A. Zoglat
    ,
    S. EL Adlouni
    ,
    F. Badaoui
    ,
    A. Amar
    ,
    C. G. Okou
    DOI: 10.1061/(ASCE)HE.1943-5584.0000996
    Publisher: American Society of Civil Engineers
    Abstract: The peaks over threshold (POT) is a widely used technique to describe the exceedances of hydrological data above a threshold. It is well known that, under some conditions, the exceedances distribution can be approximated by a generalized Pareto distribution (GPD). The lack of a generally accepted methodology for selecting the optimal threshold is a major issue of the POT technique. In this paper an integrated approach is proposed that combines some graphical approaches with some analytical approaches to identify the optimal threshold and estimate the shape parameter of the exceedances distribution. Such a combination intends to reduce the subjectivity in graphical methods, and to refine their finding by using rigorous mathematical tools of analytical methods. First, a statistical test is used to select the appropriate GPD fitting the exceedances. Then, three numerical approaches, namely the likelihood ratio test, square error method, and multiple threshold method, are applied to detect the optimal threshold above which exceedances can be approximated by a GPD. These techniques are illustrated in a case study of Loukkos basin, a water resource of great importance in Morocco.
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      Managing Hydrological Risks with Extreme Modeling: Application of Peaks over Threshold Model to the Loukkos Watershed, Morocco

    URI
    https://yetl.yabesh.ir/yetl1/handle/yetl/80101
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    • Journal of Hydrologic Engineering

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    contributor authorA. Zoglat
    contributor authorS. EL Adlouni
    contributor authorF. Badaoui
    contributor authorA. Amar
    contributor authorC. G. Okou
    date accessioned2017-05-08T22:24:44Z
    date available2017-05-08T22:24:44Z
    date copyrightSeptember 2014
    date issued2014
    identifier other44253355.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/80101
    description abstractThe peaks over threshold (POT) is a widely used technique to describe the exceedances of hydrological data above a threshold. It is well known that, under some conditions, the exceedances distribution can be approximated by a generalized Pareto distribution (GPD). The lack of a generally accepted methodology for selecting the optimal threshold is a major issue of the POT technique. In this paper an integrated approach is proposed that combines some graphical approaches with some analytical approaches to identify the optimal threshold and estimate the shape parameter of the exceedances distribution. Such a combination intends to reduce the subjectivity in graphical methods, and to refine their finding by using rigorous mathematical tools of analytical methods. First, a statistical test is used to select the appropriate GPD fitting the exceedances. Then, three numerical approaches, namely the likelihood ratio test, square error method, and multiple threshold method, are applied to detect the optimal threshold above which exceedances can be approximated by a GPD. These techniques are illustrated in a case study of Loukkos basin, a water resource of great importance in Morocco.
    publisherAmerican Society of Civil Engineers
    titleManaging Hydrological Risks with Extreme Modeling: Application of Peaks over Threshold Model to the Loukkos Watershed, Morocco
    typeJournal Paper
    journal volume19
    journal issue9
    journal titleJournal of Hydrologic Engineering
    identifier doi10.1061/(ASCE)HE.1943-5584.0000996
    treeJournal of Hydrologic Engineering:;2014:;Volume ( 019 ):;issue: 009
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian