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Kenza Najib - One of the best experts on this subject based on the ideXlab platform.

  • non stationary frequency analysis of Heavy Rainfall events in southern france
    Hydrological Sciences Journal-journal Des Sciences Hydrologiques, 2013
    Co-Authors: Yves Tramblay, Luc Neppel, Julie Carreau, Kenza Najib
    Abstract:

    Abstract Heavy Rainfall events often occur in southern French Mediterranean regions during the autumn, leading to catastrophic flood events. A non-stationary peaks-over-threshold (POT) model with climatic covariates for these Heavy Rainfall events is developed herein. A regional sample of events exceeding the threshold of 100 mm/d is built using daily precipitation data recorded at 44 stations over the period 1958–2008. The POT model combines a Poisson distribution for the occurrence and a generalized Pareto distribution for the magnitude of the Heavy Rainfall events. The selected covariates are the seasonal occurrence of southern circulation patterns for the Poisson distribution parameter, and monthly air temperature for the generalized Pareto distribution scale parameter. According to the deviance test, the non-stationary model provides a better fit to the data than a classical stationary model. Such a model incorporating climatic covariates instead of time allows one to re-evaluate the risk of extreme ...

  • Non-stationary frequency analysis of Heavy Rainfall events in southern France
    Hydrological Sciences Journal, 2013
    Co-Authors: Yves Tramblay, Luc Neppel, Julie Carreau, Kenza Najib
    Abstract:

    Heavy Rainfall events often occur in southern French Mediterranean regions during the autumn, leading to catastrophic flood events. A non-stationary peaks-over-threshold (POT) model with climatic covariates for these Heavy Rainfall events is developed herein. A regional sample of events exceeding the threshold of 100 mm/d is built using daily precipitation data recorded at 44 stations over the period 19582008. The POT model combines a Poisson distribution for the occurrence and a generalized Pareto distribution for the magnitude of the Heavy Rainfall events. The selected covariates are the seasonal occurrence of southern circulation patterns for the Poisson distribution parameter, and monthly air temperature for the generalized Pareto distribution scale parameter. According to the deviance test, the non-stationary model provides a better fit to the data than a classical stationary model. Such a model incorporating climatic covariates instead of time allows one to re-evaluate the risk of extreme precipitation on a monthly and seasonal basis, and can also be used with climate model outputs to produce future scenarios. Existing scenarios of the future changes projected for the covariates included in the model are tested to evaluate the possible future changes on extreme precipitation quantiles in the study area.

P. Bois - One of the best experts on this subject based on the ideXlab platform.

  • topographic effects on statistical characteristics of Heavy Rainfall and mapping in the french alps
    Journal of Applied Meteorology, 2001
    Co-Authors: Kieffer A Weisse, P. Bois
    Abstract:

    Abstract This paper uses detailed topographic characterization of relief for mapping statistical parameters of Heavy Rainfall in the French Alps. After determination of statistical parameters of Heavy Rainfall for time steps ranging from 1 to 24 h at rain gauging stations (10- and 100-yr Rainfall), multivariate linear regression is used to identify the relationships between Rainfall and morphometric parameters. Statistical characteristics of Heavy Rainfall events measured on short time steps (less than 3 h) are better linked to relief characteristics than are those on longer time steps. Furthermore, the Rainfall parameters are shown to be closely linked to geographic features at a location in the Alps, such as distance to the Mediterranean Sea. More-local variables, such as altitude, slope, or azimuth, are less relevant. A mapping methodology based on linear relationships between Rainfall parameters and topographic parameters is defined. This methodology takes into account the spatial structure of multiva...

  • Topographic effects on statistical characteristics of Heavy Rainfall and mapping in the French Alps
    Journal of Applied Meteorology, 2001
    Co-Authors: A. Weisse, P. Bois
    Abstract:

    This paper uses detailed topographic characterization of relief for mapping statistical parameters of Heavy Rainfall in the French Alps. After determination of statistical parameters of Heavy Rainfall for time steps ranging from 1 to 24 h at rain gauging stations (10- and 100-yr Rainfall), multivariate linear regression is used to identify the relationships between Rainfall and morphometric parameters. Statistical characteristics of Heavy Rainfall events measured on short time steps (less than 3 h) are better linked to relief characteristics than are those on longer time steps. Furthermore, the Rainfall parameters are shown to be closely linked to geographic features at a location in the Alps, such as distance to the Mediterranean Sea. More local variables, such as altitude, slope, or azimuth, are less relevant. A mapping methodology based on linear relationships between Rainfall parameters and topographic parameters is defined. This methodology takes into account the spatial structure of multivariate regression residuals. The performance of this method is compared with the simple interpolation method of kriging. For time steps shorter than 3 h, the information on relief improves the interpolation of Heavy Rainfall.

Yves Tramblay - One of the best experts on this subject based on the ideXlab platform.

  • non stationary frequency analysis of Heavy Rainfall events in southern france
    Hydrological Sciences Journal-journal Des Sciences Hydrologiques, 2013
    Co-Authors: Yves Tramblay, Luc Neppel, Julie Carreau, Kenza Najib
    Abstract:

    Abstract Heavy Rainfall events often occur in southern French Mediterranean regions during the autumn, leading to catastrophic flood events. A non-stationary peaks-over-threshold (POT) model with climatic covariates for these Heavy Rainfall events is developed herein. A regional sample of events exceeding the threshold of 100 mm/d is built using daily precipitation data recorded at 44 stations over the period 1958–2008. The POT model combines a Poisson distribution for the occurrence and a generalized Pareto distribution for the magnitude of the Heavy Rainfall events. The selected covariates are the seasonal occurrence of southern circulation patterns for the Poisson distribution parameter, and monthly air temperature for the generalized Pareto distribution scale parameter. According to the deviance test, the non-stationary model provides a better fit to the data than a classical stationary model. Such a model incorporating climatic covariates instead of time allows one to re-evaluate the risk of extreme ...

  • Non-stationary frequency analysis of Heavy Rainfall events in southern France
    Hydrological Sciences Journal, 2013
    Co-Authors: Yves Tramblay, Luc Neppel, Julie Carreau, Kenza Najib
    Abstract:

    Heavy Rainfall events often occur in southern French Mediterranean regions during the autumn, leading to catastrophic flood events. A non-stationary peaks-over-threshold (POT) model with climatic covariates for these Heavy Rainfall events is developed herein. A regional sample of events exceeding the threshold of 100 mm/d is built using daily precipitation data recorded at 44 stations over the period 19582008. The POT model combines a Poisson distribution for the occurrence and a generalized Pareto distribution for the magnitude of the Heavy Rainfall events. The selected covariates are the seasonal occurrence of southern circulation patterns for the Poisson distribution parameter, and monthly air temperature for the generalized Pareto distribution scale parameter. According to the deviance test, the non-stationary model provides a better fit to the data than a classical stationary model. Such a model incorporating climatic covariates instead of time allows one to re-evaluate the risk of extreme precipitation on a monthly and seasonal basis, and can also be used with climate model outputs to produce future scenarios. Existing scenarios of the future changes projected for the covariates included in the model are tested to evaluate the possible future changes on extreme precipitation quantiles in the study area.

Gabriel A Vecchi - One of the best experts on this subject based on the ideXlab platform.

  • changing frequency of Heavy Rainfall over the central united states
    Journal of Climate, 2013
    Co-Authors: Gabriele Villarini, J. A. Smith, Gabriel A Vecchi
    Abstract:

    AbstractRecords of daily Rainfall accumulations from 447 rain gauge stations over the central United States (Minnesota, Wisconsin, Michigan, Iowa, Illinois, Indiana, Missouri, Kentucky, Tennessee, Arkansas, Louisiana, Alabama, and Mississippi) are used to assess past changes in the frequency of Heavy Rainfall. Each station has a record of at least 50 yr, and the data cover most of the twentieth century and the first decade of the twenty-first century. Analyses are performed using a peaks-over-threshold approach, and, for each station, the 95th percentile is used as the threshold. Because of the count nature of the data and to account for both abrupt and slowly varying changes in the Heavy Rainfall distribution, a segmented regression is used to detect changepoints at unknown points in time. The presence of trends is assessed by means of a Poisson regression model to examine whether the rate of occurrence parameter is a linear function of time (by means of a logarithmic link function). The results point to...

Julie Carreau - One of the best experts on this subject based on the ideXlab platform.

  • non stationary frequency analysis of Heavy Rainfall events in southern france
    Hydrological Sciences Journal-journal Des Sciences Hydrologiques, 2013
    Co-Authors: Yves Tramblay, Luc Neppel, Julie Carreau, Kenza Najib
    Abstract:

    Abstract Heavy Rainfall events often occur in southern French Mediterranean regions during the autumn, leading to catastrophic flood events. A non-stationary peaks-over-threshold (POT) model with climatic covariates for these Heavy Rainfall events is developed herein. A regional sample of events exceeding the threshold of 100 mm/d is built using daily precipitation data recorded at 44 stations over the period 1958–2008. The POT model combines a Poisson distribution for the occurrence and a generalized Pareto distribution for the magnitude of the Heavy Rainfall events. The selected covariates are the seasonal occurrence of southern circulation patterns for the Poisson distribution parameter, and monthly air temperature for the generalized Pareto distribution scale parameter. According to the deviance test, the non-stationary model provides a better fit to the data than a classical stationary model. Such a model incorporating climatic covariates instead of time allows one to re-evaluate the risk of extreme ...

  • Non-stationary frequency analysis of Heavy Rainfall events in southern France
    Hydrological Sciences Journal, 2013
    Co-Authors: Yves Tramblay, Luc Neppel, Julie Carreau, Kenza Najib
    Abstract:

    Heavy Rainfall events often occur in southern French Mediterranean regions during the autumn, leading to catastrophic flood events. A non-stationary peaks-over-threshold (POT) model with climatic covariates for these Heavy Rainfall events is developed herein. A regional sample of events exceeding the threshold of 100 mm/d is built using daily precipitation data recorded at 44 stations over the period 19582008. The POT model combines a Poisson distribution for the occurrence and a generalized Pareto distribution for the magnitude of the Heavy Rainfall events. The selected covariates are the seasonal occurrence of southern circulation patterns for the Poisson distribution parameter, and monthly air temperature for the generalized Pareto distribution scale parameter. According to the deviance test, the non-stationary model provides a better fit to the data than a classical stationary model. Such a model incorporating climatic covariates instead of time allows one to re-evaluate the risk of extreme precipitation on a monthly and seasonal basis, and can also be used with climate model outputs to produce future scenarios. Existing scenarios of the future changes projected for the covariates included in the model are tested to evaluate the possible future changes on extreme precipitation quantiles in the study area.