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Oriani, Fabio

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Oriani, Fabio
Affiliation principale
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https://libra.unine.ch/handle/123456789/627

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  • Publication
    Accès libre
    Simulating rainfall time-series: how to account for statistical variability at multiple scales?
    (2018)
    Oriani, Fabio 
    ;
    Mehrotra, R
    ;
    Mariéthoz, Grégoire
    ;
    Straubhaar, Julien 
    ;
    Sharma, A
    ;
    Renard, Philippe 
    Daily rainfall is a complex signal exhibiting alternation of dry and wet states, seasonal fluctuations and an irregular behavior at multiple scales that cannot be preserved by stationary stochastic simulation models. In this paper, we try to investigate some of the strategies devoted to preserve these features by comparing two recent algorithms for stochastic rainfall simulation: the first one is the modified Markov model, belonging to the family of Markov-chain based techniques, which introduces non-stationarity in the chain parameters to preserve the long-term behavior of rainfall. The second technique is direct sampling, based on multiple-point statistics, which aims at simulating a complex statistical structure by reproducing the same data patterns found in a training data set. The two techniques are compared by first simulating a synthetic daily rainfall time-series showing a highly irregular alternation of two regimes and then a real rainfall data set. This comparison allows analyzing the efficiency of different elements characterizing the two techniques, such as the application of a variable time dependence, the adaptive kernel smoothing or the use of low-frequency rainfall covariates. The results suggest, under different data availability scenarios, which of these elements are more appropriate to represent the rainfall amount probability distribution at different scales, the annual seasonality, the dry-wet temporal pattern, and the persistence of the rainfall events.
  • Publication
    Accès libre
    Missing data simulation inside flow rate time-series using multiple-point statistics
    (2016-10)
    Oriani, Fabio 
    ;
    Borghi, Andrea 
    ;
    Straubhaar, Julien 
    ;
    Mariethoz, Grégoire 
    ;
    Renard, Philippe 
    The direct sampling (DS) multiple-point statistical technique is proposed as a non-parametric missing data simulator for hydrological flow rate time-series. The algorithm makes use of the patterns contained inside a training data set to reproduce the complexity of the missing data. The proposed setup is tested in the reconstruction of a flow rate time-series while considering several missing data scenarios, as well as a comparative test against a time-series model of type ARMAX. The results show that DS generates more realistic simulations than ARMAX, better recovering the statistical content of the missing data. The predictive power of both techniques is much increased when a correlated flow rate time-series is used, but DS can also use incomplete auxiliary time-series, with a comparable prediction power. This makes the technique a handy simulation tool for practitioners dealing with incomplete data sets.