Stochastic simulation of nonstationary oscillation hydroclimatic processes using empirical mode decomposition.

Lee, Taesam et Ouarda, Taha B. M. J. (2012). Stochastic simulation of nonstationary oscillation hydroclimatic processes using empirical mode decomposition. Water Resources Research , vol. 48 , nº 2. W02514. DOI: 10.1029/2011WR010660.

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Résumé

Nonstationary oscillation (NSO) processes are observed in a number of hydroclimatic data series. Stochastic simulation models are useful to study the impacts of the climatic variations induced by NSO processes into hydroclimatic regimes. Reproducing NSO processes in a stochastic time series model is, however, a difficult task because of the complexity of the nonstationary behaviors. In the current study, a novel stochastic simulation technique that reproduces the NSO processes embedded in hydroclimatic data series is presented. The proposed model reproduces NSO processes by utilizing empirical mode decomposition (EMD) and nonparametric simulation techniques (i.e., k-nearest-neighbor resampling and block bootstrapping). The model was first tested with synthetic data sets from trigonometric functions and the Rssler system. The North Atlantic Oscillation (NAO) index was then examined as a real case study. This NAO index was then employed as an exogenous variable for the stochastic simulation of streamflows at the Romaine River in the province of Quebec, Canada. The results of the application to the synthetic data sets and the real-world case studies indicate that the proposed model preserves well the NSO processes along with the key statistical characteristics of the observations. It was concluded that the proposed model possesses a reasonable simulation capacity and a high potential as a stochastic model, especially for hydroclimatic data sets that embed NSO processes.

Type de document:	Article
Mots-clés libres:	climate index; empirical mode decomposition; nonstationary oscillation; stochastic simulation; streamblow; time series
Centre:	Centre Eau Terre Environnement
Date de dépôt:	19 oct. 2018 15:00
Dernière modification:	26 nov. 2019 19:41
URI:	https://espace.inrs.ca/id/eprint/7295

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