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Lee T.,Gyeongsang National University | Ouarda T.B.M.J.,Masdar Institute of Science and Technology | Ouarda T.B.M.J.,Canada Research Chair on the Estimation of Hydrometeorological Variables
International Journal of Climatology | Year: 2012

One of the important issues in climate change detection is the selection of climate models for the background noise. The background noise is generally chosen in a somewhat subjective manner. In the current study, we propose an approach of detecting climate change signal in order to mitigate the effects of background noise and to improve climate change detection ability. At first, the high-frequency components of three climate datasets (climate signal, observation, background noise) induced from the random noise process are extracted from empirical mode decomposition (EMD) analysis. Then, statistical detection techniques are applied to the datasets from which the high-frequency random components are excluded. The proposed approach is tested with synthetically generated data and with a real-world case study represented by global surface temperature anomaly (GSTA) data. The case study reveals that each component of the observed GSTA data from EMD contains the information related to external and internal forcings such as solar activity and oceanic circulation. Among these components, the statistically significant low-frequency components are employed in climate change detection. Compared to one of the existing approaches, some improvements in the slope coefficient estimates and the signal-to-noise ratio (SNR) are observed in the synthetic application of the proposed model. The application to the GSTA data shows higher SNR in the proposed approach than in the existing approach. © 2011 Royal Meteorological Society. Source


Lee T.,Gyeongsang National University | Ouarda T.B.M.J.,Masdar Institute of Science and Technology | Ouarda T.B.M.J.,Canada Research Chair on the Estimation of Hydrometeorological Variables
Journal of Hydrology | Year: 2011

Among the various stochastic models used in hydrology and meteorology, the k-nearest neighbor resampling (KNNR) has been one of the most common alternatives to supplement the short historical records. In the KNNR model one needs to select the model order (d) and the number of nearest neighbors (k). Traditionally, the prescriptive selection (k=n 1/2 where n is the record length) has been used for k and no practical solutions were provided to choose d. Another applicable approach is generalized cross-validation (GCV). However, it has been reported in the literature that GCV is not practical for the selection of d and k in the KNNR model. In the current study we propose an approach to select d and k based on the Akaike information criterion (AIC). The proposed approach was validated on a number of simulated datasets and applied to the case study of the Colorado River system. The results indicate that the proposed AIC-based approach represents a robust model for the selection of d and k. In the simulation study, the model led particularly to the selection of the same model orders as the real orders of the simulated datasets. It also gave acceptable k values in the case study. © 2011 Elsevier B.V. Source


Lee T.,Gyeongsang National University | Ouarda T.B.M.J.,Masdar Institute of Science and Technology | Ouarda T.B.M.J.,Canada Research Chair on the Estimation of Hydrometeorological Variables | Jeong C.,Induk University
Journal of Hydrology | Year: 2012

A multivariate stochastic generation model for daily weather variables is proposed that extends the multivariate . k-nearest neighbor resampling approach (MKNN). Major drawbacks of the MKNN approach include repetitive historical multivariate patterns, underestimating variance and serial correlation, and reshuffling of historical data. These drawbacks cause under-generation of events that are extreme in their frequency and magnitude. In this study, these drawbacks are addressed by applying a stochastic optimization technique (i.e., a genetic algorithm (GA)) and a perturbation using a gamma kernel density estimate (GKDE). The competitive selection operator in the GA was used to better preserve the historical variance and serial correlation as well as to produce unprecedented multivariate patterns. By employing the GKDE, the resampled precipitation data are perturbed, and thus new precipitation values are generated. To preserve the distribution of the annual maximum events fitted to a general extreme value (GEV), the GKDE bandwidth was selected by employing the statistics of the historical annual maximum. The proposed method was applied to generate six daily weather variables (maximum temperature, minimum temperature, dew point temperature, solar radiation, wind speed, and precipitation) of the summer season (June-September) for a station in Seoul, South Korea. The presented results indicate that the suggested weather generator is an appropriate alternative for generating daily weather variables while reproducing the historical extreme distribution. © 2012 Elsevier B.V. Source


Chebana F.,Canada Research Chair on the Estimation of Hydrometeorological Variables | Ouarda T.B.M.J.,Canada Research Chair on the Estimation of Hydrometeorological Variables
Environmetrics | Year: 2011

Several hydrological phenomena are described by two or more correlated characteristics. These dependent characteristics should be considered jointly to be more representative of the multivariate nature of the phenomenon. Consequently, probabilities of occurrence cannot be estimated on the basis of univariate frequency analysis (FA). The quantile, representing the value of the variable(s) corresponding to a given risk, is one of the most important notions in FA. The estimation of multivariate quantiles has not been specifically treated in the hydrological FA literature. In the present paper, we present a new and general framework for local FA based on a multivariate quantile version. The multivariate quantile offers several combinations of the variable values that lead to the same risk. A simulation study is carried out to evaluate the performance of the proposed estimation procedure and a case study is conducted. Results show that the bivariate estimation procedure has an analogous behaviour to the univariate one with respect to the risk and the sample size. However, the dependence structure between variables is ignored in the univariate case. The univariate estimates are obtained as special combinations by the multivariate procedure and with equivalent accuracy. Copyright © 2009 John Wiley & Sons, Ltd. Source


Nezhad M.K.,Hydro - Quebec | Chokmani K.,Canada Research Chair on the Estimation of Hydrometeorological Variables | Ouarda T.B.M.J.,Canada Research Chair on the Estimation of Hydrometeorological Variables | Barbet M.,Hydro - Quebec | Bruneau P.,Hydro - Quebec
Hydrological Processes | Year: 2010

In this article, an approach using residual kriging (RK) in physiographical space is proposed for regional flood frequency analysis. The physiographical space is constructed using physiographical/climatic characteristics of gauging basins by means of canonical correlation analysis (CCA). This approach is a modified version of the original method, based on ordinary kriging (OK). It is intended to handle effectively any possible spatial trends within the hydrological variables over the physiographical space. In this approach, the trend is first quantified and removed from the hydrological variable by a quadratic spatial regression. OK is therefore applied to the regression residual values. The final estimated value of a specific quantile at an ungauged station is the sum of the spatial regression estimate and the kriged residual. To evaluate the performance of the proposed method, a cross-validation procedure is applied. Results of the proposed method indicate that RK in CCA physiographical space leads to more efficient estimates of regional flood quantiles when compared to the original approach and to a straightforward regression-based estimator. © 2010 John Wiley & Sons, Ltd. Source

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