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Québec, Canada

Modarres R.,INRS ETE
Water Resources Management | Year: 2010

The spatial variation of the statistical characteristics of the extreme dry events, such as the annual maximum dry spell length (AMDSL), is a key practice for regional drought analysis and mitigation management. For arid and semi arid regions, where the data set is short and insufficient, the regionalization methods are applied to transfer at-site data to a region. The spatial variation and regional frequency distribution of annual maximum dry spell length for Isfahan Province, located in the semi arid region of Iran, was investigated using a daily database compiled from 31 rain gauges and both L-moment and multivariate analysis. The use of L-moment method showed a homogeneous region over entire province with generalized logistic distribution (GLOG) as the regional frequency distribution. However, the cluster analysis performed two regions in west and east of the province where L-moment method demonstrated the homogeneity of the regions and GLOG and Pearson Type III (PIII) distributions as regional frequency distributions for each region, respectively. The principal component analysis was applied on at-site statistics of AMDSL and found the L-coefficient of skewness (LCs) and maximum AMDSL the main variables describing the spatial variation of AMDSL over the Isfahan Province. The comparison of two homogeneous regions also proved the difference between two regions. Therefore, this study indicates the advantage of the use of multivariate methods with L-moment method for hydrologic regionalization and spatial variation of drought statistical characteristics. © Springer Science+Business Media B.V. 2009. Source

Lee T.,INRS ETE | Salas J.D.,Colorado State University | Prairie J.,University of Colorado at Boulder
Water Resources Research | Year: 2010

Stochastic streamflow generation is generally utilized for planning and management of water resources systems. For this purpose, a number of parametric and nonparametric models have been suggested in literature. Among them, temporal and spatial disaggregation approaches play an important role particularly to make sure that historical variance-covariance properties are preserved at various temporal and spatial scales. In this paper, we review the underlying features of existing nonparametric disaggregation methods, identify some of their pros and cons, and propose a disaggregation algorithm that is capable of surmounting some of the shortcomings of the current models. The proposed models hinge on k-nearest neighbor resampling, the accurate adjusting procedure, and a genetic algorithm. The models have been tested and compared to an existing nonparametric disaggregation approach using data of the Colorado River system. It has been shown that the model is capable of (1) reproducing the season-to-season correlations including the correlation between the last season of the previous year and the first season of the current year, (2) minimizing or avoiding the generation of flow patterns across the year that are literally the same as those of the historical records, and (3) minimizing or avoiding the generation of negative flows. In addition, it is applicable to intermittent river regimes. Copyright 2010 by the American Geophysical Union. Source

Lee T.,Gyeongsang National University | Ouarda T.B.M.J.,INRS ETE | Ouarda T.B.M.J.,Masdar Institute of Science and Technology
Journal of Geophysical Research: Atmospheres | Year: 2011

Long-term nonstationary oscillations (NSOs) are commonly observed in climatological data series such as global surface temperature anomalies (GSTA) and low-frequency climate oscillation indices. In this work, we present a stochastic model that captures NSOs within a given variable. The model employs a data-adaptive decomposition method named empirical mode decomposition (EMD). Irregular oscillatory processes in a given variable can be extracted into a finite number of intrinsic mode functions with the EMD approach. A unique data-adaptive algorithm is proposed in the present paper in order to study the future evolution of the NSO components extracted from EMD. To evaluate the model performance, the model is tested with the synthetic data set from Rössler attractor and with GSTA data. The results of the attractor show that the proposed approach provides a good characterization of the NSOs. For GSTA data, the last 30 observations are truncated and compared to the generated data. Then the model is used to predict the evolution of GSTA data over the next 50 years. The results of the case study confirm the power of the EMD approach and the proposed NSO resampling (NSOR) method as well as their potential for the study of climate variables. Copyright 2011 by the American Geophysical Union. Source

This paper presents an implementation of the convolutional perfectly matched layer for the velocity-stress formulation of poroviscoelastic equations in anisotropic media, with high-order time and space formulations. After its introduction by Bérenger in 1994 for Maxwell's equation, the perfectly matched layer (PML) quickly became the standard approach to absorb outgoing waves on a numerical mesh. Subsequent developments include generalization and formulation in terms of a convolutional operator and auxiliary differential equations that improve efficiency and allow for the implementation of high-order time integration schemes. In this work, a fourth-order Runge-Kutta scheme is employed. Also, with the convolutional formulation, the wavefields need not be split as in the original formalism. Such unsplit PMLs allow for the implementation with a pseudospectral operator as presented in this paper. Although efforts have been deployed for the optimization of PML parameters, such an approach is not always tractable, especially for seismic wave propagation because different types of waves interact with the medium. Hence, the performance of the implementation is evaluated through a series of numerical experiments. Tests are performed for waves impinging on the PML interface both at normal and grazing incidence, for both isotropic and anisotropic media. The results highlight the advantage of the convolutional formulation for waves at grazing incidence and show that anisotropic media require a larger number of absorbing layers to achieve performances comparable to those obtained in isotropic media. © 2011 Elsevier Ltd. Source

Modarres R.,INRS ETE | Sarhadi A.,Isfahan University of Technology
Global and Planetary Change | Year: 2011

Iran is a large country with diverse geophysical and climatic conditions which are influenced by both large atmospheric circulation patterns and local effects. The density of rainfall station network of Iran is not enough for rainfall estimation at ungauged regions.Therefore, rainfall regionalization should be used to extend rainfall data to regions where rainfall data are not available. The aim of this study is to use cluster analysis and L-moment methods together to quantify regional rainfall patterns of Iran using annual rainfall of 137 stations for the period of 1952-2003. The cluster analysis follows "Ward's method" and shows eight regions of rainfall in Iran. The homogeneity test of L-moments shows that most of these regions are homogeneous. Using the goodness-of-fit test, Z Dist, the regional frequency distribution functions for each group are then selected. The 3-parameter Log Normal (LN3), Generalized Extreme Value (GEV) and generalize logistic (GLOG) distributions are selected for the first, second and the remaining 6 regions of rainfall over Iran, respectively. However, because of different rainfall generating mechanisms in Iran such as elevation, sea neighborhood and large atmospheric circulation systems, no parent distribution could be found for the entire country. © 2010 Elsevier B.V. Source

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