a b s t r a c tFor the evaluation of wind energy potential, probability density functions (pdfs) are usually used to describe wind speed distributions. The selection of the appropriate pdf reduces the wind power estimation error. The most widely used pdf for wind energy applications is the 2-parameter Weibull probability density function. In this study, a selection of pdfs are used to model hourly wind speed data recorded at 9 stations in the United Arab Emirates (UAE). Models used include parametric models, mixture models and one non-parametric model using the kernel density concept. A detailed comparison between these three approaches is carried out in the present work. The suitability of a distribution to fit the wind speed data is evaluated based on the log-likelihood, the coefficient of determination R 2 , the Chi-square statistic and the Kolmogorov-Smirnov statistic. Results indicate that, among the one-component parametric distributions, the Kappa and Generalized Gamma distributions provide generally the best fit to the wind speed data at all heights and for all stations. The Weibull was identified as the best 2-parameter distribution and performs better than some 3-parameter distributions such as the Generalized Extreme Value and 3-parameter Lognormal. For stations presenting a bimodal wind speed regime, mixture models or nonparametric models were found to be necessary to model adequately wind speeds. The two-component mixture distributions give a very good fit and are generally superior to non-parametric distributions.
21Arid and semiarid climates occupy more than 1/4 of the land surface of our planet, and 22 42Results show that a decreasing shift in the mean has occurred in the total annual rainfall 43 and the number of rainy days at all four stations, and that the variance has decreased for 44 3 the total annual rainfall at two stations. Frequency analysis was also performed on data 45 before and after the change point. Results show that rainfall quantile values are 46 significantly lower after 1999. The change point around the year 1999 is linked to various 47 52 Southern Oscillation Index. 55 56 4 1. Introduction 57 The United Arab Emirates (UAE) is located in the arid southeast part of the Arabian 58Peninsula. This region is characterized by very scarce and variable rainfall. Without 59 permanent surface water resources, groundwater resources were extensively used for 60 water supply. Recently, strong economic and demographic growth in UAE has put even 61 more stress on water resources. The deficit in water availability between the increasing 62 demand and water resources availability has been met by non-conventional sources such 63 as desalinated water. Groundwater aquifers rely on recharge from rainfall. For this 64 purpose, a large number of small recharge dams were built to capture rainfall water from 65 infrequent but usually intense events. For optimal water resources management, it is 66 important to understand the temporal evolution of rainfall. The main objective of the 67 present study is to analyze rainfall trends in the arid region of the UAE. The variables 68 analyzed in this study are: the total annual, seasonal and monthly rainfall; the annual, 69 seasonal and monthly maximum rainfall, and the number of rainy days per year, season 70 and month. 71A relatively limited number of studies dealing with rainfall trend analysis in arid and 72 semi-arid regions have been conducted, with very few dealing with desert environments 73 and the Arabian Peninsula. Modarres and Sarhadi (2009) found that, in Iran, annual 74 rainfall is decreasing at 67% of 145 stations studied while annual maximum rainfall is 75 decreasing at only 50% of the stations. However, only 24 stations exhibit significantly 76 negative trends. Törnros (2010) reported a statistically significant decreasing trend at 5 77 stations among a total of 37 stations in the southeastern Mediterranean region. 78Decreasing but non-significant trends in rainfall characteristics were found in the region 79
The present paper provides a brief review of statistical models that are commonly used in the estimation of low flows both at sites with a reliable streamflow record and sites remote from data sources. Opportunities are identified for the regional estimation of low-flow characteristics at ungauged sites. The adaptation of the neighbourhood regionalization approach, commonly used in regional flood frequency analysis, can be extended to low-flow variables. Estimation approaches extending the usefulness of recession information in regional low-flow frequency analysis to ungauged sites using a canonical correlation analysis approach for the identification of hydrological neighbourhoods is described. The validity of recession parameters when estimated from very short hydrological data records is also discussed. Promising new directions for future research in the field of statistical low-flow frequency estimation are identified. Résumé : La présente communication offre un bref survol des modèles statistiques couramment utilisés pour l'estimation des basses eaux tant à des sites offrant un enregistrement fiable des débits d'un cours d'eau qu'à des sites éloignés des sources de données. Des possibilités d'estimation régionale des caractéristiques des basses eaux dans des sites non jaugés sont décrites. L'adaptation de l'approche de régionalisation par voisinage, couramment employée dans l'analyse régionale de la fréquence des crues, peut être étendue aux variables des basses eaux. Sont également décrites certaines approches d'estimation qui accroissent l'utilité des données sur les décrues dans l'analyse régionale de la fréquence des basses eaux pour des sites non jaugés, et ce, à l'aide d'une approche de l'analyse de corrélation canonique pour l'identification des voisinages hydrologiques. Il est aussi question de la validité des paramètres de décrue lorsque les estimations reposent sur des enregistrements de données hydrologiques s'étalant sur de très courtes périodes. De nouvelles orientations prometteuses pour les recherches futures dans le domaine de l'estimation statistique de la fréquence de l'étiage sont également dégagées.
The log-linear regression model is one of the most commonly used models to estimate flood quantiles at ungauged sites within the regional frequency analysis (RFA) framework. However, hydrological processes are naturally complex in several aspects including nonlinearity. The aim of the present paper is to take into account this nonlinearity by introducing the generalized additive model (GAM) in the estimation step of RFA. A neighborhood approach using canonical correlation analysis (CCA) is used to delineate homogenous regions. GAMs possess a number of advantages such as flexibility in shapes of the relationships as well as the distribution of the output variable. The regional model is applied on a dataset of 151 hydrometrical stations located in the province of Québec, Canada. A stepwise procedure is employed to select the appropriate physiometeorological variables. A comparison is performed based on different elements (regional model, variable selection, and delineation). Results indicate that models using GAM outperform models using the log-linear regression as well as other methods applied to this dataset. In addition, GAM is flexible and allows for the inclusion and presentation of nonlinear effects of explanatory variables, in particular, basin area effect (scale). Another finding is the reduced effect of CCA delineation when combined with GAM.
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