For 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 R2, 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 non-parametric 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

Rainfall intensity‐duration‐frequency (IDF) curves are commonly used for the design of water resources infrastructure. Numerous studies reported non‐stationarity in meteorological time series. Neglecting to incorporate non‐stationarities in hydrological models may lead to inaccurate results. The present work focuses on the development of a general methodology that copes with non‐stationarities that may exist in rainfall, by making the parameters of the IDF relationship dependent on the covariates of time and climate oscillations. In the recent literature, non‐stationary models are generally fit on data series of specific durations. In the approach proposed here, a single model with a separate functional relation with the return period and the rainfall duration is instead defined. This model has the advantage of being simpler and extending the effective sample size. Its parameters are estimated with the maximum composite likelihood method. Two sites in Ontario, Canada and one site in California, USA, exhibiting non‐stationary behaviours are used as case studies to illustrate the proposed method. For these case studies, the time and the climate indices Atlantic Multi‐decadal Oscillation (AMO) and Western Hemisphere Warm Pool (WHWP) for the stations in Canada, and the time and the climate indices Southern Oscillation Index (SOI) and Pacific Decadal Oscillation (PDO) for the stations in United States are used as covariates. The Gumbel and the generalized extreme value distributions are used as the time‐dependent functions in the numerator of the general IDF relationship. Results show that the non‐stationary framework for IDF modelling provides a better fit to the data than its stationary counterpart according to the Akaike information criterion. Results indicate also that the proposed generalized approach is more robust than the common approach especially for stations with short rainfall records (e.g., R² of 0.98 compared to 0.69 for duration of 30 min and a sample size of 27 years).

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.