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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 behaviors 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 USA 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 shows that the non‐stationary framework for IDF modeling 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 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).

Quantile estimates are generally interpreted in association with the return period concept in practical engineering. To do so with the peaks‐over‐threshold (POT) approach, combined Poisson‐generalized Pareto distributions (referred to as PD‐GPD model) must be considered. In this article, we evaluate the incorporation of non‐stationarity in the generalized Pareto distribution (GPD) and the Poisson distribution (PD) using, respectively, the smoothing‐based B‐spline functions and the logarithmic link function. Two models are proposed, a stationary PD combined to a non‐stationary GPD (referred to as PD0‐GPD1) and a combined non‐stationary PD and GPD (referred to as PD1‐GPD1). The teleconnections between hydro‐climatological variables and a number of large‐scale climate patterns allow using these climate indices as covariates in the development of non‐stationary extreme value models. The case study is made with daily precipitation amount time series from southeastern Canada and two climatic covariates, the Arctic Oscillation (AO) and the Pacific North American (PNA) indices. A comparison of PD0‐GPD1 and PD1‐GPD1 models showed that the incorporation of non‐stationarity in both POT models instead of solely in the GPD has an effect on the estimated quantiles. The use of the B‐spline function as link function between the GPD parameters and the considered climatic covariates provided flexible non‐stationary PD‐GPD models. Indeed, linear and nonlinear conditional quantiles are observed at various stations in the case study, opening an interesting perspective for further research on the physical mechanism behind these simple and complex interactions.

Wind energy accounts for a small share of the global energy consumption in spite of its widespread availability. One of the obstacles hindering exploitation of wind energy is the lack of proper wind speed assessment models. The wind energy field credibility has occasionally suffered from wind power potential estimation studies that were conducted based on very short wind speed records and which did not give consideration to inter-annual wind variability. The objective of this paper is to examine the long-term variability of wind speed in the United Arab Emirates (UAE) and its teleconnections with various global climate indices by using wind speed collected from six ground stations and a reanalysis dataset. Linear correlation analysis and wavelet analysis were used to characterize the interaction. The modified Mann–Kendall test and linear regression indicated that half of the stations show a significant wind speed trend at the 5% level. The cumulative sum and Bayesian change detection methods indicated that five of the stations present change points. Continuous wavelet transform of wind speed showed biannual periodicity in some stations, in addition to the annual one. Wavelet coherence analysis demonstrated that wind speed in the UAE is mainly associated with the North Atlantic Oscillation, East Atlantic Oscillation, El Nino Southern Oscillation and the Indian Ocean Dipole indices. The first two indices simultaneously modulate wind speed in the summer while the last two influence winter and autumn wind speeds. Step-wise multiple linear regression models were developed to select appropriate predictors among the various climate indices.

The aim of this article is to present a methodology that describes the relationship between two time series according to their oscillatory modes. Cross-wavelet analysis is used to analyse the connection between the outputs of the empirical mode decomposition (EMD). The combined EMD and cross-wavelet methodology is used for the description of the connection between the annual mean streamflow of Quebec rivers and the North Atlantic Oscillation index (NAO). The relationship between the two time series is analysed by cross-wavelet analysis at the level of the mode of oscillation extracted from the EMD algorithm. The resulting cross-spectra are obtained individually for 18 stations and show intermittent intensity in these relationships between 1970 and 1990 for different oscillation modes. To highlight its particularity, the present methodology is compared with the results of a similar combination of multiresolution analysis (MRA) and cross-wavelet analysis. It shows that EMD isolates clearer bands of frequencies than MRA. Finally, a multi-site analysis is proposed, which performs a principal component analysis of the cross-spectra. This analysis illustrates the evolution of the relationships according to the geographic location. Finally, the advantages and limitations of the proposed methodology are discussed.

In this study, we investigate the influence of global climate oscillations on the local temperature and precipitation over the United Arab Emirates (UAE), which is one of the driest regions in the world with very high temperatures and low precipitation. The identification and assessment of remote interactions (teleconnections) are carried out by using ground station and gridded data sets. Monthly rainfall data from six ground stations over the UAE for the period of 1982–2010 is used in this study along with the long-term gridded precipitation and temperature data from the Global Precipitation Climatology Center and Global Historic Climatic Network. Linear correlations, wavelet analysis, and cross-wavelet analysis have been applied to identify the relation between climate indices and precipitation (temperature). The analysis reveals that the strong variability in precipitation is closely associated with the Southern Oscillation Index (SOI) and the Indian Ocean Dipole Index (IOD) during the months of August–March, September–January, respectively. In case of temperature, the strong variability is associated with the North Atlantic Oscillation Index (NAO) and the East Atlantic Oscillation Index (EAO) during the months of April–October, July–December. Spatial analysis of cross-wavelet reveals that the winter precipitation is significantly influenced by SOI and temperature during summer by the NAO. This research concludes that the negative phases of SOI (NAO) play a significant role in the increase of precipitation (decrease in summer temperatures) over the UAE region.

This study discusses the evolution of temperature, precipitation and soil moisture patterns over the United Arab Emirates (UAE) region, which is characterized by hot climate and scarce precipitation. A stochastic model that reproduces non-stationary oscillation (NSO) processes by utilizing ensemble empirical mode decomposition (EEMD) and non-parametric techniques is used to predict the evolution of temperature, precipitation and soil moisture. The long-term gridded temperature, precipitation and soil moisture data from the Global Historic Climatic Network, Global Precipitation Climatology Center and Climate Prediction Center are used in this study. The data consists of 65 years of average monthly temperature and soil moisture measurements and 110 years of average monthly precipitation over the UAE. The last 20 years of observations of temperature, precipitation and soil moisture are reserved for the validation of the methodology and the rest of the data is used for prediction. The results show that future long-term patterns are well captured by the model and hence confirm the potential of the EEMD technique and the NSO resampling (NSOR) modelling process. The model is also used for forecasting the evolution of temperature, precipitation and soil moisture patterns for the next 30 years. This procedure is finally used to produce the spatial patterns of temperature, precipitation and soil moisture. Significant increase in temperature and decrease in precipitation and soil moisture are observed particularly over Abu Dhabi. The spatial map shows strong increase (decrease) in temperature (precipitation and soil moisture) over most of the UAE. The results are quite different for the south eastern part of the UAE. Western parts of the UAE are projected to see larger temperature increases than other parts. The results are coherent with the previous findings over this region.

This study proposes a multi-site statistical downscaling model (MSDM), which can downscale daily precipitation series at multiple sites in a regional study area by utilizing Global Climate Models' (GCMs) precipitation outputs directly. The at-site precipitation occurrences and amount characteristics are reproduced by first-order Markov chain and probability mapping approaches, respectively. The spatial coherence of precipitation series among multiple sites is reproduced by adding correlated random noise series to GCM precipitation outputs. The model is applied for two regional study areas in southern Quebec (Canada). The MSDM results are compared to those of the local intensity scaling (LOCI) model, which is a single site downscaling model that uses GCM precipitation outputs. Both models reproduce probabilities of precipitation occurrence and mean wet-day precipitation amounts. However, the MSDM reproduces the observed precipitation occurrence Lag-1 autocorrelation, the standard deviation of the wet-day precipitation amounts, maximum 3-d precipitation total (R3days), and 90th percentile of the rain day amount (PREC90) better than the LOCI model. The MSDM also accurately reproduces cross-site correlations of precipitation occurrence and amount among multiple observation series.

The potential impact of large-scale climate patterns of El Nino Southern Oscillation (ENSO), North Atlantic Oscillation (NAO) and Pacific Decadal Oscillation (PDO) in modulating precipitation regimes across the upper Medjerda River Basin in northern Tunisia is assessed. As the case study is located in the Mediterranean region (North Africa), the regional Mediterranean Oscillation (MOAC) and Western Mediterranean Oscillation (WeMO) are also investigated. Six precipitation time series are also observed. Strong correlations are identified between ENSO and precipitation series at lag - 2 years. Extreme ENSO years are reflected in the precipitation as periods of severe water deficit or excess. Wavelet spectra driven to seasonal precipitation reveal that precipitation is organized in preferred bands with distinct activities in each scale band, and that most of the precipitation variance is explained by the 2-8-year scales. Using cross-wavelet analysis, climate patterns that are most associated with precipitation variability are identified. The analysis demonstrates also that precipitations are simultaneously controlled by different climate patterns. Particularly, the influence of ENSO on precipitation is stronger as well as that of PDO and MO. Results indicate that precipitation variability at the upper Medjerda River Basin is associated with global-scale ENSO processes at the annual as well as seasonal time scales and is aligned with changing phase difference between periods. Moreover, separation of annual precipitation into two seasons reveals statistically significant associations between El Nino and La Nina phases of ENSO with dry and wet seasonal precipitation, respectively. Complementing this, three streamflow records with length up to 104 years are used, and relationships with rainfall series are analysed using wavelets. A strong coherence between rainfall and streamflow observations is found and justifies undertaking the study of climate-streamflow relationships where ENSO exhibits potential impacts on annual streamflows.

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. Copyright © 2011 Royal Meteorological Society

A Bayesian Normal Homogeneity Test (BNHT) for the detection of artificial discontinuities in climatic series is presented. The test is simple to use and allows the integration of prior knowledge on the date of change from various sources of information (e.g. metadata or expert belief) in the analysis. The performance of the new test was evaluated on synthetic series with similar statistical properties as observed total annual precipitation in the southern and central parts of the province of Quebec, Canada. Different priors were used to investigate the sensitivity of the test to the choice of priors. It was found that (1) high-prior probability of no change yields low false detection rates on the homogeneous series; (2) the test has a very high power of detection on series with a single shift (the best power of detection if compared with previous methods applied to the same synthetic series); (3) shifts having a small magnitude are detectable with a low prior probability of no change and (4) when applied to series with multiple shifts with a segmentation procedure and a high probability of no change, the test proved to be performing well in detecting multiple shifts (as performing as the best techniques previously applied to the same synthetic series). An example of application to total annual precipitation in Quebec City, Canada is also presented to illustrate (1) a case for which the results are not affected by the choice of the prior parameters and (2) a case for which information about potential changes found in the metadata was integrated in the analysis and allowed the detection of a change that would not have been detected with a non-informative prior.

Positive/upward shifts in the rate of occurrence of heat spells can considerably impact socioeconomic sectors. Particularly, populous urban areas and centers of regional socioeconomic activities are more vulnerable to the enhanced activity of heat spells. In this study, 24 time series of annual counts of summer-season (June–August) heat spells are derived from homogenized records of daily minimum and maximum temperatures (i.e. Tmin and Tmax) observed at McTavish station, located in the center of Montreal (Canada), over the period 1896–1991. Twelve of these time series, which fulfill the assumption of the Poisson process for heat spell occurrences, are examined for abrupt changes in the rate of occurrences using hierarchical Bayesian change-point approach. In these analyses, a heat spell is defined as an extreme climate event with Tmin and Tmax simultaneously above selected thresholds and a duration ≥ 1-day. The results of the Bayesian change-point analyses suggest structural inhomogeneities within the heat spell observations, i.e. the results do not support abrupt changes for all time series of annual counts of heat spells; this may not have been possible to detect by studying heat spells defined on the basis of just a single combination of Tmin and Tmax thresholds. Furthermore, the overall results of the Bayesian change-point analyses and those of commonly employed nonparametric trend detection and estimation techniques, when applied to change-point free smaller samples, suggest that there is inadequate evidence in favor of increased activity of heat spells in Montreal during the third last and second last decades (i.e. 1970s and 1980s) of the 20th century, which are the most recent decades of the observation period analyzed. Copyright © 2006 Royal Meteorological Society

The use of the standard normal homogeneity test (SNHT) for homogenization of climatological records and studying changes in their patterns has increased in recent years. The critical values of this test were originally developed for sample sizes ranging from 10 to 250 using relatively short Monte Carlo simulations (MCS). The objective of this paper is to improve the critical values of the SNHT and extend them to large sample sizes. The critical values, along with their standard errors, are developed for 108 sample sizes ranging from 10 to 50 000 using 30 replicates of one million samples for each sample size. These critical values mimic the tails of the SNHT statistic better and therefore are more accurate, and would be useful for making correct statistical inference for climate data homogenization and assessment of climate variability in future studies. Copyright © 2006 Royal Meteorological Society

Heatwaves can have adverse affects on public health and can considerably impact social and economic activities. Climate-change scenarios have shown that the temperature regime will likely be modified significantly over the course of the next 50 years and more. The frequency of occurrence and amplitude of heatwaves may be impacted by changes in the temperature regime. A heatwave can best be characterized by its magnitude and duration. Thus, both of these characteristics need to be studied together. This paper presents an approach based on the principle of parsimony by extending methodologies developed for the analysis of extreme hydrological events: the index-flood method and the regional flood frequency approach to perform at-site heatwave–duration–frequency (HDF) analysis. The approach is very similar to intensity–duration–frequency often used in the analysis of extreme precipitation events. The HDF analysis is performed using annual maximum series of heatwaves of 1–10 days duration observed at four selected sites in southern Quebec with long (i.e. >80 years) time series covering most of the 20th century. The two main tasks in this approach consist of modelling (1) µ(D), a function that relates mean heatwave to its duration, and (2) g(T), a function describing the at-site dimensionless growth curve, where T is the return period. It is found that the µ(D) function can best be modelled using a relationship of the form µ(D) = aDb (where a and b are parameters to be estimated). The dimensionless growth curve g(T) was modelled using the generalized extreme value distribution. The HDF approach can model various quantiles of heatwaves in a fairly acceptable manner when assessed on the basis of relative root-mean-square error and 95% bootstrap confidence intervals. An analysis of the pattern of occurrences of heatwaves indicates that heatwaves of short durations (1–5 days) has shifted over time and occur earlier in the summer than before. Median heatwaves occur during the second and third weeks of July and the majority of heatwaves are concentrated over the time interval from the last 10 days of June to the first 10 days of August. Copyright © 2005 Royal Meteorological Society.