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De nombreuses méthodes régionales ont été développées pour améliorer l'estimation de la distribution des débits de crues en des sites où l'on dispose de peu d'information ou même d'aucune information. Cet article présente une synthèse de modèles hydrologiques utilisés en France et au Québec (Canada), à l'occasion d'un séminaire relatif aux " méthodes d'estimation régionale en hydrologie " tenu à Lyon en mai 1997. Les modèles français sont fortement liés à une technique d'extrapolation de la distribution des crues, la méthode du Gradex, qui repose sur l'exploitation probabiliste conjointe des séries hydrométriques et pluviométriques. Ceci explique les deux principaux volets d'études régionales pratiquées en France : les travaux liés à la régionalisation des pluies et ceux liés à la régionalisation des débits. Les modèles québecois comprennent généralement deux étapes : la définition et la détermination de régions hydrologiquement homogènes, puis l'estimation régionale, par le transfert à l'intérieur d'une même région de l'information des sites jaugés à un site non-jaugé ou partiellement jaugé pour lequel on ne dispose pas d'information suffisante. Après avoir donné un aperçu des méthodes pratiquées dans les deux pays, une discussion dégage les caractéristiques principales et les complémentarités des différentes approches et met en évidence l'intérêt de développer une collaboration plus étroite pour mieux tenir compte des particularités et des complémentarités des méthodes développées de part et d'autre. Une des pistes évoquées consiste à combiner l'information régionale pluviométrique (approche française) et hydrométrique (approche québécoise)., Design flood estimates at ungauged sites or at gauged sites with short records can be obtained through regionalization techniques. Various methods have been employed in different parts of the world for the regional analysis of extreme hydrological events. These regionalization approaches make different assumptions and hypotheses concerning the hydrological phenomena being modeled, rely on various types of continuous and non-continuous data, and often fall under completely different theories. A research seminar dealing with " regional estimation methods in hydrology " took place in Lyon during the month of May 1997, and brought together various researchers and practitioners mainly from France and the Province of Quebec (Canada). The present paper is based on the conferences and discussions that took place during this seminar and aims to review, classify, comparatively evaluate, and potentially propose improvements to the most prominent regionalization techniques utilized in France and Quebec. The specific objectives of this paper are :· to review the main regional hydrologic models that have been proposed and commonly used during the last three decades ;· to classify the literature into different groups according to the origin of the method, its specific objective, and the technique it adopts ; · to present a comprehensive evaluation of the characteristics of the methods, and to point out the hypotheses, data requirements, strengths and weaknesses of each particular one ; and · to investigate and identify potential improvements to the reviewed methods, by combining and extending the various approaches and integrating their particular strengths.Regionalization approaches adopted in France include the Gradex method which represents a simplified rainfall-runoff model which provides estimates of flood magnitudes of given probabilities and is based on rainfall data which often cover longer periods and are more reliable than flow data (Guillot and Duband, 1967 ; CFGB, 1994). It is based on the hypotheses that beyond a given rainfall threshold (known as the pivot point), all water is transformed into runoff, and that a rainfall event of a given duration generates runoff for the same length of time. These hypotheses are equivalent to assuming that, beyond the pivot point, the rainfall-runoff relationship is linear and that the precipitation and runoff probability curves are parallel on a Gumbel plot.In Quebec (and generally in North America), regional flood frequency analysis involves usually two steps : delineation of homogeneous regions, and regional estimation. In the first step, the focus is on identifying and regrouping sites which seem sufficiently homogeneous or sufficiently similar to the target ungauged site to provide a basis for information transfer. The second step of the analysis consists in inferring flood information (such as quantiles) at the target site using data from the stations identified in the first step of the analysis. Two types of " homogeneous " regions can be proposed : fixed set regions (geographically contiguous or non-contiguous) and neighborhood type of regions. The second type includes the methods of canonical correlation analysis and of the regions of influence. Regional estimation can be accomplished using one of two main approaches : index flood or quantile regression methods.The results of this work indicate that the philosophies of regionalization and the methods utilized in France and Quebec are complementary to each other and are based on different needs and outlooks. While the approaches followed in France are characterized by strong conceptual and geographic aspects with an emphasis on the utilization of information related to other phenomena (such as precipitations), the approaches adopted in Quebec rely on the strength of their statistical and stochastic components and usually condense the spatial and temporal information to a realistic functional form. This dissimilarity in the approaches being followed on either side may be originated by the distinct topographic and climatic characteristics of each region (France and Quebec) and by the differences in basin sizes and hydrometeorologic network densities. The conclusions of the seminar point to the large potential of improvements in regional estimation methods, which may result from an enhanced exchange between scientists from both sides : indeed, there is much to gain from learning about the dissimilarities between the various approaches, comparing their performances, and devising new methods that combine their individual strengths. Hence, the Gradex method for example could benefit from an increased utilization of regional flood information, while flood regionalization methods utilized in Quebec could gain much from the formalization of the use of rainfall information and from the integration of an improved modeling of physical hydrologic phenomena. This should result in the enhancement of the efficiency of regional estimation methods and their ability to handle various practical conditions.It is hoped that this research will contribute towards closing the gap between French and Quebec literature, and more generally between the European and the North American hydrological schools of thought, by narrowing the large literature that is available, by providing the necessary cross-evaluation of regional flood analysis models, and by providing comprehensive propositions for improved approaches for regional hydrologic modeling.

Dans le domaine de la prévision des débits, une grande variété de méthodes sont disponibles: des modèles stochastiques et conceptuels mais aussi des approches plus novatrices telles que les réseaux de neurones artificiels, les modèles à base de règles floues, la méthode des k plus proches voisins, la régression floue et les splines de régression. Après avoir effectué une revue détaillée de ces méthodes et de leurs applications récentes, nous proposons une classification qui permet de mettre en lumière les différences mais aussi les ressemblances entre ces approches. Elles sont ensuite comparées pour les problèmes différents de la prévision à court, moyen et long terme. Les recommandations que nous effectuons varient aussi avec le niveau d'information a priori. Par exemple, lorsque l'on dispose de séries chronologiques stationnaires de longue durée, nous recommandons l'emploi de la méthode non paramétrique des k plus proches voisins pour les prévisions à court et moyen terme. Au contraire, pour la prévision à plus long terme à partir d'un nombre restreint d'observations, nous suggérons l'emploi d'un modèle conceptuel couplé à un modèle météorologique basé sur l'historique. Bien que l'emphase soit mise sur le problème de la prévision des débits, une grande partie de cette revue, principalement celle traitant des modèles empiriques, est aussi pertinente pour la prévision d'autres variables., A large number of models are available for streamflow forecasting. In this paper we classify and compare nine types of models for short, medium and long-term flow forecasting, according to six criteria: 1. validity of underlying hypotheses, 2. difficulties encountered when building and calibrating the model, 3. difficulties in computing the forecasts, 4. uncertainty modeling, 5. information required by each type of model, and 6. parameter updating. We first distinguish between empirical and conceptual models, the difference being that conceptual models correspond to simplified representations of the watershed, while empirical model only try to capture the structural relationships between inputs to the watershed and outputs, such as streamflow. Amongst empirical models, we distinguish between stochastic models, i.e. models based on the theory of probability, and non-stochastic models. Three types of stochastic models are presented: statistical regression models, Box-Jenkins models, and the nonparametric k-nearest neighbor method. Statistical linear regression is only applicable for long term forecasting (monthly flows, for example), since it requires independent and identically distributed observations. It is a simple method of forecasting, and its hypotheses can be validated a posteriori if sufficient data are available. Box-Jenkins models include linear autoregressive models (AR), linear moving average models (MA), linear autoregressive - moving average models (ARMA), periodic ARMA models (PARMA) and ARMA models with auxiliary inputs (ARMAX). They are more adapted for weekly or daily flow forecasting, since the yallow for the explicit modeling of time dependence. Efficient methods are available for designing the model and updating the parameters as more data become available. For both statistical linear regression and Box-Jenkins models, the inputs must be uncorrelated and linearly related to the output. Furthermore, the process must be stationary. When it is suspected that the inputs are correlated or have a nonlinear effect on the output, the k-nearest neighbor method may be considered. This data-based nonparametric approach simply consists in looking, among past observations of the process, for the k events which are most similar to the present situation. A forecast is then built from the flows which were observed for these k events. Obviously, this approach requires a large database and a stationary process. Furthermore, the time required to calibrate the model and compute the forecasts increases rapidly with the size of the database. A clear advantage of stochastic models is that forecast uncertainty may be quantified by constructing a confidence interval. Three types of non-stochastic empirical models are also discussed: artificial neural networks (ANN), fuzzy linear regression and multivariate adaptive regression splines (MARS). ANNs were originally designed as simple conceptual models of the brain. However, for forecasting purposes, these models can be thought of simply as a subset of non linear empirical models. In fact, the ANN model most commonly used in forecasting, a multi-layer feed-forward network, corresponds to a non linear autoregressive model (NAR). To capture the moving average components of a time series, it is necessary to use recurrent architectures. ANNs are difficult to design and calibrate, and the computation of forecasts is also complex. Fuzzy linear regression makes it possible to extract linear relationships from small data sets, with fewer hypotheses than statistical linear regression. It does not require the observations to be uncorrelated, nor does it ask for the error variance to be homogeneous. However, the model is very sensitive to outliers. Furthermore, a posteriori validation of the hypothesis of linearity is not possible for small data sets. MARS models are based on the hypothesis that time series are chaotic instead of stochastic. The main advantage of the method is its ability to model non-stationary processes. The approach is non-parametric, and therefore requires a large data set.Amongst conceptual models, we distinguish between physical models, hydraulic machines, and fuzzy rule-based systems. Most conceptual hydrologic models are hydraulic machines, in which the watershed is considered to behave like a network of reservoirs. Physical modeling of a watershed would imply using fundamental physical equations at a small scale, such as the law of conservation of mass. Given the complexity of a watershed, this can be done in practice only for water routing. Consequently, only short term flow forecasts can be obtained from a physical model, since the effects of precipitation, infiltration and evaporation must be negligible. Fuzzy rule-based systems make it possible to model the water cycle using fuzzy IF-THEN rules, such as IF it rains a lot in a short period of time, THEN there will be a large flow increase following the concentration time. Each fuzzy quantifier is modeled using a fuzzy number to take into account the uncertainty surrounding it. When sufficient data are available, the fuzzy quantifiers can be constructed from the data. In general, conceptual models require more effort to develop than empirical models. However, for exceptional events, conceptual models can often provide more realistic forecasts, since empirical models are not well suited for extrapolation.A fruitful approach is to combine conceptual and empirical models. One way of doing this, called extended streamflow prediction or ESP, is to combine a stochastic model for generating meteorological scenarios with a conceptual model of the watershed.Based on this review of flow forecasting models, we recommend for short term forecasting (hourly and daily flows) the use of the k-nearest neighbor method, Box-Jenkins models, water routing models or hydraulic machines. For medium term forecasting (weekly flows, for example), we recommend the k-nearest neighbor method and Box-Jenkins models, as well as fuzzy-rule based and ESP models. For long term forecasting (monthly flows), we recommend statistical and fuzzy regression, Box-Jenkins, MARS and ESP models. It is important to choose a type of model which is appropriate for the problem at hand and for which the information available is sufficient. Each type of model having its advantages, it can be more efficient to combine different approaches when forecasting streamflow.

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.

Specific conductivity, pH, dissolved oxygen, carbon,phosphorous, and nitrogen species were measured at 36 stations inthe Richibucto River drainage basin, including the estuary, in NewBrunswick, Canada, over the six-year period 1996 through 2001.Each station was sampled between 1 and 26 times (mean = 7.5,standard deviation = 6.0) during the ice free seasons withoutregard to tide. There was significant variance among stations inmost parameters. Principal component analysis (PCA) was used toidentify the processes explaining the observed variance in waterquality. Because of the high variability in specific conductance, sta-tions were first grouped in a freshwater subset and an estuarine(brackish water) subset. For freshwater stations, most of the vari-ance in water quality was explained by pH and total organic car-bon, as well as high nutrient concentrations. These high nutrientconcentrations, along with water salinity, which varies with flowand tides, are also important in determining water quality variabil-ity in brackish water. It is recommended that water quality param-eters that were found to explain most of the variance by PCA bemonitored more closely, as they are key elements in understandingthe variability in water quality in the Richibucto drainage basin.Cluster analyses showed that high phosphorous and nitrate concen-trations were mostly found in areas of peat runoff, tributariesreceiving treated municipal effluent, and lentic zones upstream ofculverts. Peat runoff was also shown to be acidic, whether it isrunoff from a harvested area or a natural bog.(KEY TERMS: water quality; statistical analysis; multivariate;watershed management; peat.)St-Hilaire, Andre, Guy Brun, Simon C. Courtenay, Taha B.M.J. Ouarda, AndrewD. Boghen, and Bernard Bobee, 2004. Multivariate Analysis of Water Quality inthe Richibucto Drainage Basin (New Brunswick, Canada). Journal of the Ameri-can Water Resources Association (JAWRA) 40(3):691-703.

Log-linear models are frequently used in hydrology, especially for the regional estimation of flood volumes based on the physiographic data of a set of basins. A log-linear model describes a linear relationship between the log of a dependant variable and independent variables which are functions of parameters, of which the value remains to be determined. It is determined by using a set of basins with known values of dependant and independent variables. The model is then used to obtain a prediction for the dependant variable logarithm of a basin of interest, based on the known values of independent variables in the model. This prediction is unbiased with relation to the log of the target variable. However, the exponential value of this prediction is biased with relation to the target variable. This paper addresses the measures to correct the bias in the prediction, which is introduced by exponentiation; the impacts on the variance of the ensuing predictions is also discussed. Key words: bias, transformation, log-linear model.[Journal translation]

Flood risk management for residences requires an economical analysis involving the mean annual damage by floods, taking into account the whole range of probability of floods and the cost of projected fluvial enhancements and measures, taking also into account the residual level of risk. Efficient methods are therefore necessary to estimate these risk values. The proposed approach is of a "distributed" type; it involves numerical modeling for estimating "residential submersion depth", a variable, which explains most of the direct damages to residences. The method relies on an individualized georeferenced definition of each residence. Measured submersion data and the compensations obtained from the huge Saguenay flood in 1996 (Ville de Laterrière) were used to build empirical laws based on submersion. Four distinct relationships were established: residences with or without a basement and valued below or above $50,000 each were assigned a specific relationship. With these relationships, direct residential damages in Laterrière were assessed by using only simulation results at the georeferenced position of sector residences as input. It was then possible to evaluate the total amount of direct home damages in Laterrière.Key words: flood, risk, damage rating curves, Saguenay floods, numerical modeling, predictive model, georeference, geographic information system.

This study has been conducted within the "Fonds pour la formation de chercheurs et l'aide a la recherche (FCAR) - Action concertee RADARSAT" program. The study shows the potential of RADARSAT-1 standard mode data (S1 and S7 beams) for mapping natural and exploited wetlands in southern Quebec. The best period for the acquisition of S1 beam data is during the growing season. However, an S7 beam mode image acquired in February (winter) can help to discriminate different vegetation densities within wetlands. With a maximum likelihood classification method, the data set giving the best results is the winter S7 image and two summer S1 images (May 7 or June 11 and July 28 or August 3). The large wetlands can be easily classified amongst other areas (i.e., agricultural, open water, forest, etc.) by this method and data set, but the different categories of vegetation communities within wetlands cannot be well discriminated. However, the use of a texture parameter (mean, 7 × 7 windows) can significantly improve the...

Most often, flood frequency analysis describes a flood event only by its peak. However, the true flood severity is also defined by its volume and duration. This paper presents an approach allowing flood events to be considered in a more complete way: the flood-duration–frequency (QdF) approach. In a similar manner to the rainfall intensity–duration–frequency analysis, averaged discharges are computed over different fixed durations d. For each duration a frequency distribution of maximum averaged discharges is studied. Finally, a continuous formulation is fitted, as a function of the return period T and the duration d over which discharges have been averaged. The proposed model has been tested for 169 catchments in the provinces of Quebec and Ontario, Canada. The shapes of the QdF curves enabled us to define different types of flood behaviour and to identify the corresponding geographic regions. This mapping of flood behaviour was the basis for the delineation of seven homogeneous geographical regions, containing catchments having the same hydrological behaviour as is required for regional flood frequency analysis. Copyright © 2003 John Wiley & Sons, Ltd.

In recent years in North America, a number of government agencies and industries have begun to reinvest in meteorological networks. This investment must be based on sound scientific advice. Increased meteorological station network density can be beneficial for a number of purposes, including flood forecasting. This study aimed at investigating the impact of network density at two temporal scales, i.e. for the estimation of total annual precipitation and for the estimation of daily precipitation during specific rain events. This was done using kriging as a means to estimate the spatial distribution and variance of rainfall. Kriged precipitation from two network scenarios (sparse and dense) were used as input into the HSAMI hydrological model and simulations were compared on five drainage basins in the Mauricie area (Qu´ ebec, Canada). A comparison of the distribution of total annual precipitation interpolated from the two network scenarios showed that adding stations changed the distribution and magnitude of rainfall in the study area. High precipitation cells were better defined with the denser network, and decreases in the relative spatial variance were observed. Similarly, kriged daily precipitation provided a more defined spatial distribution of rainfall during important rain events of 1999, and variance was also reduced when the denser network was used. Finally, simulations performed with the HSAMI model were generally improved when the precipitation inputs were estimated using a denser station network for most drainage basins studied, as expressed by increased Nash coefficients and a decreased root-mean-square error. Peak flows during important summer flood events were generally better simulated when a denser network was used to calculate the mean daily precipitation used as input. Total cumulated volume estimations during the rain events were also generally improved with a denser network. This study showed that the estimation of variance remains an important tool for rain gauge network design. Moreover, network density was shown to have an important impact on the quality of flow simulations, even when a lumped model is used. Copyright  2003 John Wiley & Sons, Ltd.

It is often necessary to estimate extreme events at sites where little or no hydrometric data are available. In such cases, one may use a regional estimation procedure, utilizing data available from other sites in the same hydrologic region. In general, a regional flood frequency procedure consists of two steps: determination of hydrologically homogeneous region and regional estimation. This paper presents the development of a regional flood frequency procedure based on canonical correlation analysis and multiple regression: (i) the canonical correlation analysis allows us to link a set of hydrological variables and a set of physiographical and (or) meteorological variables and, therefore, to determine the hydrological neighborhood of an ungauged site; (ii) the multiple regression allows us to transfer data from sites of the homogeneous region to the target site. The developed methodology was applied to the Saint-Maurice river region (Quebec, Canada). Using a jackknife procedure, several pairs of hydrological variables were compared to identify the most appropriate grouping. Key words: regionalization, frequency analysis, canonical correlation analysis, multiple regression.

The shape of a flood hydrograph is a random event. This study develops a method for describing statistical properties of the shape of a flood hydrograph. Two shape variables, namely, shape mean ( Sm) and shape variance (Sv), are defined to express the randomness of a flood hydrograph. The two-parameter beta probability density function ~pdf! is adopted to represent the shape of a flood hydrograph. The parameters are estimated using the shape variables. On the basis of this approach, one can derive the design-flood hydrograph shape corresponding to various combinations of flood peak, volume, and duration. The applicability of the proposed method is demonstrated by using observed flood data from the Ashuapmushuan basin in the province of Quebec, Canada. It is concluded that the method is appropriate for representing the statistical properties of the shape of a flood hydrograph.

© 2015 Elsevier Inc. Using GPS radio occultation refractivity data collected over the period of 2002-2013, we present a new method for identification of the intertropical convergence zone (ITCZ). The ITCZ is identified by estimating the maximum in the monthly meridional refractivity and specific humidity field by applying a Gaussian fit at each longitude. The interannual variability and climatology of the ITCZ is presented from 12. years of refractivity data. This new method captures all the general features of ITCZ extent and its variability. We also examine the effects of the ITCZ vertically during different seasons. The ITCZ is observed mostly at eastern Pacific in May month, and it is zonally distributed in the September and October months of each year. The zonal variability is large between lower and higher levels, particularly over the Indian monsoon and western Pacific. The latitudinal difference in the vertical extent between 850. hPa and higher levels is larger during the northern hemisphere (NH) summer than NH winter.

We live in a fast-paced busy world where one of the key biomedical factors we compensate with is sleep. Sleep impacts many facets of our lives from chronic disorders to workplace productivity, happiness and many other dimensions that we are continually discovering. Advances in technology and health informatics have made it possible to measure and understand sleep and related social factors in the real world. We are specifically interested in understanding sleep before and during the month of Ramadan - a time when people fast from sunrise to sunset. This fast is observed by more than a billion humans yearly and consequently there are some impacts to the society. In this paper we introduce the development of a health information system capable of acquiring, managing and visualizing health data for 1. Sleep signals were acquired by utilizing wireless sensing technology to measure sleep in a naturalistic setting rather than asking users to sleep in a sleep laboratory and 2. Social factors captured from surveys through a web platform rather than utilizing paper surveys. The system was developed to be easy-to-use by users and study administrators. We introduce to our knowledge the first research study, Fasting In Ramadan Research sTudy (FIRST@Masdar), capturing sleep and social factors before and during Ramadan from a total of 40 participants over two consecutive fasting years of 2012 and 2013.

Objective: The aim of this study is to understand the relationship between automatically captured social exposure and detailed sleep parameters of healthy young adults. Methods: This study was conducted in a real-world setting in a graduate-student housing community at a US university. Social exposure was measured using Bluetooth proximity sensing technology in mobile devices. Sleep was monitored in a naturalistic setting using a headband sleep monitoring device over a period of 2 weeks. The analysis included a total of 11 subjects (6 males and 5 females) aged 24–35 (149 subject nights). Results: Slow-wave sleep showed a significant positive correlation (Spearman’s rho = 0.51, p, MIT Media Lab Consortium, MIT and Masdar Institute Cooperative Program (MIT/Masdar Collaborative Research Grant), Masdar Institute of Science and Technology (Masdar Institute Fellowship)

A nonparametric simulation model (k-nearest neighbor resampling, KNNR) for water quality analysis involving geographic information is suggested to overcome the drawbacks of parametric models. Geographic information is, however, not appropriately handled in the KNNR nonparametric model. In the current study, we introduce a novel statistical notion, called a “depth function,” in the classical KNNR model to appropriately manipulate geographic information in simulating stormwater quality. An application is presented for a case study of the total suspended solids throughout the entire United States. The stormwater total suspended solids concentration data indicated that the proposed model significantly improves the simulation performance compared with the existing KNNR model.

Despite its potential advantages, canonical correlation analysis (CCA) has been little used in the fields of hydrology and water resources. In a regional flood frequency analysis, canonical correlations can be used to investigate the correlation structure between the two sets of variables represented by watershed characteristics and flood peaks. This paper presents a clear theoretical framework for the use of canonical correlations in regional flood frequency analysis. Some additional results are also presented for the case of gauged target-basins. The approach described in this paper allows one to carry out the determination of homogeneous hydrologic neighborhoods and identifies the variables to use during the step of regional estimation. A data set of 106 stations from the province of Ontario (Canada) is used to demonstrate the advantages of this method and investigate various aspects in relation with its robustness. Results indicate that the method is robust to such factors as the number of stations and the type of parametric distribution being used. Step-by-step algorithms for the delineation of hydrologic neighborhoods in the cases of gauged and ungauged basins are also presented.

A canonical correlation method for determining the homogeneous regions used for estimating flood characteristics of ungauged basins is described. The method emphasizes graphical and quantitative analysis of relationships between the basin and flood variables before the data of the gauged basins are used for estimating the flood variables of the ungauged basin. The method can be used for both homogeneous regions, determined a priori by clustering algorithms in the space of the flood-related canonical variables, as well as for “regions of influence” or “neighbourhoods” centred on the point representing the estimated location of the ungauged basin in that space.

A vast array of techniques have been developed and applied to optimal operation of large-scale multireservoir systems. Researchers continue to be challenged by the highly complex, stochastic, nonlinear, and high dimensional nature of this dynamic optimization problem. An optimal control model is presented which incorporates chance-constraints on system state variables that assure satisfaction of operational restrictions under specified levels of reliability. The chance-constrained optimal control (CCOC) model is tested on a four-reservoir case study, and its performance assessed based on various quantitative and qualitative criteria, including maintenance of acceptable levels of risk and provision of risk-benefit trade-off information. The concepts of reliability, resiliency and vulnerability are utilized to characterize operating policies generated by the algorithm. CCOC is recommended for operational guidance of large-scale multireservoir systems due to its robustness, flexibility, modest computational requirements, and ability to include risk considerations directly impacting the choice of operational schemes.

A univariate gamma distribution is one of the most commonly adopted statistical distributions in hydrological frequency analysis. A bivariate gamma distribution constructed from specified gamma marginals may be useful for representing joint probabilistic properties of multivariate hydrological events such as floods and storms. This article presents a review of various bivariate gamma distribution models that are constructed from gamma marginals. Advantages and limitations of each of these models are pointed out. Applicability of a few bigamma distributions whose gamma marginal distributions have different scale and shape parameters is investigated. The dependence of these models is directly or indirectly measured via the Pearson's product-moment correlation coefficient. The scale and shape parameters of the models are estimated from their marginal distributions by the method of moments. Results indicate that these bigamma distribution models will be useful for describing the joint probability distribution of two correlated random variables with gamma marginals.

This paper proposes a methodology for the regional analysis of drought flows. This approach lies on the combination of two procedures. (i) The simple scale invariance method for regional series of drought flows, based on the analysis of the relation moments-surfaces or the relation quantiles-surfaces, explains the spatial variability of drought processes by their indexation on a series of scale parameters, essentially the size of the drainage basin. This procedure principally aims at delimitating homogeneous regions, and this characterizes the first condition of a regional estimation. (ii) Frequency analysis of minimum flows, with the approach using tail conditional models, consists in adjusting a probability distribution to values that are smaller than or equal to some given threshold. As a fact, this procedure gives some more weight to the lower part of the distribution by defining a priori a level of censure, mainly a threshold ui called ceiling value. This procedure establishes the second condition of a regional estimation, that is, the determination of a regional estimation model. This methodology has been applied to analyze drought flow characteristics of 187 hydrometric stations scattered in the province of Quebec. Results have shown that the first to the sixth order non-central moments follow a simple scale invariance with respect to the area of the watershed. As a result, analysis of the residuals of these six moments has allowed classifying hydrologically similar watersheds. On the basis of the results from the analysis on the residuals of the sixth moment, the province has been divided into two homogeneous regions. The behaviour of the minimum flows from these different regions has shown that they, too, followed a simple scale invariance.Key words: regionalization, flow, drought, scale invariance, tail model, non-central moment.[Journal translation]

It is often necessary to estimate extreme events at sites where little or no hydrometric data are available. In such cases, one may use a regional estimation procedure, utilizing data available from other stations in the same hydrologic region. In general, a regional flood frequency procedure consists of two steps, delineation of hydrologically homogeneous regions and regional estimation. This paper focuses on the development of a regional flood frequency procedure based on canonical correlation analysis (CCA) and its application to data from a northern Canadian basin in which floods are dominated by spring snowmelt. This CCA-based procedure allows the joint regional estimation of spring flood peaks and volumes. The CCA method allows the determination of pairs of canonical variables such that the correlation between the canonical variables of one pair is maximized and between the variables of different pairs is equal to zero. Therefore, it is possible to infer hydrological canonical variables, knowing the physiographical-meteorological canonical variables. The methodology developed was applied to the St. Maurice River basin system, which is operated by Hydro-Quebec and characterized by the relatively low precision of flow data available. Results show that the proposed method allows for a significant reduction in the 100-year spring flood and volume quantile estimation bias and mean square error. The study also shows that, in 60% of cases, the method that was previously used overestimates quantile values, which leads to an overdesign of retention structures.

Many hydrological engineering planning, design, and management problems require a detailed knowledge of flood event characteristics, such as flood peak, volume and duration. Flood frequency analysis often focuses on flood peak values, and hence, provides a limited assessment of flood events. This paper proposes the use of the Gumbel mixed model, the bivariate extreme value distribution model with Gumbel marginals, to analyze the joint probability distribution of correlated flood peaks and volumes, and the joint probability distribution of correlated flood volumes and durations. Based on the marginal distributions of these random variables, the joint distributions, the conditional probability functions, and the associated return periods are derived. The model is tested and validated using observed flood data from the Ashuapmushuan river basin in the province of Quebec, Canada. Results indicate that the model is suitable for representing the joint distributions of flood peaks and volumes, as well as flood volumes and durations.

Annual maximum flood (AMF) sampling remains the most popular approach to flood frequency analysis. An alternative, the " peaks over threshold " (POT) approach, deals with the selection of over-threshold values. However, the POT approach remains under-employed mainly because of the complexities associated with its use. Among the difficulties are the choice of threshold and the selection of criteria for retaining flood peaks. The literature remains sparse and incoherent concerning the various elements and complexities of the POT model. The purpose of the present paper is to shed some light on some of the recurrent and most important questions with regard to the practice of POT modeling, and to make a first step in establishing a set of coherent practice-oriented guidelines for the use of the POT model. This paper reviews tests and methods useful for modeling the process of over-threshold values, the choice of the threshold level, the verification of the independence of the values and the stationnarity of the process, and also presents an application., L'analyse fréquentielle des crues repose généralement sur l'extraction des valeurs maximales annuelles. Une alternative consiste à retenir toutes les valeurs dépassant un certain seuil. Cette approche reste sous-employée du fait notamment de difficultés liées au choix du seuil d'extraction et à la définition de critères permettant de respecter l'indépendance des valeurs. L'objet de cet article est de proposer, au travers d'une revue bibliographique, une série de recommandations opérationnelles pour l'emploi de cette approche. Il s'agit de tests et de méthodes pour modéliser le processus d'occurrence des crues, choisir le seuil d'extraction, vérifier l'indépendance et la stationnarité des valeurs. Une application pratique illustre ces différentes recommandations.

The presence of zeroes in a record of low flows usually has been interpreted as being indicative of a stream that goes dry during some periods. However, in some instances, zeroes may appear in a gauging record simply because the actual discharge was below a measurement threshold. For example, in some regions it is sometimes true that the lower limit of a stream gauge is above the true stream bottom, and any (censored) discharges not registered by the gauges are recorded as zeroes. This paper presents a comparison of alternative methods for treatment of zeroes in low-flow estimation procedures when tail models are employed for modeling purposes. Simulation experiments indicate that a Weibull tail model, as fitted using a likelihood-based method for inclusion of censored data, is the best estimator of the 10-year low-flow quantile when zeroes in a data set have arisen as a consequence of censoring. When zeroes in a data set exist because the stream is an ephemeral one, a lognormal mixed tail model fitted us...

As with all dynamic programming formulations, differential dynamic programming (DDP) successfully exploits the sequential decision structure of multi-reservoir optimization problems, overcomes difficulties with the nonconvexity of energy production functions for hydropower systems, and provides optimal feedback release policies. DDP is particularly well suited to optimizing large-scale multi-reservoir systems due to its relative insensitivity to state-space dimensionality. This advantage of DDP encourages expansion of the state vector to include additional multi-lag hydrologic information and/or future inflow forecasts in developing optimal reservoir release policies. Unfortunately, attempts at extending DDP to the stochastic case have not been entirely successful. A modified stochastic DDP algorithm is presented which overcomes difficulties in previous formulations. Application of the algorithm to a four-reservoir hydropower system demonstrates its capabilities as an efficient approach to solving stochastic multi-reservoir optimization problems. The algorithm is also applied to a single reservoir problem with inclusion of multi-lag hydrologic information in the state vector. Results provide evidence of significant benefits in direct inclusion of expanded hydrologic state information in optimal feedback release policies.

In the fields of statistics and hydrology, a considerable amount of research has been directed toward robust methods of estimation. This paper broadly reviews these methods and presents results of the application of a specific robust estimation procedure, namely trimming, in flood frequency analysis. Using simulation, the effect of various proportions of symmetric trimming on the estimation of moments, distribution parameters, and quantiles is examined. The influence of the sample size and parent distribution parameters on the estimation performance is also investigated for several return periods and for three different fitting methods for the log-Pearson Type III distribution. While the results point to the potential usefulness of robust estimators in hydrological analysis, they also serve as a precaution that certain distribution fitting techniques may be overly robust to extreme observations in the sample (those in the tails).

In hydraulic design, one often needs to estimate flood quantiles for use as design values. It is important to assess the estimation error by constructing confidence intervals (CIs) for these quantiles. Fitting probability distributions to hydrologic data is used widely for estimating quantiles of hydrological variables. The two-parameter gamma (G2) is among the distributions commonly used, but the three-parameter generalized gamma (GG3) (also known as Kritsky-Menkel distribution) is an alternative when more shape flexibility is needed to fit the data. We use an approximate method to construct CIs for the quantiles of the G2 and GG3 distributions. This method is shown to be useful for hydrological applications where the data record is short.

The indexed sequential hydrologic modeling (ISM) methodology is utilized by the Western Area Power Administration as the basis for risk‐based estimation of project‐dependable hydropower capacity for several federally owned/operated projects. ISM is a technique based on synthetic generation of a series of overlapping short‐term inflow sequences obtained directly from the historical record. The validity of ISM is assessed through application to the complex multireservoir hydropower system of the Colorado River basin for providing risk estimates associated with determination of reliable hydrogeneration capacity. Performance of ISM is compared with results from stochastically generated streamflow input data to the Colorado River Simulation System (CRSS). Statistical analysis and comparison of results are based on monthly power capacity, energy generation, and downstream water deliveries. Results indicate that outputs generated from ISM synthetically generated sequences display an acceptable correspondence with those obtained from stochastically generated hydrologic data for the Colorado River Basin.

Changes in extreme meteorological variables and the demographic shift towards an older population have made it important to investigate the association of climate variables and hip fracture by advanced methods in order to determine the climate variables that most affect hip fracture incidence. The nonlinear autoregressive moving average with exogenous variable-generalized autoregressive conditional heteroscedasticity (ARMAX-GARCH) and multivariate GARCH (MGARCH) time series approaches were applied to investigate the nonlinear association between hip fracture rate in female and male patients aged 40–74 and 75+ years and climate variables in the period of 1993–2004, in Montreal, Canada. The models describe 50–56 % of daily variation in hip fracture rate and identify snow depth, air temperature, day length and air pressure as the influencing variables on the time-varying mean and variance of the hip fracture rate. The conditional covariance between climate variables and hip fracture rate is increasing exponentially, showing that the effect of climate variables on hip fracture rate is most acute when rates are high and climate conditions are at their worst. In Montreal, climate variables, particularly snow depth and air temperature, appear to be important predictors of hip fracture incidence. The association of climate variables and hip fracture does not seem to change linearly with time, but increases exponentially under harsh climate conditions. The results of this study can be used to provide an adaptive climate-related public health program and ti guide allocation of services for avoiding hip fracture risk.

The two-parameter generalized Pareto distribution (GPD) has been recommended for the frequency analysis of environmental extreme events. In the present paper, we concentrate on one form of the GPD (which we will call GPDB) which can be useful in the frequency analysis of two types of hydrological variables: (1) when the shape parameter α is positive, the distribution (denoted by GPDB-2) can be used to study phenomena such as flood flows, which are bounded from below but have a long right tail; (2) when α is negative, the resulting distribution (GPDB-3) could be used to study variables such as low flows which are bounded from both sides but with a left tail. Six versions of the generalized method of moments (GMM) for fitting GPDB are investigated. The flexibility of the GMM provides the user with the possibility of choosing a version of the method (i.e. the moment pair that is used in fitting the distribution) which assigns larger weight to the larger elements of the sample, or another version which gives more weight to the smaller elements, depending on the problem at hand. A general formula for the asymptotic variance of the T-year event XT obtained by combining any two moments of GPDB is presented and applied. It is shown that the adequate choice of the order of these two moments to fit the distribution can lead, in some cases, to a considerable reduction in the variance of the estimator of XT, in comparison with estimation by the traditional method of moments (which uses moments of order one and two). The performance indices that are used to compare the different versions of the GMM are based on root mean square error, bias, and variance—both asymptotic and observed (based on simulation)—of GPDB quantiles and parameters. It is shown that moments of order (0),−1) and (2,−1) lead to the best results when the shape parameter a is positive (GPDB − 2), and the traditional method of moments can be considered as most efficient for negative values of α (GPDB −3). The GMM with moments of order 0 and one is shown to be rather consistent and moderately satisfactory for both GPDB-2 and GPDB-3.

[1] Over the last 10 years, downscaling techniques, including both dynamical (i.e., the regional climate model) and statistical methods, have been widely developed to provide climate change information at a finer resolution than that provided by global climate models (GCMs). Because one of the major aims of downscaling techniques is to provide the most accurate information possible, data analysts have tried a number of approaches to improve predictor selection, which is one of the most important steps in downscaling techniques. Classical methods such as regression techniques, particularly stepwise regression (SWR), have been employed for downscaling. However, SWR presents some limits, such as deficiencies in dealing with collinearity problems, while also providing overly complex models. Thus, the least absolute shrinkage and selection operator (LASSO) technique, which is a penalized regression method, is presented as another alternative for predictor selection in downscaling GCM data. It may allow for more accurate and clear models that can properly deal with collinearity problems. Therefore, the objective of the current study is to compare the performances of a classical regression method (SWR) and the LASSO technique for predictor selection. A data set from 9 stations located in the southern region of Quebec that includes 25 predictors measured over 29 years (from 1961 to 1990) is employed. The results indicate that, due to its computational advantages and its ease of implementation, the LASSO technique performs better than SWR and gives better results according to the determination coefficient and the RMSE as parameters for comparison.

The optimal design and operation of hydraulic structures relies on the availability of appropriate estimates of hydrological variables at the local and regional levels. Regional frequency analysis is commonly used for the estimation of hydrological or meteorological events (such as floods) at sites where little or no data are available. A number of methodologies have been developed for the regional analysis of hydrological events, with different target hydrological variables, assumptions, limitations, data needs, and outputs. This article presents a review and classification of recent developments in regional hydrological frequency analysis with a focus on the strengths and potentials of the various theories. Keywords: hydrometric networks; historical data; prediction; extremes; homogeneous regions

Reflectance observations from the high-resolution visible (HRV) channel of the SEVIRI instrument, onboard Meteosat Second Generation, have shown to be sensitive to dust over bright surfaces, i.e. bright desert sand. This was observed when deriving the cloud index from the HRV channel for dusty days over the desert environment of the United Arab Emirates (UAE). Sensitivity of the HRV channel to dust could then be used to derive an index giving an indication of the dust conditions in the atmosphere. Therefore, the purpose of this paper is to propose an index, the interception index, for dust detection over bright desert surfaces in the UAE using the HRV SEVIRI channel. The ability of the interception index to estimate the direct normal irradiance is also investigated.

The existence of time-dependent variance or conditional variance, commonly called heteroscedasticity, in hydrologic time series has not been thoroughly investigated. This paper deals with modelling the heteroscedasticity in the residuals of the seasonal autoregressive integrated moving average (SARIMA) model using a generalized autoregressive conditional heteroscedasticity (GARCH) model. The model is applied to two monthly rainfall time series from humid and arid regions. The effect of Box–Cox transformation and seasonal differencing on the remaining seasonal heteroscedasticity in the residuals of the SARIMA model is also investigated. It is shown that the seasonal heteroscedasticity in the residuals of the SARIMA model can be removed using Box–Cox transformation along with seasonal differencing for the humid region rainfall. On the other hand, transformation and seasonal differencing could not remove heteroscedasticity from the residuals of the SARIMA model fitted to rainfall data in the arid region. Therefore, the GARCH modelling approach is necessary to capture the heteroscedasticity remaining in the residuals of a SARIMA model. However, the evaluation criteria do not necessarily show that the GARCH model improves the performance of the SARIMA model. Copyright © 2012 John Wiley & Sons, Ltd.

[1] The prevention of flood risks and the effective planning and management of water resources require river flows to be continuously measured and analyzed at a number of stations. For a given station, a hydrograph can be obtained as a graphical representation of the temporal variation of flow over a period of time. The information provided by the hydrograph is essential to determine the severity of extreme events and their frequencies. A flood hydrograph is commonly characterized by its peak, volume, and duration. Traditional hydrological frequency analysis (FA) approaches focused separately on each of these features in a univariate context. Recent multivariate approaches considered these features jointly in order to take into account their dependence structure. However, all these approaches are based on the analysis of a number of characteristics and do not make use of the full information content of the hydrograph. The objective of the present work is to propose a new framework for FA using the hydrographs as curves: functional data. In this context, the whole hydrograph is considered as one infinite-dimensional observation. This context allows us to provide more effective and efficient estimates of the risk associated with extreme events. The proposed approach contributes to addressing the problem of lack of data commonly encountered in hydrology by fully employing all the information contained in the hydrographs. A number of functional data analysis tools are introduced and adapted to flood FA with a focus on exploratory analysis as a first stage toward a complete functional flood FA. These methods, including data visualization, location and scale measures, principal component analysis, and outlier detection, are illustrated in a real-world flood analysis case study from the province of Quebec, Canada.

[1] In this paper, improved flow duration curve (FDC) and area ratio (AR) based methods are developed to obtain better daily streamflow estimation at ungauged sites. A regression based logarithmic interpolation method which makes no assumption on the distribution or shape of a FDC is introduced in this paper to estimate regional FDCs. The estimated FDC is combined with a spatial interpolation algorithm to obtain daily streamflow estimates. Multiple source sites based AR methods, especially the geographical distance weighted AR (GWAR) method, are introduced in an effort to maximize the use of regional information and improve the standard AR method (SAR). Performances of the proposed approaches are evaluated using a jackknife procedure. The application to 109 stations in the province of Quebec, Canada indicates that the FDC based methods outperform AR based methods in all the summary statistics including Nash, root mean squared error (RMSE), and Bias. The number of sites that show better performances using the FDC based approaches is also significantly larger than the number of sites showing better performances using AR based methods. Using geographical distance weighted multiple sources sites based approaches can improve the performance at the majority of the catchments comparing with using the single source site based approaches.

[1] Nonstationary oscillation (NSO) processes are observed in a number of hydroclimatic data series. Stochastic simulation models are useful to study the impacts of the climatic variations induced by NSO processes into hydroclimatic regimes. Reproducing NSO processes in a stochastic time series model is, however, a difficult task because of the complexity of the nonstationary behaviors. In the current study, a novel stochastic simulation technique that reproduces the NSO processes embedded in hydroclimatic data series is presented. The proposed model reproduces NSO processes by utilizing empirical mode decomposition (EMD) and nonparametric simulation techniques (i.e., k-nearest-neighbor resampling and block bootstrapping). The model was first tested with synthetic data sets from trigonometric functions and the Rossler system. The North Atlantic Oscillation (NAO) index was then examined as a real case study. This NAO index was then employed as an exogenous variable for the stochastic simulation of streamflows at the Romaine River in the province of Quebec, Canada. The results of the application to the synthetic data sets and the real-world case studies indicate that the proposed model preserves well the NSO processes along with the key statistical characteristics of the observations. It was concluded that the proposed model possesses a reasonable simulation capacity and a high potential as a stochastic model, especially for hydroclimatic data sets that embed NSO processes.

Several watch and warning systems have been established in the world in recent years to prevent the effects of heat waves. However, many of these approaches can be applied only in regions with perfect conditions (e.g., enough data, stationary series or homogeneous regions). Furthermore, a number of these approaches do not account for possible trend in mortality and/or temperature series, whereas others are generally not adapted to regions with low population densities or low daily mortality levels. In addition, prediction based on multiple days preceding the event can be less accurate if it attributes the same importance to each of these days, since the forecasting accuracy actually decreases with the period. The aim of the present study was to identify appropriate indicators as well as flexible and general thresholds that can be applied to a variety of regions and conditions. From a practical point of view, the province of Quebec constitutes a typical case where a number of the above-mentioned constraints are present. On the other hand, until recently, the province’s watch and warning system was based on a study conducted in 2005, covering only the city of Montreal and applied to the whole province. The proposed approach is applied to each one of the other health regions of the province often experiencing low daily counts of mortality and presenting trends. The first constraint led to grouping meteorologically homogeneous regions across the province in which the number of deaths is sufficient to carry out the appropriate data analyses. In each region, mortality trends are taken into account. In addition, the proposed indicators are defined by a 3-day weighted mean of maximal and minimal temperatures. The sensitivity of the results to the inclusion of traumatic deaths is also checked. The application shows that the proposed method improved the results in terms of sensitivity, specificity and number of yearly false alarms, compared to those of the existing and other classical approaches. An additional criterion based on the Humidex is applied in a second step and a local validation is applied to historical observations at reference forecasting stations. An integrated heat health watch and warning system with thresholds that are adapted to the regional climate has thus been established for each sub-region of the province of Quebec and became operational in June 2010.

[1] This study proposes a method to reconstruct past spring flood discharge from continuous and discrete tree ring chronologies, since both have their respective strengths and weaknesses in northern environments. Ring width or density series provide uninterrupted records that are indirectly linked to regional discharge through a concomitant effect of climate on tree growth and streamflow. Conversely, discrete event chronologies constitute conspicuous records of past high water levels since they are constructed from trees that are directly damaged by the flood. However, the uncertainty of discrete series increases toward the past, and their relationships with spring discharge are often nonlinear. To take advantage of these two sources of information, we introduce a new transfer model technique on the basis of generalized additive model (GAM) theory. The incorporation of discrete predictors and the evaluation of the robustness of the nonlinear relationships are assessed using a jackknife procedure. We exemplify our approach in a reconstruction of May water supplies to the Caniapiscau hydroelectric reservoir in northern Quebec, Canada. We used earlywood density measurements as continuous variables and ice-scar dates around Lake Montausier in the James Bay area as a discrete variable. Strong calibration (0.57 < 0.61 < 0.75) and validation (0.27 < 0.44 < 0.58) R2 statistics were obtained, thus highlighting the usefulness of the model. Our reconstruction suggests that, since ∼1965, spring floods have become more intense and variable in comparison with the last 150 years. We argue that a similar procedure can be used in each case where discrete and continuous tree ring proxies are used together to reconstruct past spring floods.

There are several deficiencies in the statistical approaches proposed in the literature for the assessment and redesign of surface water-quality-monitoring locations. These deficiencies vary from one approach to another, but generally include: (i) ignoring the attributes of the basin being monitored; (ii) handling multivariate water quality data sequentially rather than simultaneously; (iii) focusing mainly on locations to be discontinued; and (iv) ignoring the reconstitution of information at discontinued locations. In this paper, a methodology that overcomes these deficiencies is proposed. In the proposed methodology, the basin being monitored is divided into sub-basins, and a hybrid-cluster analysis is employed to identify groups of sub-basins with similar attributes. A stratified optimum sampling strategy is then employed to identify the optimum number of monitoring locations at each of the sub-basin groups. An aggregate information index is employed to identify the optimal combination of locations to be discontinued. The proposed approach is applied for the assessment and redesign of the Nile Delta drainage water quality monitoring locations in Egypt. Results indicate that the proposed methodology allows the identification of (i) the optimal combination of locations to be discontinued, (ii) the locations to be continuously measured and (iii) the sub-basins where monitoring locations should be added. To reconstitute information about the water quality variables at discontinued locations, regression, artificial neural network (ANN) and maintenance of variance extension (MOVE) techniques are employed. The MOVE record extension technique is shown to result in a better performance than regression or ANN for the estimation of information about water quality variables at discontinued locations.

[1] Hydrological events are often described through various characteristics which are generally correlated. To be realistic, these characteristics are required to be considered jointly. In multivariate hydrological frequency analysis, the focus has been made on modeling multivariate samples using copulas. However, prior to this step, data should be visualized and analyzed in a descriptive manner. This preliminary step is essential for all of the remaining analysis. It allows us to obtain information concerning the location, scale, skewness, and kurtosis of the sample as well as outlier detection. These features are useful to exclude some unusual data, to make different comparisons, and to guide the selection of the appropriate model. In the present paper we introduce methods measuring these features, which are mainly based on the notion of depth function. The application of these techniques is illustrated on two real-world streamflow data sets from Canada. In the Ashuapmushuan case study, there are no outliers and the bivariate data are likely to be elliptically symmetric and heavy-tailed. The Magpie case study contains a number of outliers, which are identified to be real observed data. These observations cannot be removed and should be accommodated by considering robust methods for further analysis. The presented depth-based techniques can be adapted to a variety of hydrological variables.

[1] 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 Rossler 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.

The present study investigates the role of equatorial Pacific sea surface temperatures (SSTs) on the precipitation variability over the United Arab Emirates (UAE) and adjoining Middle East regions. Monthly precipitation data (1981–2011) assembled from rain gauge stations located in the UAE along with other global reanalysis data sets are used to explore the teleconnections. It is observed that statistically significant correlations exist between precipitation over the UAE and the equatorial Pacific and North Atlantic SSTs. Canonical correlation analysis between the monthly winter precipitation and the global SSTs (60°S to 60°N) reveals that the major portion of the precipitation variability is influenced by equatorial Pacific SSTs associated with El Nino–Southern Oscillation (ENSO). The moisture budget analysis reveals the distinct change in the anomalous circulation (cyclonic and anticyclonic) associated with strong convergence and divergence of the moisture flux during the warm and cold phases of ENSO, respectively. Further, the composite analysis of upper troposheric zonal wind shows the equatorward shift (~2° latitude) of subtropical jet stream (STJ) over the Middle East during the warm phase of ENSO affecting the weather in the UAE. The findings suggest that the teleconnection linking ENSO and the precipitation over UAE and adjoining regions is mediated by the response of STJ to Rossby waves.

[1] Nonstationary oscillations in climatic variables and indices have been the focus of many studies. Since climate indices or their associated hydrometeorological variables might contain nonstationary oscillation processes, it would be useful to be able to divide the intrinsic nonstationary oscillation into a finite number of components. Those components can then be used to predict the future system evolution. In the current study nonstationary oscillations of certain time series are extracted using a decomposition analysis called the empirical mode decomposition (EMD). In EMD the most important components are modeled with a nonstationary oscillation resampling (NSOR) technique. To predict a long-term oscillation pattern, a time series with a long record is required. The normalized regional precipitation of eastern Canada is one such series. In a second example, the future evolution of extreme streamflows at two stations from the province of Quebec, Canada, is studied by using the long-term patterns of climatic indices. Results indicate that the future long-term patterns are well-modeled with the NSOR and EMD. However, the indirect approach to finding the interconnection sometimes gives rise to a high prediction uncertainty.

Background Studies have suggested an association between climate variables and circulatory diseases. The short-term effect of climate conditions on the incidence of ischemic heart disease (IHD) over the 1989-2006 period was examined for Quebec's 18 health regions. Methods Analyses were carried out for two age groups. A GAM statistical model, that blends the properties of generalized linear models with additive models, was used to fit the standardized daily hospitalization rates for IHD and their relationship with climatic conditions up to two weeks prior to the day of admission, controlling for time trends, day of the season and gender. Results Results show that, in most of Quebec's regions, cold temperatures during winter months and hot episodes during the summer months are associated with an increase of up to 12% in the daily hospital admission rate for IHD but also show decreased risks in some areas. The risk of hospitalization is higher for men and women of 45-64 years and varies spatially. In most regions, exposure to a continuous period of cold or hot temperature was more harmful than just one isolated day of extreme weather. Men aged 45-64 years showed higher risk levels of IHD than women of the same age group. In most regions, the annual maximum of daily IHD admissions for 65 years old was reached earlier in the season for both genders and both seasons compared to younger age groups. The effects of meteorological variables on the daily IHD admissions rate were more pronounced in regions with high smoking prevalence and high deprivation index. Conclusion This study highlights the differential effects of cold and hot periods on IHD in Quebec health regions depending on age, sex, and other factors such as smoking, behaviour and deprivation levels.

International audience; The total amount of suspended sediment load carried by a stream during a year is usually transported during one or several extreme events related to high river flow and intense rainfall, leading to very high suspended sediment concentrations (SSCs). In this study quantiles of SSC derived from annual maximums and the 99th percentile of SSC series are considered to be estimated locally in a site-specific approach using regional information. Analyses of relationships between physiographic characteristics and the selected indicators were undertaken using the localities of 5-km radius draining of each sampling site. Multiple regression models were built to test the regional estimation for these indicators of suspended sediment transport. To assess the accuracy of the estimates, a Jack-Knife re-sampling procedure was used to compute the relative bias and root mean square error of the models. Results show that for the 19 stations considered in California, the extreme SSCs can be estimated with 40–60% uncertainty, depending on the presence of flow regulation in the basin. This modelling approach is likely to prove functional in other Mediterranean climate watersheds since they appear useful in California, where geologic, climatic, physiographic, and land-use conditions are highly variable.

An up-to-date review of the statistical approaches utilized for the assessment and redesign of surface water quality monitoring (WQM) networks is presented. The main technical aspects of network design are covered in four sections, addressing monitoring objectives, water quality variables, sampling frequency and spatial distribution of sampling locations. This paper discusses various monitoring objectives and related procedures used for the assessment and redesign of long-term surface WQM networks. The appropriateness of each approach for the design, contraction or expansion of monitoring networks is also discussed. For each statistical approach, its advantages and disadvantages are examined from a network design perspective. Possible methods to overcome disadvantages and deficiencies in the statistical approaches that are currently in use are recommended.

[1] In this paper, artificial neural networks (ANNs) are introduced to obtain improved regional low-flow estimates at ungauged sites. A multilayer perceptron (MLP) network is used to identify the functional relationship between low-flow quantiles and the physiographic variables. Each ANN is trained using the Levenberg-Marquardt algorithm. To improve the generalization ability of a single ANN, several ANNs trained for the same task are used as an ensemble. The bootstrap aggregation (or bagging) approach is used to generate individual networks in the ensemble. The stacked generalization (or stacking) technique is adopted to combine the member networks of an ANN ensemble. The proposed approaches are applied to selected catchments in the province of Quebec, Canada, to obtain estimates for several representative low-flow quantiles of summer and winter seasons. The jackknife validation procedure is used to evaluate the performance of the proposed models. The ANN-based approaches are compared with the traditional parametric regression models. The results indicate that both the single and ensemble ANN models provide superior estimates than the traditional regression models. The ANN ensemble approaches provide better generalization ability than the single ANN models.

Because of their multivariate nature, several hydrological phenomena can be described by more than one correlated characteristic. These characteristics are generally not independent and should be jointly considered. Consequently, univariate regional frequency analysis (FA) cannot provide complete assessment of true probabilities of occurrence. The objective of the present paper is to propose a procedure for regional flood FA in a multivariate framework. In the present paper, the focus is on the estimation step of regional FA. The proposed procedure represents a multivariate version of the index flood model and is based on copulas and a multivariate quantile version with a focus on the bivariate case. The model offers increased flexibility to designers by leading to several scenarios associated with the same risk. The univariate quantiles represent special cases corresponding to the extreme scenarios. A simulation study is carried out to evaluate the performance of the model in a bivariate framework. Simulation results show that bivariate FA provides the univariate quantiles with equivalent accuracy. Similarity is observed between results of the bivariate model and those of the univariate one in terms of the behavior of the corresponding performance criteria. The procedure performs better when the regional homogeneity is high. Furthermore, the impacts of small variations in the record length at gauged sites and the region size on the performance of the proposed procedure are not significant.

In this paper, two new Bayesian change point techniques are described and compared to eight other techniques presented in previous work to detect inhomogeneities in climatic series. An inhomogeneity can be defined as a change point (a time point in a series such that the observations have a different distribution before and after this time) in the data series induced from changes in measurement conditions at a given station. It is important to be able to detect and correct an inhomogeneity, as it can interfere with the real climate change signal. The first technique is a Bayesian method of multiple change point detection in a multiple linear regression. The second one allows the detection of a single change point in a multiple linear regression. These two techniques have never been used for homogenization purposes. The ability of the two techniques to discriminate homogeneous and inhomogeneous series was evaluated using simulated data series. Various sets of synthetic series (homogeneous, with a single shift, and with multiple shifts) representing the typical total annual precipitation observed in the southern and central parts of the province of Quebec, Canada, and nearby areas were generated for the purpose of this study. The two techniques gave small false detection rates on the homogeneous series. Furthermore, the two techniques proved to be efficient for the detection of a single shift in a series. For the series with multiple shifts, the Bayesian method of multiple change point detection performed better. An application to a real data set is also provided and validated with the available metadata.