Fateh Chebana

Index flood–based multivariate regional frequency analysis

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.

Spatial variability of climate effects on ischemic heart disease hospitalization rates for the period 1989-2006 in Quebec, Canada

BackgroundStudies 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 Quebecs 18 health regions.MethodsAnalyses 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.ResultsResults show that, in most of Quebecs 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.ConclusionThis 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.

Health impacts of the July 2010 heat wave in Québec, Canada

BackgroundOne of the consequences of climate change is the increased frequency and intensity of heat waves which can cause serious health impacts. In Québec, July 2010 was marked by an unprecedented heat wave in recent history. The purpose of this study is to estimate certain health impacts of this heat wave.MethodsThe crude daily death and emergency department admission rates during the heat wave were analyzed in relation to comparison periods using 95% confidence intervals.ResultsDuring the heat wave, the crude daily rates showed a significant increase of 33% for deaths and 4% for emergency department admissions in relation to comparison periods. No displacement of mortality was observed over a 60-day horizon.ConclusionsThe all-cause death indicator seems to be sufficiently sensitive and specific for surveillance of exceedences of critical temperature thresholds, which makes it useful for a heat health-watch system. Many public health actions combined with the increased use of air conditioning in recent decades have contributed to a marked reduction in mortality during heat waves. However, an important residual risk remains, which needs to be more vigorously addressed by public health authorities in light of the expected increase in the frequency and severity of heat waves and the aging of the population.

Regional Frequency Analysis at Ungauged Sites with the Generalized Additive Model

AbstractThe log-linear regression model is one of the most commonly used models to estimate flood quantiles at ungauged sites within the regional frequency analysis (RFA) framework. However, hydrological processes are naturally complex in several aspects including nonlinearity. The aim of the present paper is to take into account this nonlinearity by introducing the generalized additive model (GAM) in the estimation step of RFA. A neighborhood approach using canonical correlation analysis (CCA) is used to delineate homogenous regions. GAMs possess a number of advantages such as flexibility in shapes of the relationships as well as the distribution of the output variable. The regional model is applied on a dataset of 151 hydrometrical stations located in the province of Quebec, Canada. A stepwise procedure is employed to select the appropriate physiometeorological variables. A comparison is performed based on different elements (regional model, variable selection, and delineation). Results indicate that mo...

Probabilistic Gaussian Copula Regression Model for Multisite and Multivariable Downscaling

AbstractAtmosphere–ocean general circulation models (AOGCMs) are useful to simulate large-scale climate evolutions. However, AOGCM data resolution is too coarse for regional and local climate studies. Downscaling techniques have been developed to refine AOGCM data and provide information at more relevant scales. Among a wide range of available approaches, regression-based methods are commonly used for downscaling AOGCM data. When several variables are considered at multiple sites, regression models are employed to reproduce the observed climate characteristics at small scale, such as the variability and the relationship between sites and variables. This study introduces a probabilistic Gaussian copula regression (PGCR) model for simultaneously downscaling multiple variables at several sites. The proposed PGCR model relies on a probabilistic framework to specify the marginal distribution for each downscaled variable at a given day through AOGCM predictors, and handles multivariate dependence between sites ...

Non-linear canonical correlation analysis in regional frequency analysis

Hydrological processes are complex non-linear phenomena. Canonical correlation analysis (CCA) is frequently used in regional frequency analysis (RFA) to delineate hydrological neighborhoods. Although non-linear CCA (NL-CCA) is widely used in several fields, it has not been used in hydrology, particularly in RFA. This paper presents an overview of techniques used to reproduce non-linear relationships between two sets of variables. The approaches considered in this work are based on NL-CCA using neural networks (CCA-NN), coupled to a log-linear regression model for flood quantile estimation. In order to demonstrate the usefulness of these approaches in RFA, a comparative study between the latter and linear CCA is performed using three different databases from North America. Results show that CCA-NN is more robust and can better reproduce the non-linear relationship structures between physiographical and hydrological variables. This reflects the high flexibility of this approach. Results indicate that for all three databases, it is more advantageous to proceed with the non-linear CCA approach.

A general and flexible methodology to define thresholds for heat health watch and warning systems, applied to the province of Québec (Canada)

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 Québec 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.

Generalized Extreme Value versus Halphen System: Exploratory Study

The Halphen system and the generalized extreme-value (GEV) distributions are recommended for the estimation of the hydrological extreme events. The GEV system is widely used, whereas the Halphen distributions are less known by hydrologists. The objective of this study is to compare these two systems on the basis of various criteria. A simulation study was undertaken to assess the errors related to the fit of the Halphen distributions to samples generated by the GEV distribution and vice versa. Results show that in the case of power-tail type, the Halphen type Inverse B (HIB) distribution is more adequate and safer than the Frechet (EV2) or Gumbel (EV1) distributions especially in the case of the small sample size. For subexponential class of distributions, both Halphen Type A (HA) and Type B (HB) perform better than the Gumbel (EV1) distribution. When considering limiting distribution cases, results show a very similar behavior of EV1, gamma, and inverse gamma distribution with a small advantage to the EV...

A Nonlinear Approach to Regional Flood Frequency Analysis Using Projection Pursuit Regression

AbstractThis paper presents an approach for regional flood frequency analysis (RFFA) in the presence of nonlinearity and problematic stations, which require adapted methodologies. To this end, the projection pursuit regression (PPR) is proposed. The PPR is a family of regression models that applies smooth functions on intermediate predictors to fit complex patterns. The PPR approach can be seen as a hybrid method between the generalized additive model (GAM) and the artificial neural network (ANN), which combines the advantages of both methods. Indeed, the PPR approach has the structure of a GAM to describe nonlinear relations between hydrological variables and other basin characteristics. On the other hand, PPR can consider interactions between basin characteristics to improve the predictive capabilities in a similar way to ANN, but simpler. The methodology developed in the present study is applied to a case study represented by hydrometric stations from southern Quebec, Canada. It is shown that flood qua...

Bivariate index-flood model: case study in Québec, Canada

Abstract Floods, as extreme hydrological phenomena, can be described by more than one correlated characteristic, such as peak, volume and duration. These characteristics should be jointly considered since they are generally not independent. For an ungauged site, univariate regional flood frequency analysis (FA) provides a limited assessment of flood events. A recent study proposed a procedure for regional FA in a multivariate framework. This procedure represents a multivariate version of the index-flood model and is based on copulas and multivariate quantiles. The performance of the proposed procedure was evaluated by simulation. However, the model was not tested on a real-world case study data. In the present paper, practical aspects are investigated jointly for flood peak (Q) and volume (V) of a dataset from the Côte-Nord region in the province of Quebec, Canada. The application of the proposed procedure requires the identification of the appropriate marginal distribution, the estimation of the index flood and the selection of an appropriate copula. The results of the case study show that the regional bivariate FA procedure performed well. This performance depends strongly on the performance of the two univariate models and, more specifically, the univariate model of Q. The results show also the impact of the homogeneity of the region on the performance of the univariate and bivariate models. Editor D. Koutsoyiannis