Fateh Chebana

Institut National de la Recherche Scientifique | INRS · Eau Terre Environnement Centre

Several hydrological phenomena are described by two or more correlated characteristics. These dependent characteristics should be considered jointly to be more representative of the multivariate nature of the phenomenon. Consequently, probabilities of occurrence cannot be estimated on the basis of univariate frequency analysis (FA). The quantile, r...

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...

Hydrological frequency analysis (HFA) relies on a number of assumptions on the data series, especially independence, homogeneity and stationarity. In the univariate setting, these assumptions are generally checked before the modeling step. During the last decade, multivariate HFA approaches have gained popularity since most hydrological events can...

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 hydr...

The book provides comprehensive and detailed descriptions of the approaches and techniques used in multivariate frequency analysis with illustrative examples and real-life case studies provided. The book presents all background material and new developments in one place, presenting the material in a homogeneous and pedagogical way in order to all...

Extreme heat events have significant health impacts that need to be adequately quantified in the context of climate change. Traditionally, heat-health association methods have relied on statistical models using a single air temperature index, without considering other heat-related variables that may influence the relationship and their potentially...

The study area is in the Ghrib–Cheliff sub-watershed, extending over ~1380 km2 and is located ~100 km South-West of Alger. The proposed model was tested using monthly data, total rainfall (R), peak discharge (Qmax), and suspended sediment load (SSL). Data is obtained from Ghrib stations (hydrometric and rainfall) located on the Cheliff wadi. Twenty...

Extreme heat events pose a significant threat to population health that is amplified by climate change. Traditionally, statistical models have been used to model heat-health relationships, but they do not consider potential interactions between temperature-related and air pollution predictors. Artificial intelligence (AI) methods, which have gained...

Hydrological extreme events are characterized by several correlated variables. For a better associated risk assessment, the dependence structure between these variables must be taken into account by considering copulas. On the other hand, extreme events are generated from different phenomena. In such cases, the margins and/or copula may be affected...

The widespread increase of dissolved organic carbon (DOC) in northern hemisphere surface waters have been generally attributed to the recovery from acidic deposition and to climatic variations. The long-term responses of DOC to environmental drivers could be better predicted with a better understanding of the mechanisms taking place at the soil lev...

In environmental epidemiology, there is wide interest in creating and using comprehensive indices that can summarize information from different environmental exposures while retaining strong predictive power on a target health outcome. In this context, the present article proposes a model called the constrained groupwise additive index model (CGAIM...

Heat-related mortality is an increasingly important public health burden that is expected to worsen with climate change. In addition to long-term trends, there are also interannual variations in heat-related mortality that are of interest for efficient planning of health services. Large-scale climate patterns have an important influence on summer w...

Habitat suitability curves (HSC) synthesize the preference of a species for important habitat variables and are, therefore, key components of various fish habitat models. However, HSC are developed at large scales (e.g. river or regional scales) that do not consider the differences that exist in available habitat conditions at smaller scales. To ad...

Although the relationship between weather and health is widely studied, there are still gaps in this knowledge. The present paper proposes data transformation as a way to address these gaps and discusses four different strategies designed to study particular aspects of a weather–health relationship, including (i) temporally aggregating the series,...

Cardiovascular morbidity and mortality are influenced by meteorological conditions, such as temperature or snowfall. Relationships between cardiovascular health and meteorological conditions are usually studied based on specific meteorological events or means. However, those studies bring little to no insight into health peaks and unusual events fa...

During the last two decades, a number of countries or cities established heat-health warning systems in order to alert public health authorities when some heat indicator exceeds a predetermined threshold. Different methods were considered to establish thresholds all over the world, each with its own strengths and weaknesses. The common ground is th...

During the last two decades, a number of countries or cities established heat-health warning systems in order to alert public health authorities when some heat indicator exceeds a predetermined threshold. Different methods were considered to establish thresholds all over the world, each with its own strengths and weaknesses. The common ground is th...

Background Many countries have developed heat-health watch and warning systems (HHWWS) or early-warning systems to mitigate the health consequences of extreme heat events. HHWWS usually focuses on the four hottest months of the year and imposes the same threshold over these months. However, according to climate projections, the warm season is expec...

In this paper, a new fish habitat modelling approach is introduced using the full probability density functions (PDF), rather than single measurements or central tendency metrics, to describe each predictor. To model habitat selection using PDFs, functional regression models (FRM) are used to allow for the inclusion of curves or functions (smoothed...

To study hydrological events, such as floods and droughts, frequency analysis (FA) techniques are commonly employed. FA relies on some assumptions, especially, the stationarity of the data series. However, the stationarity assumption is not always fulfilled for a variety of reasons such as climate change and human activities. Thus, it is essential...

Low-flow estimation at ungagged sites is a challenging task. Ensemble-based machine learning regression has recently been utilized in modeling hydrologic phenomena and showed improved performance compared to classical regional regression approaches. Ensemble modeling mainly revolves around developing a proper training framework of the individual le...

The Cheliff watershed has a considerable expanse from east to west, beginning with M'Sila to Mostaganem, as seen from this expanse, with great climatic, topographic, geological diversity etc., hence the magnitude of the solid transport phenomenon and the resulting siltation. The scarcity of the region's water resource (average 450 mm·y-1), hence th...

Background Many countries have developed heat-health watch and warning systems (HHWWS) or early-warning systems in an attempt to mitigate the health consequences of extreme heat events. HHWWS usually focus on the four hottest months of the year and impose the same threshold over these months. However, according to climate projections, hot season is...

Background Many countries have developed heat-health watch and warning systems (HHWWS) or early-warning systems in an attempt to mitigate the health consequences of extreme heat events. HHWWS usually focus on the four hottest months of the year and impose the same threshold over these months. However, according to climate projections, hot season is...

Context A number of studies have shown that cold has an important impact on human health. However, almost no studies focused on cold warning systems to prevent those health effects. For Nordic regions, like the province of Quebec in Canada, winter is long and usually very cold with an observed increase in mortality and hospitalizations throughout t...

Extreme hydrologic events are commonly described by several dependent characteristics, such as duration, volume and peak flow for floods. Traditionally in Algeria and North Africa, flood frequency analysis (FFA) is conducted as a univariate approach focusing separately on each single of flood characteristics. On the other hand, elsewhere, multivari...

Investigating the nature of trends in time series is one of the most common analyses performed in hydro-climate research. However, trend analysis is also widely abused and misused, often overlooking its underlying assumptions, which prevent its application to certain types of data. A mechanistic application of graphical diagnostics and statistical...

Extended streamflow forecasting is nowadays important for early flood warning and risk mitigation under a changing climate. However, the absence of reliable estimates of the usual streamflow descriptors, at the considered time horizons, renders common input–output modeling approaches inapt for extended forecasting. For such a problem, system recurr...

Change point detection methods have an important role in many hydrological and hydraulic studies of river basins. These methods are very useful to characterize changes in hydrological regimes and can, therefore, lead to better understanding changes in extreme flows behavior. Flood events are generally characterized by a finite number of characteris...

Recently, there have been an increasing number of studies dealing with change detection in multivariate series. However, a major drawback with most of the currently used methods is the lack of flexibility. Indeed, these methods are only able to detect changes in the strength of dependence assuming invariant shape of the dependence structure. Howeve...

Stream temperature is one of the most important environmental variables in lotic habitats as it has important and direct impacts on the ecosystem. Given the continuous nature of this variable, the aim of this paper was to introduce functional regression for the air‐stream temperature relation, being capable to model an entire seasonal or annual cur...

Hydrological and climatological extreme events are characterized by several correlated random variables. For a better associated risk assessment, the dependence structure between these variables must be taken into account by considering copulas. Multiparameter copulas (M-copulas) play an important role by their flexibility and ability to capture mo...

The regional nature of liquefaction records and limited information available for a certain set of explanatories motivate the development of complex prediction techniques. Indirect methods are commonly applied to incidentally derive a hyperplane to this binary classification problem. Machine learning approaches offer evolutionary prediction models...

Seismic-induced liquefaction prediction is an important application of classification problems. Machine learning offers evolutionary prediction models. Moreover, Ensemble learning is a recent advancement in this field, where a number of learners are trained and their inferences are integrated to produce stable as well as improved generalization abi...

Like any time series generated by complex systems, river flows can be represented by a time-varying parameter (TVP) model. TVP modeling frameworks often assume that the system evolution exhibits superstatistical random walks. Also, the TVPs should hone a predefined model ability to capture system's recurrence. In this work, we develop a computation...

The nature of pollutants involved in smog episodes can vary significantly in various cities and contexts and will impact local populations differently due to actual exposure and pre-existing sensitivities for cardiovascular or respiratory diseases. While regulated standards and guidance remain important, it is relevant for cities to have local warn...

A major challenge of climate change adaptation is to assess the effect of changing weather on human health. In spite of an increasing literature on the weather-related health subject, many aspect of the relationship are not known, limiting the predictive power of epidemiologic models. The present paper proposes new models to improve the performance...

Air temperature is a significant meteorological variable that affects social activities and economic sectors. In this paper, a non-parametric and a parametric approach are used to forecast hourly air temperature up to 24 h in advance. The former is a regression model in the Functional Data Analysis framework. The nonlinear regression operator is es...

Generalized Additive Models (GAMs) are introduced in this study for the regional estimation of low-flow characteristics at ungauged basins and compared to other approaches commonly used for this purpose. GAMs provide more flexibility in the shape of the relationships between the response and explanatory variables in comparison to classical models s...

The performance of a hydrological model depends strongly on the calibration procedure, and in particular on the goodness-offit measure used. It is widely recognized that traditional goodness-of-fit measures such as the Nash-Sutcliffe efficiency (NSE) are biased toward securing a particular aspect of a hydrograph (high flows, in the case of the NSE)...

Estimation of low-flow quantiles or indices at ungauged sites is traditionally done through regional low-flow frequency analysis. However, traditional methods imply a prior aggregation of the regional information due to the usual focus on a given quantile. This leads to loss of information for estimating additional quantiles or performing additiona...

inspa

In environmental epidemiology studies, health response data (e.g. hospitalization or mortality) are often noisy because of hospital organization and other social factors. The noise in the data can hide the true signal related to the exposure. The signal can be unveiled by performing a temporal aggregation on health data and then using it as the res...

Flow duration curves (FDC) are used to obtain daily streamflow series at ungauged sites. In this study, functional multiple regression (FMR) is proposed for FDC estimation. Its natural framework for dealing with curves allows obtaining the FDC as a whole instead of a limited number of single points. FMR assessment is performed through a case study...

In a number of environmental studies, relationships between natural processes are often assessed through regression analyses, using time series data. Such data are often multi-scale and non-stationary, leading to a poor accuracy of the resulting regression models and therefore to results with moderate reliability. To deal with this issue, the prese...

Estimation of flood quantiles in ungauged catchments is a common problem in hydrology. For this, the log-linear regression model is widely adopted. However, in many cases, a simple log transformation may not be able to capture the complexity and nonlinearity in flood generation processes. This paper develops generalized additive model (GAM) to deal...

Probabilistic regression approaches for downscaling daily precipitation are very useful. They provide the whole conditional distribution at each forecast step to better represent the temporal variability. The question addressed in this paper is: How to simulate spatiotemporal characteristics of multisite daily precipitation from probabilistic regre...

It has been argued that rainfall-runoff model calibration based solely on streamflow is not sufficient to evaluate the realism of a hydrological model to represent the internal fluxes. Therefore, model calibration has evolved to evaluating model performance using a number of hydrological signatures that link the model to the underlying processes. H...

Performance measures are widely used in hydrological modeling to provide objective evaluation of the match between simulated and observed system output (i.e. discharge). Each performance measure emphasises a particular aspect of a hydrograph, and the use of a particular performance measure on a specific metric typically means discounting one aspect...

This study presents an analysis of atmospheric ammonia (NH3) concentration from four different sites located in the Portneuf municipality and Quebec City over 2010-2013 years. The determination of NH3 concentration was performed using passive samplers. Seasonal Mann-Kendall test at 5% significance level was used to analyse the trend in each of the...

Water resources planning and management require complete data sets of a number of hydrological variables, such as flood peaks and volumes. However, hydrologists are often faced with the problem of missing data (MD) in hydrological databases. Several methods are used to deal with the imputation of MD. During the last decade, multivariate approaches...

Estimation of flood events at ungauged sites is often performed through regional flood frequency analysis (RFFA). RFFA uses the available information at gauged sites to estimate the desired design events at the ungauged site. These regional methods are based on a prior aggregation of the hydrological information at the gauged sites, which implies l...

It is accepted that human-induced climate change is unavoidable and it will have effects on physical, chemical, and biological properties of aquatic habitats. This will be especially important for cold water fishes such as trout. The objective of this study is to simulate water temperature for future periods under the climate change situations. Fut...

The high complexity of hydrological systems has long been recognized. Despite the increasing number of statistical techniques that aim to estimate hydrological quantiles at ungauged sites, few approaches were designed to account for the possible nonlinear connections between hydrological variables and catchments characteristics. Recently, a number...

Some regional procedures to estimate hydrological quantiles at ungauged sites, such as the index-flood method, require the delineation of homogeneous regions as a basic step for their application. The homogeneity of these delineated regions is usually tested providing a yes/no decision. However, complementary measures that are able to quantify the...

Extreme hydrological events, such as floods and droughts, are one of the natural disasters that occur in several parts of the world. They are regarded as being the most costly natural risks in terms of the disastrous consequences in human lives and in property damages. The main objective of the present study is to estimate flood events of Abiod wad...

Regional frequency analysis is an important tool to properly estimate hydrological characteristics at ungauged or partially gauged sites in order to prevent hydrological disasters. The delineation of homogeneous groups of sites is an important first step in order to transfer information and obtain accurate quantile estimates at the target site. The...

Hydrological frequency analysis (HFA) is commonly used for the assessment of the risk associated to hydrological events. HFA is generally based on the assumptions of homogeneity, independence and stationarity of the hydrological data. Hydrological events are often described through a number of dependent characteristics, such as peak, volume and dur...

This study investigates the utilization of hydrological information in regional flood frequency analysis (RFFA) to enforce desired properties for a group of gauged stations. Neighbourhoods are particular types of regions that are centred on target locations. A challenge for using neighbourhoods in RFFA is that hydrological information is not availa...

To estimate quantiles (representing the hydrologic risk) at an ungauged site, regional frequency analysis (RFA) is commonly used. To provide accurate estimates, in RFA regions must be homogeneous. Thus, the first step of such an analysis is to check this homogeneity, usually through the Hosking-Wallis (HW) homogeneity test. Though this test is usef...

Time series are effective at forecasting streamflow. For example, ARMA models and their various extensions are commonly used. They usually consider discrete observations, such as hourly or daily observations. This means that there is a gap between time t and t+1 because it is not physically possible to measure real continuous streamflow. Due to thi...

This paper reviews the different criteria used in the field of wind energy to compare the goodness-of-fit of candidate probability density functions (pdfs) to wind speed records, and discusses their advantages and disadvantages. The moment ratio and L-moment ratio diagram methods are also proposed as alternative methods for the choice of the pdfs....

Statistical downscaling techniques are required to refine atmosphere–ocean global climate data and provide reliable meteorological information such as a realistic temporal variability and relationships between sites and variables in a changing climate. To this end, the present paper introduces a modular structure combining two statistical tools of...

Hospitalizations and deaths belong to the most studied health variables in public health. Those variables are usually analyzed through mean events and trends, based on the whole dataset. However, this approach is not appropriate to comprehend health outcome peaks which are unusual events that strongly impact the health care network (e.g. overflow i...

Given the non-Gaussian and non-linear processes included in hydrologic models, on existing data assimilation algorithms, sequential Monte Carlo methods known as particle filters appear as one of the best tools for data assimilation. Given that particle filters rely on a large number of simulation runs at each step, their usage necessarily leads to...

Statistical models usually consider streamflow and meteorological processes as a succession of regular data values (for instance daily or hourly values). However, those natural processes are intrinsically continuous. Thus, exhaustive forecasting can benefit from considering streamflow and meteorological variables as continuous. This is made possibl...

To estimate quantiles (representing the hydrologic risk) at an ungauged site, regional frequency analysis (RFA) is commonly used. To provide accurate estimates, in RFA regions must be homogeneous. Thus, the first step of such an analysis is to check this homogeneity, usually through the Hosking-Wallis (HW) homogeneity test. Though this test is usef...

This study describes the data assimilation method that uses streamflow observation to update hydrology model states. Data assimilation has been receiving growing attention in the field of hydrology due to its ability to improve streamflow simulations and forecasts. Because the non-Gaussian and non-linear processes are included in hydrologic models,...

Analysis and forecasting of water temperature are important for water-ecological management. The objective of this study is to compare models for water temperature during the summer season for an impounded river. In a case study, we consider hydro-climatic and water temperature data of the Fourchue River (St-Alexandre-de-Kamouraska, Quebec, Canada)...

Streamflow, as a natural phenomenon, is continuous in time and so are the meteorological variables which influence its variability. In practice, it can be of interest to forecast the whole flow curve instead of points (daily or hourly). To this end, this paper introduces the functional linear models and adapts it to hydrological forecasting. More p...

The adoption of hydrological neighborhoods is one of the common approaches employed for the delineation step in regional frequency analysis (RFA). Traditional methods proposed for building hydrological neighborhoods are mainly based on distance metrics. These methods have some limitations. They are not robust against outliers, they are not affine i...

Classical regression models are widely used in hydrological regional frequency analysis (RFA) in order to provide quantile estimates at ungauged sites given physio-meteorological information. Since classical regression-based methods only provide the conditional mean of the response variable, estimated at-site quantiles at gauged sites are commonly...

Regional frequency analysis is needed to estimate hydrological quantiles at ungauged sites or to improve estimates at sites with short record lengths, by transferring information from gauged sites. Some regional procedures, such as the index-flood method, require the delineation of homogeneous regions as a basic step for their application. The homo...

The clustering of catchments is important for prediction in ungauged basins, model parameterization and watershed development and management. The aim of this study is to explore a new measure of similarity among catchments, using a data depth function and comparing it with catchment clustering indices based on flow and physical characteristics. A c...