Synthèse des développements récents en analyse régionale des extrêmes hydrologiques

L’estimation adéquate des événements hydrologiques extrêmes (événements de conception) est primordiale en raison des risques importants associés à une connaissance insuffisante de ces événements. Dans les sites où l’on dispose de peu ou même d’aucune information hydrologique, on a recours aux méthodologies d’estimation régionale pour l’estimation des extrêmes hydrologiques. De nombreuses méthodologies ont été développées durant les dernières années pour améliorer l’estimation régionale de la distribution des extrêmes hydrologiques. Cet article présente une synthèse exhaustive des derniers développements en matière d’analyse hydrologique régionale. Une discussion dégage les directions principales de ces développements récents, met en évidence les défis majeurs en matière d’analyse régionale pour les années futures et évoque des pistes prometteuses de travaux de recherche afin de répondre à ces nouveaux défis.Adequate estimation of extreme hydrological variables is essential for the rational design and operation of a variety of hydraulic structures, due to the significant risk that is associated with these activities. Local frequency analysis is commonly used for the estimation of extreme hydrological events at sites where an adequate amount of data is available. However, data are usually only collected at a relatively limited number of sites. In practice, it frequently happens that little or no streamflow data is available at a site of interest (where a dam is to be constructed for example). In such cases, hydrologists can utilize a regional flood frequency procedure, relying on data available from other basins with a similar hydrologic regime.Various methods have been developed over the last few years for the regional analysis of extreme hydrological events. These regionalization approaches aim to estimate different characteristics of the extreme hydrological phenomena of interest, make different assumptions and hypotheses concerning these hydrological phenomena, rely on various types of data, and often fall under completely different theories. The present paper aims to review and classify recent developments in regional frequency analysis of extreme hydrological variables.The specific objectives of the paper are to: i) review the main recent developments in regional hydrologic modeling that have been proposed during the last few years; ii) classify these developments into different groups according to the theoretical background of the method, its specific objectives, and the characteristics of hydrological extreme phenomena it is intended to deal with; iii) propose a comprehensive discussion of these methods, and point out the hypotheses, limitations, data requirements, and potential of each one; iv) identify the new challenges facing engineers in terms of regional frequency analysis of hydrological extremes; and v) propose potential promising directions for future research work which aim to meet these new challenges.Recent developments reviewed in the present paper include improvements in classical approaches for regional delineation and for information transfer, methods combining the delineation and estimation steps, seasonality-based methods, multivariate models for regional frequency analysis, the QdF approach, non stationary models, and approaches for the combination of local and regional data. The paper provides also a discussion of the various hydrological variables treated with regional estimation methodologies, comparative studies of these methodologies, and practical tools that were developed for regional frequency analysis. It is hoped that this document will contribute towards closing the gap between theory and practice, by narrowing the wide body of literature that is available, and by providing comprehensive propositions for regional frequency analysis approaches that meet the new challenges facing hydrologic engineers

Utilisation des réseaux de neurones et de la régularisation bayésienne en modélisation de la température de l’eau en rivière

Dans ce travail, nous avons élaboré un modèle de prédiction des variations de la température d’un cours d’eau en fonction de variables climatiques, telles que la température de l’air ambiant, le débit d’eau et la quantité de précipitation reçue par le cours d’eau. Les réseaux de neurones statiques ont été utilisés pour approximer la relation entre ces différentes variables avec une erreur moyenne de 0,7 °C. Par ailleurs, nous proposons un modèle de prédiction de l’évolution de la température de l’eau à court et moyen termes pour les jours (j + i, i = 1,2,..). Deux méthodes ont été appliquées : la première, de type itérative, utilise la valeur estimée du jour j pour prédire la valeur de la température de l’eau au jour j + 1; la seconde méthode, beaucoup plus simple à mettre en oeuvre, consiste à estimer la température de tous les jours considérés en une seule fois.L’optimisation de la fonction de coût par l’algorithme de Levenberg-Marquardt, disponible dans l’outil « réseaux de neurones » de MATLAB a permis d’améliorer nettement la performance des modèles. Des résultats très satisfaisants sont alors obtenus en testant la validité du modèle par la validation croisée avec des erreurs moyennes de prédiction à sept jours de 1,5 °C.Understanding and predicting water temperatures is essential in order to help prevent or forecast high temperature problems. To attain this objective, we define in this work a model that predicts temperature variations in a small stream according to climatic variables, such as air temperature, water flow and quantity of rainfall in the river catchment. Static neural networks were used as a technique for evaluation of the relations among the various variables, with a mean error of 0.7°C.In addition, we developed a forecasting model able to estimate the short-term and mid-term variations of water temperature, i.e., to forecast the temperature of days (j+i , i=1,2…) from climatic parameters of day j. Two methods were used: the first one is iterative and uses the estimated value of day j to estimate the value of the water temperature for day j+1. The second method is much simpler, involving an estimate of the temperature of all days at once. The Levenberg-Marquardt algorithm implemented in the Matlab neural network toolbox allowed a marked improvement in the performance of the model. Very satisfactory results were then obtained by testing the validity by cross validation technique with a mean error of 1.5°C for long term prediction of 7 days

A new look at weather-related health impacts through functional regression.

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 performances of the currently used ones. The proposed models are based on functional data analysis (FDA), a statistical framework dealing with continuous curves instead of scalar time series. The models are applied to the temperature-related cardiovascular mortality issue in Montreal. By making use of the whole information available, the proposed models improve the prediction of cardiovascular mortality according to temperature. In addition, results shed new lights on the relationship by quantifying physiological adaptation effects. These results, not found with classical model, illustrate the potential of FDA approaches

Aggregating the response in time series regression models, applied to weather-related cardiovascular mortality.

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 response in regression analysis. From aggregated series, a general methodology is introduced to account for the particularities of an aggregated response in a regression setting. This methodology can be used with usually applied regression models in weather-related health studies, such as generalized additive models (GAM) and distributed lag nonlinear models (DLNM). In particular, the residuals are modelled using an autoregressive-moving average (ARMA) model to account for the temporal dependence. The proposed methodology is illustrated by modelling the influence of temperature on cardiovascular mortality in Canada. A comparison with classical DLNMs is provided and several aggregation methods are compared. Results show that there is an increase in the fit quality when the response is aggregated, and that the estimated relationship focuses more on the outcome over several days than the classical DLNM. More precisely, among various investigated aggregation schemes, it was found that an aggregation with an asymmetric Epanechnikov kernel is more suited for studying the temperature-mortality relationship

EMD-regression for modelling multi-scale relationships, and application to weather-related cardiovascular mortality.

In a number of environmental studies, relationships between nat4ural 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 present paper introduces the EMD-regression methodology consisting in applying the empirical mode decomposition (EMD) algorithm on data series and then using the resulting components in regression models. The proposed methodology presents a number of advantages. First, it accounts of the issues of non-stationarity associated to the data series. Second, this approach acts as a scan for the relationship between a response variable and the predictors at different time scales, providing new insights about this relationship. To illustrate the proposed methodology it is applied to study the relationship between weather and cardiovascular mortality in Montreal, Canada. The results shed new knowledge concerning the studied relationship. For instance, they show that the humidity can cause excess mortality at the monthly time scale, which is a scale not visible in classical models. A comparison is also conducted with state of the art methods which are the generalized additive models and distributed lag models, both widely used in weather-related health studies. The comparison shows that EMD-regression achieves better prediction performances and provides more details than classical models concerning the relationship

Combining Landsat TIR ‐imagery data and ERA5 reanalysis information with different calibration strategies to improve simulations of streamflow and river temperature in the Canadian Subarctic

Arctic and Subarctic environments are among the most vulnerable regions to climate change. Increases in liquid precipitation and changes in snowmelt onset are cited as the main drivers of change in streamflow and water temperature patterns in some of the largest rivers of the Canadian Arctic. However, in spite of this evidence, there is still a lack of research on water temperature, particularly in the eastern Canadian Arctic. In this paper, we use the CEQUEAU hydrological‐water temperature model to derive consistent long‐term daily flow and stream temperature time series in Aux Mélèzes River, a non‐regulated basin (41 297 km2) in the eastern Canadian subarctic. The model was forced using reanalysis data from the fifth‐generation ECMWF atmospheric reanalyses (ERA5) from 1979 to 2020. We used water temperature derived from thermal infrared (TIR) images as reference data to calibrate CEQUEAU's water temperature model, with calibration performed using single‐site, multi‐site, and upscaling factors approaches. Our results indicate that the CEQUEAU model can simulate streamflow patterns in the river and shows excellent spatiotemporal performance with Kling‐Gupta Efficiency (KGE) metric >0.8. Using the best‐performing flow simulation as one of the inputs allowed us to produce synthetic daily water temperature time series throughout the basin, with the multi‐site calibration approach being the most accurate with root mean square errors (RMSE) <2.0°C. The validation of the water temperature simulations with a three‐year in situ data logger dataset yielded an RMSE = 1.38°C for the summer temperatures, highlighting the robustness of the calibrated parameters and the chosen calibration strategy. This research demonstrates the reliability of TIR imagery and ERA5 as sources of model calibration data in data‐sparse environments and underlines the CEQUEAU model as an assessment tool, opening the door to its use to assess climate change impact on the arctic regions of Canada

Faire-face aux changements ensemble (FACE) : mieux s’adapter aux changements climatiques au Canada et en Afrique de l’Ouest dans le domaine des ressources en eau - rapport final

Vu la fin prématurée du projet initial, un court nouveau projet intitulé « Les leishmanioses canines: réévaluation des transmissions, prophylaxie et indicateur épidémiologique en sante publique », sous la direction du professeur Abdelkbir Rhalem, Laboratoire de Parasitologie et des zoonoses parasitaires de l’Institut Agronomique et Vétérinaire Hassan II au Maroc, en collaboration avec le ministère de la santé (Dr Abderrahmane Laamrani El Idrissi, Chef de Service des Maladies parasitaires) a été amorcé en janvier 2015. Ce projet vise (1) à évaluer le rôle réservoir du chien pour des parasites du genre Leishmania sp. responsables chez l’homme de la forme viscérale (L.infantum) et les formes cutanées (L.infantum Mon- 24 et L. tropica) et (2) à utiliser le modèle chien comme indicateur de santé publique pour déterminer les conditions de transmission de la maladie et son évolution (prévalence et incidence). La première phase s’est terminée avec succès et la deuxième phase est amorcée (rapport prévu fin 2016)

The Importance of Including Water Temperature Simulations in a 2D Fish Habitat Model for the St. Lawrence River

Extreme climatic conditions likely caused a massive fish mortality during the summer of 2001 in the St. Lawrence River. To corroborate this hypothesis, we used a physical habitat simulation approach incorporating hydraulic and water temperature models. Spawning Habitat Suitability Indices (HSI) for common carp (Cyprinus carpio) were developed using fuzzy logic and applied to the model outputs to estimate habitat weighted usable area during the event. The results revealed that areas suitable for common carp spawning (HSI > 0.3) were severely reduced by high water temperatures, which exceeded 28 °C during the mortality event. During the mortality event, the amount of suitable habitat was reduced to ≤200 ha/day, representing less than 15% of the maximum potential suitable habitat in the study reach. In addition, the availability of cooler habitats that could have been used as thermal refuges was also reduced. These results indicate that the high water temperature in spawning areas and reduced accessibility to thermal refuge habitats exposed the carp to substantial physiological and environmental stress. The high water temperatures were highly detrimental to the fish and eventually led to the observed mortalities. This study demonstrates the importance of including water temperature in habitat suitability models

Considering Fish as Recipients of Ecosystem Services Provides a Framework to Formally Link Baseline, Development, and Post-operational Monitoring Programs and Improve Aquatic Impact Assessments for Large Scale Developments.

In most countries, major development projects must satisfy an Environmental Impact Assessment (EIA) process that considers positive and negative aspects to determine if it meets environmental standards and appropriately mitigates or offsets negative impacts on the values being considered. The benefits of before-after-control-impact monitoring designs have been widely known for more than 30 years, but most development assessments fail to effectively link pre- and post-development monitoring in a meaningful way. Fish are a common component of EIA evaluation for both socioeconomic and scientific reasons. The Ecosystem Services (ES) concept was developed to describe the ecosystem attributes that benefit humans, and it offers the opportunity to develop a framework for EIA that is centred around the needs of and benefits from fish. Focusing an environmental monitoring framework on the critical needs of fish could serve to better align risk, development, and monitoring assessment processes. We define the ES that fish provide in the context of two common ES frameworks. To allow for linkages between environmental assessment and the ES concept, we describe critical ecosystem functions from a fish perspective to highlight potential monitoring targets that relate to fish abundance, diversity, health, and habitat. Finally, we suggest how this framing of a monitoring process can be used to better align aquatic monitoring programs across pre-development, development, and post-operational monitoring programs