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Accurate estimation of daily rainfall return levels associated with large return periods is needed for a number of hydrological planning purposes, including protective infrastructure, dams, and retention basins. This is especially relevant at small spatial scales. The ERA-5 reanalysis product provides seasonal daily precipitation over Europe on a 0.25 x 0.25 grid (about 27 x 27 km). This translates more than 20,000 land grid points and leads to models with a large number of parameters when estimating return levels. To bypass this abundance of parameters, we build on the regional frequency analysis (RFA), a well-known strategy in statistical hydrology. This approach consists in identifying homogeneous regions, by gathering locations with similar distributions of extremes up to a normalizing factor and developing sparse regional models. In particular, we propose a step-by-step blueprint that leverages a recently developed and fast clustering algorithm to infer return level estimates over large spatial domains. This enables us to produce maps of return level estimates of ERA-5 reanalysis daily precipitation over continental Europe for various return periods and seasons. We discuss limitations and practical challenges and also provide a git hub repository. We show that a relatively parsimonious model with only a spatially varying scale parameter can compete well against statistical models of higher complexity., 13 pages and 11 figures

A recurrent question in climate risk analysis is determining how climate change will affect heavy precipitation patterns. Dividing the globe into homogeneous sub-regions should improve the modelling of heavy precipitation by inferring common regional distributional parameters. In addition, in the detection and attribution (D&A) field, biases due to model errors in global climate models (GCMs) should be considered to attribute the anthropogenic forcing effect. Within this D&A context, we propose an efficient clustering algorithm that, compared to classical regional frequency analysis (RFA) techniques, is covariate-free and accounts for dependence. It is based on a new non-parametric dissimilarity that combines both the RFA constraint and the pairwise dependence. We derive asymptotic properties of our dissimilarity estimator, and we interpret it for generalised extreme value distributed pairs. As a D&A application, we cluster annual daily precipitation maxima of 16 GCMs from the coupled model intercomparison project. We combine the climatologically consistent subregions identified for all GCMs. This improves the spatial clusters coherence and outperforms methods either based on margins or on dependence. Finally, by comparing the natural forcings partition with the one with all forcings, we assess the impact of anthropogenic forcing on precipitation extreme patterns., 16 pages, 6 figures, proofs are attached

International audience; Natural risk studies such as flood risk assessments require long series of weather variables. As an alternative to observed series, which have a limited length, these data can be provided by weather generators. Among the large variety of existing ones, resampling methods based on analogues have the advantage of guaranteeing the physical consistency between local weather variables at each time step. However, they cannot generate values of predictands exceeding the range of observed values. Moreover, the length of the simulated series is typically limited to the length of the synoptic meteorological records used to characterize the large-scale atmospheric configuration of the generation day. To overcome these limitations, the stochastic weather generator proposed in this study combines two sampling approaches based on atmospheric analogues: (1) a synoptic weather generator in a first step, which recombines days of the 20th century to generate a 1000-year sequence of new atmospheric trajectories, and (2) a stochastic downscaling model in a second step applied to these atmospheric trajectories, in order to simulate long time series of daily regional precipitation and temperature. The method is applied to daily time series of mean areal precipitation and temperature in Switzerland. It is shown that the climatological characteristics of observed precipitation and temperature are adequately reproduced. It also improves the reproduction of extreme precipitation values, overcoming previous limitations of standard analogue-based weather generators.

International Union For Quaternary Research, Dublin, 25th and 31st July 2019. -- http://www.inqua2019.org/

AGU Fall Meeting 2019 in San Francisco, 9-13 december 2019, Extreme Flood Events (EFE) and related disasters are becoming an increased economic and social concern in the context of global environmental change. The current and expected impacts of EFEs at regional scale are highly variable, as our knowledge of climate and land cover/use dynamic interactions is hampered by the limited availability of long time series. We investigate such relationships during the last millennia in the northern Iberian Peninsula where both Atlantic and Mediterranean climate patterns interact, and the landscapes have been heavily modified since Medieval times. Three lacustrine sedimentary archives located along an W-E transect in Northern Spain (Sanabria, Arreo and Montcortès) and with robust age models provide high-resolution reconstructions to assess the effects of climate variability and land use change on flood frequency and intensity. Preliminary results show a large spatio-temporal flood variability at decadal to centennial scales. The northwestern (Lake Sanabria) and central (Lake Arreo) sites show an increase in extreme events since the 17th century. On the other hand, more extreme floods occurred in Lake Montcortès (NE Iberia) during the Late Middle Ages and the 19th century. A see-saw pattern in flood frequency seems to occur during the Medieval Climate Anomaly/Little Ice Age transition with more (less) flood events occurring during cold (warm) phases in Atlantic and Mediterranean areas respectively. The occurrence of a large flooding event on January 9th, 1959 caused by the Vega de Tera dam failure in the Sanabria watershed provides a unique opportunity to compare its depositional signatures with previous paleofloods. Detailed geomorphologic mapping and sedimentological analyses of a transect of cores in the lake underline the uniqueness of the 1959 flood in terms of sediment transport, lake-watershed connectivity and hydraulic properties. The depopulation of the mountains since the mid 1950s have changed the land covers and consequently the hydrological behavior of the watersheds. Although at a regional scale, climate variability remains the main flood control, land-use changes have strongly modulated the magnitude of run-off events and ultimately affecting soil erosion and land degradation in northern Iberian Peninsula during the last millennium.

International audience; Precipitation amounts at daily or hourly scales are skewed to the right and heavy rainfall is poorly modeled by a simple gamma distribution. An important, yet challenging topic in hydrometeorology is to find a probability distribution that is able to model well low, moderate and heavy rainfall. To address this issue, we present a semiparametric distribution suitable for modeling the entire-range of rainfall amount. This model is based on a recent parametric statistical model called the class of Extended Generalized Pareto Distributions (EGPD). The EGPD family is in compliance with Extreme Value Theory for both small and large values, while it keeps a smooth transition between these tails and bypasses the hurdle of selecting thresholds to define extremes. In particular, return levels beyond the largest observation can be inferred. To add flexibility to this EGPD class, we propose to model the transition function in a non-parametric fashion. A fast and efficient nonparametric scheme based on Bernstein polynomial approximations is investigated. We perform simulation studies to assess the performance of our approach. It is compared to two parametric models: a parametric EGPD and the classical Generalized Pareto Distribution (GPD), the later being only fitted to excesses above high threshold. We also apply our semiparametric version of EGPD to daily rainfall data recorded at Mont-Aigoual weather station in France.

National audience

International audience; River discharge is one of the most important quantities in hydrology. It provides fundamentalrecords for water resources management and climate change monitoring. Even very short data-gaps in thisinformation can cause extremely different analysis outputs. Therefore, reconstructing missing data ofincomplete data sets is an important step regarding the performance of the environmental models, engineering,and research applications, thus it presents a great challenge. The objective of this paper is to introducean effective technique for reconstructing missing daily discharge data when one has access to onlydaily streamflow data. The proposed procedure uses a combination of regression and autoregressive integratedmoving average models (ARIMA) called dynamic regression model. This model uses the linear relationshipbetween neighbor and correlated stations and then adjusts the residual term by fitting an ARIMAstructure. Application of the model to eight daily streamflow data for the Durance river watershed showedthat the model yields reliable estimates for the missing data in the time series. Simulation studies were alsoconducted to evaluate the performance of the procedure.

International audience

International audience; Nous analysons 47 années de couples pointe-volume de crues de la rivière Romaine au Québec à l'aide d'un modèle paramètrique de copule. Les deux marges sont modélisées soit par une loi gamma ou normale, et la dépendance par une famille paramétrique de copules (Arch12 et Clayton). L'inférence, la sélection de modéles et l'aide à la décision en avenir incertain sont alors réalisées sous la perpective bayésienne dans un contexte paramétrique.

The unusual frequency of hydro-meteorological events in recent decades, often with catastrophic consequences for society and the environment, require new methods for designing water management projects and the structures meant to protect us from natural hazards. These methods and techniques are often based on the statistical modeling techniques of frequency analysis. Predictive Hydrology: A Frequency Analysis Approach is the first book to address both the theoretical concepts and the methodological approaches used in frequency hydrology—spelling out the fundamental methods to consider, providing concise instruction on the techniques that are involved, and including examples and critiques based on practical applications. It explores some of the recent research developments in the field. Published originally in French, this English translation targets students in civil engineering, environmental sciences and technology, hydrology, geography, geology and ecology. This book will also serve as a useful reference not only for teachers and researchers, but for engineering practitioners, who are constantly faced with the problems of handling data, but often find themselves without the appropriate analytical tools.

La fréquence inhabituelle d'évènements hydrométéorologiques particuliers constatés ces dernières décennies et leurs conséquences souvent catastrophiques sur la société et l'environnement illustrent bien l'importance de l'analyse statistique fréquentielle lors de la conception d'ouvrages de gestion des eaux et de prévention des risques naturels. Cet ouvrage définit et précise les concepts et approches méthodologiques de cette discipline, recense les méthodes essentielles à considérer, explique rigoureusement et pédagogiquement les techniques qui s'y rapprochent, illustre et critique à l'aide d'exemples leurs applications. Un chapitre présente en plus les développements récents en recherche et les perspectives en la matière.

Cette thèse porte sur l'analyse bayésienne de données fonctionnelles dans un contexte hydrologique. L'objectif principal est de modéliser des données d'écoulements d'eau d'une manière parcimonieuse tout en reproduisant adéquatement les caractéristiques statistiques de celles-ci. L'analyse de données fonctionnelles nous amène à considérer les séries chronologiques d'écoulements d'eau comme des fonctions à modéliser avec une méthode non paramétrique. Dans un premier temps, les fonctions sont rendues plus homogènes en les synchronisant. Ensuite, disposant d'un échantillon de courbes homogènes, nous procédons à la modélisation de leurs caractéristiques statistiques en faisant appel aux splines de régression bayésiennes dans un cadre probabiliste assez général. Plus spécifiquement, nous étudions une famille de distributions continues, qui inclut celles de la famille exponentielle, de laquelle les observations peuvent provenir. De plus, afin d'avoir un outil de modélisation non paramétrique flexible, nous traitons les noeuds intérieurs, qui définissent les éléments de la base des splines de régression, comme des quantités aléatoires. Nous utilisons alors le MCMC avec sauts réversibles afin d'explorer la distribution a posteriori des noeuds intérieurs. Afin de simplifier cette procédure dans notre contexte général de modélisation, nous considérons des approximations de la distribution marginale des observations, nommément une approximation basée sur le critère d'information de Schwarz et une autre qui fait appel à l'approximation de Laplace. En plus de modéliser la tendance centrale d'un échantillon de courbes, nous proposons aussi une méthodologie pour modéliser simultanément la tendance centrale et la dispersion de ces courbes, et ce dans notre cadre probabiliste général. Finalement, puisque nous étudions une diversité de distributions statistiques au niveau des observations, nous mettons de l'avant une approche afin de déterminer les distributions les plus adéquates pour un échantillon de courbes donné., This thesis is about Bayesian functional data analysis in hydrology. The main objective is to model water flow data in a parsimonious fashion while still reproducing the statistical features of the data. Functional data analysis leads us to consider the water flow time series as functions to be modelled with a nonparametric method. First, the functions are registered in order to make them more homogeneous. With a more homogeneous sample of curves, we proceed to model their statistical features by relying on Bayesian regression splines in a fairly broad probabilistic framework. More specifically, we study a family of continuous distributions, which include those of the exponential family, from which the data might have arisen. Furthermore, to have a flexible nonparametric modeling tool, we treat the interior knots, which define the basis elements of the regression splines, as random quantities. We then use MCMC with reversible jumps in order to explore the posterior distribution of the interior knots. In order to simplify the procedure in our general modeling context, we consider some approximations for the marginal distribution of the observations, namely one based on the Schwarz information criterion and another which relies on Laplace's approximation. In addition to modeling the central tendency of a sample of curves, we also propose a methodology to simultaneously model the central tendency and the dispersion of the curves in our general probabilistic framework. Finally, since we study several statistical distributions for the observations, we put forward an approach to determine the most adequate distributions for a given sample of curves.