Peu de travaux se sont attachés à estimer les statistiques relatives aux crues fréquentes en sites non jaugés. Celles-ci ont de fait été délaissées par la communauté hydrologique, plus encline à s’intéresser aux événements extrêmes (périodes de retour d’au moins 10 ans) utilisés dans la gestion du risque inondation. Cependant, le régime des hautes eaux ne se limite pas à ces seules caractéristiques. Une bonne connaissance des crues modérées est requise dans de nombreux domaines comme l’hydroécologie ou l’hydromorphologie. La fréquente occurrence de ces crues implique en effet un modelage régulier du lit. Elles concourent ainsi à conditionner les habitats écologiques au sein des hydrosystèmes d’eau douce.L’objectif de cette thèse consiste à caractériser le régime des crues fréquentes, i.e. de périodes de retour de 1 à 5 ans, en France métropolitaine. Pour cela, il est nécessaire de considérer les chroniques disponibles au plan national, et d’en extraire l’information hydrologique pertinente. La constitution d’un échantillon fiable permettant une analyse robuste représente à ce titre une étape importante. La sélection de stations s’appuie sur une analyse des valeurs extrêmes de débit, extraites des chroniques de débit à pas de temps variable (longueur de la série, stationnarité, comportement des distributions statistiques…), ainsi que sur les informations fournies par les gestionnaires des stations hydrométriques. La démarche adoptée consiste à décrire les évènements de crues modérées dans un souci d’exhaustivité, à la fois en termes de débits mais aussi de volumes, selon une analyse multi-durées décrite par les courbes QdF (débit-durée-fréquence), qui fournissent les quantiles de crue (pic et volumes). Le modèle QdF convergent exploité ici permet de réduire à 3 le nombre de paramètres descriptifs du régime des crues.Pour caractériser le régime des crues fréquentes sur l’ensemble du réseau hydrographique français, la démarche intègre la mise en œuvre de méthodes dites « de régionalisation ». Il s’agit de transférer l’information hydrologique disponible aux sites de mesures vers l’ensemble du réseau hydrographique français. Plusieurs approches ont été envisagées. Ainsi, des formulations empiriques établies sur des découpages régionaux ont été mises en œuvre. Fréquemment utilisée, cette technique nécessite de limiter le nombre de stations présentant des enregistrements disjoints afin d’éviter le risque de représenter une variabilité temporelle plutôt qu’un effet spatial. Le respect de cette contrainte entraîne une perte de 30% de stations hydrométriques de l’échantillon initial.C’est pour limiter cette perte d’information non négligeable que la méthode TREK (Time-REferenced data Kriging) a été développée. Cet algorithme de cartographie a été conçu afin de prendre en compte le support temporel des données disponibles en plus du support spatial. Les données disponibles participent plus ou moins aux estimations selon leur période d'observation propre. TREK permet ainsi d'atténuer la perte de données provoquée par le recours à une période de référence commune ou un seuil maximal de lacunes autorisées. Pour répondre aux objectifs de la thèse, les différentes méthodes d’estimation en sites non jaugés sont mises en œuvre et leur efficience est évaluée dans le cadre d’une validation croisée. Cette démarche de comparaison objective permet de sélectionner le modèle optimal pour caractériser le régime des crues fréquentes sur le réseau hydrographique français. / Only a few studies have focused on frequent floods regimes at ungauged locations. Most of works have put their efforts on extreme flood events (return periods of 10 years or more) needed for solving many engineering issues in flood risk management. However, high flows regime is not confined to extremes values. A good understanding of frequent floods is required in a wide array of topics like hydroecology and hydromorphomology. Frequent floods provide many functions, maintaining and rejuvenating ecological habitats and influencing the geomorphology of the streambed, so their distribution must be also known.The main objective of this work is to characterise the frequent floods from a statistical point of view (with a return period between 1 and 5 years) in France. Forming the dataset is a preliminary crucial step to derive both robust and reliable statistics. The selection relies on different criteria, for example related to the quality of discharge measurements, the length of records, the self-assessment of people in charge, and finally on an analysis of extreme values extracted from time series (stationarity, shape of the distributions…).A comprehensive description of frequent floods regimes (intensity, duration and frequency) is required. It is achieved by applying the flow-duration–frequency (QdF) model which takes into account the temporal dynamics of floods. This approach is analogous to the intensity-duration–frequency (IdF) model commonly used for extreme rainfall analysis. At gauged locations, the QdF model can be summarised with only three parameters: the position and scale parameters of the exponential distribution fitted to the samples of instantaneous peak floods and a parameter homogeneous to a decay time computed from observed data.Different regionalisation methods were applied for estimating these three QdF parameters at ungauged locations. Regionalisation methods rely on the concept of transferring hydrological information from a site of measurement to ungauged sites. However these approaches require simultaneous records to avoid that the map is spoiled by temporal variability rather than display truly spatial patterns. Regional empirical formulas were derived but the constraints discussed above lead to discard 30% of the dataset.Time-REferenced data Kriging method (TREK) has been developed to overcome this issue. This alogrithm was developped in order to account the temporal support over which the variable of interest has been calculated, in addition to its spatial support. This approach aims at reducing the loss of data caused by the selection of a common reference period of records required to build a reliable dataset. The performances of each method have been assessed by cross-validation and a combination of best features is finally selected to map the frequent flow features over France.

There is growing concern for increased frequency of extreme events due to several severe floods and droughts occurring globally in recent years. Improving knowledge on the complexity of hydrological systems and interactions with climate is essential to be able to determine drivers and predict changes in the future. This is especially true in cold regions such as the Swedish Subarctic. This thesis explored changes in high peak flows and linked trends to climate. Trend analyses were applied on 18 catchments in the Swedish Subarctic over their entire periods of record and a common period (1990-2013) among the data to explore changes in flood magnitude, flood occurrence, mean summer flow, snowmelt onset and center of mass. Further, a flood frequency analysis was applied using the extreme value type I (Gumbel) distribution and selected flood percentiles were tested for stationarity. The results show the complexity of the hydrological system and interactions with climate. No clear overall pattern could be determined suggesting that changes are happening at catchment scale. Indications for a shift in flow regime from snowmelt-dominated to rainfall-dominated are evident with all significant trends pointing towards lower flood magnitudes in the spring flood, earlier flood occurrence and snowmelt onset, and decreasing mean summer flows. The shift in flow regime suggests that air temperature is more clearly reflected in streamflow than precipitation in the Swedish Subarctic. Decreasing trends in flood magnitude and mean summer flows are suggestive of permafrost thawing, which agrees with the increasing trends in the annual minimum flow. Long streamflow records can further link variability in streamflow to multidecadal atmospheric circulations over the North Atlantic. Most evident are changes towards lower mean summer flows (ten catchments significant at a 95% confidence interval) and earlier snowmelt onset (eight catchments significant). Trends in the selected flood percentiles show indications towards an increase in extreme events over the entire period (significant for four catchments), with all significant trends being positive. Over the common period, no pattern is notable and the sensitivity of trend analyses is evident.

Coastal regions are at risk of environmental threats. Flooding in coastal rivers is the result of intense precipitation which is triggered by climate change. Coastal watersheds are prone to losing significant amounts of sediment and nutrients because of the shorter transport pathway that drains directly into the coastal water. In this study, the hydrology, flood frequency, and water quality assessment of two coastal watersheds, Wolf River watershed (WRW) and Jourdan River watershed (JRW), were conducted using the Soil and Water Assessment Tool (SWAT). Since WRW and JRW are the main tributaries to fetch freshwater to Saint Louis Bay (SLB) of Western Mississippi Sound, an integrated approach to assess the influence of freshwater influx into the coastal water is also performed by coupling SWAT with hydrodynamic visual Environment Fluid Dynamics Code (v-EFDC). An auto-calibration tool, SWAT Calibration and Uncertainty Programs (SWAT-CUP) was used to calibrate and validate the flow, total suspended solids and mineral phosphorous for obtaining satisfactory statistical results. While comparing the flood frequency of historical, baseline and projected scenario in both watersheds, the results illustrated that using annual maximum series, 1% exceedance probability was the highest for WRW baseline scenario, whereas for JRW, 1% exceedance probability was the highest for projected scenario. The water quality assessment study of WRW and JRW suggested that ponds and wetlands were more effective in reducing TSS and riparian buffers were more effective in reducing MinP at the outlet of both the watersheds. The integrated approach of coupling SWAT-vEFDC model result indicated that major impact on water quality was observed at the location where the freshwater inflow into the SLB, and the impact was diminished while moving further along the Western Mississippi Sound. Overall, this study gives an insight for integrated coastal watershed management which includes prediction of future flood frequency, the application of best management practices for reducing sediment and nutrient load, and estimation of upstream watershed pollutant load draining along with runoff including its effect on the coastal water quality.

Flood design necessitates discharge estimates for large recurrence intervals. However, in a flood frequency analysis, the uncertainty of discharge estimates increases with higher recurrence intervals, particularly due to the small number of available flood data. Furthermore, traditional distribution functions increase unlimitedly without consideration of an upper bound discharge. Hence, additional information needs to be considered which is representative for high recurrence intervals. Envelope curves which bound the maximum observed discharges of a region are an adequate regionalisation method to provide additional spatial information for the upper tail of a distribution function. Probabilistic regional envelope curves (PRECs) are an extension of the traditional empirical envelope curve approach, in which a recurrence interval is estimated for a regional envelope curve (REC). The REC is constructed for a homogeneous pooling group of sites. The estimation of this recurrence interval is based on the effective sample years of data considering the intersite dependence among all sites of the pooling group. The core idea of this thesis was an improvement of discharge estimates for high recurrence intervals by integrating empirical and probabilistic regional envelope curves into the flood frequency analysis. Therefore, the method of probabilistic regional envelope curves was investigated in detail. Several pooling groups were derived by modifying candidate sets of catchment descriptors and settings of two different pooling methods. These were used to construct PRECs. A sensitivity analysis shows the variability of discharges and the recurrence intervals for a given site due to the different assumptions. The unit flood of record which governs the intercept of PREC was determined as the most influential aspect. By separating the catchments into nested and unnested pairs, the calculation algorithm for the effective sample years of data was refined. In this way, the estimation of the recurrence intervals was improved, and therefore the use of different parameter sets for nested and unnested pairs of catchments is recommended. In the second part of this thesis, PRECs were introduced into a distribution function. Whereas in the traditional approach only discharge values are used, PRECs provide a discharge and its corresponding recurrence interval. Hence, a novel approach was developed, which allows a combination of the PREC results with the traditional systematic flood series while taking the PREC recurrence interval into consideration. An adequate mixed bounded distribution function was presented, which in addition to the PREC results also uses an upper bound discharge derived by an empirical envelope curve. By doing so, two types of additional information which are representative for the upper tail of a distribution function were included in the flood frequency analysis. The integration of both types of additional information leads to an improved discharge estimation for recurrence intervals between 100 and 1000 years. / Abschätzungen von Abflüssen mit hohen Wiederkehrintervallen werden vor allem für die Bemessung von Extremhochwässern benötigt. In der Hochwasserstatistik bestehen insbesondere für hohe Wiederkehrintervalle große Unsicherheiten, da nur eine geringe Anzahl an Messwerten für Hochwasserereignisse verfügbar ist. Zudem werden zumeist Verteilungsfunktionen verwendet, die keine obere Grenze beinhalten. Daher müssen zusätzliche Informationen zu den lokalen Pegelmessungen berücksichtigt werden, die den Extrembereich einer Verteilungsfunktion abdecken. Hüllkurven ermitteln eine obere Grenze von Hochwasserabflüssen basierend auf beobachteten maximalen Abflusswerten. Daher sind sie eine geeignete Regionalisierungsmethode. Probabilistische regionale Hüllkurven sind eine Fortentwicklung des herkömmlichen Ansatzes der empirischen Hüllkurven. Hierbei wird einer Hüllkurve einer homogenen Region von Abflusspegeln ein Wiederkehrintervall zugeordnet. Die Berechnung dieses Wiederkehrintervalls basiert auf der effektiven Stichprobengröße und berücksichtigt die Korrelationsbeziehungen zwischen den Pegeln einer Region. Ziel dieser Arbeit ist eine Verbesserung der Abschätzung von Abflüssen mit großen Wiederkehrintervallen durch die Integration von empirischen und probabilistischen Hüllkurven in die Hochwasserstatistik. Hierzu wurden probabilistische Hüllkurven detailliert untersucht und für eine Vielzahl an homogenen Regionen konstruiert. Hierbei wurden verschiedene Kombinationen von Einzugsgebietsparametern und Variationen von zwei Gruppierungsmethoden verwendet. Eine Sensitivitätsanalyse zeigt die Variabilität von Abfluss und Wiederkehrintervall zwischen den Realisationen als Folge der unterschiedlichen Annahmen. Die einflussreichste Größe ist der maximale Abfluss, der die Höhe der Hüllkurve bestimmt. Eine Einteilung in genestete und ungenestete Einzugsgebiete führt zu einer genaueren Ermittlung der effektiven Stichprobe und damit zu einer verbesserten Abschätzung des Wiederkehrintervalls. Daher wird die Verwendung von zwei getrennten Parametersätzen für die Korrelationsfunktion zur Abschätzung des Wiederkehrintervalls empfohlen. In einem zweiten Schritt wurden die probabilistischen Hüllkurven in die Hochwasserstatistik integriert. Da in traditionellen Ansätzen nur Abflusswerte genutzt werden, wird eine neue Methode präsentiert, die zusätzlich zu den gemessenen Abflusswerten die Ergebnisse der probabilistischen Hüllkurve – Abfluss und zugehöriges Wiederkehrintervall - berücksichtigt. Die Wahl fiel auf eine gemischte begrenzte Verteilungsfunktion, die neben den probabilistischen Hüllkurven auch eine absolute obere Grenze, die mit einer empirischen Hüllkurve ermittelt wurde, beinhaltet. Damit werden zwei Arten von zusätzlichen Informationen verwendet, die den oberen Bereich einer Verteilungsfunktion beschreiben. Die Integration von beiden führt zu einer verbesserten Abschätzung von Abflüssen mit Wiederkehrintervallen zwischen 100 und 1000 Jahren.

Two locations on Scott Creek, located 12 miles north of Santa Cruz California, are being considered for the installation of a streamgage to measure discharge. Each location offers unique considerations and challenges in gage construction and discharge measurement capabilities. A detailed flood frequency analysis was completed using a direct watershed comparison, direct equations developed by Waananen and Crippen, a Log Pearson Type III Frequency Distribution, a regional analysis, and two-station comparisons. Final results indicate a 100-year recurrence interval of 6,310 ft3/s at the Upper Scott Creek location and 6,520 ft3/s at the lower location. A detailed indirect measurement revealed that the Lower Scott Creek gage location can only maintain a discharge of 2,500 ft3/s, or a 10-year frequency event, before bank overflow. Therefore, a cableway spanning the width of the design flow cannot be constructed and stage readings at extreme peak events will not accurately represent the true hydrograph. A bridge at the Upper Scott Creek gage location will provide a means for measuring high flow events; however, the channel is in a state of disequilibrium due to debris jams within the 140 foot reach above the bridge. This site is also problematic due to the occurrence of channel avulsion which is scouring and incising a new channel which threatens to undermine the left bank wingwall of the bridge. Remediation measures have been proposed, including the installation of a cross-vane and wing-deflectors, to mitigate negative effects of erosion and reestablish a natural channel condition. The upstream location has been selected as the preferred alternative given the remediation measures are successful.

This study addresses flood risk assessment in the Catalpa Creek watershed, located in northeast Mississippi, USA. Employing the Hydrological Modeling System (HEC-HMS) and the River Analysis System (HEC-RAS), integrated models were developed and calibrated, to predict flood behavior within the watershed. The study conducted flood frequency analyses for return periods ranging from 2 to 100 years and generated flood inundation maps, pinpointing flood-prone areas. Mitigation measures for flood risk management were recommended. The results underscore the effectiveness of the integrated modeling approach for simulating and understanding the complex dynamics of flood events. The research identified critical flood-prone zones, emphasizing the importance of proactive flood risk management. The calibrated hydrological model serves as a valuable tool for stormwater management, water resource planning, and watershed assessment. The study provides insights into flood risk in the Catalpa Creek watershed, offering valuable guidance to regional decision-makers. This study lays the foundation for future investigations in floodplain encroachment, sediment transport, stream restoration, and flood inundation hazard mapping.

Reliable estimates of design flood quantiles are often necessary at sparsely gauged/ungauged target locations in river basins for various applications in water resources engineering. Development of effective methods for use in this task has been a long-standing challenge in hydrology for over five decades.. Hydrologists often consider various regional flood frequency analysis (RFFA) approaches that involve (i) use of regionalization approach to delineate a homogeneous group of watersheds resembling watershed of the target location, and (ii) use of a regional frequency analysis (RFA) approach to transfer peak flow related information from gauged watersheds in the group to the target location, and considering the information as the basis to estimate flood quantile(s) for the target site. The work presented in the thesis is motivated to address various shortcomings/issues associated with widely used regionalization and RFA approaches. Regionalization approaches often determine regions by grouping data points in multidimensional space of attributes depicting watershed’s hydrology, climatology, topography, land-use/land-cover and soils. There are no universally established procedures to identify appropriate attributes, and modelers use subjective procedures to choose a set of attributes that is considered common for the entire study area. This practice may not be meaningful, as different sets of attributes could influence extreme flow generation mechanism in watersheds located in different parts of the study area. Another issue is that practitioners usually give equal importance (weight) to all the attributes in regionalization, though some attributes could be more important than others in influencing peak flows. To address this issue, a two-stage clustering approach is developed in the thesis. It facilitates identification of appropriate attributes and their associated weights for use in regionalization of watersheds in the context of flood frequency analysis. Effectiveness of the approach is demonstrated through a case study on Indiana watersheds. Conventional regionalization approaches could prove effective for delineating regions when data points (depicting watersheds) in watershed related attribute space can be segregated into disjoint groups using straight lines or linear planes. They prove ineffective when (i) data points are not linearly separable, (ii) the number of attributes and watersheds is large, (iii) there are outliers in the attribute space, and (iv) most watersheds resemble each other in terms of their attributes. In real world scenario, most watersheds resemble each other, and regions may not always be segregated using straight lines or linear planes, and dealing with outliers and high-dimensional data is inevitable in regionalization. To address this, a fuzzy support vector clustering approach is proposed in the thesis and its effectiveness over commonly used region-of-influence approach, and different cluster analysis based regionalization methods is demonstrated through a case study on Indiana watersheds. For the purpose of regional frequency analysis (RFA), index-flood approach is widely used over the past five decades. Conventional index-flood (CIF) approach assumes that values of scale and shape parameters of frequency distribution are identical across all the sites in a homogeneous region. In real world scenario, this assumption may not be valid even if a region is statistically homogeneous. Logarithmic index-flood (LIF) and population index-flood (PIF) methodologies were proposed to address the problem, but even those methodologies make unrealistic assumptions. PIF method assumes that the ratio of scale to location parameters is a constant for all the sites in a region. On the other hand, LIF method assumes that appropriate frequency distribution to fit peak flows could be found in log-space, but in reality the distribution of peak flows in log space may not be closer to any of the known theoretical distributions. To address this issue, a new mathematical approach to RFA is proposed in L-moment and LH-moment frameworks that can overcome shortcomings of the CIF approach and its related LIF and PIF methods that make various assumptions but cannot ensure their validity in RFA. For use with the proposed approach, transformation mechanisms are proposed for five commonly used three-parameter frequency distributions (GLO, GEV, GPA, GNO and PE3) to map the random variable being analyzed from the original space to a dimensionless space where distribution of the random variable does not change, and deviations of regional estimates of all the distribution’s parameters (location, scale, shape) with respect to their population values as well as at-site estimates are minimal. The proposed approach ensures validity of all the assumptions of CIF approach in the dimensionless space, and this makes it perform better than CIF approach and related LIF and PIF methods. Monte-Carlo simulation experiments revealed that the proposed approach is effective even when the form of regional frequency distribution is mis-specified. Case study on watersheds in conterminous United States indicated that the proposed approach outperforms methods based on index-flood approach in real world scenario. In recent decades, fuzzy clustering approach gained recognition for regionalization of watersheds, as it can account for partial resemblance of several watersheds in watershed related attribute space. In working with this approach, formation of regions and quantile estimation requires discerning information from fuzzy-membership matrix. But, currently there are no effective procedures available for discerning the information. Practitioners often defuzzify the matrix to form disjoint clusters (regions) and use them as the basis for quantile estimation. The defuzzification approach (DFA) results in loss of information discerned on partial resemblance of watersheds. The lost information cannot be utilized in quantile estimation, owing to which the estimates could have significant error. To avert the loss of information, a threshold strategy (TS) was considered in some prior studies, but it results in under-prediction of quantiles. To address this, a mathematical approach is proposed in the thesis that allows discerning information from fuzzy-membership matrix derived using fuzzy clustering approach for effective quantile estimation. Effectiveness of the approach in estimating flood quantiles relative to DFA and TS was demonstrated through Monte-Carlo simulation experiments and case study on mid-Atlantic water resources region, USA. Another issue with index flood approach and its related RFA methodologies is that they assume linear relationship between each of the statistical raw moments (SMs) of peak flows and watershed related attributes in a region. Those relationships form the basis to arrive at estimates of SMs for the target ungauged/sparsely gauged site, which are then utilized to estimate parameters of flood frequency distribution and quantiles corresponding to target return periods. In reality, non-linear relationships could exist between SMs and watershed related attributes. To address this, simple-scaling and multi-scaling methodologies have been proposed in literature, which assume that scaling (power law) relationship exists between each of the SMs of peak flows at sites in a region and drainage areas of watersheds corresponding to those sites. In real world scenario, drainage area alone may not completely describe watershed’s flood response. Therefore flood quantile estimates based on the scaling relationships can have large errors. To address this, a recursive multi-scaling (RMS) approach is proposed that facilitates construction of scaling (power law) relationship between each of the SMs of peak flows and a set of site’s region-specific watershed related attributes chosen/identified in a recursive manner. The approach is shown to outperform index-flood based region-of-influence approach, simple-and multi-scaling approaches, and a multiple linear regression method through leave-one-out cross validation experiment on watersheds in and around Indiana State, USA. The conventional approaches to flood frequency analysis (FFA) are based on the assumption that peak flows at the target site represent a sample of independent and identically distributed realization drawn from a stationary homogeneous stochastic process. This assumption is not valid when flows are affected by changes in climate and/or land use/land cover, and regulation of rivers through dams, reservoirs and other artificial diversions/storages. In situations where evidence of non-stationarity in peak flows is strong, it is not appropriate to use quantile estimates obtained based on the conventional FFA approaches for hydrologic designs and other applications. Downscaling is one of the options to arrive at future projections of flows at target sites in a river basin for use in FFA. Conventional downscaling methods attempt to downscale General Circulation Model (GCM) simulated climate variables to streamflow at target sites. In real world scenario, correlation structure exists between records of streamflow at sites in a study area. An effective downscaling model must be parsimonious, and it should ensure preservation of the correlation structure in downscaled flows to a reasonable extent, though exact reproduction/mimicking of the structure may not be necessary in a climate change (non-stationary) scenario. A few recent studies attempted to address this issue based on the assumption of spatiotemporal covariance stationarity. However, there is dearth of meaningful efforts especially for multisite downscaling of flows. To address this, multivariate support vector regression (MSVR) based methodology is proposed to arrive at flood return levels (quantile estimates) for target locations in a river basin corresponding to different return periods in a climate change scenario. The approach involves (i) use of MSVR relationships to downscale GCM simulated large scale atmospheric variables (LSAVs) to monthly time series of streamflow at multiple locations in a river basin, (ii) disaggregation of the downscaled streamflows corresponding to each site from monthly to daily time scale using k-nearest neighbor disaggregation methodology, (iii) fitting time varying generalized extreme value (GEV) distribution to annual maximum flows extracted from the daily streamflows and estimating flood return levels for different target locations in the river basin corresponding to different return periods.

Since 1971, a number of flood frequency models have been developed for river basins in Malawi for use in the design of hydraulic structures, but the varied nature of their results have most often given a dilemma to the design engineer due to differences in magnitudes of calculated floods for given return periods. All the known methods for flood frequency analysis developed in country so far have not used a homogeneity test for the river basins from which the hydrological data has been obtained. This study was thus conducted with a view to resolving this problem and hence improve the design of hydraulic structures such as culverts, bridges, water intake points for irrigation schemes, and flood protection dykes. In light of the above, during the course of this study the applicability of existing methods in the design of hydraulic structures was assessed. Also, the study investigated how land use and land cover change influence the frequency and magnitude of floods in the study area, and how their deleterious impacts on the socio-economic and natural environment in the river basins could be mitigated / Environmental Sciences / M. Sc. (Environmental Management)

L'estimation de crues de projet est requise pour le dimensionnement de barrages et de bassins de rétention, de même que pour la gestion des inondations lors de l’élaboration de cartes d’aléas ou lors de la modélisation et délimitation de plaines d’inondation. Généralement, les crues de projet sont définies par leur débit de pointe à partir d’une analyse fréquentielle univariée. Cependant, lorsque le dimensionnement d’ouvrages hydrauliques ou la gestion de crues nécessitent un stockage du volume ruisselé, il est également nécessaire de connaître les caractéristiques volume, durée et forme de l’hydrogramme de crue en plus de son débit maximum. Une analyse fréquentielle bivariée permet une estimation conjointe du débit de pointe et du volume de l’hydrogramme en tenant compte de leur corrélation. Bien qu’une telle approche permette la détermination du couple débit/volume de crue, il manque l’information relative à la forme de l’hydrogramme de crue. Une approche attrayante pour caractériser la forme de la crue de projet est de définir un hydrogramme représentatif normalisé par une densité de probabilité. La combinaison d’une densité de probabilité et des quantiles bivariés débit/volume permet la construction d’un hydrogramme synthétique de crue pour une période de retour donnée, qui modélise le pic d’une crue ainsi que sa forme. De tels hydrogrammes synthétiques sont potentiellement utiles et simples d’utilisation pour la détermination de crues de projet. Cependant, ils possèdent actuellement plusieurs limitations. Premièrement, ils reposent sur la définition d’une période de retour bivariée qui n’est pas univoque. Deuxièmement, ils décrivent en général le comportement spécifique d’un bassin versant en ne tenant pas compte de la variabilité des processus représentée par différents types de crues. Troisièmement, les hydrogrammes synthétiques ne sont pas disponibles pour les bassins versant non jaugés et une estimation de leurs incertitudes n’est pas calculée.Pour remédier à ces manquements, cette thèse propose des avenues pour la construction d’hydrogrammes synthétiques de projet pour les bassins versants jaugés et non jaugés, de même que pour la prise en compte de la diversité des types de crue. Des méthodes sont également développées pour la construction d’hydrogrammes synthétiques de crue spécifiques au bassin et aux événements ainsi que pour la régionalisation des hydrogrammes. Une estimation des diverses sources d’incertitude est également proposée. Ces travaux de recherche montrent que les hydrogrammes synthétiques de projet constituent une approche qui s’adapte bien à la représentation de différents types de crue ou d’événements dans un contexte de détermination de crues de projet. Une comparaison de différentes méthodes de régionalisation montre que les hydrogrammes synthétiques de projet spécifiques au bassin peuvent être régionalisés à des bassins non jaugés à l’aide de méthodes de régression linéaires et non linéaires. Il est également montré que les hydrogrammes de projet spécifiques aux événements peuvent être régionalisés à l’aide d’une approche d’indice de crue bivariée. Dans ce contexte, une représentation fonctionnelle de la forme des hydrogrammes constitue un moyen judicieux pour la délimitation de régions ayant un comportement hydrologique de crue similaire en terme de réactivité. Une analyse de l’incertitude a montré que la longueur de la série de mesures et le choix de la stratégie d’échantillonnage constituent les principales sources d’incertitude dans la construction d’hydrogrammes synthétiques de projet. Cette thèse démontre qu’une approche de crues de projet basée sur un ensemble de crues permet la prise en compte des différents types de crue et de divers processus. Ces travaux permettent de passer de l’analyse fréquentielle statistique de crues vers l’analyse fréquentielle hydrologique de crues permettant de prendre en compte les processus et conduisant à une prise de décision plus éclairée. / Design flood estimates are needed in hydraulic design for the construction of dams and retention basins and in flood management for drawing hazard maps or modeling inundation areas. Traditionally, such design floods have been expressed in terms of peak discharge estimated in a univariate flood frequency analysis. However, design or flood management tasks involving storage, in addition to peak discharge, also require information on hydrograph volume, duration, and shape . A bivariate flood frequency analysis allows the joint estimation of peak discharge and hydrograph volume and the consideration of their dependence. While such bivariate design quantiles describe the magnitude of a design flood, they lack information on its shape. An attractive way of modeling the whole shape of a design flood is to express a representative normalized hydrograph shape as a probability density function. The combination of such a probability density function with bivariate design quantiles allows the construction of a synthetic design hydrograph for a certain return period which describes the magnitude of a flood along with its shape. Such synthetic design hydrographs have the potential to be a useful and simple tool in design flood estimation. However, they currently have some limitations. First, they rely on the definition of a bivariate return period which is not uniquely defined. Second, they usually describe the specific behavior of a catchment and do not express process variability represented by different flood types. Third, they are neither available for ungauged catchments nor are they usually provided together with an uncertainty estimate.This thesis therefore explores possibilities for the construction of synthetic design hydrographs in gauged and ungauged catchments and ways of representing process variability in design flood construction. It proposes tools for both catchment- and flood-type specific design hydrograph construction and regionalization and for the assessment of their uncertainty.The thesis shows that synthetic design hydrographs are a flexible tool allowing for the consideration of different flood or event types in design flood estimation. A comparison of different regionalization methods, including spatial, similarity, and proximity based approaches, showed that catchment-specific design hydrographs can be best regionalized to ungauged catchments using linear and nonlinear regression methods. It was further shown that event-type specific design hydrograph sets can be regionalized using a bivariate index flood approach. In such a setting, a functional representation of hydrograph shapes was found to be a useful tool for the delineation of regions with similar flood reactivities.An uncertainty assessment showed that the record length and the choice of the sampling strategy are major uncertainty sources in the construction of synthetic design hydrographs and that this uncertainty propagates through the regionalization process.This thesis highlights that an ensemble-based design flood approach allows for the consideration of different flood types and runoff processes. This is a step from flood frequency statistics to flood frequency hydrology which allows better-informed decision making.

further development of the simple and promising analytical probabilistic approach / Urban stormwater management aims at mitigating the adverse impacts of urbanization. Hydrological models are used in support of stormwater management planning and design. There are three main approaches that can be applied for this modeling purpose: (1) continuous simulation approach which is accurate but time-consuming; (2) design storm approach, which is widely used and its accuracy highly depends on the selected antecedent moisture conditions and temporal distribution of design storms; and (3) the analytical probabilistic approach which is recently developed and still not used in practice. Although it is time-effective and it can produce results as accurate as the other two approaches; the analytical probabilistic approach requires further developments in order to make it more reliable and accurate. For this purpose, three subtopics are investigated in this thesis. (1) Rainfall data analysis as required by the analytical probabilistic approach with emphasis on testing the exponentiality of rainfall event duration, volume and interevent time (i.e., time separating it from its preceding rainfall event). A goodness-of-fit testing procedure that is suitable for this kind of data analysis was proposed. (2) Derivation of new analytical probabilistic models for peak discharge rate incorporating trapezoidal and triangular hydrograph shapes in order to include all possible catchment’s responses. And (3) the infiltration process is assumed to continue until the end of the rainfall event; however, the soil may get saturated earlier and the excess amount would contribute to the runoff volume which may have adverse impact if not taken into consideration. Thus, in addition to the infiltration process, the saturation excess runoff is also included and new models for flood frequencies are developed. All the models developed in this thesis are tested and compared to methods used in practice, reasonable results were obtained. / Thesis / Doctor of Philosophy (PhD) / Urban stormwater management aims at mitigating the adverse impacts of urbanization. Hydrological models are used in support of stormwater management planning and design. The analytical probabilistic stormwater management model (APSWM) is a promising tool for planning and design analysis. The purpose of this thesis is to further develop APSWM in order to make it more reliable and accurate. First, a clear procedure for rainfall data analysis as required by APSWM is provided. Second, a new APSWM is derived incorporating other runoff temporal-distribution patterns. Finally, the possibility of soil layer saturation while it is still raining is added to the model. All the models developed in this thesis are tested and compared to methods used in engineering practice, reasonable results were obtained.