Floods cause widespread damage to property and life in different parts of the world. Hence there is a paramount need to develop effective methods for design flood estimation to alleviate risk associated with these extreme hydrologic events. Methods that are conventionally considered for analysis of floods focus on estimation of continuous frequency relationship between peak flow observed at a location and its corresponding exceedance probability depicting the plausible conditions in the planning horizon. These methods are commonly known as at-site flood frequency analysis (FFA) procedures. The available FFA procedures can be classified as parametric and nonparametric. Parametric methods are based on the assumption that sample (at-site data) is drawn from a population with known probability density function (PDF). Those procedures have uncertainty associated with the choice of PDF and the method for estimation of its parameters. Moreover, parametric methods are ineffective in modeling flood data if multimodality is evident in their PDF. To overcome those artifacts, a few studies attempted using kernel based nonparametric (NP) methods as an alternative to parametric methods. The NP methods are data driven and they can characterize the uncertainty in data without prior assumptions as to the form of the PDF. Conventional kernel methods have shortcomings associated with boundary leakage problem and normal reference rule (considered for estimation of bandwidth), which have implications on flood quantile estimates. To alleviate this problem, focus of NP flood frequency analysis has been on development of new kernel density estimators (kdes). Another issue in FFA is that information on the whole hydrograph (e.g., time to the peak flow, volume of the flood flow and duration of the flood event) is needed, in addition to peak flow for certain applications. An option is to perform frequency analysis on each of the variables independently. However, these variables are not independent, and hence there is a need to perform multivariate analysis to construct multivariate PDFs and use the corresponding cumulative distribution functions (CDFs) to arrive at estimates of characteristics of design flood hydrograph. In this perspective, recent focus of flood frequency analysis studies has been on development of methods to derive joint distributions of flood hydrograph related variables in a nonparametric setting. Further, in real world scenario, it is often necessary to estimate design flood quantiles at target locations that have limited or no data. Regional Flood Frequency analysis (RFFA) procedures have been developed for use in such situations. These procedures involve use of a regionalization procedure for identification of a homogeneous group of watersheds that are similar to watershed of the target site in terms of flood response. Subsequently regional frequency analysis (RFA) is performed, wherein the information pooled from the group (region) forms basis for frequency analysis to construct a CDF (growth curve) that is subsequently used to arrive at quantile estimates at the target site. Though there are various procedures for RFFA, they are largely confined to only univariate framework considering a parametric approach as the basis to arrive at required quantile estimates. Motivated by these findings, this thesis concerns development of a linear diffusion process based adaptive kernel density estimator (D-kde) based methodologies for at-site as well as regional FFA in univariate as well as bivariate settings. The D-kde alleviates boundary leakage problem and also avoids normal reference rule while estimating optimal bandwidth by using Botev-Grotowski-Kroese estimator (BGKE). Potential of the proposed methodologies in both univariate and bivariate settings is demonstrated by application to synthetic data sets of various sizes drawn from known unimodal and bimodal parametric populations, and to real world data sets from India, USA, United Kingdom and Canada. In the context of at-site univariate FFA (considering peak flows), the performance of D- kde was found to be better when compared to four parametric distribution based methods (Generalized extreme value, Generalized logistic, Generalized Pareto, Generalized Normal), thirty-two ‘kde and bandwidth estimator’ combinations that resulted from application of four commonly used kernels in conjunction with eight bandwidth estimators, and a local polynomial–based estimator. In the context of at-site bivariate FFA considering ‘peakflow-flood volume’ and ‘flood duration-flood volume’ bivariate combinations, the proposed D-kde based methodology was shown to be effective when compared to commonly used seven copulas (Gumbel-Hougaard, Frank, Clayton, Joe, Normal, Plackett, and student’s-T copulas) and Gaussian kernel in conjunction with conventional as well as BGKE bandwidth estimators. Sensitivity analysis indicated that selection of optimum number of bins is critical in implementing D-kde in bivariate setting. In the context of univariate regional flood frequency analysis (RFFA) considering peak flows, a methodology based on D-kde and Index-flood methods is proposed and its performance is shown to be better when compared to that of widely used L-moment and Index-flood based method (‘regional L-moment algorithm’) through Monte-Carlo simulation experiments on homogeneous as well as heterogeneous synthetic regions, and through leave-one-out cross validation experiment performed on data sets pertaining to 54 watersheds in Godavari river basin, India. In this context, four homogeneous groups of watersheds are delineated in Godavari river basin using kernel principal component analysis (KPCA) in conjunction with Fuzzy c-means cluster analysis in L-moment framework, as an improvement over heterogeneous regions in the area (river basin) that are currently being considered by Central Water Commission, India. In the context of bivariate RFFA two methods are proposed. They involve forming site-specific pooling groups (regions) based on either L-moment based bivariate homogeneity test (R-BHT) or bivariate Kolmogorov-Smirnov test (R-BKS), and RFA based on D-kde. Their performance is assessed by application to data sets pertaining to stations in the conterminous United States. Results indicate that the R-BKS method is better than R-BHT in predicting quantiles of bivariate flood characteristics at ungauged sites, although the size of pooling groups formed using R-BKS is, in general, smaller than size of those formed using R-BHT. In general, the performance of the methods is found to improve with increase in size of pooling groups. Overall the results indicate that the D-kde always yields bona fide PDF (and CDF) in the context of univariate as well as bivariate flood frequency analysis, as probability density is nonnegative for all data points and integrates to unity for the valid range of the data. The performance of D-kde based at-site as well as regional FFA methodologies is found to be effective in univariate as well as bivariate settings, irrespective of the nature of population and sample size. A primary assumption underlying conventional FFA procedures has been that the time series of peak flow is stationarity (temporally homogeneous). However, recent studies carried out in various parts of the World question the assumption of flood stationarity. In this perspective, Time Varying Gaussian Copula (TVGC) based methodology is proposed in the thesis for flood frequency analysis in bivariate setting, which allows relaxing the assumption of stationarity in flood related variables. It is shown to be effective than seven commonly used stationary copulas through Monte-Carlo simulation experiments and by application to data sets pertaining to stations in the conterminous United States for which null hypothesis that peak flow data were non-stationary cannot be rejected.

The objective of this present thesis was to determine whether GEV (Generalized Extreme Value) itself can be a more conservative distribution than LP3 (Log Pearson III) associated with other methods, such as the B17B weighting procedure with Single Grubbs-Beck (SGB) for low outliers, when determining the projected floods in a flood frequency analysis (FFA) for Santa Ana and San Gabriel regions and other urbanized stream gages present in California. In this work, USGS PeakFQ was utilized. From the results obtained, it was possible to state that GEV fitting results were directly affected by the length of the data. When the length of the record is short, it is not accurate to use a projection of 100-year return period, for example, to represent future projection. Comparing the LP3 and GEV CDFs, for the majority of the stream gages analyzed in this project, GEV proves to be the most conservative method, with smaller return periods.

Hydraulic geometry relationships and regional curves are used in natural channel design to assist engineers, biologists, and fluvial geomorphologists in the efforts undertaken to ameliorate previous activities that have diminished, impaired or destroyed the structure and function of stream systems. Bankfull channel characteristics were assessed for 14 United States Geological Survey (USGS) gaged sites in the Inner Bluegrass and 15 USGS gaged sites in the Outer Bluegrass Regions of Kentucky. Hydraulic geometry relationships and regional curves were developed for the aforementioned regions. Analysis of the regression relationships showed that bankfull discharge is a good explanatory variable for bankfull parameters such as area, width and depth. The hydraulic geometry relationships developed produced high R2 values up to 0.95. The relationships were also compared to other studies and show strong relationships to both theoretical and empirical data. Regional curves, relating drainage area to bankfull parameters, were developed and show that drainage area is a good explanatory variable for bankfull parameters. R2 values for the regional curves were as high as 0.98.

L’estimation de l’aléa hydrologique en sites non jaugés présente un enjeu important pour la gestion des risques. La complexité du phénomène réside à la fois dans la nécessité d’avoir une approche multivariée (estimation de caractéristiques multiples des crues : durées, périodes de retour) qui propose une extrapolation raisonnable des événements. SHYREG est une méthode basée sur la simulation de scénarios de crues, qui présente ces avantages. Évaluée lors du projet ANR ExtraFlo, elle présente de bonnes performances en justesse et en stabilité lorsqu'elle est calée sur des données locales de débits. Cette méthode vise à estimer des débits de crue en tout point du territoire. Elle doit donc pouvoir être appliquée en site non jaugés.Le travail de thèse présenté ici se focalise sur le transfert de la méthode vers le non jaugé en s’intéressant aux valeurs des débits simulés mais aussi à leur cohérence. Tout d’abord, une révision du calage a permis de s’assurer de la cohérence des débits simulés le long d’un cours d’eau. Ensuite, l’application d’un large panel de méthodes de régionalisation a permis de déterminer que la régionalisation devait s’appuyer à la fois sur la structure spatiale et sur les caractéristiques physiographiques des bassins. Finalement, une méthode qui régionalise SHYREG simultanément à son calage a été retenue. Une comparaison avec d’autres approches régionalisées a mis en évidence la qualité du modèle développé. / Flood hazard estimation in ungauged sites presents a major challenge for risk management. The complexity of the phenomenon arises from both the need for a multivariate approach (estimation of different flood characteristics: peak flow, volume, duration ...), and for an approach which offers a reasonable extrapolation of extreme events. The SHYREG method is based on the simulation of flood scenarios and presents these benefits. It has been evaluated during the ANR ExtraFlo project. It showed good performance in both accuracy and stability when calibrated against local discharge data. However, weaknesses have been identified when implemented in ungauged sites.The objective of the present thesis is to develop the method in order to improve the SHYREG performances in ungauged sites. Two kinds of modifications were implemented. First, the calibration of the method in gauged sites was reviewed. The main idea was to integrate more data and to take more into account the coherence between simulated discharges in different sites. Then, diverse regionalisation schemes extracted from the scientific literature were considered. Their application demonstrated the necessity to exploit information from nearby sites and the physical properties of the catchments. Finally, a version which realises the regionalisation simultaneously to the calibration has been selected. Its comparison with other method showed the quality of this new version of SHYREG.

Regional flood frequency techniques are commonly used to estimate flood quantiles when flood data are unavailable or the record length at an individual gauging station is insufficient for reliable analyses. These methods compensate for limited or unavailable data by pooling data from nearby gauged sites. This requires the delineation of hydrologically homogeneous regions in which the flood regime is sufficiently similar to allow the spatial transfer of information. Therefore, several Regional Flood Frequency Analysis (RFFA) methods are applied to the Ceyhan Basin. Dalyrmple (1960) Method is applied as a common RFFA method used in Turkey. Multivariate statistical techniques which are Stepwise and Nonlinear Regression Analysis are also applied to flood statistics and basin characteristics for gauging stations. Rainfall, Perimeter, Length of Main River, Circularity, Relative Relief, Basin Relief, Hmax, Hmin, Hmean and H&Delta / are the simple additional basin characteristics. Moreover, before the analysis started, stations are clustered according to their basin characteristics by using the combination of Ward&rsquo / s and k-means clustering techniques. At the end of the study, the results are compared considering the Root Mean Squared Errors, Nash-Sutcliffe Efficiency Index and % difference of results. Using additional basin characteristics and making an analysis with multivariate statistical techniques have positive effect for getting accurate results compared to Dalyrmple (1960) Method in Ceyhan Basin. Clustered region data give more accurate results than non-clustered region data. Comparison between clustered region and non-clustered region Q100/Q2.33 reduced variate values for whole region is 3.53, for cluster-2 it is 3.43 and for cluster-3 it is 3.65. This show that clustering has positive effect in the results. Nonlinear Regression Analysis with three clusters give less errors which are 29.54 RMSE and 0.735 Nash-Sutcliffe Index, when compared to other methods in Ceyhan Basin.

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.

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.