京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第20352号 / 工博第4289号 / 新制||工||1664(附属図書館) / 京都大学大学院工学研究科都市環境工学専攻 / (主査)教授 清水 芳久, 教授 米田 稔, 准教授 KIM,SUNMIN / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Thesis (MScEng (Civil Engineering))--University of Stellenbosch, 2010. / ENGLISH ABSTRACT: The primary aim of this study was to evaluate, calibrate and verify the SDF run-off coefficients at a quaternary catchment level in the C5 secondary drainage region (SDF basin 9) and other selected SDF basins in South Africa by establishing the catchment parameters and SDF/probability distribution-ratios. The probability distribution-ratios were based on the comparison between the flood peaks estimated by the SDF method and statistical analyses of observed flow data. These quaternary run-off coefficients were then compared with the existing regional SDF run-off coefficients, whilst the run-off coefficient adjustment factors as proposed by Van Bladeren (2005) were also evaluated. It was evident from this study that the calibrated run-off coefficients obtained are spread around those of Alexander (2003), but were generally lower in magnitude. The adjusted run-off coefficients (Van Bladeren, 2005) had a tendency to decrease in magnitude with increasing recurrence interval, whilst some of the adjusted run-off coefficients exceeded unity. The extent to which the original SDF method overestimated the magnitude and frequency of flood peaks varied form basin to basin, with the SDF/probability distribution-ratios the highest in the Highveld and southern coastal regions with summer convective precipitation. In these regions the flood peak-ratios were occasionally different by up to a factor of 3 or even more. The southern coastal regions with winter orographic/frontal precipitation demonstrated the best flood peak-ratios, varying from 0.78 to 1.63. The adjusted SDF method results (Van Bladeren, 2005) were only better in 26% of all the basins under consideration when compared to those estimated by the original SDF method. On average, the adjusted SDF/probability distribution-ratios varied between 0.30 and 6.58, which is unacceptable. The calibrated version of the SDF method proved to be the most accurate in all the basins under consideration. On average, the calibrated SDF/probability distribution-ratios varied between 0.85 and 1.15, whilst at some basins and individual return periods, less accurate results were evident. Verification tests were conducted in catchments not considered during the calibration process with a view to establish whether the calibrated run-off coefficients are predictable and to confirm that the method is reliable. The verification results showed that the calibrated/verified SDF method is the most accurate and similar trends were evident in all the basins under consideration. On average, the verified SDF/probability distribution-ratios varied between 0.82 and 1.19, except in SDF basins 6 and 21 where the 5 to 20-year return period flood peaks were overestimated by 41% and 56% respectively, which is still conservative. The secondary aim of this study was to develop a customised, user-friendly Design Flood Estimation Tool (DFET) in a Microsoft Office Excel/Visual Basic for Applications environment in order to assess the use and applicability of the various design flood estimation methods. The developed DFET will provide designers with a software tool for the rapid investigation and evaluation of alternative design flood estimation methods either at a regional or site specific scale. The focus user group of the application will comprises of engineering technicians, engineering technologist and engineers employed at civil engineering consultants, not necessarily specialists in the field of flood hydrology. The DFET processed all the catchment, meteorological (precipitation) and hydrological (observed flows) data used as input for the various design flood estimation methods. / AFRIKAANSE OPSOMMING: Die primêre doelwit van die studie was om die SDF-afloopkoëffisiënte op ‘n kwartinêre opvangsgebiedvlak in die C5-sekondêre dreineringsgebied (SDFopvangsgebied 9) en ander gekose SDF-opvangsgebiede in Suid-Afrika te evalueer, te kalibreer en te verifieer deur die opvangsgebiedparameters en SDF/waarskynlikheidsverspreiding-verhoudings vas te stel. Dié waarskynlikheidsverspreiding-verhoudings was gebaseer op die vergelyking tussen die vloedpieke soos beraam deur die SDF-metode en statistiese analises van waargenome vloeidata. Dié kwartinêre afloopkoëffisiënte is met die bestaande streeksgebonde SDF-afloopkoëffisiënte vergelyk, terwyl die afloopkoëffisiënt-aanpassingsfaktore soos voorgestel deur Van Bladeren (2005) ook geëvalueer is. Dit het duidelik uit die studie geblyk dat die gekalibreerde afloopkoëffisiënte verspreid rondom die van Alexander (2003) is, maar in die algemeen laer in omvang. Die aangepaste afloopkoëffisiënte (Van Bladeren, 2005) was geneig om af te neem in grootte met ‘n toename in die herhalingsperiode, terwyl sommige afloopkoëffisiënte ‘n waarde van 1 oorskry het. Die omvang waartoe die oorspronklike SDF metode die grootte en herhaalperiode van vloedpieke oorskat het, wissel van opvangsgebied tot opvangsgebied, met die SDF/waarskynlikheidsverspreiding-verhoudings die hoogste in die Hoëveld en suidelike kusstreke gekenmerk deur konveksie-somerreënval. In hierdie streke het die vloedpiekverhoudings gereeld verskil tot en met ‘n faktor van 3 of selfs meer. Die suidelike kusstreke met kenmerkende ortografiese/frontale winterreënval het oor die beste vloedpiekverhoudings beskik wat gewissel het tussen 0.78 en 1.63. Die resultate van die aangepaste SDF-metode (Van Bladeren, 2005) was slegs in 26% van al die opvangsgebiede beter as die beramings van die oorspronklike SDF-metode. Die aangepaste SDF/waarskynlikheidsverspreiding-verhoudings het, met verwysing na gemiddeldes, tussen 0.30 en 6.58 gewissel, wat onaanvaarbaar is. Die gekalibreerde weergawe van die SDF-metode was die mees akkurate metode in al die opvangsgebiede van belang. Die gekalibreerde SDF/waarskynlikheidsverspreiding-verhoudings het, met verwysing na gemiddeldes, tussen 0.85 en 1.15 gewissel, terwyl die resultate van sommige opvangsgebiede en individuele herhalingsperiodes minder akkuraat was. Verifikasietoetse is uitgevoer in die opvangsgebiede wat nie tydens die kalibrasieproses gebruik was nie om vas te stel of die gekalibreerde afloopkoëffisiënte voorspelbaar is en om te bevestig dat die metode betroubaar is. Die verifikasieresultate het getoon dat die gekalibreerde/geverifieerde SDFmetode die mees akkurate metode is en dat soortgelyke tendense duidelik was in al die relevante opvangsgebiede. Die geverifieerde SDF/waarskynlikheidsverspreiding-verhoudings het, met verwysing na gemiddeldes, tussen 0.82 en 1.19 gewissel, behalwe in SDF-opvangsgebiede 6 en 21 waar die 5- en 20-jaar herhalingsperiode-vloedpieke onderskeidelik met 41% en 56% oorskat is, wat steeds konserwatief is. Die sekondêre doelwit van die studie was om ‘n gebruikersvriendelike “Design Flood Estimation Tool” (DFET) in ‘n Microsoft Office Excel/Visual Basic for Applications omgewing te ontwikkel om die gebruik en toepaslikheid van die verskeie ontwerpvloedberamingsmetodes te bepaal. Die DFET sal ontwerpers voorsien van ‘n sagtewareprogram om alternatiewe ontwerpvloedberamingsmetodes op streek- of plaaslike skaal te ondersoek en te evalueer. Die fokus-gebruikersgroep vir die toepassing van die program sal bestaan uit ingenieurstegnici, ingenieurstegnoloë en ingenieurs werksaam by raadgewende siviele ingenieurs, nie noodwendig vakkundiges in die veld van hidrologie nie. Die DFET was gebruik om al die opvangsgebied-, meteorologiese (reënval) en hidrologiese (waargenome vloeie) data vir die verskeie ontwerpvloedberamingsmetodes te verwerk.

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.

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.