Academic literature on the topic 'Flood frequency analysis'

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<br>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<br>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.

Parametric methods, currently used in regional flood frequency analysis, have numerous drawbacks and limitations, especially with regard to flood distribution selection and regional relationship form. Alternative approaches involving nonparametric methods are investigated in this thesis on a set of New Brunswick annual maximum floods. Nonparametric methods were employed at the three steps of regional analysis: at-site flood frequency analysis, homogeneous region delineation and regional relationship development. Nonparametric flood frequency analysis indicated that an annual maximum flood data set from New Brunswick contained some unimodal distributions along with many mixed distributions of bimodal and heavy-tailed shapes. A simulation study showed that sampling variability from a unimodal distribution could not account for the bimodality in nonparametric frequency analysis, confirming the existence of mixed distributions. L-moment analysis, a parametric method, confirmed that the entire set of floods from New Brunswick could not be appropriately described by a unimodal distribution. In this study, a new method is proposed for the purpose of homogeneous region delineation which effectively combines geographical considerations and flood data characteristics. The technique is based on the grouping of stations with similar density function shape, which reflect similar flood generating mechanisms. In New Brunswick, flood densities of three different shapes were grouped on a geographical basis to delineate homogeneous regions. Statistical tests based on L-moment analysis confirmed that the stations within a homogeneous bimodal region came from the same distribution. But L-moment analysis would propose either the Generalized Logistic or the Generalized Extreme Value as the regional distribution. Nonparametric frequency analysis revealed, however, that the floods within that region actually came from a mixed distribution. Nonparametric regression was employed for regional relationship development in New Brunswick; however, no significant improvement over the parametric approach of linear regression resulted. Using bootstrapping of pairs, a new method to compute the confidence interval at the center of a nonparametric regression was investigated. A comparison of linear and nonparametric regression confidence intervals can assist in evaluating the appropriateness of a linear model, and thus the need to employ nonparametric regression. Nonparametric regression was shown to be useful in screening irrational relationships that could be developed with the parametric approach. A new regional analysis methodology, involving nonparametric methods at the three steps of regional analysis, is proposed in this study, resulting in improved homogeneous region delineation, in more accurate at-site quantile estimates, and more realistic regional relationships.

Estimation of the probability of occurrence of future flood events at one or more locations across a river system is frequently required for the design of bridges, culverts, spillways, dams and other engineering works. This study investigates some of the statistical aspects for estimating the flood frequency distribution at a single site and on regional basis. It is demonstrated that generalized logistic (GL) distribution has many properties well suited for the modelling of flood frequency data. The GL distribution performs better than the other commonly recommended flood frequency distributions in terms of several key properties. Specifically, it is capable of reproducing almost the same degree of skewness typically present in observed flood data. It appears to be more robust to the presence of extreme outliers in the upper tail of the distribution. It has a relatively simpler mathematical form. Thus all the well known methods of parameter estimation can be easily implemented. It is shown that the method of probability weighted moments (PWM) using the conventionally recommended plotting position substantially effects the estimation of the shape parameter of the generalized extreme value (GEV) distribution by relocating the annual maximum flood series. A location invariant plotting position is introduced to use in estimating, by the method of PWM, the parameters of the GEV and the GL distributions. Tests based on empirical distribution function (EDF) statistics are proposed to assess the goodness of fit of the flood frequency distributions. A modified EDF test is derived that gives greater emphasis to the upper tail of a distribution which is more important for flood frequency prediction. Significance points are derived for the GEV and GL distributions when the parameters are to be estimated from the sample data by the method of PWMs. The critical points are considerably smaller than for the case where the parameters of a distribution are assumed to be specified. Approximate formulae over the whole range of the distribution for these tests are also developed which can be used for regional assessment of GEV and GL models based on all the annual maximum series simultaneously in a hydrological region. In order to pool at-site flood data across a region into a single series for regional analysis, the effect of standardization by at-site mean on the estimation of the regional shape parameter of the GEV distribution is examined. Our simulation study based on various synthetic regions reveals that the standardization by the at-site mean underestimates the shape parameter of the GEV by about 30% of its true value and also contributes to the separation of skewness of observed and simulated floods. A two parameter standardization by the at-site estimates of location and scale parameters is proposed. It does not distort the shape of the flood frequency data in the pooling process. Therefore, it offers significantly improved estimate of the shape parameter, allows pooling data with heterogeneous coefficients of variation and helps to explain the separation of skewness effect. Regions on the basis of flood statistics L-CV and USKEW are derived for Scotland and North England. Only about 50% of the basins could be correctly identified as belonging to these regions by a set of seven catchment characteristics. The alternative approach of grouping basins solely on the basis of physical properties is preferable. Six physically homogeneous groups of basins are identified by WARD's multivariate clustering algorithm using the same seven characteristics. These regions have hydrological homogeneity in addition to their physical homogeneity. Dimensionless regional flood frequency curves are produced by fitting GEV and GL distributions for each region. The GEV regional growth curves imply a larger return period for a given magnitude flood. When floods are described by GL model the respective return periods are considerably smaller.

Engineers in the water resources field frequently have to estimate the probability of exceedance related to a determined flow value at a chosen river cross-section, also known as flood quantile. This estimation is necessary in order to proceed with the design of different structures, such as dam spillways, and for other functions, such as risk management. The accurate estimation of the flood quantile of interest is not an easy task, as return periods of interest usually require a data record that exceeds the length of the available gauging record at the site of concern. In order to accurately estimate flood quantiles at a given river cross-section, the regional flood frequency analysis (RFFA) is often used, allowing to compensate the lack of data by gathering information from other gauging stations which are supposed to be similar to the target station. The present Thesis deals with the evaluation of the 1,000-year flood quantile for five Brazilian dams based on a RFFA. The five studied dams are: Cachoeira Dourada, Ponte Alta do Bom Jesus, Quatiara, Poxoréo and Volta Grande. An index-flood method is applied as a usual approach in RFFA. The Region of Influence approach is then considered in order to define a homogeneous pooling-group of sites for a given target site. Overall, 79 gauging stations are examined for the evaluation of the 1,000-year flood quantile.

Stratigraphic records in four basins in the central Black Hills in combination with hydraulic calculations show that all basins have experienced multiple large floods in the last 2,000 years with flow rates substantially larger than those gaged historically. Flood-frequency analyses for the study reaches account for 29 paleofloods inferred from interpretation of stratigraphic records locally extending back 1,000 to almost 2,000 years. The addition of paleoflood data to the gaged and historical data significantly reduced uncertainties related to flood-frequency. For all study reaches the 95-percent confidence intervals about the low-probability quantile estimates (100-, 200-, and 500-year recurrence-intervals) were reduced by at least 78 percent relative to those for the gaged records only. In some cases, 95-percent uncertainty intervals were reduced by 99 percent or more. Additionally, a stratigraphic record of 35 large paleofloods and four large historical floods during the last 2,000 years (including several floods not used in the frequency analyses due to age constraints) reveal four flooding episodes at A.D.: 130-40, 640-670, 900-1290, and 1410 to present. During the Medieval Climate Anomaly (~A.D. 900-1300) the Black Hills experienced 13 large floods compared to nine large floods in the previous 800 years. This high concentration of large flooding events were likely caused by: 1) instability of air masses caused by stronger than normal westerlies; 2) larger or more frequent hurricanes in the Gulf of Mexico and Atlantic Ocean; and/or 3) reduced land covering vegetation and an increase in forest fires caused by the severe drought. By examining the response of streamflow to the MCA, it seems likely that if severe long-term drought conditions persist for the Black Hills region, an increase in the frequency and magnitude of large floods can be expected. The Black Hills paleofloods represent some of the largest known floods, relative to drainage area, for the United States. Many of the other largest known United States floods are in areas with physiographic and climatologic conditions broadly similar to the Black Hills--semi-arid and rugged landscapes that intercept and focus heavy precipitation from convective storm systems.