ESTIMATING PARAMETERS OF EV1 DISTRIBUTION FOR FLOOD FREQUENCY ANALYSIS1

ABSTRACT: The parameters of the extreme value type 1 distribution were estimated for 55 annual flood data sets by seven methods. These are the methods of (1) moments, (2) probability weighted moments, (3) mixed moments, (4) maximum likelihood estimation, (5) incomplete means, (6) principle of maximum entropy, and (7) least squares. The method of maximum likelihood estimation was found to be the best and the method of incomplete means the worst. The differences between the methods of principle of maximum entropy, probability weighted moments, moments, and least squares were only minor. The difference between these methods and the method of maximum likelihood was not pronounced.     

GENERALIZED SKEW COEFFICIENTS FOR FLOOD FREQUENCY ANALYSIS1

ABSTRACT: In order to promote a uniform and consistent approach for floodflow frequency studies, the U.S. Water Resources Council has recommended the use of the log-Pearson type III distribution with a generalized skew coefficient. This paper investigates various methods of determining generalized skew coefficients. A new method is introduced that determines generalized skew coefficients using a weighting procedure based upon the variance of regional (map) skew coefficients and the variance of sample skew coefficients. The variance of skew derived from sample data is determined using either of two non-parametric methods called the jackknife or bootstrap. Applications of the new weighting procedure are presented along with an experimental study to test various weighting procedures to derive generalized skew coefficients.     

CLIMATE FACTOR FOR SMALL-BASIN FLOOD FREQUENCY1

ABSTRACT: A climate factor, C_T, (T = 2–, 25-, and 100-year recurrence intervals) that delineates regional trends in small-basin flood frequency was derived using data from 71 long-term rainfall record sites. Values of C_T at these sites were developed by a regression analysis that related rainfall-runoff model estimates of T-year floods to a sample set of 50 model calibrations. C_T was regionalized via kriging to develop maps depicting its geographic variation for a large part of the United States east of the 105th meridian. Kriged estimates of C_T and basin-runoff characteristics were used to compute regionalized T-year floods for 200 small drainage basins. Observed T-year flood estimates also were developed for these sites. Regionalized floods are shown to account for a large percentage of the variability in observed flood estimates with coefficients of determination ranging from 0.89 for 2-year floods to 0.82 for 100-year floods. The relative importance of the factors comprising regionalized flood estimates is evaluated in terms of scale (size of drainage area), basin-runoff characteristics (rainfall. runoff model parameters), and climate (C_T).     

STATISTICAL TERMINOLOGY: DEFINITIONS AND INTERPRETATION FOR FLOOD PEAK ESTIMATION1

ABSTRACT: Considerable effort is expended each year in making flood peak estimates at both gaged and ungaged sites. Many methods, both simplistic and complex, have been proposed for making such estimates. The hydrologist that must make an estimate at a particular site is interested in the accuracy of the estimate. Most methods are developed using either statistical analyses or analytical optimization schemes. While publications describing these methods often include some statistical measure of goodness-of-flt, the terminology often does not provide the potential user with an answer to the question,‘How accurate is the estimate?’ That is, statistical terminology often are not used properly, which may lead to a false sense of security. The use of the correct terminology will help potential users evaluate the usefulness of a proposed method and provide a means of comparing different methods. This study provides definitions for terms often used in literature on flood peak estimation and provides an interpretation for these terms. Specific problems discussed include the use of arbitrary levels of significance in statistical tests of hypotheses, the identification of both random and systematic variation in estimates from hydrologic methods, and the difference between accuracy of model calibration and accuracy of prediction.     

COMPARISON OF PARAMETRIC TAIL ESTIMATORS FOR LOW‐FLOW FREQUENCY ANALYSIS1

ABSTRACT: In recent years, several approaches to hydrologic frequency analysis have been proposed that enable one to direct attention to that portion of an overall probability distribution that is of greatest interest. The majority of the studies have focused on the upper tail of a distribution for flood analyses, though the same ideas can be applied to low flows. This paper presents an evaluation of the performances of five different estimation methods that place an emphasis on fitting the lower tail of the lognormal distribution for estimation of the ten‐year low‐flow quantile. The methods compared include distributional truncation, MLE treatment of censored data, partial probability weighted moments, LL‐moments, and expected moments. It is concluded that while there are some differences among the alternative methods in terms of their biases and root mean square errors, no one method consistently performs better than the others, particularly with recognition that the underlying population distribution is unknown. Therefore, it seems perfectly legitimate to make a selection of a method on the basis other criteria, such as ease of use. It is also shown in this paper that the five alternative methods can perform about as well as, if not better than, an estimation strategy involving fitting the complete lognormal distribution using L‐moments.     

PARAMETER DETERMINATION FOR THE MUSKINGUM-CUNGE FLOOD ROUTING METHOD1

ABSTRACT: The proportionality coefficient, K, and the weighing parameter, X, required for the Muskingum-Cunge Flood Routing Method are dependent on the hydraulic characteristics of the channel and the dynamic characteristic of the flood wave. This work focuses on the determination of the Muskingum-Cunge Flood Routing Method parameters for streams where measured hydrographs are not available (i.e., ungaged streams) with floods that stay within the channel banks. In the present work, a gaged creek was used and a dynamic wave was routed to test the reliability of the parameters determined through the Schaefer and Stevens technique (Schaefer and Stevens, 1978). The predicted outflow hydrographs are compared to the hydrographs obtained for the same stream determined with the Muskingum Routing option of the HEC-1 program. Cypress Creek in Harris County, Texas, was the model for this work; and the corresponding data were extracted from the Grant Road and Westfield, Texas, USGS gaging stations.     

CLIMATE VARIABILITY AND FLOOD FREQUENCY ESTIMATION FOR THE UPPER MISSISSIPPI AND LOWER MISSOURI RIVERS1

ABSTRACT: This paper considers the distribution of flood flows in the Upper Mississippi, Lower Missouri, and Illinois Rivers and their relationship to climatic indices. Global climate patterns including El Niño/Southern Oscillation, the Pacific Decadal Oscillation, and the North Atlantic Oscillation explained very little of the variations in flow peaks. However, large and statistically significant upward trends were found in many gauge records along the Upper Mississippi and Missouri Rivers: at Hermann on the Missouri River above the confluence with the Mississippi (p = 2 percent), at Hannibal on the Mississippi River (p < 0.1 percent), at Meredosia on the Illinois River (p = 0.7 percent), and at St. Louis on the Mississippi below the confluence of all three rivers (p = 1 percent). This challenges the traditional assumption that flood series are independent and identically distributed random variables and suggests that flood risk changes over time.     

REGIONALIZATION OF LOW-FLOW FREQUENCY ESTIMATES: AN ALABAMA CASE STUI)Y1

ABSTRACT: Low-flow estimates, as determined by probabilistic modeling of observed data sequences, are commonly used to describe certain streamflow characteristics. Unfortunately, however, reliable low-flow estimates can be difficult to come by, particularly for gaging sites with short record lengths. The shortness of records leads to uncertainties not only in the selection of a distribution for modeling purposes but also in the estimates of the parameters of a chosen model. In flood frequency analysis, the common approach to mitigation of some of these problems is through the regionalization of frequency behavior. The same general approach is applied here to the case of low-flow estimation, with the general intent of not only improving low-flow estimates but also illustrating the gains that might be attained in so doing. Data used for this study is that which has been systematically observed at 128 streamflow gaging sites across the State of Alabama. Our conclusions are that the log Pearson Type 3 distribution is a suitable candidate for modeling of Alabama low-flows, and that the shape parameter of that distribution can be estimated on a regional basis. Low-flow estimates based on the regional estimator are compared with estimates based on the use of only at-site estimation techniques.     

PLOTTING FORMULA FOR FLOOD FREQUENCY1

ABSTRACT: In flood frequency analysis it is required to estimate the values of probabilities based on plotting formula. All of the many existing formula provide different results, particularly at the tails of the distribution. The existing practice in selection of a particular formula is rather arbitrary; and often Weibull's formula is recommended, which provided biased and conservative results. Based on the mean square criterion, a new plotting formula is developed, and it is given by F_m= (m - 0.24)/(N + 0.5).     

SPATIAL ANALYSIS FOR MONTHLY RAINFALL IN SOUTH FLORIDA1

ABSTRACT: Several methods have been developed to interpolate point rainfall data and integrate areal rainfall data from any network of stations. From previous studies, it can be concluded that models for spatial analysis of rainfall are dependent on topography, area of analysis, type of rainfall, and density of gauging network. The purpose of this study is to evaluate a set of six appropriate models for point and areal rainfall estimations over a 4000 square mile area in South Florida. In this study, a case of developing spatial continuity model for monthly rainfall from a database that had various lengths of records and missing data is documented. The spatial correlation and variogram models for monthly rainfall were developed. Six methods of spatial interpolation were applied and the results validated with historical observations. The results of the study indicate that the multiquadric, kriging, and optimal interpolation schemes are the best three methods for interpolation of monthly rainfall within the study area. The optimal and kriging methods have the advantage of providing estimates of the error of interpolation. The optimal interpolation method uses the spatial correlation function and the kriging method uses the variogram function. The two spatial functions are related. Either of the two methods provide good estimates of monthly point and areal rainfall in the study area.     

FLOOD FREQUENCY ANALYSIS FOR SMALL WATERSHEDS IN SOUTHERN ILLINOIS1

ABSTRACT: Twenty-two gaging stations were selected for developing a regional flood frequency curve for small (area less than 2 square miles) watersheds in southern Illinois. Five probability functions were compared, and the extreme value type I function was selected to develop the regional flood curve. The curve was generated with the index flood method and also another empirical method that related the function parameters to the watershed area. Estimated peak discharges with various return periods were compared with the results obtained from multiple regression analysis.     

APPLICATION OF PARAMETER OPTIMIZATION METHOD FOR CALIBRATING TANK MODEL1

ABSTRACT: Many automatic calibration processes have been proposed to efficiently calibrate the 16 parameters involved in the four‐layered tank model. The Multistart Powell and Stuffed Complex Evolution (SCE) methods are considered the best two procedures. Two rainfall events were designed to compare the performance and efficiency of these two methods. The first rainfall event is short term and the second designed for long term rainfall data collection. Both rainfall events include a lengthy no‐rainfall period. Two sets of upper and lower values for the search range were selected for the numerical tests. The results show that the Multistart Powell and SCE methods are able to obtain the true values for the 16 parameters with a sufficiently long no‐rainfall period after a rainfall event. In addition, by using two selected objective functions, one based on root mean square error and one based on root mean square relative error criteria, it is found that the no‐rainfall period lengths necessary to obtain the converged true values for the 16 parameters are roughly the same. The SCE method provides a more efficient search based on an appropriate preliminary search range. The Multistart Powell method, on the other hand, leads to more accurate search results when there is no suitable search range selected based on the parameter calibration experience.     

FLOOD FREQUENCY ANALYSIS FOR EVALUATING WATERSHED CONDITIONS WITh RAINFALL-RUNOFF MODELS1

ABSTRACT: Many rainfall-runoff modeling studies compare flood quantiles for different land-use and/or flood mitigation scenarios. However, when flood quantiles are estimated using conventional statistical methods, comparisons may be misleading because the estimates often misrepresent the quantile relationship between scenarios. An alternate statistical procedure is proposed, in which rainfall-runoff modeling is used to evaluate an approximate relationship between flood quantiles for different scenarios. Monte Carlo experiments show that the proposed method produces flood quantile estimates that better reflect the differences between scenarios. The ratio between quantiles for different scenarios is more accurate, so comparisons of the scenarios using flood quantiles are more reliable.     

USE OF HISTORIC FLOOD INFORMATION IN ESTIMATING FLOOD PEAKS ON UNGAGED WATERSHEDS1

Regional procedures to estimate flood magnitudes for ungaged watersheds typically ignore available site-specific historic flood information such as high water marks and the corresponding flow estimates, otherwise referred to as limited site-specific historic (LSSH) flood data. A procedure to construct flood frequency curves on the basis of LSSH flood observations is presented. Simple inverse variance weighting is employed to systematically combine flood estimates obtained from the LSSH data base with those from a regional procedure to obtain improved estimtes of flood peaks on the ungaged watershed. For the region studied, the variance weighted estimates of flow had a lower logarithmic standard error than either the regional or the LSSH flow estimates, when compared to the estimates determined by three standard distributions for gaged watersheds investigated in the development of the methodology. Use of the simple inverse variance weighting procedure is recommended when “reliable” estimates of LSSH floods for the ungaged site are available.     

A FLOOD DAMAGE MODEL FOR FLOOD PLAIN STUDIES1

ABSTRACT: Hydrologic and economic information must be integrated in flood plain management. This study describes an integrated approach which includes consideration of the hydrologic, hydrodynamic, physical, and economic components of the total system. On the basis of these components, a theoretical model is proposed which provides a rational procedure for estimating flood damages from projections of economic development within an area. The utility of the model is demonstrated by applying it to a flood-prone region in Southern Quebec, Canada.     

BIVARIATE FREQUENCY ANALYSIS OF FLOODS USING COPULAS1

Abstract: Bivariate flood frequency analysis offers improved understanding of the complex flood process and useful information in preparing flood mitigation measures. However, difficulties arise from limited bivariate distribution functions available to jointly model the correlated flood peak and volume that have different univariate marginal distributions. Copulas are functions that link univariate distribution functions to form bivariate distribution functions, which can overcome such difficulties. The objective of this study was to analyze bivariate frequency of flood peak and volume using copulas. Separate univariate distributions of flood peak and volume are first fitted from observed data. Copulas are then employed to model the dependence between flood peak and volume and join the predetermined univariate marginal distributions to construct the bivariate distribution. The bivariate probabilities and associated return periods are calculated in terms of univariate marginal distributions and copulas. The advantage of using copulas is that they can separate the effect of dependence from the effects of the marginal distributions. In addition, explicit relationships between joint and univariate return periods are made possible when copulas are employed to construct bivariate distribution of floods. The annual floods of Tongtou flow gauge station in the Jhuoshuei River, Taiwan, are used to illustrate bivariate flood frequency analysis.     

SOME TECHNIQUES FOR USING FREQUENCY ANALYSIS AND REALTIME DATA TO INTERPRET FLOOD POTENTIAL DATA1

ABSTRACT: Flood potential data can be effectively interpreted if simple frequency analysis concepts are used to explain the significance of flood potential. Instead of simply presenting data as a quantitative amount or as a percentage of the average condition, predictions can be discussed in terms of their probabilities of exceedance, or return periods. Criteria are presented for evaluating the significance of various return periods. Frequency interpretations are applied to snow course data, peak flow forecasts, and streamflow volume forecasts in northern Utah to illustrate these concepts. In addition, access to realtime data allows tracking of snowmelt progression and identification of any deviations from the forecast flood potential situation. Several data elements, including snowpack, streamfiow volume and peak, and realtime data are jointly evaluated to assess potential hazard and probable risk.     

APPLICATION OF ADVANCES IN FLOOD FREQUENCY ANALYSIS1

ABSTRACT: Flood frequency analyses are frequently being made using widely available computer programs. Serious errors can result from blind acceptance of such results. Visual interpretation of observed flood series can be used for evaluation on frequency paper with compatible scales. Such frequency papers are presented in the paper. In ephemeral streams, more infrequent floods may constitute a separate set from the more frequent floods because (a) runoff producing storms cover only a portion of the contributing area, (b) transmission losses in the normally dry streambed may reduce the peak flow, and (c) some runoff may be stored in stock water ponds which therefore leads to partial area runoff. The Cunnane plotting position used in this paper is superior to the more widely used Weibull equation, having a mathematically sound basis for locating observed floods on an assumed probability.     

A COMPARISON OF TRANSFORMATION METHODS FOR FLOOD FREQUENCY ANALYSIS1

ABSTRACT: The SMEMAX transformation, its modified versions and power transformation were applied to 55 long-term records of annual maximum flood flows tested previously for independence, homogeneity and completeness. Even though SMEMAX transformation reduced the coefficient of skewness to near zero for flood data, their distribution was not a true normal distribution. In almost all cases, the coefficient of kurtosis was quite different from 3.0 of the normal distribution. Empirical criteria showed that SMEMAX transformation performed well only for 40 (70 percent) of the 55 stations tested. Its performance level dropped, especially for stations which had both the coefficient of skewness and kurtosis greater than 3.0 and 10.0, respectively. Power transformation was generally better in transforming the flood data to a normal distribution. It performed well for 50 (90 percent) of the 55 stations tested. The coefficient of skewness in case of the data transformed by power transformation was much closer to the zero value than in the case of SMEMAX transformed series. The SMEMAX transformation and its two modified versions yielded identical results when flood frequency analysis was performed. Computationally, all three methods were equally simple and easy to apply for flood frequency analysis. In some cases, the coefficient of kurtosis for the transformed distributions obtained both by SMEMAX and power transformations deviated farther from that for the normal distribution than for the parent distribution.