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Estimation of design flood is formed as a basis for planning and management of civil engineering construction especially hydraulic structures such as water use and flood control structures. The dimensions and capacity of the buildings depend on flood magnitude for a given return period that may have some uncertainty due to model error. This can be achieved through Flood Frequency Analysis (FFA) that involves fitting probability distribution to the series of Annual Maximum Discharge (AMD) data. In this paper, a study on assessment of uncertainty of error in design flood estimates was carried out by fitting Method of Moments (MoM), Maximum Likelihood Method (MLM) and method of L- Moments (LMO) of 2- parameter Log Normal (LN2) distribution to the series of AMD data for river Tapi at Prakasha barrage. The selection of best fit method of LN2 was made through Goodness-of-Fit (GoF) tests viz., Chi-Square and Kolmogorov-Smirnov, and Model Performance Indicators (MPIs) such as correlation coefficient, root mean absolute error and root mean squared error. GoF tests results confirmed the suitability of all three methods of LN2 for estimation of Peak Flood Discharge (PFD). The results indicated that the predicted PFD by MLM is closer to the observed data. From FFA results, it was witnessed that the standard error on the estimated PFD by MoM is minimum than those values of MLM and LMO. The outcomes of FFA results were weighed with MPIs and found that MLM is better suited amongst three methods of LN2 for estimation of PFD at Prakasha barrage. [ABSTRACT FROM AUTHOR]

The estimation of Peak Flood Discharge (PFD) for a specific return period is of paramount importance in hydrological studies for the planning, design, and management of civil and hydraulic structures. This research investigates the evaluation of probability distributions through statistical tests, viz., the Chi-Square, Kolmogorov-Smirnov, and D-index. These tests aid in estimating the PFD using the Flood Frequency Analysis (FFA) approach for the Tapi River at Prakasha Barrage. In this analysis, various distributions from the extreme value family such as Extreme Value Type-1, Extreme Value Type-2, Generalized Extreme Value (GEV), and Pareto, were employed in the FFA. The distribution parameters are determined by standard techniques, such as the method of moments, Maximum Likelihood Method (MLM), and the Method of L-Moments (LMO). A qualitative assessment, along with the D-index value, was employed to select the most suitable distribution for the analyzed data. This study demonstrates that among the four distributions adopted in FFA, the GEV (LMO) distribution exhibits the best fit. Furthermore, this research suggests the use of PFDs derived from the GEV (LMO) distribution for design purposes. [ABSTRACT FROM AUTHOR]

Estimation of rainfall for a given duration and return period is considered as one of the important design parameters in hydrological studies for planning, design and management of civil and hydraulic structures. This can be computed by fitting a probability distribution to the annual 1-day maximum rainfall observed at the rain-gauge site located within the vicinity of the study area. This paper presents an analysis of rainfall estimation at Dahanu using an extreme value family of probability distributions, namely, Generalized Extreme Value (GEV), Extreme Value Type-1, Extreme Value Type-2, and 3-parameter Pareto, wherein the parameters are determined using the Method of Moments, Maximum Likelihood Method, and Method of L-Moments (LMO). The adequacy of fitting probability distributions adopted in rainfall analysis is evaluated by quantitative assessment through Goodness-of-Fit (viz., Chi-Square and Kolmogorov-Smirnov) and diagnostic (viz., D-index) tests, and qualitative assessment by using the fitted curves of the estimated rainfall. The study shows that the estimated 1-day maximum rainfall given by GEV (LMO) distribution could be considered as a design parameter while designing the civil and hydraulic structures at Dahanu. [ABSTRACT FROM AUTHOR]

Estimation of rainfall for a given return period is of utmost importance for planning and design of minor and major hydraulic structures. This can be achieved through Extreme Value Analysis (EVA) of rainfall by fitting probability distributions viz., 2-parameters Normal, 2-parameters Log Normal, Pearson Type-3, Log Pearson Type-3, Extreme Value Type-1 (EV1) and Generalized Extreme Value (GEV) to the series of annual 1-day maximum rainfall. Based on the intended applications and the variate under consideration, Method of Moments (MoM), Maximum Likelihood Method (MLM) and L-Moments (LMO) are used for determination of parameters of the distributions. The adequacy of fitting six probability distributions adopted in EVA of rainfall for Afzalpur, Aland and Kalaburagi sites is quantitatively assessed by Goodness-of-Fit (viz., Chi-square and Kolmogorov-Smirnov) and diagnostic test (viz., D-index) tests, and qualitatively assessed by the fitted curves of the estimated rainfall. The outcomes of the study indicates that the GEV (LMO) is better suited amongst six distributions studied in EVA for rainfall estimation for Afzalpur and Kalaburagi whereas EV1 (MLM) for Aland. [ABSTRACT FROM AUTHOR]

Estimation of maximum flood discharge (MFD) at a desired location on a river is important for planning, design and management of hydraulic structures. This can be achieved using deterministic models with extreme storm events or through frequency analysis by fitting of probability distributions to the recorded annual maximum discharge data. In the latter approach, suitable probability distributions and associated parameter estimation methods are applied. In the present study, method of moments and L-moments (LMO) are used for determination of parameters of six probability distributions. Goodness-of-Fit tests such as Chi-square and Kolmogorov- Smirnov are applied for checking the adequacy of fitting of probability distributions to the recorded data. Diagnostic test of D-index is used for the selection of a suitable distribution for estimation of MFD. The study reveals that the Extreme Value Type-1 distribution (using LMO) is better suited amongst six distributions used in the estimation of MFD at Malakkara and Neeleswaram gauging stations in Pampa and Periyar river basins, respectively. [ABSTRACT FROM AUTHOR]