Intercomparison of Probability Distributions using Goodness-of-Fit and Diagnostic Tests for Extreme Value Analysis of Rainfall

N. Vivekanandan*
*Scientist - B, Central Water and Power Research Station, Pune, Maharashtra, India.
Periodicity:December - February'2017
DOI : https://doi.org/10.26634/jste.5.4.10389

Abstract

Estimation of extreme rainfall for a desired return period is a prerequisite for planning, design and operation of various hydraulic structures, such as dams, bridges, barrages, and storm water drainage systems. Depending on the size and the design-life of the structure, the estimated extreme rainfall corresponding to particular return period is used. This can be computed through Extreme Value Analysis (EVA) of rainfall by fitting probability distributions to the recorded values of annual 1-day maximum rainfall. This paper illustrates the adoption of Extreme Value Type-1, Extreme Value Type-2, 2- parameter Log Normal and Log Pearson Type-3 (LP3) probability distributions in EVA of rainfall for Hissar and Tohana. Based on the applicability, standard parameter estimation procedures, viz., Method of Moments (MoM), Maximum Likelihood Method (MLM), and Order Statistics Approach are used for determination of parameters of distributions. The adequacy on the fitting of probability distributions used in EVA of rainfall is evaluated by applying Goodness-of-Fit (GoF) tests, viz., Anderson-Darling and Kolmogorov-Smirnov. In addition to GoF tests, D-index is employed to evaluate the best suitable probability distribution for estimation of extreme rainfall. The study suggests the LP3 (using MLM) is better suited amongst four probability distributions adopted in EVA for estimation of extreme rainfall for Hissar and Tohana.

Keywords

Anderson-Darling, D-index, Kolmogorov-Smirnov, Log Pearson Type-3, Rainfall.

How to Cite this Article?

Vivekanandan, N. (2017). Intercomparison of Probability Distributions using Goodness-of-Fit and Diagnostic Tests for Extreme Value Analysis of Rainfall. i-manager’s Journal on Structural Engineering, 5(4), 9-16. https://doi.org/10.26634/jste.5.4.10389

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