In this study LH-moment proposed by Wang (Water Resour Res 33(12):2841-2848, 1997) has been used for regional flood frequency analysis of the North-Bank region of the river Brahmaputra, India. Three probability distributions i.e. generalized extreme value (GEV), generalized logistic (GLO) and generalized Pareto (GPA) has been used for each level of LH-moments i.e. L, L 1 , L 2 , L 3 and L 4 . The regional frequency analysis procedure proposed by Hosking and Wallis (Water Resour Res 29(2):271-281, 1993) for L-moments i.e. discordancy measure for screening the data, heterogeneity measure for formation of homogeneous region and goodness-of-fit test have been used for each level of LH-moments. Based on the LH-moment ratio diagram and |Z|-statistic criteria, GEV distribution for level one LH-moment is identified as the robust distribution for the study area. For estimation of floods of various return periods for both gauged and ungauged catchments of the study area, regional flood frequency relationships have been developed by using the level one LH-moment based on GEV distribution. A comparative study has been performed between L-moments and LH-moments for the study area. It is observed from comparative study that the regional flood frequency analysis based on the GEV distribution by using level one LH-moment (L 1 ) is superior to the use of L-moments.

Rainfall data of the northeast region of India has been considered for selecting best fit model for rainfall frequency analysis. The methods of L-moment has been employed for estimation of parameters five probability distributions, namely Generalized extreme value (GEV), Generalized Logistic(GLO), Pearson type 3 (PE3), 3 parameter Log normal (LN3) and Generalized Pareto (GPA) distributions. The methods of LH-moment of four orders (L1 L2, L3 & L4-moments) have also been used for estimating the parameters of three probability distributions namely Generalized extreme value (GEV), Generalized Logistic (GLO) and Generalized Pareto (GPA) distributions. PE3 distribution has been selected as the best fitting distribution using L-moment, GPA distribution using L1-moment and GLO distribution using L2, L3 & L4-moments. Relative root mean square error (RRMSE) and RBIAS are employed to compare between the results found from L-moment and LH-moment analysis. It is found that GPA distribution designated by L1-moment method is the most suitable and the best fitting distribution for rainfall frequency analysis of the northeast India. Also the L1-moment method is significantly more efficient than L-moment and other orders of LH-moment for rainfall frequency analysis of the northeast India.

Abstract:In this study our main objective is to determine the best fitting probability distribution for annual maximum flood discharge data of river Kopili, Assam. Various probability distributions i.e. Gumbel (G), generalized extreme value (GEV), normal (N), log-normal (LN3), generalized logistic (GLO), generalized pareto (GPA) and Pearson type-III (PE3) have been used for our study. The L-moments methods have been used for estimating the parameters of all the distributions. The root mean square error (RMSE), model efficiency and D-index (fit in the top six values) together with L-moment ratio diagram is used for goodness of fit measure. It has been observed that Generalized Pareto is the best fitting probability distribution for annual maximum discharge data of river Kopili.

The LQ-moment proposed by Mudholkar, et al. (1998) is used for regional flood frequency analysis of the North-Bank region of the river Brahmaputra, India. Five probability distributions are used for the LQmoment: generalized extreme value (GEV), generalized logistic (GLO) and generalized Pareto (GPA), lognormal (LN3) and Pearson Type III (PE3). The same regional frequency analysis procedure proposed by Hosking (1990) for the L-moment is used for the LQ-moment. Based on the LQ-moment ratio diagram and dist i Z-statistic criteria, the PE3 distribution is identified as the robust distribution for the study area. For estimation of floods of various return periods for both gauged and ungauged catchments of the study area, regional flood frequency relationships are developed using the LQ-moment based PE3 distribution.

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