Regional analysis of maximum rainfall using L-moment and LH-moment: A comparative case study for the northeast India

Abstract

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.

Predicting Flood Frequency with the LH-Moments Method: A Case Study of Prigor River, Romania

“…In In the case of both parameter estimation methods, the application of a certain probability distribution of three parameters in the frequency analysis of extreme events in hydrology is carried out only if the theoretical values of the indicators L-skewness, L-kurtosis, LH-skewness and LH-kurtosis are closest to the corresponding values of the indicators of the analyzed data set, an aspect also highlighted in the literature [2,3,14,15,17,22,23]. The inconvenience of three-parameter distributions is due to the fact that they cannot calibrate higher-order linear moments (the fourth-order linear moment that characterizes Lkurtosis).…”

“…Considering that the main selection criterion in the case of these parameter estimation methods is represented by the values and the variation graphs of the statistical indicators, the variation relations for a wide range of distributions, including those analyzed in this article, are presented, providing information of real help in using these distributions. In the case of both parameter estimation methods, the application of a certain probability distribution of three parameters in the frequency analysis of extreme events in hydrology is carried out only if the theoretical values of the indicators L-skewness, L-kurtosis, LHskewness and LH-kurtosis are closest to the corresponding values of the indicators of the analyzed data set, an aspect also highlighted in the literature [2,3,14,15,17,22,23]. The inconvenience of three-parameter distributions is due to the fact that they cannot calibrate higher-order linear moments (the fourth-order linear moment that characterizes L-kurtosis).…”

“…In hydrology, the LH-moments method has been generally applied for low-flow frequency analysis [7], flood frequency analysis [1,8-10], regional flood frequency analysis [11][12][13][14] and frequency analysis of the annual maximum rainfall series [15][16][17].…”

An inovative regional frequency analysis approach for robust extreme precipitation assessment in data-rich and climatically diverse regions

“…L-moments provide a notable benefit compared to traditional moments since they are resilient to the impact of sampling variability. L-moments offer more reliable estimates of population parameters compared to traditional moments, as they are less susceptible to the influence of outliers or extreme values in the data (Bora et al 2017). This is particularly useful when working with small sample sizes or data sets that contain anomalies (Sahu et al 2021).…”

“…More recently, hydrology researchers have focused on the L-moment approach and it is increasingly used in regional frequency analysis of flood (Meshgi and Khalili 2009a, b;Bhuyan et al 2010;Gheidari 2013;Rahman et al 2014;Rutkowska et al 2017;Igor Leščešen et al 2019;Lee and Kim 2019;Mohamad Hamzah et al 2019;Kohnová et al 2020), precipitation (Haddad et al 2011;Modarres and Sarhadi 2011;Chen and Hong 2012;Sarmadi and Shokoohi 2015;Zakaria et al 2018;Gocic et al 2021), extreme precipitation (Modarres and Modarres 2009;Abolverdi and Khalili 2010;Deka et al 2011;Zakaria and Shabri 2013;Malekinezhad and Zare-Garizi 2014;Yin et al 2016;Bora et al 2017;Zhou et al 2017;Journal 2017;Liang et al 2017;Hu et al 2019;Ibrahim 2019), air temperature (Sarmadi and Azmi 2016), and drought (Eslamian et al 2011;Zhang et al 2015;Tosunoglu and Can 2016;Adib and Marashi 2019;Ullah et al 2020;Kuswanto et al 2021;Adib et al 2021).…”