Maximum rainfall probability distributions pattern in Haryana –A case study

Kumar, Manoj; Shekhar, Chander; Manocha, Veena

doi:10.31018/jans.v8i4.1082

Cited by 5 publications

(2 citation statements)

References 5 publications

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“…Coronado-Hernández et al [26] reported GEV as the best fit probability distribution for frequency analysis of maximum daily rainfall for a series of return periods for selected precipitation recording stations in Colombia and also reported GEV to be the best fit for most of the rain-gauge stations considered in Bangladesh. Likewise, Kumar et al [27] gave credence to GEV as the best-performed distribution for most of the rainfall period considered for Haryana, India. Furthermore, Młyński et al [12] recommended GEV distribution to predict maximum daily rainfall with the specific probability of exceedance for catchments in the upper Vistula basin of Poland.…”

Section: Resultsmentioning

confidence: 91%

Modelling the Effects of Meteorological Factors on Maximum Rainfall Intensities Using Exponentiated Standardized Half Logistic Distribution

Sasanya

Awodutire

Ufuoma

et al. 2022

Journal of Applied Mathematics

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Rainfall intensity prediction or forecast is vital in designing hydraulic structures and flood and erosion control structures. In this work, meteorological data were obtained from the National Aeronautics and Space Administration’s (NASA) website. Models estimating maximum rainfall intensities were derived, and some meteorological factors’ effects on the models were tested. The meteorological factors considered include annual relative humidity averages, specific humidity, temperature range at 2 m, maximum temperature, and minimum temperature. This research was aimed at developing a model for estimating maximum rainfall intensities, and the effects of various meteorological factors on the models were investigated. The exponentiated standardized half logistic distribution (ESLD) was used to model the effects of the factors and return periods on 35 years’ (1984–2018) annual maxima monthly rainfall intensities for Port Harcourt metropolis, Nigeria. The model parameters were estimated using the maximum likelihood estimation method. Compared with the results from the five standard distributions, three criteria were used to determine the best-performed distribution. These indicated that the ESLD performed considerably better than the other five compared distributions. Only the return period had significant effects on the model for the rainfall intensity prediction since p < 0.05 , while the effects of the meteorological factors are insignificant.

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Section: Resultsmentioning

confidence: 91%

Modelling the Effects of Meteorological Factors on Maximum Rainfall Intensities Using Exponentiated Standardized Half Logistic Distribution

Sasanya

Awodutire

Ufuoma

et al. 2022

Journal of Applied Mathematics

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“…Furthermore, Młyński et al (2019) identi ed GEV as suitable for estimating maximum daily rainfall and probability of exceedance in the Upper Vistula Basin of southern Poland. Kumar et al (2016) tted maximum weekly, monthly, seasonal and annual 47 years rainfall data to different probability distributions and found GEV as the best distribution for most periods considered for Ambala district of Haryana State, India. Beskow et al (2015) found out that rainfall modelled in extreme southern Brazil gives the best result when modelled with multi-parameter probability distributions, of which the kappa multi-parameter distribution had better performance than GEV from this study.…”

Section: Introductionmentioning

confidence: 99%

Modelling rainfall in selected agricultural hubs in Nigeria: a comparative probability distributions study

Sasanya,

Awodutire,

Ufuoma

2024

Theor Appl Climatol

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The most appropriate probability distribution for rainfall data varies from one region to the other. The need to research the best distribution for different locations is thus a continuous process. This research was designed to investigate rainfall variability and the best-t distribution model for monthly rainfall in Oke Ogun, the agricultural hub of Oyo state, Nigeria. Thirty-six years ' rainfall data (1982-2017) were statistically analysed and tted to ten different probability distributions for each calendar month. Parameters of each distribution were estimated from maximum likelihood estimates. Best t distribution models for each month were selected using Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC) and Corrected Akaike Information Criteria (CAIC) as model selection criteria. Gamma distribution had the best-t for six of the twelve-monthly rainfall data. Weibull distribution had best-t for three months, while Lognormal tted data for March and July best and Gumbel distribution had best-t for August. This study will enable adequate integrated water resources management as well as proper water resources evaluation and planning, for agricultural productivity in the Oke Ogun region of Oyo state. Nigeria.

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Choosing the best fit probability distribution in rainfall design analysis for Pulau Pinang, Malaysia

Mudashiru

Abustan

Sabtu

et al. 2023

Model. Earth Syst. Environ.

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Maximum rainfall probability distributions pattern in Haryana –A case study

Cited by 5 publications

References 5 publications

Modelling the Effects of Meteorological Factors on Maximum Rainfall Intensities Using Exponentiated Standardized Half Logistic Distribution

Modelling the Effects of Meteorological Factors on Maximum Rainfall Intensities Using Exponentiated Standardized Half Logistic Distribution

Modelling rainfall in selected agricultural hubs in Nigeria: a comparative probability distributions study

Choosing the best fit probability distribution in rainfall design analysis for Pulau Pinang, Malaysia

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