The Statistical Distribution of Annual Maximum Rainfall in Colombo District

Mayooran, T.; Laheetharan, A.

doi:10.4038/sljastats.v15i2.7411

Cited by 17 publications

(6 citation statements)

References 10 publications

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“…Likewise, the Gumbel distribution model was found more suitable for predicting 3, 4 5, 6 and 7-days maximum annual rainfall, because of their lowest ( 2 ) values i.e., 4.66, 5.62, 4.60, 4.45 and 8.61, respectively. The result seems to be similar as reported by (Agarwal et al, 1988, Jeevnatghvam and Jay Kumar, 1979, Mayooran and Laheetharan, 2014.…”

Section: Evaluation Of Modelssupporting

confidence: 91%

Best-fit probability distribution model for predicting annual maximum rainfall of Pusa (Bihar)

Kumar

Suresh²,

Kumari³

2018

IJAI

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In present study an attempt has been made to evaluate the suitable probability distribution models for predicting 1, 2, 3, 4, 5, 6 and 7-days annual maximum rainfall amounts based on 39 years (1964 to 2002) daily rainfall data. Three probability distribution models namely: Log Normal distribution, Log Pearson Type-III distribution and Gumbel distribution models were considered to evaluate their goodness of fit. The Weibull’s method was used for computation of observed rainfall values at1, 5, 20, 30, 50, 95 and 99 percent probability levels. The Log Pearson type –III distribution was found suitable for 1 and 2 days maximum annual rainfall, while Gumbel distribution was found to be the best for predicting 3, 4, 5, 6 and 7- days annual maximum rainfall amounts. The relationships between annual maximum rainfall and return periods were also developed. The non – linear relationships (i.e. logarithmic) were found to be most suitable for all the cases.

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Section: Evaluation Of Modelssupporting

confidence: 91%

Best-fit probability distribution model for predicting annual maximum rainfall of Pusa (Bihar)

Kumar

Suresh²,

Kumari³

2018

IJAI

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“…Table S2 shows a list of papers where the GEV and a broad pool of non-asymptotic probability distributions are used to represent the maximum annual daily precipitation. Using standard goodness-of-fit tests, or statistical indicators, many of these experiments (which often involve a large number of alternatives–even more than 30 28 ) came to the conclusion that the GEV is not always the best probability distribution to represent the maximum annual daily precipitation; in some cases the GEV works better in humid sites than in dry sites 29 – 31 . While comparing extreme-value-type distributions versus standard probability distributions might be questionable 32 , 33 , we argue that, from a statistical point of view, it is always possible to test the agreement between a data sample and a probability distribution and a better agreement for a non-asymptotic distribution should be critically addressed in view of possible non-asymptotic conditions.…”

Section: Introductionmentioning

confidence: 99%

Superstatistical distribution of daily precipitation extremes: A worldwide assessment

Michele

Avanzi

2018

Sci Rep

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Maximum annual daily precipitation is a fundamental hydrologic variable that does not attain asymptotic conditions. Thus the classical extreme value theory (i.e., the Fisher-Tippett’s theorem) does not apply and the recurrent use of the Generalized Extreme Value distribution (GEV) to estimate precipitation quantiles for structural-design purposes could be inappropriate. In order to address this issue, we first determine the exact distribution of maximum annual daily precipitation starting from a Markov chain and in a closed analytical form under the hypothesis of stochastic independence. As a second step, we formulate a superstatistics conjecture of daily precipitation, meaning that we assume that the parameters of this exact distribution vary from a year to another according to probability distributions, which is supported by empirical evidence. We test this conjecture using the world GHCN database to perform a worldwide assessment of this superstatistical distribution of daily precipitation extremes. The performances of the superstatistical distribution and the GEV are tested against data using the Kolmogorov-Smirnov statistic. By considering the issue of model’s extrapolation, that is, the evaluation of the estimated model against data not used in calibration, we show that the superstatistical distribution provides more robust estimations than the GEV, which tends to underestimate (7–13%) the quantile associated to the largest cumulative frequency. The superstatistical distribution, on the other hand, tends to overestimate (10–14%) this quantile, which is a safer option for hydraulic design. The parameters of the proposed superstatistical distribution are made available for all 20,561 worldwide sites considered in this work.

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“…The methodology presented above was applied to the 47 years weather data in which maximum rainfall Table 1. Description of various probability distribution functions( (Mayooran, and laheetharan, 2014).…”

Section: Resultsmentioning

confidence: 99%

“…It has long been a topic of interest in the fields of climatology to find a probability distribution that provides a good fit to daily rainfall. Several studies have been conducted in India and abroad on rainfall analysis and best fit probability distribution function such as normal, log-normal, gumbel, weibull and Pearson type distribution were identified (Mayooran, and laheetharan, 2014). Rama Rao et al (1975) analyzed the daily rainfall data collected at Bijapur for the year 1921 to 1970 at Bihapur.…”

Section: Introductionmentioning

confidence: 99%

Maximum rainfall probability distributions pattern in Haryana –A case study

Kumar

Shekhar

Manocha

2016

JANS

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The present study has been undertaken to fit best probability distribution of rainfall in Ambala District of Haryana State. The analysis showed that the maximum daily rainfall among the years ranged between 41mm (1980) to 307.9mm (2009) indicating a very large variation during the period of study. The mean of maximum daily rainfall of all years annually is 112.13mm. The means of monthly and weekly values ranged from 33.10-88.92mm and 8.77- 46.28 mm, respectively. The maximum daily rainfall in a year/monsoon season was307.9 mm and monthly maximum daily rainfall in monsoon season ranged from 105 -307.9mm. The weekly maximum daily rainfall ranged from48 mm-307.9 mm. It was also observed that the minimum among the maximum daily rainfall was 41mm for annual, 34mm for season and 0 in all the months and weeks. The maximum value of coefficient of variation was observed in the first week which indicated a large fluctuation in the rainfall data set and minimum value of coefficient of variation 0.464 was observed for the whole year which shows that fluctuation was minimum for the whole year. Generalized extreme value distribution was found to be best fit probability distribution for most of the periods.

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The Statistical Distribution of Annual Maximum Rainfall in Colombo District

Cited by 17 publications

References 10 publications

Best-fit probability distribution model for predicting annual maximum rainfall of Pusa (Bihar)

Best-fit probability distribution model for predicting annual maximum rainfall of Pusa (Bihar)

Superstatistical distribution of daily precipitation extremes: A worldwide assessment

Maximum rainfall probability distributions pattern in Haryana –A case study

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