Stochastic Generation of the Occurrence and Amount of Daily Rainfall

Barkotulla, M. A. B.

doi:10.18187/pjsor.v6i1.122

Cited by 14 publications

(10 citation statements)

References 20 publications

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“…For example Barkotulla (2010) in Mahadevpur (Bangladesh), Muamaraldin (2009) in the Middle East (Egypt, Jordan and Palestine), and Srikanthan and McMaho (1985) in Australia used the technique suggested by Richardson (1981) and obtained synthetic data statistically similar to that recorded. In West Africa, similar study has not yet been carried out despite the various campaigns it has experienced.…”

Section: Introductionmentioning

confidence: 86%

Untitled

2017

Int.J.Curr.Res.Aca.Rev.

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Article InfoStochastic weather generators used in meteorological and hydrological studies are mostly statistical models that produce random numbers resembling the observed data on which they have been fitted. They can generate weather sequences that statistically resemble the real observed data. In this study we developed a single-site daily rainfall generator for the simulations of rainfall occurrences and amounts in northwestern of Benin. A first-order two-state Markov chain was used to determine the occurrence of daily precipitation. The rainfall amounts on wet days were generated by using the oneparameter Exponential distribution and the two-parameter Gamma distribution. The Markov model was successful in simulating the rainfall occurrences and the Exponential distribution as the more reliability in preserving most of the important daily characteristics of the historical rainfall amounts. The test of the two distributions on data that are not taken into account in the elaboration of the model has shown that the exponential distribution reproduces well the statistics of the daily precipitation than the gamma distribution

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Section: Introductionmentioning

confidence: 86%

Untitled

2017

Int.J.Curr.Res.Aca.Rev.

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“…Two of the most attractive features Markov chain models are the ease with which seasonality is accommodated and availability of effective statistical inference procedures for parameter estimation and model selection (Stern and Coe, 1984).The distribution pattern of rainfall rather than the total amount of rainfall within the entire period of time is more important for studying the pattern of rainfall occurrence (Garg and Singh, 2010). Rainfall is the principal phenomenon driving many hydrological extremes such as foods, droughts, landslides, debris and mudflows; its analysis and modeling are typical problems in applied hydrometeorology (Barkotulla, 2010). Rainfall exhibits a strong variability in time and space across the globe.…”

Section: Introductionmentioning

confidence: 99%

Markov chain modeling of daily rainfall in Lay Gaint Woreda, South Gonder Zone, Ethiopia

Getachew

Teshome

2018

J.Degrade.Min.Land Manage.

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Abstract:An understanding and knowledge of seasonal rainfall distribution, e.g. length of the growing season and rainfall extremes, is very important in agro-based economies like Ethiopia, where 95% of the farmers depend on rainfed agricultural production. The distribution pattern of rainfall rather than the total amount of rainfall within the entire period of time is more important for studying the pattern of rainfall occurrence. Zero, first and second order Markov chain was used to describe the characteristics of rainfall occurrences in this woreda. The states considered were; dry (d) and rainy (r).The overall chance of rain and the fitted curve tells us that the chance of getting rain in the main rainy season is quadruple as compared to the small rainy season. The first order Markov chain model indicates that the probability of getting rain in the small rainy season is significantly dependent on whether the earlier date was dry or wet while the second order Marko chain indicates that during the main rainy season the dependence of the probability of rain on the previous two dates' conditions is less as compared with the small rainy season. Rainfall amounts are very variable and are usually modeled by a gamma distribution.Therefore, the pattern of rainfall is somewhat unimodial having only one extreme value in August. Onset, cessation and length of growing season of rainfall for the main rainy season show medium variation compared to the small rainy season.

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“…Estimating and predicting precipitation is a problem of fundamental importance for agriculture, hydrology and ecology. Information on the probability of precipitation, its size and the number of days without rainfall, is necessary for designing sanitary systems for draining rainwater, or planning irrigation systems as an alternative system of growing plants in order to build a rational management of water in the soil [1]. Determining the distribution of rainfall is also necessary for planning the use of water resources on a larger scale, eg in the national economy.…”

Section: Introductionmentioning

confidence: 99%

Markov chain as a tool for forecasting daily precipitation in the vicinity of the city of Bydgoszcz, Poland

et al. 2018

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The crop yield depends on numerous weather factors, but mainly on the rainfall pattern and course of air temperature during vegetation period. Investigating the dependence of yields on rainfall, apart from its amount, there also should be taken into account dry spell periods. The two-state Markov chain was considered as a precipitation pattern in the investigation, since it is generally recognized as a simple and effective model of the precipitation occurrence. Based on the daily precipitation totals from the period 1971—2013, the Markov chain was designated. The data were derived from a measuring point of the University of Science and Technology in Bydgoszcz, Poland. As one of the objectives was to determine the order of the Markov chain examined describing the change of precipitation in subsequent days. Another aim was to investigate rainfall dependencies on a month of a year. An analysis of this data leads to the conclusion that the chain is second order. This is confirmed by the two criteria used: BIC (Bayesian Information Criteria) and AIC (Akaike Information Criteria). The research regarded the precipitation volume dependence on a month of the year.

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Stochastic Generation of the Occurrence and Amount of Daily Rainfall

Cited by 14 publications

References 20 publications

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Markov chain modeling of daily rainfall in Lay Gaint Woreda, South Gonder Zone, Ethiopia

Markov chain as a tool for forecasting daily precipitation in the vicinity of the city of Bydgoszcz, Poland

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