Modelling weekly rainfall data for crop planning in a sub-humid climate of India

Sharda, V. N.; Das, Purushottam

doi:10.1016/j.agwat.2005.01.010

Cited by 38 publications

(32 citation statements)

References 14 publications

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“…This is largely due to the Weibull distribution, which is also often applied to rainfall data, being a subdistribution of the GEV. The use of the Weibull distribution is consistent with the findings of Burgueno et al (2004), which found the Weibull distribution well suited to fit time intervals between rainfall, and Sharda and Das (2005) which found the Weibull distribution fit weekly rainfall data better than the gamma distribution as well as other rainfall research. As a check, the gamma parameters of the original fits were also allowed to vary with time with very similar trend identification results.…”

Section: Temporal Analysissupporting

confidence: 75%

“…The use of the gamma distribution for precipitation data has been established in the literature (Groisman et al,1999;Semenov and Bengstsson, 2002;Watterson and Dix, 2003;Zolina et al, 2004;), although other distributions such as the Weibull and Poisson have also been used (Burgueno et al, 2004;Sharda and Das, 2005). The gamma distribution is positively skewed and has good flexibility by allowing for variability in both mean and variance with its shape and scale parameters.…”

Section: Gamma Distributionmentioning

confidence: 99%

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Regional scale spatio‐temporal consistency of precipitation variables related to water resource management and planning

Paynter

Nachabe

2009

Meteorological Applications

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ABSTRACT:The focus of this research was to determine the extent of spatio-temporal changes in precipitation patterns using methods that can be regionalized and applied to various water resources such as lakes, streams, reservoirs or other water bodies. Many water resources throughout the world have demonstrated changes in historic water levels. As precipitation is the major input to these water bodies, it is important to investigate any changes in rainfall in both time and space. To investigate these patterns regionally, appropriate distributions, either gamma or generalized extreme value (GEV), were fitted to variables at a number of rainfall gauges within 40 km of a particular lake using maximum likelihood estimation. The variables included total annual rainfall, rainfall days per year, maximum annual event rainfall and maximum annual interevent days. Once the distribution parameters were estimated, an average, representative, distribution for each variable with 99% confidence limits was developed to determine if the fits for all gauges and variables were contained by these limits. To examine the temporal variation, distribution parameters were allowed to vary with time and then compared to constant parameter fits via likelihood ratio tests to determine if the varying parameter model significantly improved the fit. The spatial distribution of rainfall variables was found to be quite homogenous within the given confidence limits with some exceptions at high and low percentiles. Furthermore, the temporal distribution of rainfall variables was found to be stationary with only one gauge showing a significant trend. The average distribution for each precipitation variable developed can be applied at water resources within the region without rainfall gauges in proximity due to the spatial homogeneity and temporal stationarity identified. The methodology can be adapted to water resource management and planning in other regions.

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Section: Temporal Analysissupporting

confidence: 75%

Section: Gamma Distributionmentioning

confidence: 99%

Regional scale spatio‐temporal consistency of precipitation variables related to water resource management and planning

Paynter

Nachabe

2009

Meteorological Applications

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“…Changing variables and after some tedious algebra we obtain Finally, substituting equations (37) and (38) into equation (5) and simplifying yields equation (18).…”

Section: Discussionmentioning

confidence: 99%

“…That is, the functional form of   (  ) does not change the functional form of the asset speci…c pricing kernel in equation (18).…”

Section: The Pricing Kernelmentioning

confidence: 99%

General equilibrium and preference free model for pricing options under transformed gamma distribution

Vitiello

Poon

2009

Journal of Futures Markets

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“…The gamma distribution is frequently and successfully used for modeling the non-zero amounts of rain (Stern and Coe, 1984;Siddiqi, 1992;Hussain, 2004;Sharda and Das, 2005). The p.d.f.…”

Section: Fitting Of Gamma Distributionmentioning

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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Modelling weekly rainfall data for crop planning in a sub-humid climate of India

Cited by 38 publications

References 14 publications

Regional scale spatio‐temporal consistency of precipitation variables related to water resource management and planning

Regional scale spatio‐temporal consistency of precipitation variables related to water resource management and planning

General equilibrium and preference free model for pricing options under transformed gamma distribution

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

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