Nonparametric kernel estimation of annual precipitation over Iran

Jou, Parviz Haghighat; Akhoond‐Ali, Ali Mohammad; Nazemosadat, M. J.

doi:10.1007/s00704-012-0727-6

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…For the rainfall stations showing a significant trend, a direct estimation of the unknown density function was made using the kernel estimators. The kernel estimation of the density function is a commonly used tool for analyzing the behavior of hydrological phenomena, including atmospheric precipitation, as evidenced by the works of numerous research teams [40][41][42]. The outcomes allowed us to estimate the variability of the maximum annual daily precipitation.…”

Section: Identification Of Empirical Distributions With Kernel Estimamentioning

confidence: 99%

Estimating Maximum Daily Precipitation in the Upper Vistula Basin, Poland

et al. 2019

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The aim of this study was to determine the best probability distributions for calculating the maximum annual daily precipitation with the specific probability of exceedance (Pmaxp%). The novelty of this study lies in using the peak-weighted root mean square error (PWRMSE), the root mean square error (RMSE), and the coefficient of determination (R2) for assessing the fit of empirical and theoretical distributions. The input data included maximum daily precipitation records collected in the years 1971–2014 at 51 rainfall stations from the Upper Vistula Basin, Southern Poland. The value of Pmaxp% was determined based on the following probability distributions of random variables: Pearson’s type III (PIII), Weibull’s (W), log-normal, generalized extreme value (GEV), and Gumbel’s (G). Our outcomes showed a lack of significant trends in the observation series of the investigated random variables for a majority of the rainfall stations in the Upper Vistula Basin. We found that the peak-weighted root mean square error (PWRMSE) method, a commonly used metric for quality assessment of rainfall-runoff models, is useful for identifying the statistical distributions of the best fit. In fact, our findings demonstrated the consistency of this approach with the RMSE goodness-of-fit metrics. We also identified the GEV distribution as recommended for calculating the maximum daily precipitation with the specific probability of exceedance in the catchments of the Upper Vistula Basin.

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Section: Identification Of Empirical Distributions With Kernel Estimamentioning

confidence: 99%

Estimating Maximum Daily Precipitation in the Upper Vistula Basin, Poland

et al. 2019

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“…It is easy to apply and can often uncover structural features in the data set which a parametric approach might not reveal. Recently, nonparametric methods have been extensively applied to rainfall studies Sharma and Lall (1999), Sharma (2000), Haghighat jou et al (2013), and Kim et al (2006) used kernel density estimation to estimate the rainfall probability density function. In this paper, we will estimate the marginal distribution functions using both parametric and non-parametric approach.…”

Section: Kernel Density Estimationmentioning

confidence: 99%

“…For example, Abdul Zeephongsekul (2011, 2014) used parametric distributions to investigate rainfall severity and duration patterns in the state of Victoria, Australia; Shiau (2006) and Shiau and Modarres (2009) estimated the Standard Precipitation Index by fitting rainfall intensity using the Gamma distribution. As would be expected, this parametric approach does not work well for every precipitation data and appears to fit poorly near the tails of the distribution (Haghighat jou et al 2013). To alleviate this problem, a nonparametric kernel density approach has been applied to fit precipitation data.…”

Section: Introductionmentioning

confidence: 97%

“…To alleviate this problem, a nonparametric kernel density approach has been applied to fit precipitation data. Examples of such works are Haghighat jou et al (2013), where nonparametric kernel density is used to estimate the annual precipitation series in Iran, and Sharma (2000), who presented a nonparametric long-term probabilistic forecast model based on estimation of the conditional probability distribution of rainfall using nonparametric kernel density estimation techniques.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Rainfall Severity and Duration in Victoria, Australia using Non-parametric Copulas and Marginal Distributions

Rauf

Zeephongsekul

2014

Water Resour Manage

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The analysis of joint probability distributions of rainfall characteristics such as severity and duration is important in water resources management. Deriving their distributions using standard statistical techniques are often problematical due to its complexity. Standard methods usually assume that the rainfall characteristics are independent or that their marginal distributions belong to the same family of distributions. The use of copulas based methodologies can circumvent these restrictions and are therefore increasingly popular. However, the copulas and marginal distributions that are commonly used belong to specific parametric families and their adoption could lead to spurious inferences if the underlying assumptions are violated. For this reason, we recommend a nonparametric or semiparametric approach to estimate the joint distribution of rainfall characteristics. In this paper, we introduce and compare several copula-based approaches, each involving a combination of parametric or nonparametric marginal distributions conjoined by a parametric or nonparametric copula. An empirical illustration of the different approaches using rainfall data collected from six stations in the state of Victoria, Australia, demonstrated that a nonparametric approach can often give better results than a purely parametric approach.

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Frequency analysis of extreme daily rainfall over an arid zone of Iran using Fourier series method

Jou

Mirhashemi

2022

Appl Water Sci

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In this paper frequency analysis of annual extreme daily rainfall of 14 gauging stations located in an arid zone of Iran were performed using parametric and nonparametric approaches. The parametric methods include normal, two- and three-parameter log-normal, two-parameter gamma, Pearson and log-Pearson type III, extreme value type I (Gumbel), generalized extreme value and generalized logistic distributions. The nonparametric approach is Fourier series method. The data were fitted to all of above mentioned models and the results showed that the goodness of fit of the data to Fourier series is much better than other parametric methods. Thus the Fourier series can be used as an alternative approach for frequency analysis of extreme daily rainfall in an arid zone. In addition, the quantiles can be calculated by the Fourier series.

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Nonparametric kernel estimation of annual precipitation over Iran

Cited by 4 publications

References 18 publications

Estimating Maximum Daily Precipitation in the Upper Vistula Basin, Poland

Estimating Maximum Daily Precipitation in the Upper Vistula Basin, Poland

Analysis of Rainfall Severity and Duration in Victoria, Australia using Non-parametric Copulas and Marginal Distributions

Frequency analysis of extreme daily rainfall over an arid zone of Iran using Fourier series method

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