scholarly journals Determining suitable probability distribution models for annual precipitation data (a case study of Mazandaran and Golestan provinces)

2010 ◽  
Vol 3 (1) ◽  
Author(s):  
Mohammad Mahdavi ◽  
Khaled Osati ◽  
Sayed Ali Naghi Sadeghi ◽  
Bakhtiar Karimi ◽  
Jalil Mobaraki
Keyword(s):  
Probability Distribution ◽  
Annual Precipitation ◽  
Precipitation Data ◽  
Distribution Models

scholarly journals Probability Distributions for a Quantile Mapping Technique for a Bias Correction of Precipitation Data: A Case Study to Precipitation Data Under Climate Change

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1475 ◽  
Author(s):  
Jun-Haeng Heo ◽  
Hyunjun Ahn ◽  
Ju-Young Shin ◽  
Thomas Rodding Kjeldsen ◽  
Changsam Jeong
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Shape Parameter ◽  
Mapping Method ◽  
Distribution Model ◽  
Parameter Distribution ◽  
Precipitation Data ◽  
Distribution Models ◽  
Quantile Mapping

The quantile mapping method is a bias correction method that leads to a good performance in terms of precipitation. Selecting an appropriate probability distribution model is essential for the successful implementation of quantile mapping. Probability distribution models with two shape parameters have proved that they are fit for precipitation modeling because of their flexibility. Hence, the application of a two-shape parameter distribution will improve the performance of the quantile mapping method in the bias correction of precipitation data. In this study, the applicability and appropriateness of two-shape parameter distribution models are examined in quantile mapping, for a bias correction of simulated precipitation data from a climate model under a climate change scenario. Additionally, the impacts of distribution selection on the frequency analysis of future extreme precipitation from climate are investigated. Generalized Lindley, Burr XII, and Kappa distributions are used, and their fits and appropriateness are compared to those of conventional distributions in a case study. Applications of two-shape parameter distributions do lead to better performances in reproducing the statistical characteristics of observed precipitation, compared to those of conventional distributions. The Kappa distribution is considered the best distribution model, as it can reproduce reliable spatial dependences of the quantile corresponding to a 100-year return period, unlike the gamma distribution.

scholarly journals Application of Harmony Search to Design Storm Estimation from Probability Distribution Models

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Sukmin Yoon ◽  
Changsam Jeong ◽  
Taesam Lee
Keyword(s):  
Parameter Estimation ◽  
Probability Distribution ◽  
Harmony Search ◽  
Computation Time ◽  
The Other ◽  
Distribution Model ◽  
Distribution Models ◽  
Design Storm ◽  
Estimated Parameters

The precision of design storm estimation depends on the selection of an appropriate probability distribution model (PDM) and parameter estimation techniques. Generally, estimated parameters for PDMs are provided based on the method of moments, probability weighted moments, and maximum likelihood (ML). The results using ML are more reliable than the other methods. However, the ML is more laborious than the other methods because an iterative numerical solution must be used. In the meantime, metaheuristic approaches have been developed to solve various engineering problems. A number of studies focus on using metaheuristic approaches for estimation of hydrometeorological variables. Applied metaheuristic approaches offer reliable solutions but use more computation time than derivative-based methods. Therefore, the purpose of the current study is to enhance parameter estimation of PDMs for design storms using a recently developed metaheuristic approach known as a harmony search (HS). The HS is compared to the genetic algorithm (GA) and ML via simulation and case study. The results of this study suggested that the performance of the GA and HS was similar and showed more accurate results than that of the ML. Furthermore, the HS required less computation time than the GA.

Tenderfoot Creek Experimental Forest Annual Precipitation Data: 1993-2010

2011 ◽  
Author(s):  
David K. Wright ◽  
Lance S. Glasgow ◽  
Ward W. McCaughey ◽  
Elaine K. Sutherland
Keyword(s):  
Annual Precipitation ◽  
Precipitation Data

Calibrating GPM IMERG Late-Run product using ground-based CPC daily precipitation data: a case study in the Beijing-Tianjin-Hebei urban agglomeration

2021 ◽  
Vol 12 (9) ◽  
pp. 848-858
Author(s):  
Jintao Xu ◽  
Siyu Zhu ◽  
Ziqiang Ma ◽  
Hui Liu ◽  
Yulin Shangguan ◽  
...  
Keyword(s):  
Daily Precipitation ◽  
Urban Agglomeration ◽  
Precipitation Data ◽  
Daily Precipitation Data

scholarly journals MLE-Based Parameter Estimation for Four-Parameter Exponential Gamma Distribution and Asymptotic Variance of Its Quantiles

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2092
Author(s):  
Songbai Song ◽  
Yan Kang ◽  
Xiaoyan Song ◽  
Vijay P. Singh
Keyword(s):  
Parameter Estimation ◽  
Confidence Intervals ◽  
Asymptotic Variance ◽  
Information Matrix ◽  
Estimation Method ◽  
Flood Frequency Analysis ◽  
Annual Precipitation ◽  
Precipitation Data ◽  
Marquardt Algorithm ◽  
Design Values

The choice of a probability distribution function and confidence interval of estimated design values have long been of interest in flood frequency analysis. Although the four-parameter exponential gamma (FPEG) distribution has been developed for application in hydrology, its maximum likelihood estimation (MLE)-based parameter estimation method and asymptotic variance of its quantiles have not been well documented. In this study, the MLE method was used to estimate the parameters and confidence intervals of quantiles of the FPEG distribution. This method entails parameter estimation and asymptotic variances of quantile estimators. The parameter estimation consisted of a set of four equations which, after algebraic simplification, were solved using a three dimensional Levenberg-Marquardt algorithm. Based on sample information matrix and Fisher’s expected information matrix, derivatives of the design quantile with respect to the parameters were derived. The method of estimation was applied to annual precipitation data from the Weihe watershed, China and confidence intervals for quantiles were determined. Results showed that the FPEG was a good candidate to model annual precipitation data and can provide guidance for estimating design values

Determining the best fitting distribution of annual precipitation data in Serbia using L-moments method

2020 ◽  
Author(s):  
Milan Gocic ◽  
Lazar Velimirovic ◽  
Miomir Stankovic ◽  
Slavisa Trajkovic
Keyword(s):  
Annual Precipitation ◽  
Precipitation Data ◽  
Moments Method ◽  
Best Fitting ◽  
L Moments
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