Flood analysis using generalized logistic models in partial duration series
(2012) In Journal of Hydrology 420. p.5971 Abstract (Swedish)
 Abstract in Undetermined
As a generalization of the commonly assumed Poisson distribution (PD) used to estimate the annual number of peaks over threshold in partial duration series (PDS) model, the negative binomial (NB) distribution is proposed in this study. Instead of generalized pareto distribution (GPD) and exponential distribution (ED) models popularly applied to predict the probability of the exceedances of peak over threshold, the performance of the general logistic distribution (GLD) models is analyzed. Two different models for analyzing extreme hydrologic events are compared, based on, PDS and annual maximum series (AMS), respectively. The performance of the two models in terms of uncertainty of Tyear event... (More)  Abstract in Undetermined
As a generalization of the commonly assumed Poisson distribution (PD) used to estimate the annual number of peaks over threshold in partial duration series (PDS) model, the negative binomial (NB) distribution is proposed in this study. Instead of generalized pareto distribution (GPD) and exponential distribution (ED) models popularly applied to predict the probability of the exceedances of peak over threshold, the performance of the general logistic distribution (GLD) models is analyzed. Two different models for analyzing extreme hydrologic events are compared, based on, PDS and annual maximum series (AMS), respectively. The performance of the two models in terms of uncertainty of Tyear event estimator [q(T)] is evaluated in the cases of estimation with the method of moments (MOMs), maximum likelihood (ML), and probability weighted moments (PWMs). The annual maximum distribution corresponding to a PDS model with Poisson distributed count of peaks above threshold and GLD for flood exceedances was found to be an extreme value type I (EV1) distribution. The comparison between PDS and AMS is made using ratio of variance of the Tyear event estimates, which is derived analytically after checking the reliability of the expressions with Monte Carlo simulations. The results reveal that the AMS/NBGLD and PDS/GLD models using PWM estimation method give least variance of flood estimates with the PDS model giving marginally better results. From the overall results, it was observed that the Poisson distribution performs better, where the difference between mean (mu) and variance of counts of threshold exceedances is small otherwise the NB distribution is found to be efficient when used in combination with generalized logistic distribution in the PDS model, and this is more prominent for it mu < 1.4. Hence, in such cases when the PDS data have a mean less than this, the AMS/NBGLD and PDS/GLD should be a better model for q(T) estimation as compared to PDS/ED. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/record/2199549
 author
 Bhunya, Pradeep; Singh, P.K.; Ojha, C.S.P. and Berndtsson, Ronny ^{LU}
 organization
 publishing date
 2012
 type
 Contribution to journal
 publication status
 published
 subject
 keywords
 Negative binomial distribution, Generalized pareto distribution, Exponential distribution, Probability weighted moments, Monte Carlo simulations, General logistic distribution
 in
 Journal of Hydrology
 volume
 420
 pages
 59  71
 publisher
 Elsevier
 external identifiers

 wos:000301082000006
 scopus:84856208778
 ISSN
 00221694
 DOI
 10.1016/j.jhydrol.2011.11.037
 language
 English
 LU publication?
 yes
 id
 e9811b03f7964c599e2f8c714681a017 (old id 2199549)
 date added to LUP
 20111031 12:19:22
 date last changed
 20180107 07:02:32
@article{e9811b03f7964c599e2f8c714681a017, abstract = {<b>Abstract in Undetermined</b><br/><br> As a generalization of the commonly assumed Poisson distribution (PD) used to estimate the annual number of peaks over threshold in partial duration series (PDS) model, the negative binomial (NB) distribution is proposed in this study. Instead of generalized pareto distribution (GPD) and exponential distribution (ED) models popularly applied to predict the probability of the exceedances of peak over threshold, the performance of the general logistic distribution (GLD) models is analyzed. Two different models for analyzing extreme hydrologic events are compared, based on, PDS and annual maximum series (AMS), respectively. The performance of the two models in terms of uncertainty of Tyear event estimator [q(T)] is evaluated in the cases of estimation with the method of moments (MOMs), maximum likelihood (ML), and probability weighted moments (PWMs). The annual maximum distribution corresponding to a PDS model with Poisson distributed count of peaks above threshold and GLD for flood exceedances was found to be an extreme value type I (EV1) distribution. The comparison between PDS and AMS is made using ratio of variance of the Tyear event estimates, which is derived analytically after checking the reliability of the expressions with Monte Carlo simulations. The results reveal that the AMS/NBGLD and PDS/GLD models using PWM estimation method give least variance of flood estimates with the PDS model giving marginally better results. From the overall results, it was observed that the Poisson distribution performs better, where the difference between mean (mu) and variance of counts of threshold exceedances is small otherwise the NB distribution is found to be efficient when used in combination with generalized logistic distribution in the PDS model, and this is more prominent for it mu < 1.4. Hence, in such cases when the PDS data have a mean less than this, the AMS/NBGLD and PDS/GLD should be a better model for q(T) estimation as compared to PDS/ED.}, author = {Bhunya, Pradeep and Singh, P.K. and Ojha, C.S.P. and Berndtsson, Ronny}, issn = {00221694}, keyword = {Negative binomial distribution,Generalized pareto distribution,Exponential distribution,Probability weighted moments,Monte Carlo simulations,General logistic distribution}, language = {eng}, pages = {5971}, publisher = {Elsevier}, series = {Journal of Hydrology}, title = {Flood analysis using generalized logistic models in partial duration series}, url = {http://dx.doi.org/10.1016/j.jhydrol.2011.11.037}, volume = {420}, year = {2012}, }