NON-STATIONARY APPROACH TO AT-SITE FLOOD FREQUENCY MODELLING I. MAXIMUM LIKELIHOOD ESTIMATION
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dc.contributor.author | Strupczewski W.G. | |
dc.contributor.author | Singh V.P. | |
dc.contributor.author | Feluch W. | |
dc.date.accessioned | 2021-03-10T04:26:10Z | |
dc.date.available | 2021-03-10T04:26:10Z | |
dc.date.issued | 2001 | |
dc.identifier | https://www.elibrary.ru/item.asp?id=689461 | |
dc.identifier.citation | Journal of Hydrology, 2001, 248, 1-4, 123-142 | |
dc.identifier.issn | 0022-1694 | |
dc.identifier.uri | https://repository.geologyscience.ru/handle/123456789/26520 | |
dc.description.abstract | For dealing with hydrological non-stationarity in flood frequency modelling (FFM) and hydrological design, it is necessary to account for trends. Taking the case of at-site FFM, statistical parametric techniques are discussed for investigation of the time-trend. The investigation entails (1) an identification of a probability distribution, and (2) development of a trend software. The Akaike Information Criterion (AIC) was used to identify the optimum distribution, i.e. the distribution and trend function, which enabled an identification of the optimum non-stationary FFM in a class of 56 competing models. The maximum likelihood (ML) method was used to estimate the parameters of the identified model using annual peak discharge series. A trend can be assumed in the first two moments of a probability distribution function and it can be of either linear or parabolic form. Both the annual maximum series (AMS) and partial duration series (PDS) approach were considered in the at-site frequency modeling. | |
dc.subject | FLOOD | |
dc.subject | FREQUENCY | |
dc.subject | PARTIAL DURATION SERIES | |
dc.subject | TIME SERIES | |
dc.subject | TREND | |
dc.title | NON-STATIONARY APPROACH TO AT-SITE FLOOD FREQUENCY MODELLING I. MAXIMUM LIKELIHOOD ESTIMATION | |
dc.type | Статья |
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