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| Titel |
Modeling the extreme precipitation using the generalized extreme value models with Southern Oscillation Index (SOI) as a covariate in the Beas basin, India |
| VerfasserIn |
Yixing Yin, Chong-Yu Xu, Haishan Chen, Lu Li, Hong Li, Hongliang Xu |
| Konferenz |
EGU General Assembly 2014
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 16 (2014) |
| Datensatznummer |
250092409
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| Publikation (Nr.) |
 EGU/EGU2014-6751.pdf |
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| Zusammenfassung |
| Variations in frequency and intensity of extreme hydrological events greatly affect the human
society and the environment. Currently one of the major challenges that face the hydrologic
science communities is to understand the characteristics, processes and mechanisms of
extreme hydrological events. The hydrology of Himalayan basins is not well understood due
to the complexities in the climatic and geographic conditions, and the scarcity of data. The
Beas River, one of the Western Himalayan rivers in India, is one of the main branches of the
Indus River system. However, the characteristics of extreme precipitation in this river basin
have rarely been explored yet. In this study, the monthly maximum rainfall data from 1982
to 2005 are modeled using generalized extreme value (GEV) models for seven
stations in the Beas River basin. Firstly, the autocorrelation, Mann-Kendall test, and
wavelet analysis were used to detect the presence of serial correlations, trends, and
periodical components. Secondly, the modeling of extreme precipitation was applied to
the monthly block maxima and the likelihood ratio test was used to determine the
best-fitting model. The Mann-Kendall test showed the existence of trend for some
stations and suggested a non-stationary model. Therefore, we fitted the extreme
precipitation with both stationary and non-stationary GEV models. The non-stationary
model fitted the GEV with El Nino-Southern Oscillation index (Southern Oscillation
Index, SOI) as a covariate with a linear link to the location parameter. The results
suggested that the covariate SOI model was a significant improvement over the model
without a covariate. Thirdly, the return periods and return levels of the extreme
precipitation were estimated based on the best fitting models. The 10, 20, 50 and 100-years
return levels and their 95% confidence intervals were provided. This research has
important implications for policy makers for designing flood prevention plans, and in
anticipating future severe rainfall and the associated consequences of severe flooding. |
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