Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1002/joc.4774 |
Modelling nonlinear trend for developing non-stationary rainfall intensity-duration-frequency curve | |
Agilan, V.; Umamahesh, N. V. | |
2017-03-15 | |
发表期刊 | INTERNATIONAL JOURNAL OF CLIMATOLOGY
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ISSN | 0899-8418 |
EISSN | 1097-0088 |
出版年 | 2017 |
卷号 | 37期号:3 |
文章类型 | Article |
语种 | 英语 |
国家 | India |
英文摘要 | The infrastructure design is primarily based on rainfall intensity-duration-frequency (IDF) curves, and the existing IDF curves are based on the concept of stationary extreme value theory (EVT) (i.e. the occurrence probability of extreme precipitation is not expected to change significantly over time). But, the extreme precipitation events are increasing due to global climate change and questioning the reliability of our current infrastructure design. Based on recent developments in the EVT, recent studies proposed a method for developing non-stationary rainfall IDF curve by incorporating linear trend in the location parameter of the generalized extreme value (GEV) distribution. Upon detecting a significant trend in the extreme rainfall series, directly applying the linear trend to develop non-stationary IDF curves may increase the bias of the non-stationary model. Hence, it is important to develop non-stationary GEV model which has less bias than the stationary model by modelling nonlinear trend in the series. In this study, we try to develop non-stationary GEV models with less bias by modelling nonlinear trend in the series using multi-objective genetic algorithm (MOGA). In addition, the proposed GEV model is compared with the stationary GEV model and the linear trend-based non-stationary GEV model. Furthermore, the Wilmington city and the Hyderabad city non-stationary IDF curves are developed and compared with stationary IDF curves. From the study results, it is observed that the proposed MOGA-based method is able to build the good quality and less bias non-stationary GEV models by modelling nonlinear trend in the series. In addition, it is also observed that the usage of linear trend for modelling non-stationarity in the time series sometimes increase the bias of non-stationary model. |
英文关键词 | extreme rainfall IDF curves nonlinear trend non-stationarity optimization |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000395349500011 |
WOS关键词 | EXTREME-VALUE ANALYSIS ; CLIMATE VARIABILITY ; GENETIC ALGORITHM ; PRECIPITATION ; CALIBRATION ; EVENTS ; TESTS |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/36902 |
专题 | 气候变化 |
作者单位 | Natl Inst Technol, Dept Civil Engn, Warangal 506004, Telangana, India |
推荐引用方式 GB/T 7714 | Agilan, V.,Umamahesh, N. V.. Modelling nonlinear trend for developing non-stationary rainfall intensity-duration-frequency curve[J]. INTERNATIONAL JOURNAL OF CLIMATOLOGY,2017,37(3). |
APA | Agilan, V.,&Umamahesh, N. V..(2017).Modelling nonlinear trend for developing non-stationary rainfall intensity-duration-frequency curve.INTERNATIONAL JOURNAL OF CLIMATOLOGY,37(3). |
MLA | Agilan, V.,et al."Modelling nonlinear trend for developing non-stationary rainfall intensity-duration-frequency curve".INTERNATIONAL JOURNAL OF CLIMATOLOGY 37.3(2017). |
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