globalchange  > 全球变化的国际研究计划
DOI: 10.1061/(ASCE)HE.1943-5584.0001821
WOS记录号: WOS:000475473500001
论文题名:
Rainfall Generator for Nonstationary Extreme Rainfall Condition
作者: Agilan, V.1; Umamahesh, N. V.2
通讯作者: Umamahesh, N. V.
刊名: JOURNAL OF HYDROLOGIC ENGINEERING
ISSN: 1084-0699
EISSN: 1943-5584
出版年: 2019
卷: 24, 期:9
语种: 英语
英文关键词: Climate change ; Extreme rainfall ; Nonstationary ; Rainfall generator
WOS关键词: STOCHASTIC WEATHER GENERATOR ; DAILY PRECIPITATION ; CLIMATE VARIABILITY ; FREQUENCY-ANALYSIS ; GENETIC ALGORITHM ; NON-STATIONARITY ; TEMPERATURE ; SIMULATION ; EVENTS ; INTENSITY
WOS学科分类: Engineering, Civil ; Environmental Sciences ; Water Resources
WOS研究方向: Engineering ; Environmental Sciences & Ecology ; Water Resources
英文摘要:

Stochastic weather generators are generally used to produce scenarios of climate variability on a daily timescale for hydrological modeling and water resource planning applications. Most of the available weather generators assume extreme rainfall series as stationary series. However, it is currently perceived that global climate change is increasing the intensity and frequency of extreme rainfall events and creating a nonstationary component in extreme rainfall time series. Consequently, the realistic modeling of rainfall extremes in a nonstationary context is indispensable. In this study, we propose a modified version of a k-nearest neighbor (KNN) weather generator that incorporates nonstationarity in the extreme rainfall series. The proposed algorithm first models the nonlinear trend in the extreme rainfall series that exceeds the defined threshold u and perturbs the original-KNN-simulated extreme rainfall using the knowledge available in the nonstationary model. The proposed algorithm is demonstrated with three case studies, and the performance of the proposed algorithm is validated using various extreme precipitation indices. The results of the three case studies indicate that extreme rainfall characteristics are consistently well simulated with the proposed algorithm. Particularly, based on the results of the three case studies, the proposed algorithm decreases the root-mean-square error (RMSE) in rainfall simulation with respect to the original KNN algorithm by at least 40%. (c) 2019 American Society of Civil Engineers.


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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/145945
Appears in Collections:全球变化的国际研究计划

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作者单位: 1.Natl Inst Technol, Dept Civil Engn, Calicut 673601, Kerala, India
2.Natl Inst Technol, Dept Civil Engn, Warangal 506004, Andhra Pradesh, India

Recommended Citation:
Agilan, V.,Umamahesh, N. V.. Rainfall Generator for Nonstationary Extreme Rainfall Condition[J]. JOURNAL OF HYDROLOGIC ENGINEERING,2019-01-01,24(9)
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