DOI: 10.5194/hess-22-1525-2018
Scopus记录号: 2-s2.0-85042733821
论文题名: Multiple causes of nonstationarity in the Weihe annual low-flow series
作者: Xiong B ; , Xiong L ; , Chen J ; , Xu C ; -Y ; , Li L
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2018
卷: 22, 期: 2 起始页码: 1525
结束页码: 1542
语种: 英语
Scopus关键词: Catchments
; Irrigation
; Anthropogenic activity
; Generalized linear model
; Global climate changes
; Gross domestic products
; Non-stationary analysis
; Non-stationary condition
; Potential evapotranspiration
; Time-varying distribution
; Climate change
; aridity
; climate change
; climate modeling
; global climate
; human activity
; index method
; potential evapotranspiration
; temperature effect
; China
; Wei River
英文摘要: Under the background of global climate change and local anthropogenic activities, multiple driving forces have introduced various nonstationary components into low-flow series. This has led to a high demand on low-flow frequency analysis that considers nonstationary conditions for modeling. In this study, through a nonstationary frequency analysis framework with the generalized linear model (GLM) to consider time-varying distribution parameters, the multiple explanatory variables were incorporated to explain the variation in low-flow distribution parameters. These variables are comprised of the three indices of human activities (HAs; i.e., population, POP; irrigation area, IAR; and gross domestic product, GDP) and the eight measuring indices of the climate and catchment conditions (i.e., total precipitation P, mean frequency of precipitation events λ, temperature T, potential evapotranspiration (EP), climate aridity index AIEP, base-flow index (BFI), recession constant K and the recession-related aridity index AIK). This framework was applied to model the annual minimum flow series of both Huaxian and Xianyang gauging stations in the Weihe River, China (also known as the Wei He River). The results from stepwise regression for the optimal explanatory variables show that the variables related to irrigation, recession, temperature and precipitation play an important role in modeling. Specifically, analysis of annual minimum 30-day flow in Huaxian shows that the nonstationary distribution model with any one of all explanatory variables is better than the one without explanatory variables, the nonstationary gamma distribution model with four optimal variables is the best model and AIK is of the highest relative importance among these four variables, followed by IAR, BFI and AIEP. We conclude that the incorporation of multiple indices related to low-flow generation permits tracing various driving forces. The established link in nonstationary analysis will be beneficial to analyze future occurrences of low-flow extremes in similar areas. © Author(s) 2018. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79376
Appears in Collections: 气候变化事实与影响
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作者单位: State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China; Department of Geosciences, University of Oslo, P.O. Box 1022 Blindern, Oslo, Norway
Recommended Citation:
Xiong B,, Xiong L,, Chen J,et al. Multiple causes of nonstationarity in the Weihe annual low-flow series[J]. Hydrology and Earth System Sciences,2018-01-01,22(2)