Non-Stationary Annual Maximum Flood Frequency Analysis Using the Norming Constants Method to Consider Non-Stationarity in the Annual Daily Flow Series

被引:44
作者
Xiong, Lihua [1 ]
Du, Tao [1 ]
Xu, Chong-Yu [1 ,2 ]
Guo, Shenglian [1 ,3 ]
Jiang, Cong [1 ]
Gippel, Christopher J. [4 ]
机构
[1] Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China
[2] Univ Oslo, Dept Geosci, N-0315 Oslo, Norway
[3] Wuhan Univ, Hubei Prov Collaborat Innovat Ctr Water Resources, Wuhan 430072, Peoples R China
[4] Griffith Univ, Australian Rivers Inst, Nathan, Qld 4111, Australia
基金
中国国家自然科学基金;
关键词
Climate change; Non-stationarity; Flood frequency analysis (FFA); Norming constants method (NCM); CLIMATE-CHANGE; STREAMFLOW; TRENDS; MODEL; RIVER; STATISTICS; RAINFALL;
D O I
10.1007/s11269-015-1019-6
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Flood frequency analysis is concerned with fitting a probability distribution to observed data to make predictions about the occurrence of floods in the future. Under conditions of climate change, or other changes to the water cycle that impact flood runoff, the flood series is likely to exhibit non-stationarity, in which case the return period of a flood event of a certain magnitude would change over time. In non-stationary flood frequency analysis, it is customary to examine only the non-stationarity of annual maximum flood data. We developed a way of considering the effect of non-stationarity in the annual daily flow series on the non-stationarity in the annual maximum flood series, which we termed the norming constants method (NCM) of non-stationary flood frequency analysis (FFA). After developing and explaining a framework for application of the method, we tested it using data from the Wei River, China. After detecting significant non-stationarity in both the annual maximum daily flood series and the annual daily flow series, application of the method revealed superior model performance compared to modelling the annual maximum daily flood series under the assumption of stationarity, and the result was further improved if explanatory climatic variables were considered. We conclude that the NCM of non-stationary FFA has potential for widespread application due to the now generally accepted weakness of the assumption of stationarity of flood time series.
引用
收藏
页码:3615 / 3633
页数:19
相关论文
共 58 条
[1]   NEW LOOK AT STATISTICAL-MODEL IDENTIFICATION [J].
AKAIKE, H .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1974, AC19 (06) :716-723
[2]  
[Anonymous], 2012, EXTREMES CHANGING CL
[3]   DETECTION OF TREND OR CHANGE IN ANNUAL FLOW OF AUSTRALIAN RIVERS [J].
CHIEW, FHS ;
MCMAHON, TA .
INTERNATIONAL JOURNAL OF CLIMATOLOGY, 1993, 13 (06) :643-653
[4]  
Coles S, 2001, An introduction to statistical modeling of extreme values, P45, DOI [DOI 10.1007/978-1-4471-3675-0, 10.1007/978-1-4471-3675-0]
[5]  
Cramer H., 1999, Mathematical methods of statistics, V9
[6]  
Dunn P., 1996, J COMPUT GRAPH STAT, V5, P236, DOI DOI 10.2307/1390802
[7]  
Embrechts P., 1997, MODELLING EXTREMAL E, DOI [DOI 10.1007/978-3-642-33483-2, 10.1007/978-3-642-33483-2]
[8]   REGIONAL FLOW-DURATION CURVES FOR UNGAUGED SITES IN MASSACHUSETTS [J].
FENNESSEY, N ;
VOGEL, RM .
JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT-ASCE, 1990, 116 (04) :530-549
[9]   PROBABILITY PLOT CORRELATION COEFFICIENT TEST FOR NORMALITY [J].
FILLIBEN, JJ .
TECHNOMETRICS, 1975, 17 (01) :111-117
[10]   Limiting forms of the frequency distribution of the largest or smallest member of a sample [J].
Fisher, RA ;
Tippett, LHC .
PROCEEDINGS OF THE CAMBRIDGE PHILOSOPHICAL SOCIETY, 1928, 24 :180-190