水文序列突变点识别方法比较研究
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摘要
水文序列突变点的识别是研究水文序列突变特征的重要环节,对水文分析和水文模拟预测意义重大。选取了目前国内外较为常用的9种水文序列突变点识别方法,即累积距平法、Mann-Kendall检验法(M-K)、有序聚类分析法、双累积曲线法、Pettitt法、BFAST法、水文情势突变法(RSI)、Lee-Heghinian法和Yamamoto法,采用黄河中游头道拐站和龙门站1960-2016年的57年长时间序列输沙数据,对比分析各种方法的适用性及准确性。结果表明:累积距平法、有序聚类分析法、双累积曲线法和Lee-Heghinian法能够较准确地获取输沙序列突变点,RSI和Pettitt检验法的适用性最好(p<0.01),M-K检验法次之(p<0.05),Yamamoto法最差;BFAST法不仅能够识别月尺度水文序列的突变点,也可用于解析其阶段性变化趋势。
Identification of the change-point of hydrological time series is important to hydrological analysis and prediction. Nine commonly used methods were selected in this study to identify the change points of the sediment load time series at Toudaoguai and Longmen stations from 1960 to 2016, including cumulative anomaly, Mann-Kendall test(M-K), order cluster analysis, double mass curve, Pettitt test, BFAST, Regime Shift Index(RSI), Lee-Heghinian and Yamamoto method. The applicability and accuracy of various methods were compared and analyzed. Results show that cumulative anomaly, order cluster analysis, double mass curve and Lee-Heghinian can accurately identify abrupt change points in sediment load data. The RSI and Pettitt exhibited best performance(p<0.01), and M-K performed well(p<0.05), whereas the Yamamoto method wasn't so good(p<0.05). BFAST can be used to identify breakpoints, as well to analyze the seasonal changes of the monthly hydrological series.
Identification of the change-point of hydrological time series is important to hydrological analysis and prediction. Nine commonly used methods were selected in this study to identify the change points of the sediment load time series at Toudaoguai and Longmen stations from 1960 to 2016, including cumulative anomaly, Mann-Kendall test(M-K), order cluster analysis, double mass curve, Pettitt test, BFAST, Regime Shift Index(RSI), Lee-Heghinian and Yamamoto method. The applicability and accuracy of various methods were compared and analyzed. Results show that cumulative anomaly, order cluster analysis, double mass curve and Lee-Heghinian can accurately identify abrupt change points in sediment load data. The RSI and Pettitt exhibited best performance(p<0.01), and M-K performed well(p<0.05), whereas the Yamamoto method wasn't so good(p<0.05). BFAST can be used to identify breakpoints, as well to analyze the seasonal changes of the monthly hydrological series.
引文
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[2] 赵广举,穆兴民,田鹏,等.近60年黄河中游水沙变化趋势及其影响因素分析[J].资源科学,2012,34(6):1070-1078.
[3] Lee A F S, Heghinian S M. A Shift of the mean level in a sequence of independent normal random variables: abayesianapproach[J]. Technometrics, 1977, 19(4):503-506.
[4] Pettitt A N. A Non-parametric approach to the change-point problem[J]. Journal of the Royal Statistical Society, 1979, 28(2):126-135.
[5] 丁晶.洪水时间序列干扰点的统计推估[J].武汉水利电力学院学报,1986(5):36-41.
[6] Wang F, Zhao G J, Mu X M, et al. Regime shift identification of runoff and sediment loads in the Yellow River Basin, China[J]. Water, 2014, 6(10):3012-3032.
[7] Weber K, Stewart M. A critical analysis of the cumulative rainfall departure concept[J]. Groundwater, 2004, 42(6):935-938.
[8] Mann H B. Nonparametric test against trend[J]. Econometrica, 1945, 13(3):245-259.
[9] Kendall M G. Rank correlation methods[J]. British Journal of Psychology, 1955, 25(1):86–91.
[10] Verbesselt J, Hyndman R, Zeileis A, et al. Phenological change detection while accounting for abrupt and gradual trends in satellite image time series[J]. Remote Sensing of Environment, 2010, 114(12):2970-2980.
[11] 穆兴民,张秀勤,高鹏,等.双累积曲线方法理论及在水文气象领域应用中应注意的问题[J].水文,2010,30(4):47-51.
[12] Yamamoto R. An analysis of climate jump[J]. Journal of the Meteorological Society of Japan, 1986, 64(2):273-281.
[13] 冉大川,姚文艺,焦鹏,等.黄河上游头道拐站年径流输沙系列突变点识别与综合诊断[J].干旱区研究,2014,31(5):928-936.
[14] 赵静,黄强,刘登峰.渭河流域水沙演变规律分析[J].水力发电学报,2015,34(3):14-20.
[15] 徐学选,高朝侠,赵娇娜.1956-2009年延河水沙变化特征及其驱动力研究[J].泥沙研究,2012(2):12-18.
[16] Zhao G J, Li E H, Mu X M, et al. Changing trends and regime shift of streamflow in the Yellow River basin[J]. Stochastic Environmental Research & Risk Assessment, 2015, 29(5):1331-1343.
[17] Shi H L, Hu C H, Wang Y G, et al. Analyses of trends and causes for variations in runoff and sediment load of the Yellow River[J]. International Journal of Sediment Research, 2017, 32(02):171-179.
[18] Zhao G J, Mu X M, Jiao J Y, et al. Evidence and causes of spatiotemporal changes in runoff and sediment yield on the Chinese Loess Plateau[J]. Land Degradation & Development, 2016, 28(2):579-590.
[19] 欧阳潮波,王文龙,田勇,等.60年来黄河河龙区间水沙变化特征及人类活动影响评价[J].泥沙研究,2016(4):55-61.
[20] Wang H J, Wu X, Bi N S, et al. Impacts of the dam-orientated water-sediment regulation scheme on the lower reaches and delta of the Yellow River (Huanghe): A review[J]. Global & Planetary Change, 2017, 157:93-113.
[21] Gao P, Mu X M, Wang F, et al. Changes in streamflow and sediment discharge and the response to human activities in the middle reaches of the Yellow River [J]. Hydrology & Earth System Sciences Discussions, 2010, 7(5):347-350.