水文时间序列高阶自相关性识别及其对水文分析的影响研究
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摘要
以往水文序列分析中,对序列自相关性的研究多集中于一阶自相关性及其处理方法,而对于水文序列的高阶自相关性则鲜有表述。以陕西省典型流域实测年径流序列为研究对象,开展序列高阶自相关性的研究,探讨高阶自相关存在与否的问题。结果表明陕西省渭河、汉江、无定河三大典型流域中,仅有无定河流域风沙区的韩家峁、横山以及流域出口的白家川水文站的年径流序列检测到了高阶自相关,其余站点的径流序列均不存在高阶自相关性。为了验证高阶自相关性对水文分析的影响,通过BP神经网络预测模型对高阶自相关性的影响进行了评估,结果显示在输入变量中加入合适的高阶变量序列后,可在一定程度上提高模型的预测效果,从侧面验证了高阶自相关性客观存在的假设。同时,研究结果还表明对径流关系不佳的小样本资料地区,考虑高阶自相关性的影响,可有效提升小样本径流资料的展延效果,为区域水文设计的科学性与应用效果提供保障。
In the past, the study on the autocorrelation of hydrological series mainly focused on the first-order autocorrelation and its processing methods, while the high-order autocorrelation of hydrological series was rarely described. This paper intended to study the high-order autocorrelation of the observed annual runoff series in typical watersheds of Shanxi Province, and discussed the existence of high-order autocorrelation. The results showed that among the three typical basins of Weihe River, Hanjiang River and Wuding River, only Hanjiamao, Hengshan and Baijiachuan hydrology station at the outlet of the Wuding River basin had detected high-order autocorrelation in their annual runoff series, and there was no high-order autocorrelation in the runoff series of other stations. This paper used BP neural network prediction model to evaluate the impact of high-order autocorrelation on hydrological analysis, and the results showed that the prediction effect of the model could be improved to a certain extent by adding the appropriate sequence of higher-order variables into the input variables, and the hypothesis of objective existence of higher-order autocorrelation was verified from the side.At the same time, the research results also indicated that taking into account the influence of high order autocorrelation in small sample data areas with poor runoff relationship, the extension effect of small sample runoff data could be effectively improved, and the scientific and applied effect of regional hydrological design could be guaranteed.
In the past, the study on the autocorrelation of hydrological series mainly focused on the first-order autocorrelation and its processing methods, while the high-order autocorrelation of hydrological series was rarely described. This paper intended to study the high-order autocorrelation of the observed annual runoff series in typical watersheds of Shanxi Province, and discussed the existence of high-order autocorrelation. The results showed that among the three typical basins of Weihe River, Hanjiang River and Wuding River, only Hanjiamao, Hengshan and Baijiachuan hydrology station at the outlet of the Wuding River basin had detected high-order autocorrelation in their annual runoff series, and there was no high-order autocorrelation in the runoff series of other stations. This paper used BP neural network prediction model to evaluate the impact of high-order autocorrelation on hydrological analysis, and the results showed that the prediction effect of the model could be improved to a certain extent by adding the appropriate sequence of higher-order variables into the input variables, and the hypothesis of objective existence of higher-order autocorrelation was verified from the side.At the same time, the research results also indicated that taking into account the influence of high order autocorrelation in small sample data areas with poor runoff relationship, the extension effect of small sample runoff data could be effectively improved, and the scientific and applied effect of regional hydrological design could be guaranteed.
引文
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[24]顾磊,张洪波,陈克宇,等.陕北地区河川基流的时空演变规律[J].地球科学进展,2015,30(7):802-811.
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[26]张宏利,陈豫,任广鑫,等.近50年来渭河流域降水变化特征分析[J].干旱地区农业研究,2008,26(4):236-241.
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[28]陕西省地方志编撰委员会.陕西省省志·水利志[M].西安:陕西人民出版社,1999.
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[2]王秀丽.基于bootstrap方法的北江流域径流趋势分析[D].广州:中山大学,2008.
[3]BIRSAN M V,ZAHARIA L,CHENDES V,et al. Seasonal trends in Romanian streamflow[J]. Hydrological Processes,2014,28(15):4496-4505.
[4]张洪波,王斌,辛琛,等.去趋势预置白方法对径流序列趋势检验的影响[J].水力发电学报,2016,35(12):56-69.
[5]许继军,杨大文,雷志栋,等.长江流域降水量和径流量长期变化趋势检验[J].人民长江,2006,37(9):63-67.
[6]樊辉,杨晓阳.黄河干、支流径流量与输沙量年际变化特征[J].泥沙研究,2010(4):11-15.
[7]VON S H,NAVARRA A. Analysis of climate variability[M].Springer,Berlin Heidelberg,1999:11-26.
[8]YUE S,PILON P,PHINNEY B,et al. The influence of autocorrelation on the ability to detect trend in hydrological series[J].Hydrological Processes,2002,16(9):1807–1829.
[9]RAZAVI S,VOGEL R. Prewhitening of hydroclimatic time series?Implications for inferred change and variability across time scales[J]. Journal of Hydrology,2018,557:109-115.
[10]张洪波,王斌,兰甜,等.基于经验模态分解的非平稳水文序列预测研究[J].水力发电学报,2015,34(12):42-53.
[11]SEN Z. Hydrological trend analysis with innovative and over-whitening procedures[J]. Hydrological Sciences Journal,2016(10):294-305.
[12]KLAUS J,CHUN K P,STUMPP C. Temporal trends inδ18O composition of precipitation in Germany:insights from time series modelling and trend analysis[J]. Hydrological Processes, 2014,29(12):2668-2680.
[13]张洪波,李哲浩,席秋义,等.基于改进过白化的Mann-Kendall趋势检验法[J].水力发电学报,2018(6):36-48.
[14]周洋.极端干旱条件下渭河流域关中段生态环境承载力研究[D].西安:西安理工大学,2012.
[15]贾佳.渭河流域基流量的演化及对生态基流的贡献[D].西安:长安大学,2012.
[16]陈芳莉.汉中市汉江流域主要水文控制站降水径流关系分析[J].陕西水利,2013(3):14-16.
[17]杨汝慧.陕南汉江流域河谷型小城市空间形态特征研究[D].西安:长安大学,2016.
[18]TOME A R,MIRANDA P M A. Piecewise linear fitting and trend changing points of climate parameters[J]. Geophysical Research Letters,2004,31,L02207.
[19]崔东文,金波. WPA-BP神经网络模型在枯水期月径流预测中的应用[J].人民珠江,2016,37(1):55-59.
[20]朱颖洁.灰预测和BP神经网络在梧州站年径流预测中的应用[J].人民珠江,2014(2):86-88.
[21]陈燕哲.陕西地区地下水源热泵工程应用适应性评价及软件[D].西安:西安建筑科技大学,2014.
[22]朱芮芮,刘昌明,郑红星.无定河流域地下水更新时间估算[J].地理学报,2009,64(3):315-322.
[23]ZHU R,LI L,WANG H et al. Comparative study on the spatial variability of rainfall and its spatial interpolation methods. China Rural Water and Hydropower,2004(7):25-28.
[24]顾磊,张洪波,陈克宇,等.陕北地区河川基流的时空演变规律[J].地球科学进展,2015,30(7):802-811.
[25]CHEN X D. Hydrology of Yellow River Basin[M]. Zhengzhou:Yellow River Water Conservancy Press,1997.
[26]张宏利,陈豫,任广鑫,等.近50年来渭河流域降水变化特征分析[J].干旱地区农业研究,2008,26(4):236-241.
[27]杨士娟.无定河流域基流分割与特性分析[D].杭州:浙江师范大学,2007.
[28]陕西省地方志编撰委员会.陕西省省志·水利志[M].西安:陕西人民出版社,1999.
[29]陕西省地方志编撰委员会.陕西省省志·地理志[M].西安:陕西人民出版社,1999.
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