基于SRI与Copula函数的黑河流域水文干旱等级划分及特征分析
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摘要
【目的】基于SRI对黑河流域水文干旱进行等级划分及特征分析。【方法】利用2种不同的径流丰枯等级分类方法,对黑河流域莺落峡水文站的径流资料进行概率统计分析,提出基于SRI的水文干旱等级划分标准(标准1),分别采用标准1和基于标准化降水指数(SPI)的干旱等级划分标准(标准2),识别水文干旱历时、烈度、烈度峰值3个干旱特征变量,利用Copula函数进行水文干旱多变量联合分布研究,选取RMSE、AIC、BIC准则作为联合分布拟合优度检验的判别依据优选Copula函数类型,计算了不同干旱事件的重现期。【结果】基于标准1的水文干旱等级划分结果比标准2更符合实际干旱情况;单变量重现期介于联合重现期与同现重现期之间,可以进行干旱事件重现期估计;黑河流域发生持续2.68个月的重旱事件的重现期为3 a。【结论】基于SRI的干旱等级划分在多变量水文干旱研究中使联合分布函数拟合更优,分析得到的干旱特征更接近实际情况。
【Objective】The purpose of this paper is to present a method to classify drought based on the measured data in Heihe basin.【Method】Two methods based on runoff-frequency classification were used to realize probabilistic statistical analysis of the runoff data at the Yingluoxia hydrological station in the Heihe River Basin. The SRI-based hydrological drought classification standard(Standard 1) was proposed. The Standard 1 and drought standardization criteria(Standard 2) based on the standardized precipitation index(SPI) were used to identify three drought characteristic variables of hydrological drought, including duration, intensity, and peak intensity.The Copula function was used to study the multivariate joint distribution of hydrological drought, the RMSE, AIC,and BIC criteria was selected as the discriminant basis for the joint distribution goodness of fit test to optimize the Copula function type and then calculated the return period for different drought events.【Result】The classification of hydrological drought grade based on Standard 1 is more consistent with the real drought situation than Standard 2. The univariate return period is between the joint return period and the cooccurrence return period, it therefore is possible to estimate the return period of the drought events. A severe drought lasing for 2.68 months occurs about every three years in the basin.【Conclusion】The SRI-based classification of drought in the multivariable hydrological drought study improves the joint distribution function, and the calculated drought characteristics marched historical data.
【Objective】The purpose of this paper is to present a method to classify drought based on the measured data in Heihe basin.【Method】Two methods based on runoff-frequency classification were used to realize probabilistic statistical analysis of the runoff data at the Yingluoxia hydrological station in the Heihe River Basin. The SRI-based hydrological drought classification standard(Standard 1) was proposed. The Standard 1 and drought standardization criteria(Standard 2) based on the standardized precipitation index(SPI) were used to identify three drought characteristic variables of hydrological drought, including duration, intensity, and peak intensity.The Copula function was used to study the multivariate joint distribution of hydrological drought, the RMSE, AIC,and BIC criteria was selected as the discriminant basis for the joint distribution goodness of fit test to optimize the Copula function type and then calculated the return period for different drought events.【Result】The classification of hydrological drought grade based on Standard 1 is more consistent with the real drought situation than Standard 2. The univariate return period is between the joint return period and the cooccurrence return period, it therefore is possible to estimate the return period of the drought events. A severe drought lasing for 2.68 months occurs about every three years in the basin.【Conclusion】The SRI-based classification of drought in the multivariable hydrological drought study improves the joint distribution function, and the calculated drought characteristics marched historical data.
引文
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[7]周玉良,周平,金菊良,等.基于供水水源的干旱指数及在昆明干旱频率分析中应用[J].水利学报, 2014, 45(9):1 038-1 047.
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[15] NELSEN B. An introduction to copulas[M]. New York:Springer,1998.
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[17]董前进,谢平.水文干旱研究进展[J].水文, 2014, 34(4):1-7.
[18]金菊良,杨齐祺,周玉良,等.干旱分析技术的研究进展[J].华北水利水电大学学报(自然科学版), 2016, 37(2):1-15.
[19]李天水,王顺,庄文化,等.游程理论和Copula函数在二维干旱变量联合分布中的应用[J].干旱区资源与环境, 2016, 30(6):77-82.
[20]冯波,闫佰忠,章光新.松花江流域水文干旱联合概率分布特征研究[J].节水灌溉, 2014(5):38-42.
[21]肖名忠,张强,陈永勤,等.基于三变量Copula函数的东江流域水文干旱频率分析[J].自然灾害学报, 2013, 22(2):99-108.
[22]涂新军,陈晓宏,赵勇,等.变化环境下东江流域水文干旱特征及缺水响应[J].水科学进展, 2016, 27(6):810-821.
[23]王晓峰,张园,冯晓明,等.基于游程理论和Copula函数的干旱特征分析及应用[J].农业工程学报, 2017, 33(10):206-214.
[24]马建琴,和鹏飞,彭高辉,等.基于三维Copula函数的沙颍河流域水文干旱频率分析[J].灌溉排水学报, 2017, 36(9):102-107.
[25]冯星,孙东永,胡维登,等. Copula函数在渭河流域干旱分析中的应用研究[J].灌溉排水学报, 2017, 36(12):110-117.
[26]马明卫,宋松柏.椭圆型Copulas函数在西安站干旱特征分析中的应用[J].水文, 2010, 30(4):36-42.
[27]王学良,吴宜芳.黑河流域水文站网现状及设站年限分析[J].甘肃水利水电技术, 2010, 46(9):13-16.
[2] UNISRD. Living with Risk:An Integrated Approach to Reducing Societal Vulnerability to Drought[R]. New York:ISDR Ad Hoc Discussion Group Drought:UNISRD, 2005.
[3] American Meteorological Society. Meteorological drought:Policy statement[J]. Bulletin of American Meteorological Society,1997(78):847-849.
[4]张俊,陈桂亚,杨文发.国内外干旱研究进展综述[J].人民长江, 2011, 42(10):65-69.
[5] JOHN Keyantash, JOHN A Dracup,周跃武,等.干旱的定量化:干旱指数的评价[J].干旱气象, 2005(2):85-94.
[6]何福力,胡彩虹,王纪军,等.基于标准化降水、径流指数的黄河流域近50年气象水文干旱演变分析[J].地理与地理信息科学, 2015, 31(3):69-75.
[7]周玉良,周平,金菊良,等.基于供水水源的干旱指数及在昆明干旱频率分析中应用[J].水利学报, 2014, 45(9):1 038-1 047.
[8] KAO S C, GOVINDARAJU R S. A copula-based joint deficit index for droughts[J]. Journal of Hydrology, 2010, 380(1):121-134.
[9] NALBANTIS I, TSAKIRIS G. Assessment of hydrological drought revisited[J]. Water Resources Management, 2009, 23(5):881-897.
[10] LUCIANO T, MICHELE L, IGNACIO L M, et al. Investigation of scaling properties in monthly streamflow and Standardized Streamflow Index(SSI)time series in the Ebro basin(Spain)[J]. Physica A:Statistical Mechanics and its Applications, 2012, 391(4):1 662-1 678.
[11] HONG X, GUO S, ZHOU Y, et al. Uncertainties in assessing hydrological drought using streamflow drought index for the upper Yangtze River basin[J].Stochastic Environment Research And Risk Assessment, 2014,(29):1 235-1 247.
[12]宋松柏. Copulas函数及其在水文中的应用[M].北京:科学出版社, 2012.
[13]水利部水文局.水文情报预报规范:GB/T 22482—2008[S].北京:中国标准出版社, 2008.
[14]王劲松,郭江勇,周跃武,等.干旱指标研究的进展与展望[J].干旱区地理, 2007, 30(1):60-65.
[15] NELSEN B. An introduction to copulas[M]. New York:Springer,1998.
[16] SAVU C, TREDE M. Hierarchies of Archimedean copulas[J]. Quantitative Finance, 2010, 10(3):295-304.
[17]董前进,谢平.水文干旱研究进展[J].水文, 2014, 34(4):1-7.
[18]金菊良,杨齐祺,周玉良,等.干旱分析技术的研究进展[J].华北水利水电大学学报(自然科学版), 2016, 37(2):1-15.
[19]李天水,王顺,庄文化,等.游程理论和Copula函数在二维干旱变量联合分布中的应用[J].干旱区资源与环境, 2016, 30(6):77-82.
[20]冯波,闫佰忠,章光新.松花江流域水文干旱联合概率分布特征研究[J].节水灌溉, 2014(5):38-42.
[21]肖名忠,张强,陈永勤,等.基于三变量Copula函数的东江流域水文干旱频率分析[J].自然灾害学报, 2013, 22(2):99-108.
[22]涂新军,陈晓宏,赵勇,等.变化环境下东江流域水文干旱特征及缺水响应[J].水科学进展, 2016, 27(6):810-821.
[23]王晓峰,张园,冯晓明,等.基于游程理论和Copula函数的干旱特征分析及应用[J].农业工程学报, 2017, 33(10):206-214.
[24]马建琴,和鹏飞,彭高辉,等.基于三维Copula函数的沙颍河流域水文干旱频率分析[J].灌溉排水学报, 2017, 36(9):102-107.
[25]冯星,孙东永,胡维登,等. Copula函数在渭河流域干旱分析中的应用研究[J].灌溉排水学报, 2017, 36(12):110-117.
[26]马明卫,宋松柏.椭圆型Copulas函数在西安站干旱特征分析中的应用[J].水文, 2010, 30(4):36-42.
[27]王学良,吴宜芳.黑河流域水文站网现状及设站年限分析[J].甘肃水利水电技术, 2010, 46(9):13-16.
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