考虑气象协变量的非一致性设计枯水流量研究

杜涛, 欧阳硕, 李帅, 王琨, 卜慧, 闫磊

raybet体育在线 院报 ›› 2018, Vol. 35 ›› Issue (11) : 26-31.

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raybet体育在线 院报 ›› 2018, Vol. 35 ›› Issue (11) : 26-31. DOI: 10.11988/ckyyb.20180187
水资源与环境

考虑气象协变量的非一致性设计枯水流量研究

  • 杜涛1, 欧阳硕1, 李帅2, 王琨1, 卜慧1, 闫磊3
作者信息 +

Nonstationary Design Low-flow Analysis in Consideration of Climate Covariates

  • DU Tao1, OUYANG Shuo1, LI Shuai2, WANG Kun1, BU Hui1, YAN Lei3
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摘要

研究非一致性条件下枯水流量设计值对流域水资源管理具有重要意义,然而如今常用的时变矩法推求出的每年一个设计值很难用于实际。选取渭河流域实测枯水流量序列作为实例进行研究,将气象协变量引入到枯水流量频率分析,结合重现期的期望超过次数(Expected Number of Exceedances, ENE)概念推求非一致性条件下枯水流量设计值,并与传统以时间为协变量的情况进行比较。结果表明:2种协变量情况下非一致性设计枯水流量相比于一致性结果存在明显差别,且以气温和降水为协变量的非一致性设计结果相比于时间为协变量更为合理。研究所得设计结果可为流域枯水期水资源管理提供一定参考依据。

Abstract

Design low-flow analysis under nonstationary conditions is a critical consideration in water resources management. The design low-flow varying from one year to the next obtained by using the time-varying moment method is hard to be applied to practical application. This paper is aimed to improve the characterization of nonstationary design low-flow under the expected number of exceedances (ENE) interpretation of return period by employing meteorological covariates in the nonstationary frequency analysis. The method of using time as the only covariate is also applied for comparison. Both methods are applied to the annual minimum monthly streamflow series of the Weihe River, China. Results demonstrate that the nonstationary design low-flow results of both methods are significantly different from the stationary case. The nonstationary design low-flow result using temperature and precipitation as covariates is found more reasonable and advisable than that of the case using time as covariate. It is concluded that nonstationary design low-flow analysis can be helpful to water resources management during dry seasons exacerbated by climate change.

关键词

设计枯水流量 / 非一致性 / 时变矩法 / 重现期 / 期望超过次数(ENE) / 协变量 / 渭河流域

Key words

design low-flow / nonstationarity / time-varying moment method / return period / expected number of exceedances (ENE) / covariates / Weihe River Basin

引用本文

导出引用
杜涛, 欧阳硕, 李帅, 王琨, 卜慧, 闫磊. 考虑气象协变量的非一致性设计枯水流量研究[J]. raybet体育在线 院报. 2018, 35(11): 26-31 https://doi.org/10.11988/ckyyb.20180187
DU Tao, OUYANG Shuo, LI Shuai, WANG Kun, BU Hui, YAN Lei. Nonstationary Design Low-flow Analysis in Consideration of Climate Covariates[J]. Journal of Changjiang River Scientific Research Institute. 2018, 35(11): 26-31 https://doi.org/10.11988/ckyyb.20180187
中图分类号: P333.3   

参考文献

[1] 涂新军, 陈晓宏. 基于秩统计量的枯水期径流时序变点的非参数识别[J]. 水利学报, 2009, 40(5): 603-607.
[2] 殷福才, 王在高, 梁虹. 枯水研究进展[J]. 水科学进展, 2004, 15(2): 249-254.
[3] 洪兴骏, 郭生练, 李天元. 基于Copula函数的鄱阳湖都昌站枯水多变量频率分析[J]. raybet体育在线 院报, 2014, 31(12): 11-16.
[4] 江聪, 熊立华. 基于皮尔逊III型分布的汉口站年最小月流量趋势性分析[J]. raybet体育在线 院报, 2013, 30(7): 16-21.
[5] 梁忠民, 胡义明, 王军. 非一致性水文频率分析的研究进展[J]. 水科学进展, 2011, 22(6): 864-871.
[6] 冯平, 李新. 基于Copula函数的非一致性洪水峰量联合分析[J]. 水利学报, 2013, 44(10): 1137-1147.
[7] 李析男, 谢平, 李彬彬, 等. 变化环境下不同等级干旱事件发生概率的计算方法-以无定河流域为例[J]. 水利学报, 2014, 45(5): 585-594.
[8] MILLY P C D, BETANCOURT J, FALKENMARK M, et al. Stationarity is Dead: Whiter Water Management?[J]. Science, 2008, 319(5863): 573-574.
[9] XIONG L H, JIANG C, DU T.Statistical Attribution Analysis of the Nonstationarity of the Annual Runoff Series of the Weihe River[J]. Water Science and Technology, 2014, 70(5): 939-946.
[10] VILLARINI G, SMITH J A, NAPOLITANO F.Non-stationary Modeling of a Long Record of Rainfall and Temperature over Rome[J]. Advances in Water Resources, 2010, 33(10): 1256-1267.
[11] SALAS J D, OBEYSEKERA J.Revisiting the Concepts of Return Period and Risk for Non-stationary Hydrologic Extreme Events[J]. Journal of Hydrologic Engineering, 2014, 19(3): 554-568.
[12] WIGLEY T M L. The Effect of Changing Climate on the Frequency of Absolute Extreme Events[J]. Climatic Change, 2009, 97(1/2): 67-76.
[13] PAREY S, MALEK F, LAURENT C, et al. Trends and Climate Evolution: Statistical Approach for Very High Temperatures in France[J]. Climatic Change, 2007, 81(3/4): 331-352.
[14] COOLEY D.Return Periods and Return Levels under Climate Change[M]//Extremes in a Changing Climate. Netherlands: Springer, 2013.
[15] 左德鹏, 徐宗学, 李景玉, 等. 气候变化情景下渭河流域潜在蒸散量时空变化特征[J]. 水科学进展, 2011, 22(4): 455-461.
[16] SONG J X, XU Z X, LIU C M.Ecological and Environmental Instream Flow Requirements for the Wei River—The Largest Tributary of the Yellow River[J]. Hydrological Processes, 2007, 21(8): 1066-1073.
[17] WILBY R L, DAWSON C W, BARROW E M.SDSM—A Decision Support Tool for the Assessment of Regional Climate Change Impacts[J]. Environmental Modelling and Software, 2002, 17(2): 147-159.
[18] 熊立华, 郭生练, 王才君. 国外区域洪水频率分析方法研究[J]. 水科学进展, 2004, 15(2): 261-267.
[19] RIGBY R A, STASINOPOULOS D M.Generalized Additive Models for Location, Scale and Shape[J]. Journal of the Royal Statistical Society Series C-Applied Statistics, 2005, 54(3): 507-554.
[20] AKAIKE H.A New Look at the Statistical Model Identification[J]. IEEE Transactions on Automatic Control, 1974, 19(6): 716-723.
[21] BUUREN S V, FREDRIKS M.Worm Plot: A Simple Diagnostic Device for Modelling Growth Reference Curves[J]. Statistics in Medicine, 2001, 20(8): 1259-1277.
[22] FILLIBEN J J.The Probability Plot Correlation Coefficient Test for Normality[J]. Technometrics, 1975, 17(1): 111-117.
[23] MASSEY F J.The Kolmogorov-Smirnov Test for Goodness of Fit[J]. Journal of the American Statistical Association, 1951, 46(253): 68-78.
[24] WILBY R L, DAWSON C W.SDSM 4.2—A Decision Support Tool for the Assessment of Regional Climate Change Impacts. User Manual[K]. UK: Environment Agency of England and Wales, 2007.
[25] 陈华, 郭家力, 郭生练, 等. 统计降尺度方法及其评价指标比较研究[J]. 水利学报, 2012, 43(8):891-897.
[26] DU T, XIONG L H, XU C Y, et al. Return Period and Risk Analysis of Nonstationary Low-flow Series under Climate Change[J]. Journal of Hydrology, 2015, 527:234-250.

基金

国家重点研发计划项目(2016YFC0402201); 国家自然科学基金项目(51609007); 湖北省自然科学基金项目(2016CFB391)

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