基于GIS的流域水文数据的时空分析
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摘要
水资源对于区域经济、社会的发展具有重要的意义。格兰德河流经农场,城镇和城市,蜿蜒于湿地和森林,是联系自然和文化景观的自然纽带。格兰德河是加拿大为数不多的历史悠久的河流之一,在此流域内的文化已有超过一万年的历史。流域内的水资源肩负着农业灌溉、人口增长、经济发展的重要任务。
本文主要对格兰德河流域时空演变规律进行研究,分为两部分：一是时间序列的研究；一是空间分布上的研究。以格兰德河主河上中下游三个代表站点的年均径流量序列为时间序列的研究对象。首先对原始年均径流量时间序列进行了解,判断其平稳性,为后期工作做准备。采用希尔伯特-黄变换、小波分析法分析时间序列的周期,并相互比较确认序列的主周期；提取多年年均径流量的趋势项,定性地确定其趋势。利用12种统计方法定量确定趋势的显著性,并用赫斯特指数估计法预测未来趋势。在空间上,采用空间插值的方法进行分析。格兰德河流域共有49个观测站点,通过交叉验证,得出克里格插值法优于传统空间插值方法。根据误差均值(MEAN)、误差均方根(RMS)、平均标准误差(ASE)、标准平均值(MS)和标准均方根预测误差(RMSS)值分析,本文采用普通克里格方法对流域多年年均径流量数据进行空间插值,并绘制等值线图。利用Mann-Kendall非参数秩次相关检验对流域49个站点的年均径流量进行计算,统计流域内整体的趋势情况,绘制了统计检验量的空间分布情况图。
研究结果显示：02GA003站年均径流量序列为非平稳时间序列,趋势明显,为上升趋势,未来上升趋势会持续；主要周期为7年、14年和36年左右。02GA014站点序列为平稳时间序列,无显著趋势,主要周期为9年和13年左右；02GA016站点序列为平稳时间序列,无显著趋势,主要周期为4年和8年左右。年际变化02GA016站点最大,站点02GA003各月月均径流量最大。三个站点多年月均径流变化相似,年内分配不均匀,集中度高,集中期为3-4月份。格兰德河中下游地区径流量变化最大,等值线非常密集,越往上游变化越小,共有九个站点具有显著性趋势,整体无明显趋势变化。
Water resources is great significant for regional economic and social development.Grand River is the source of Maderia which is the longest, the largest drainage area and the most complex stream tributary of Amazon. In the Grand watershed, there are 900 million people survive, of which 80% of the water resources for agricultural irrigation. In the basin water resources is shouldering important task such as the agricultural irrigation, the population growth and the economic development.
In this paper, spatial and temporal distribution of Grand watershed is studied, which is divided into two parts:First, study on time series; Second, study on the spatial distribution. Take the annual discharge series of three typical stations in Grand watershed as time series' object of study. First of all, the original time series of annual discharge were understood to judge the stability in preparation for later work. Cycles of time series were analyzed by compared Hilbert-Huang Transform and Wavelet analysis to confirm the primary cycle sequence; the trend of multi-year average annual discharge was extracted to determine the trend qualitatively. Twelve kinds of statistical methods were used to identify significant trends quantitatively and the method of Hurst to predict. Spatial interpolation methods were used for special analysis. There are total forty-nine observation stations in Grand watershed. The result was the Kriging method was superior to the traditional interpolation methods through cross-verification.
According to the error mean (MEAN), root mean square error (RMS), average standard error (ASE), the standard average (MS) and standard root mean square error (RMSS) value analysis; this paper used ordinary kriging method to interpolation for years of average annual discharge data and contour mapping.Mann-Kendall non-parametric rank correlation test was used to calculate average annual discharge of forty-nine stations, and spatial distribution was mapped.
The results showed:The average discharge of 02GA003 Station were non-stationary time series, the upward trend was clearly and will continue in the future; the main period was 7 years,14 years and 36 years. The average discharges of 02GA014 Station was the stationary time series and was no significant trend. The main periods were 9 years and 13 years; the average discharge of 02GA016 Station were the stationary time series and no significant trend. The main periods were 4 years and 8 years. Interannual change was largest at station 02GA016; each month discharge at station 02GA003 was the largest. There were similar for average monthly discharge of three stations that uneven distribution during years with high concentration at period of 3-4 months. The intensive contour showed that discharge at Middle and Lower Grand River changed greatest. The total nine stations were significant trend, there was no significant entirety.
本文主要对格兰德河流域时空演变规律进行研究,分为两部分：一是时间序列的研究；一是空间分布上的研究。以格兰德河主河上中下游三个代表站点的年均径流量序列为时间序列的研究对象。首先对原始年均径流量时间序列进行了解,判断其平稳性,为后期工作做准备。采用希尔伯特-黄变换、小波分析法分析时间序列的周期,并相互比较确认序列的主周期；提取多年年均径流量的趋势项,定性地确定其趋势。利用12种统计方法定量确定趋势的显著性,并用赫斯特指数估计法预测未来趋势。在空间上,采用空间插值的方法进行分析。格兰德河流域共有49个观测站点,通过交叉验证,得出克里格插值法优于传统空间插值方法。根据误差均值(MEAN)、误差均方根(RMS)、平均标准误差(ASE)、标准平均值(MS)和标准均方根预测误差(RMSS)值分析,本文采用普通克里格方法对流域多年年均径流量数据进行空间插值,并绘制等值线图。利用Mann-Kendall非参数秩次相关检验对流域49个站点的年均径流量进行计算,统计流域内整体的趋势情况,绘制了统计检验量的空间分布情况图。
研究结果显示：02GA003站年均径流量序列为非平稳时间序列,趋势明显,为上升趋势,未来上升趋势会持续；主要周期为7年、14年和36年左右。02GA014站点序列为平稳时间序列,无显著趋势,主要周期为9年和13年左右；02GA016站点序列为平稳时间序列,无显著趋势,主要周期为4年和8年左右。年际变化02GA016站点最大,站点02GA003各月月均径流量最大。三个站点多年月均径流变化相似,年内分配不均匀,集中度高,集中期为3-4月份。格兰德河中下游地区径流量变化最大,等值线非常密集,越往上游变化越小,共有九个站点具有显著性趋势,整体无明显趋势变化。
Water resources is great significant for regional economic and social development.Grand River is the source of Maderia which is the longest, the largest drainage area and the most complex stream tributary of Amazon. In the Grand watershed, there are 900 million people survive, of which 80% of the water resources for agricultural irrigation. In the basin water resources is shouldering important task such as the agricultural irrigation, the population growth and the economic development.
In this paper, spatial and temporal distribution of Grand watershed is studied, which is divided into two parts:First, study on time series; Second, study on the spatial distribution. Take the annual discharge series of three typical stations in Grand watershed as time series' object of study. First of all, the original time series of annual discharge were understood to judge the stability in preparation for later work. Cycles of time series were analyzed by compared Hilbert-Huang Transform and Wavelet analysis to confirm the primary cycle sequence; the trend of multi-year average annual discharge was extracted to determine the trend qualitatively. Twelve kinds of statistical methods were used to identify significant trends quantitatively and the method of Hurst to predict. Spatial interpolation methods were used for special analysis. There are total forty-nine observation stations in Grand watershed. The result was the Kriging method was superior to the traditional interpolation methods through cross-verification.
According to the error mean (MEAN), root mean square error (RMS), average standard error (ASE), the standard average (MS) and standard root mean square error (RMSS) value analysis; this paper used ordinary kriging method to interpolation for years of average annual discharge data and contour mapping.Mann-Kendall non-parametric rank correlation test was used to calculate average annual discharge of forty-nine stations, and spatial distribution was mapped.
The results showed:The average discharge of 02GA003 Station were non-stationary time series, the upward trend was clearly and will continue in the future; the main period was 7 years,14 years and 36 years. The average discharges of 02GA014 Station was the stationary time series and was no significant trend. The main periods were 9 years and 13 years; the average discharge of 02GA016 Station were the stationary time series and no significant trend. The main periods were 4 years and 8 years. Interannual change was largest at station 02GA016; each month discharge at station 02GA003 was the largest. There were similar for average monthly discharge of three stations that uneven distribution during years with high concentration at period of 3-4 months. The intensive contour showed that discharge at Middle and Lower Grand River changed greatest. The total nine stations were significant trend, there was no significant entirety.
引文
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[4]纪忠萍,谷德军.广州近百年来气候变化的多时间尺度分析[J].热带气象学报,1999,15：48-55.
[5]杨辉,宋亚山.华北地区水资源多时间尺度分析[J].高原气象,1999,18(4)：496-507.
[6]黄友波,谢平,夏军.频谱分析方法在水文时间序列代表性分析中的应用[J].浙江水利水电专科学校学报,2002,14(3)：1-3.
[7]刘东,付强基于小波变换的三江平原低湿地井灌区年降水序列变化趋势分析[J],地理科学,2008.6,28(3)：380-384.
[8]王义民,张珏.HHT在年最大洪峰流量规律分析中的应用[J],计算机工程与应用,2009,45(34),204-211.
[9]Shi C X, Luo Q F. Hilbert-Huang Transform and wavelet analysis of time history signal.Acta Seis-mologica Sinica,2003,16(4):422-429.
[10]Norden E H,Shen Z,Long S R,et al.The empirical moded ecomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A:Mathematical, Physical and Engineering Sci-ences,1998,454:899-955.
[11]张瑞,汪亚平,潘少明.长江大通水文站径流量的时间系列分析[J]，南京大学学报(自然科学),2006.7,42(4),423-434.
[12]孙娴,林振山.经验模态分解下中国气温变化趋势的区域特征[J],地理学报，2007,62(11).
[13]万星,周建中,刘力,李英海.基于希尔伯特-黄变换与小波方法的径流序列分析[J],华中科技大学学报(自然科学版)2008,36(1).
[14]叶泽刚,汪娜娟,周文波.降水特性空间变异性初步研究[J].水利学报,1994,12：7-13.
[15]P.Goovaerts, Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall, Journal of Hydrology,2000,228:113-129.
[16]李新,程国栋,卢玲.空间内插方法比较[J].地球科学进展,2000,15(3)：260-265.
[17]林忠辉,莫兴国等,中国陆地区域气象要素的空间插值,地理学报,2002,57(1)：47-56.
[18]李丽娟,王娟,李海滨.无定河流域降雨量空间变异性研究[J].地理研究,2002,21(4)：434-440.
[19]庄立伟、王石立.东北地区逐日气象要素的空间插值方法应用研究[J].应用气象学报,2003,14(5),605-615.
[20]姜红梅,任立良,袁飞.降水空间不均匀性对径流过程模拟的影响[J].水文,2004,24(2)：1-6.
[21]Eduardo Severino, Teresa Alpuim. Spatiotemporal models in the estimation of area precipitation [J]. Environmetrics,2005,16:773-802.
[22]张善文,雷英杰,冯有前.MATLAB在时间序列分析中的应用[M].西安：西安电子科技大学出版社,2007.
[23]王振龙.时间序列分析[M].中国统计出版社,2000.
[24]黄友波,夏军,郑冬燕等.应用频谱法分析水位时间序列的代表性[J].吉林水利.2002,10(10)：1-4.
[25]刘昌明,杨胜天,孙睿.基于RS/GIS技术的黄河流域水循环要素研究[M].郑州：黄河水利出版社,2006.
[26]吴益,程维明,任立良等.新疆和田河流域河川径流时序特征分析[J].自然资源学报,2006,21(3)：375-380.
[27]丁晶,邓育仁.随机水文学[M].成都：成都科技大学出版社,1988.
[28]Huang N E, Shen Z, Long S R, et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [J].Proc R Soc Land A,1998, 454:899-955.
[29]Huang N E, Shen Z, Long S R.A new view of nonlinear water waves:the Hilbert spectrum. [J].Ann Rev Fluid Mech,1999,31:417-457.
[30]Wu, Z.andHuang, N.E.2004:Ensemble Empirical Mode decomposition:a noise-assisted data analysis method, Center for Ocean-Land-Atmosphere Studies, Technical Report No.93:1-51.
[31]P.Flandrin, G. Rilling, and P. Goncalves, Empirical mode decomposition as a filter bank, IEEE Signal Processing Letters 11 (2004), no.2:112-114.
[32]Wu, Z. and N.E.Huang:A study of the characteristics of white noise using the empirical mode decomposition method.Proc.R.Soc.London. Ser.A,2004,460:1597-1611.
[33]Huang N E,Shen Z,Long S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-station time series analysis[J].Proc R Soc,1998,A454:903-995.
[34]崔锦泰.小波分析导论[M].西安交通大学出版社,1995.
[35]刘贵忠等.小波分析及应用[M].西安电子科技大学出版社，1995.
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