Wavelet Analysis on the Temporal Series of Precipitation in Baoji

Yang Mi; Xu Pan-pan; Qian Hui

doi:doi:10.11648/j.sd.20160403.16

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Wavelet Analysis on the Temporal Series of Precipitation in Baoji

Yang Mi, Xu Pan-pan, Qian Hui

Published in Science Discovery (Volume 4, Issue 3)

Received: 1 July 2016 Accepted: Published: 5 July 2016

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Abstract

Adopting the series data of precipitationfrom 1952 to 2013 in Baoji city, shaanxi province, China, using Morlet wavelet function, the seasonal variation of precipitation and the time series of the interannual variation of precipitation in Baoji nearly 60aare analyzed by wavelet analysis, reveals the multiple time scales change complex structure of Baoji precipitation, forecasts the precipitation change trend of four seasons in Baoji city. The results show that the seasonal and annual precipitationin Baoji has the characteristics of multi time scale, different scales show the different cycles, large scale periodic variations also include small scale periodic variations. As a whole, the performance for small scale changed seriously, no clear rules of special features, and there is obvious regularity of large scale. The time-frequency localization characteristic of wavelet analysis can show the fine structure of precipitation time series, and provide a new method for the analysis of the key water saving problems, such as multi time scale variation characteristics and short-term climate prediction.

Published in	Science Discovery (Volume 4, Issue 3)
DOI	10.11648/j.sd.20160403.16
Page(s)	189-196
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Baoji City, Wavelet Analysis, Precipitation

References

[1]	唐春娥，沈冰，陈文让．宝鸡市降水变化特征分析[J]．西安建筑科技大学学报，2007，39(1):39．
[2]	吕继强，莫淑红，沈冰．近半世纪宝鸡市干旱特征及模型预测研究[J]．北京师范大学党报，2010，46(3)：333．
[3]	邱海军，曹明明，曾彬．基于小波分析的西安降水时间序列的变化特征［J］．中国农业气象，2011，32(1)1:23-27．
[4]	孙然好，潘保田，牛最荣，等．河西走廊近年来地表水资源时间序列的小波分析[J]．干旱区地理，2005，28(4)：455-459．
[5]	张愿章，段永康，郭春梅，等．河南省1951-2012年降水量的Morlet小波分析[J]．人民黄河，2015，37(10):25-28．
[6]	张彦龙，刘普幸，王允．基于干旱指数的宁夏干旱时空变化特征及其Morlet小波分析[J]．生态学杂志，2015，34 (8): 2373-2380．
[7]	李珠，沈浩，顾春霞．基于小波分析的无锡地区降水变化规律研究[J]．安徽农业科学，2015(29):198-200．
[8]	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]．2008，28(3):380-381．
[9]	张耀存，张录军．东北气候和生态过渡区近50年来降水和温度概率分布特征变化[J]．地理科学，2005，25(5):561-566．
[10]	王文圣，丁晶，李跃清．水文小波分析[M]．北京：化学工业出版社，2005:115－141．

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APA Style

Yang Mi, Xu Pan-pan, Qian Hui. (2016). Wavelet Analysis on the Temporal Series of Precipitation in Baoji. Science Discovery, 4(3), 189-196. https://doi.org/10.11648/j.sd.20160403.16

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ACS Style

Yang Mi; Xu Pan-pan; Qian Hui. Wavelet Analysis on the Temporal Series of Precipitation in Baoji. Sci. Discov. 2016, 4(3), 189-196. doi: 10.11648/j.sd.20160403.16

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AMA Style

Yang Mi, Xu Pan-pan, Qian Hui. Wavelet Analysis on the Temporal Series of Precipitation in Baoji. Sci Discov. 2016;4(3):189-196. doi: 10.11648/j.sd.20160403.16

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@article{10.11648/j.sd.20160403.16,
  author = {Yang Mi and Xu Pan-pan and Qian Hui},
  title = {Wavelet Analysis on the Temporal Series of Precipitation in Baoji},
  journal = {Science Discovery},
  volume = {4},
  number = {3},
  pages = {189-196},
  doi = {10.11648/j.sd.20160403.16},
  url = {https://doi.org/10.11648/j.sd.20160403.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20160403.16},
  abstract = {Adopting the series data of precipitationfrom 1952 to 2013 in Baoji city, shaanxi province, China, using Morlet wavelet function, the seasonal variation of precipitation and the time series of the interannual variation of precipitation in Baoji nearly 60aare analyzed by wavelet analysis, reveals the multiple time scales change complex structure of Baoji precipitation, forecasts the precipitation change trend of four seasons in Baoji city. The results show that the seasonal and annual precipitationin Baoji has the characteristics of multi time scale, different scales show the different cycles, large scale periodic variations also include small scale periodic variations. As a whole, the performance for small scale changed seriously, no clear rules of special features, and there is obvious regularity of large scale. The time-frequency localization characteristic of wavelet analysis can show the fine structure of precipitation time series, and provide a new method for the analysis of the key water saving problems, such as multi time scale variation characteristics and short-term climate prediction.},
 year = {2016}
}

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TY  - JOUR
T1  - Wavelet Analysis on the Temporal Series of Precipitation in Baoji
AU  - Yang Mi
AU  - Xu Pan-pan
AU  - Qian Hui
Y1  - 2016/07/05
PY  - 2016
N1  - https://doi.org/10.11648/j.sd.20160403.16
DO  - 10.11648/j.sd.20160403.16
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 189
EP  - 196
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20160403.16
AB  - Adopting the series data of precipitationfrom 1952 to 2013 in Baoji city, shaanxi province, China, using Morlet wavelet function, the seasonal variation of precipitation and the time series of the interannual variation of precipitation in Baoji nearly 60aare analyzed by wavelet analysis, reveals the multiple time scales change complex structure of Baoji precipitation, forecasts the precipitation change trend of four seasons in Baoji city. The results show that the seasonal and annual precipitationin Baoji has the characteristics of multi time scale, different scales show the different cycles, large scale periodic variations also include small scale periodic variations. As a whole, the performance for small scale changed seriously, no clear rules of special features, and there is obvious regularity of large scale. The time-frequency localization characteristic of wavelet analysis can show the fine structure of precipitation time series, and provide a new method for the analysis of the key water saving problems, such as multi time scale variation characteristics and short-term climate prediction.
VL  - 4
IS  - 3
ER  -

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Author Information

Yang Mi

College of Environmental Science and Engineering, Chang'an University, Xi'an, China
Xu Pan-pan

College of Environmental Science and Engineering, Chang'an University, Xi'an, China
Qian Hui

College of Environmental Science and Engineering, Chang'an University, Xi'an, China

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Plain Text BibTeX RIS

APA Style

Yang Mi, Xu Pan-pan, Qian Hui. (2016). Wavelet Analysis on the Temporal Series of Precipitation in Baoji. Science Discovery, 4(3), 189-196. https://doi.org/10.11648/j.sd.20160403.16

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ACS Style

Yang Mi; Xu Pan-pan; Qian Hui. Wavelet Analysis on the Temporal Series of Precipitation in Baoji. Sci. Discov. 2016, 4(3), 189-196. doi: 10.11648/j.sd.20160403.16

Copy | Download

AMA Style

Yang Mi, Xu Pan-pan, Qian Hui. Wavelet Analysis on the Temporal Series of Precipitation in Baoji. Sci Discov. 2016;4(3):189-196. doi: 10.11648/j.sd.20160403.16

Copy | Download

@article{10.11648/j.sd.20160403.16,
  author = {Yang Mi and Xu Pan-pan and Qian Hui},
  title = {Wavelet Analysis on the Temporal Series of Precipitation in Baoji},
  journal = {Science Discovery},
  volume = {4},
  number = {3},
  pages = {189-196},
  doi = {10.11648/j.sd.20160403.16},
  url = {https://doi.org/10.11648/j.sd.20160403.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20160403.16},
  abstract = {Adopting the series data of precipitationfrom 1952 to 2013 in Baoji city, shaanxi province, China, using Morlet wavelet function, the seasonal variation of precipitation and the time series of the interannual variation of precipitation in Baoji nearly 60aare analyzed by wavelet analysis, reveals the multiple time scales change complex structure of Baoji precipitation, forecasts the precipitation change trend of four seasons in Baoji city. The results show that the seasonal and annual precipitationin Baoji has the characteristics of multi time scale, different scales show the different cycles, large scale periodic variations also include small scale periodic variations. As a whole, the performance for small scale changed seriously, no clear rules of special features, and there is obvious regularity of large scale. The time-frequency localization characteristic of wavelet analysis can show the fine structure of precipitation time series, and provide a new method for the analysis of the key water saving problems, such as multi time scale variation characteristics and short-term climate prediction.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Wavelet Analysis on the Temporal Series of Precipitation in Baoji
AU  - Yang Mi
AU  - Xu Pan-pan
AU  - Qian Hui
Y1  - 2016/07/05
PY  - 2016
N1  - https://doi.org/10.11648/j.sd.20160403.16
DO  - 10.11648/j.sd.20160403.16
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 189
EP  - 196
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20160403.16
AB  - Adopting the series data of precipitationfrom 1952 to 2013 in Baoji city, shaanxi province, China, using Morlet wavelet function, the seasonal variation of precipitation and the time series of the interannual variation of precipitation in Baoji nearly 60aare analyzed by wavelet analysis, reveals the multiple time scales change complex structure of Baoji precipitation, forecasts the precipitation change trend of four seasons in Baoji city. The results show that the seasonal and annual precipitationin Baoji has the characteristics of multi time scale, different scales show the different cycles, large scale periodic variations also include small scale periodic variations. As a whole, the performance for small scale changed seriously, no clear rules of special features, and there is obvious regularity of large scale. The time-frequency localization characteristic of wavelet analysis can show the fine structure of precipitation time series, and provide a new method for the analysis of the key water saving problems, such as multi time scale variation characteristics and short-term climate prediction.
VL  - 4
IS  - 3
ER  -

Copy | Download