IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i14p8051-d597087.html
   My bibliography  Save this article

Analysis of Rainfall Time Series with Application to Calculation of Return Periods

Author

Listed:
  • Ramón Egea Pérez

    (Department of Planification et Travaux, Municipal Water and Sanitation Company, 30008 Murcia, Spain)

  • Mónica Cortés-Molina

    (Department of Applied Mathematics, University of Alicante, 03690 Alicante, Spain)

  • Francisco J. Navarro-González

    (Department of Applied Mathematics, University of Alicante, 03690 Alicante, Spain)

Abstract

This paper presents a study of the characteristics of rainfall in a typical Mediterranean climate, characterized by infrequent and irregular rain in the territorial area and its intensity. One of the main components of this type of climate is short-duration and high-intensity rain events that cause a large amount of damage to property and human lives, seriously affecting the operation of infrastructure and the activity of society in general. The objective of this study was to design a methodology based on peak over threshold (POT) analysis. This methodology allows us to establish reference precipitation values and more approximate return periods in the absence of sufficiently extensive historical precipitation series. In addition, the frequency of these extreme events or return periods is established. The characteristics of the precipitation regime make direct analysis difficult. Thus, the functions of the probability distributions underlying the described phenomena are improved.

Suggested Citation

  • Ramón Egea Pérez & Mónica Cortés-Molina & Francisco J. Navarro-González, 2021. "Analysis of Rainfall Time Series with Application to Calculation of Return Periods," Sustainability, MDPI, vol. 13(14), pages 1-18, July.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:14:p:8051-:d:597087
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/14/8051/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/14/8051/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Xun & Lai, K.K. & Wang, Shou-Yang, 2008. "A new approach for crude oil price analysis based on Empirical Mode Decomposition," Energy Economics, Elsevier, vol. 30(3), pages 905-918, May.
    2. Li, Muyi & Huang, Yongxiang, 2014. "Hilbert–Huang Transform based multifractal analysis of China stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 406(C), pages 222-229.
    3. Meyler, Aidan & Kenny, Geoff & Quinn, Terry, 1998. "Forecasting irish inflation using ARIMA models," MPRA Paper 11359, University Library of Munich, Germany.
    4. Kang, Jia-Ning & Wei, Yi-Ming & Liu, Lan-Cui & Han, Rong & Yu, Bi-Ying & Wang, Jin-Wei, 2020. "Energy systems for climate change mitigation: A systematic review," Applied Energy, Elsevier, vol. 263(C).
    5. Bangzhu Zhu & Ping Wang & Julien Chevallier & Yiming Wei, 2015. "Carbon Price Analysis Using Empirical Mode Decomposition," Computational Economics, Springer;Society for Computational Economics, vol. 45(2), pages 195-206, February.
    6. J. Lelieveld & P. Hadjinicolaou & E. Kostopoulou & J. Chenoweth & M. Maayar & C. Giannakopoulos & C. Hannides & M. Lange & M. Tanarhte & E. Tyrlis & E. Xoplaki, 2012. "Climate change and impacts in the Eastern Mediterranean and the Middle East," Climatic Change, Springer, vol. 114(3), pages 667-687, October.
    7. repec:zbw:bofitp:2014_005 is not listed on IDEAS
    8. Yves Tramblay & Samuel Somot, 2018. "Future evolution of extreme precipitation in the Mediterranean," Climatic Change, Springer, vol. 151(2), pages 289-302, November.
    9. Poornima Unnikrishnan & V. Jothiprakash, 2020. "Hybrid SSA-ARIMA-ANN Model for Forecasting Daily Rainfall," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(11), pages 3609-3623, September.
    10. Razdan, Ashok, 2004. "Wavelet correlation coefficient of ‘strongly correlated’ time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 333(C), pages 335-342.
    11. Ju, Keyi & Zhou, Dequn & Zhou, P. & Wu, Junmin, 2014. "Macroeconomic effects of oil price shocks in China: An empirical study based on Hilbert–Huang transform and event study," Applied Energy, Elsevier, vol. 136(C), pages 1053-1066.
    12. Oladosu, Gbadebo, 2009. "Identifying the oil price-macroeconomy relationship: An empirical mode decomposition analysis of US data," Energy Policy, Elsevier, vol. 37(12), pages 5417-5426, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ju, Keyi & Su, Bin & Zhou, Dequn & Zhang, Yuqiang, 2016. "An incentive-oriented early warning system for predicting the co-movements between oil price shocks and macroeconomy," Applied Energy, Elsevier, vol. 163(C), pages 452-463.
    2. Jianguo Zhou & Xuechao Yu & Xiaolei Yuan, 2018. "Predicting the Carbon Price Sequence in the Shenzhen Emissions Exchange Using a Multiscale Ensemble Forecasting Model Based on Ensemble Empirical Mode Decomposition," Energies, MDPI, vol. 11(7), pages 1-17, July.
    3. Quande Qin & Huangda He & Li Li & Ling-Yun He, 2020. "A Novel Decomposition-Ensemble Based Carbon Price Forecasting Model Integrated with Local Polynomial Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1249-1273, April.
    4. Wang, Delu & Ma, Gang & Song, Xuefeng & Liu, Yun, 2017. "Energy price slump and policy response in the coal-chemical industry district: A case study of Ordos with a system dynamics model," Energy Policy, Elsevier, vol. 104(C), pages 325-339.
    5. Bangzhu Zhu & Shujiao Ma & Rui Xie & Julien Chevallier & Yi-Ming Wei, 2018. "Hilbert Spectra and Empirical Mode Decomposition: A Multiscale Event Analysis Method to Detect the Impact of Economic Crises on the European Carbon Market," Computational Economics, Springer;Society for Computational Economics, vol. 52(1), pages 105-121, June.
    6. Li, Muyi & Huang, Yongxiang, 2014. "Hilbert–Huang Transform based multifractal analysis of China stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 406(C), pages 222-229.
    7. Zhu, Bangzhu & Han, Dong & Chevallier, Julien & Wei, Yi-Ming, 2017. "Dynamic multiscale interactions between European carbon and electricity markets during 2005–2016," Energy Policy, Elsevier, vol. 107(C), pages 309-322.
    8. Tian, Hu & Zheng, Xiaolong & Zeng, Daniel Danjun, 2019. "Analyzing the dynamic sectoral influence in Chinese and American stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    9. Wang, Haoyu & Di, Junpeng & Yang, Zhaojun & Han, Qing, 2020. "Assessment of mutual fund performance based on Ensemble Empirical Mode Decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 538(C).
    10. Jihong Xiao & Xuehong Zhu & Chuangxia Huang & Xiaoguang Yang & Fenghua Wen & Meirui Zhong, 2019. "A New Approach for Stock Price Analysis and Prediction Based on SSA and SVM," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 287-310, January.
    11. Chakrabarty, Anindya & De, Anupam & Gunasekaran, Angappa & Dubey, Rameshwar, 2015. "Investment horizon heterogeneity and wavelet: Overview and further research directions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 45-61.
    12. Ju, Keyi & Su, Bin & Zhou, Dequn & Wu, Junmin & Liu, Lifan, 2016. "Macroeconomic performance of oil price shocks: Outlier evidence from nineteen major oil-related countries/regions," Energy Economics, Elsevier, vol. 60(C), pages 325-332.
    13. Xu, Mengjia & Shang, Pengjian & Lin, Aijing, 2016. "Cross-correlation analysis of stock markets using EMD and EEMD," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 82-90.
    14. Tiwari, Aviral K. & Dar, Arif B. & Bhanja, Niyati & Gupta, Rangan, 2016. "A historical analysis of the US stock price index using empirical mode decomposition over 1791-2015," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 10, pages 1-15.
    15. Ftiti, Zied & Hadhri, Sinda, 2019. "Can economic policy uncertainty, oil prices, and investor sentiment predict Islamic stock returns? A multi-scale perspective," Pacific-Basin Finance Journal, Elsevier, vol. 53(C), pages 40-55.
    16. Cao, Guangxi & Xu, Wei, 2016. "Multifractal features of EUA and CER futures markets by using multifractal detrended fluctuation analysis based on empirical model decomposition," Chaos, Solitons & Fractals, Elsevier, vol. 83(C), pages 212-222.
    17. Geng, Jiang-Bo & Ji, Qiang & Fan, Ying, 2016. "The behaviour mechanism analysis of regional natural gas prices: A multi-scale perspective," Energy, Elsevier, vol. 101(C), pages 266-277.
    18. Siddiqui, Atiq W. & Basu, Rounaq, 2020. "An empirical analysis of relationships between cyclical components of oil price and tanker freight rates," Energy, Elsevier, vol. 200(C).
    19. Yu, Lean & Li, Jingjing & Tang, Ling & Wang, Shuai, 2015. "Linear and nonlinear Granger causality investigation between carbon market and crude oil market: A multi-scale approach," Energy Economics, Elsevier, vol. 51(C), pages 300-311.
    20. Liu, Ying Lin & Zhang, Jing Jie & Fang, Yan, 2023. "The driving factors of China's carbon prices: Evidence from using ICEEMDAN-HC method and quantile regression," Finance Research Letters, Elsevier, vol. 54(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:14:p:8051-:d:597087. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.