IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v31y2017i1d10.1007_s11269-016-1532-2.html
   My bibliography  Save this article

A Hybrid Statistical Downscaling Method Based on the Classification of Rainfall Patterns

Author

Listed:
  • Gwo-Fong Lin

    (National Taiwan University)

  • Ming-Jui Chang

    (National Taiwan University)

  • Jyue-Ting Wu

    (National Taiwan University)

Abstract

A hybrid statistical downscaling method based on the classification of rainfall patterns is presented which is capable of overcoming the poor representation of extreme events. The large-scale datasets, which are obtained from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data and the global circulation models (GCMs) outputs, and the local daily rainfall data are analyzed to assess the impacts of climate change on rainfall. The proposed method is composed of two steps. The first step is the classification of daily rainfall patterns. The detrended fluctuation analysis (DFA) is introduced to define the extreme rainfall. Two classification models, extreme rainfall and wet rainfall, are developed to describe the relationship between large-scale weather factors and rainfall patterns using support vector machine (SVM). These two models are able to identify the three rainfall patterns (the extreme, the normal and the dry rainfall) of the daily weather factors. The second step is the estimation of daily rainfall. The improved self-organizing linear output map (ISOLO) is adopted to estimate the rainfall for the aforementioned three different rainfall patterns. The future rainfall changes are calculated for the periods 2046–2065 and 2081–2100 under the A2 and B1 scenarios. An application to Taiwan has shown that the proposed method provides reliable and accurate rainfall-pattern classification. In addition, the improvement of the estimation of daily rainfall is significant, especially for the extreme rainfall. In conclusion, the proposed method is effective to overcome the poor representation of extreme events and the impacts of climate change on rainfall are analyzed.

Suggested Citation

  • Gwo-Fong Lin & Ming-Jui Chang & Jyue-Ting Wu, 2017. "A Hybrid Statistical Downscaling Method Based on the Classification of Rainfall Patterns," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 377-401, January.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:1:d:10.1007_s11269-016-1532-2
    DOI: 10.1007/s11269-016-1532-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-016-1532-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-016-1532-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wen-chuan Wang & Kwok-wing Chau & Dong-mei Xu & Xiao-Yun Chen, 2015. "Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2655-2675, June.
    2. Subimal Ghosh & Sudhir Katkar, 2012. "Modeling Uncertainty Resulting from Multiple Downscaling Methods in Assessing Hydrological Impacts of Climate Change," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(12), pages 3559-3579, September.
    3. Alvarez-Ramirez, Jose & Alvarez, Jesus & Rodriguez, Eduardo, 2008. "Short-term predictability of crude oil markets: A detrended fluctuation analysis approach," Energy Economics, Elsevier, vol. 30(5), pages 2645-2656, September.
    4. Lin, Boqiang & Wesseh, Presley K., 2013. "What causes price volatility and regime shifts in the natural gas market," Energy, Elsevier, vol. 55(C), pages 553-563.
    5. Grau-Carles, Pilar, 2006. "Bootstrap testing for detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 360(1), pages 89-98.
    6. Hung-Wei Tseng & Tao-Chang Yang & Chen-Min Kuo & Pao-Shan Yu, 2012. "Application of Multi-site Weather Generators for Investigating Wet and Dry Spell Lengths under Climate Change: A Case Study in Southern Taiwan," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(15), pages 4311-4326, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sultan Mahmud & Ferdausi Mahojabin Sumana & Md Mohsin & Md. Hasinur Rahaman Khan, 2022. "Redefining homogeneous climate regions in Bangladesh using multivariate clustering approaches," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(2), pages 1863-1884, March.
    2. Jun-Mao Liao & Ming-Jui Chang & Luh-Maan Chang, 2020. "Prediction of Air-Conditioning Energy Consumption in R&D Building Using Multiple Machine Learning Techniques," Energies, MDPI, vol. 13(7), pages 1-22, April.

    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. Wang, Yudong & Wu, Chongfeng & Wei, Yu, 2011. "Can GARCH-class models capture long memory in WTI crude oil markets?," Economic Modelling, Elsevier, vol. 28(3), pages 921-927, May.
    2. Gwo-Fong Lin & Ming-Jui Chang & Chian-Fu Wang, 2017. "A Novel Spatiotemporal Statistical Downscaling Method for Hourly Rainfall," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(11), pages 3465-3489, September.
    3. Cagli, Efe Caglar & Taskin, Dilvin & Evrim Mandaci, Pınar, 2019. "The short- and long-run efficiency of energy, precious metals, and base metals markets: Evidence from the exponential smooth transition autoregressive models," Energy Economics, Elsevier, vol. 84(C).
    4. Ortiz-Cruz, Alejandro & Rodriguez, Eduardo & Ibarra-Valdez, Carlos & Alvarez-Ramirez, Jose, 2012. "Efficiency of crude oil markets: Evidences from informational entropy analysis," Energy Policy, Elsevier, vol. 41(C), pages 365-373.
    5. Misund, Bård & Oglend, Atle, 2016. "Supply and demand determinants of natural gas price volatility in the U.K.: A vector autoregression approach," Energy, Elsevier, vol. 111(C), pages 178-189.
    6. Ruan, Qingsong & Bao, Junjie & Zhang, Manqian & Fan, Limin, 2019. "The effects of exchange rate regime reform on RMB markets: A new perspective based on MF-DCCA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 122-134.
    7. Zhuang, Xiaoyang & Wei, Yu & Ma, Feng, 2015. "Multifractality, efficiency analysis of Chinese stock market and its cross-correlation with WTI crude oil price," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 430(C), pages 101-113.
    8. Wesseh, Presley K. & Lin, Boqiang, 2018. "Optimal carbon taxes for China and implications for power generation, welfare, and the environment," Energy Policy, Elsevier, vol. 118(C), pages 1-8.
    9. Alvarez-Ramirez, J. & Alvarez, J. & Rodríguez, E., 2015. "Asymmetric long-term autocorrelations in crude oil markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 330-341.
    10. Jew Das & Alin Treesa & N. V. Umamahesh, 2018. "Modelling Impacts of Climate Change on a River Basin: Analysis of Uncertainty Using REA & Possibilistic Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(15), pages 4833-4852, December.
    11. Liang, Chao & Xia, Zhenglan & Lai, Xiaodong & Wang, Lu, 2022. "Natural gas volatility prediction: Fresh evidence from extreme weather and extended GARCH-MIDAS-ES model," Energy Economics, Elsevier, vol. 116(C).
    12. Alvarez-Ramirez, Jose & Alvarez, Jesus & Rodriguez, Eduardo, 2008. "Short-term predictability of crude oil markets: A detrended fluctuation analysis approach," Energy Economics, Elsevier, vol. 30(5), pages 2645-2656, September.
    13. Wesseh, Presley K. & Lin, Boqiang, 2016. "Can African countries efficiently build their economies on renewable energy?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 161-173.
    14. Salman Sharifazari & Shahab Araghinejad, 2015. "Development of a Nonparametric Model for Multivariate Hydrological Monthly Series Simulation Considering Climate Change Impacts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5309-5322, November.
    15. Sensoy, Ahmet & Hacihasanoglu, Erk, 2014. "Time-varying long range dependence in energy futures markets," Energy Economics, Elsevier, vol. 46(C), pages 318-327.
    16. Zhang, Bing, 2013. "Are the crude oil markets becoming more efficient over time? New evidence from a generalized spectral test," Energy Economics, Elsevier, vol. 40(C), pages 875-881.
    17. Wang, Gang-Jin & Xie, Chi, 2013. "Cross-correlations between Renminbi and four major currencies in the Renminbi currency basket," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(6), pages 1418-1428.
    18. Aslam, Faheem & Zil-e-huma, & Bibi, Rashida & Ferreira, Paulo, 2022. "Cross-correlations between economic policy uncertainty and precious and industrial metals: A multifractal cross-correlation analysis," Resources Policy, Elsevier, vol. 75(C).
    19. Mohammad Zounemat-Kermani, 2016. "Investigating Chaos and Nonlinear Forecasting in Short Term and Mid-term River Discharge," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(5), pages 1851-1865, March.
    20. Aslam, Faheem & Aziz, Saqib & Nguyen, Duc Khuong & Mughal, Khurrum S. & Khan, Maaz, 2020. "On the efficiency of foreign exchange markets in times of the COVID-19 pandemic," Technological Forecasting and Social Change, Elsevier, vol. 161(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:spr:waterr:v:31:y:2017:i:1:d:10.1007_s11269-016-1532-2. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.