IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v114y2022i2d10.1007_s11069-022-05444-2.html
   My bibliography  Save this article

Acquisition of rainfall in ungauged basins: a study of rainfall distribution heterogeneity based on a new method

Author

Listed:
  • Ye Zhao

    (Wuhan University
    Hubei Key Lab of Water System Science for Sponge City Construction)

  • Xiang Zhang

    (Wuhan University
    Hubei Key Lab of Water System Science for Sponge City Construction)

  • Feng Xiong

    (Changjiang Water Resources Commission)

  • Shuying Liu

    (Wuhan University
    Hubei Key Lab of Water System Science for Sponge City Construction)

  • Yao Wang

    (Wuhan University
    Hubei Key Lab of Water System Science for Sponge City Construction)

  • Changmei Liang

    (Hubei Water Resources Research Institute)

Abstract

High-density rainfall data is always desired to capture the heterogeneity of precipitation to accurately describe the components of the hydrological cycle. However, equipping and maintaining a high-density rain gauge network involve high costs, and the existing rain gauges are often unable to meet the density requirements. The objective of this study is to provide a new method to analyze the spatiotemporal variability of the precipitation field and to solve the problem of insufficient site density. To this end, the proper orthogonal decomposition (POD) method is proposed, which can analyze the spatial distribution characteristics of rainfall fields to solve data shortages. To demonstrate the feasibility and advantages of the proposed methodology, four districts and counties (Hongshan District, Jianli County, Sui County, and Xuanen County) in Hubei province in China were selected as case studies. The principal results are as follows. (1) The proposed method is effective in analyzing the spatiotemporal variability of the rainfall field to reconstruct rainfall processes in ungauged basins. (2) Compared with the commonly used Thiessen polygon method, the inverse distance weighting method, and the Kriging method, POD is more accurate and convenient, and the root mean squared error is reduced from 3.22, 1.83, 2.19 to 2.09; the correlation coefficients are improved from 0.60, 0.85, 0.79 to 0.89, respectively. (3) The POD method performs particularly well in simulating the peak value and the peak time and can offer a meaningful reference for analyzing the spatial distribution of rainfall.

Suggested Citation

  • Ye Zhao & Xiang Zhang & Feng Xiong & Shuying Liu & Yao Wang & Changmei Liang, 2022. "Acquisition of rainfall in ungauged basins: a study of rainfall distribution heterogeneity based on a new method," 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. 114(2), pages 1723-1739, November.
    • Handle: RePEc:spr:nathaz:v:114:y:2022:i:2:d:10.1007_s11069-022-05444-2
    DOI: 10.1007/s11069-022-05444-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-022-05444-2
    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/s11069-022-05444-2?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. Carl Eckart & Gale Young, 1936. "The approximation of one matrix by another of lower rank," Psychometrika, Springer;The Psychometric Society, vol. 1(3), pages 211-218, September.
    2. Gulser Meric & Christine Lentz & Wayne Smeltz & Ilhan Meric, 2012. "International Evidence on Market Linkages After the 2008 Stock Market Crash," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 6(4), pages 45-57.
    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. Sewell, Daniel K., 2018. "Visualizing data through curvilinear representations of matrices," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 255-270.
    2. Kohei Adachi & Nickolay T. Trendafilov, 2016. "Sparse principal component analysis subject to prespecified cardinality of loadings," Computational Statistics, Springer, vol. 31(4), pages 1403-1427, December.
    3. Norman Cliff, 1962. "Analytic rotation to a functional relationship," Psychometrika, Springer;The Psychometric Society, vol. 27(3), pages 283-295, September.
    4. Jushan Bai & Serena Ng, 2020. "Simpler Proofs for Approximate Factor Models of Large Dimensions," Papers 2008.00254, arXiv.org.
    5. Adele Ravagnani & Fabrizio Lillo & Paola Deriu & Piero Mazzarisi & Francesca Medda & Antonio Russo, 2024. "Dimensionality reduction techniques to support insider trading detection," Papers 2403.00707, arXiv.org.
    6. Alfredo García-Hiernaux & José Casals & Miguel Jerez, 2012. "Estimating the system order by subspace methods," Computational Statistics, Springer, vol. 27(3), pages 411-425, September.
    7. Mitzi Cubilla‐Montilla & Ana‐Belén Nieto‐Librero & Ma Purificación Galindo‐Villardón & Ma Purificación Vicente Galindo & Isabel‐María Garcia‐Sanchez, 2019. "Are cultural values sufficient to improve stakeholder engagement human and labour rights issues?," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 26(4), pages 938-955, July.
    8. Stegeman, Alwin, 2016. "A new method for simultaneous estimation of the factor model parameters, factor scores, and unique parts," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 189-203.
    9. Jos Berge & Henk Kiers, 1993. "An alternating least squares method for the weighted approximation of a symmetric matrix," Psychometrika, Springer;The Psychometric Society, vol. 58(1), pages 115-118, March.
    10. Shimeng Huang & Henry Wolkowicz, 2018. "Low-rank matrix completion using nuclear norm minimization and facial reduction," Journal of Global Optimization, Springer, vol. 72(1), pages 5-26, September.
    11. Amalendu Bhunia & Devrim Yaman, 2017. "Is There a Causal Relationship Between Financial Markets in Asia and the US?," Lahore Journal of Economics, Department of Economics, The Lahore School of Economics, vol. 22(1), pages 71-90, Jan-June.
    12. Antti J. Tanskanen & Jani Lukkarinen & Kari Vatanen, 2016. "Random selection of factors preserves the correlation structure in a linear factor model to a high degree," Papers 1604.05896, arXiv.org, revised Dec 2018.
    13. Ali Habibnia & Esfandiar Maasoumi, 2021. "Forecasting in Big Data Environments: An Adaptable and Automated Shrinkage Estimation of Neural Networks (AAShNet)," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 363-381, December.
    14. Jin-Xing Liu & Yong Xu & Chun-Hou Zheng & Yi Wang & Jing-Yu Yang, 2012. "Characteristic Gene Selection via Weighting Principal Components by Singular Values," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-10, July.
    15. Kargin, V. & Onatski, A., 2008. "Curve forecasting by functional autoregression," Journal of Multivariate Analysis, Elsevier, vol. 99(10), pages 2508-2526, November.
    16. Yoshio Takane & Forrest Young & Jan Leeuw, 1977. "Nonmetric individual differences multidimensional scaling: An alternating least squares method with optimal scaling features," Psychometrika, Springer;The Psychometric Society, vol. 42(1), pages 7-67, March.
    17. Aldrin, Magne, 1996. "Moderate projection pursuit regression for multivariate response data," Computational Statistics & Data Analysis, Elsevier, vol. 21(5), pages 501-531, May.
    18. W. Gibson, 1962. "On the least-squares orthogonalization of an oblique transformation," Psychometrika, Springer;The Psychometric Society, vol. 27(2), pages 193-195, June.
    19. Gordana Ispirova & Tome Eftimov & Barbara Koroušić Seljak, 2020. "P-NUT: Predicting NUTrient Content from Short Text Descriptions," Mathematics, MDPI, vol. 8(10), pages 1-21, October.
    20. Chester Harris, 1955. "Separation of data as a principle in factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 20(1), pages 23-28, March.

    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:nathaz:v:114:y:2022:i:2:d:10.1007_s11069-022-05444-2. 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.