IDEAS home Printed from https://ideas.repec.org/a/wly/envmet/v36y2025i2ne70006.html
   My bibliography  Save this article

New Parametric Approach for Modeling Hydrological Data: An Alternative to the Beta, Kumaraswamy, and Simplex Models

Author

Listed:
  • Thiago A. N. De Andrade
  • Frank Gomes‐Silva
  • Indranil Ghosh

Abstract

We propose a new approach of continuous distributions in the unit interval, focusing on hydrological applications. This study presents the innovative two‐parameter model called modified exponentiated generalized (MEG) distribution. The efficiency of the MEG distribution is evidenced through its application to 29 real datasets representing the percentage of useful water volume in hydroelectric power plant reservoirs in Brazil. The model outperforms the beta, simplex, and Kumaraswamy (KW) distributions, which are widely used for this type of analysis. The connection of our proposal with classical distributions, such as the Fréchet and KW distribution, broadens its applicability. While the Fréchet distribution is recognized for its usefulness in modeling extreme values, the proximity to KW allows a comprehensive analysis of hydrological data. The simple and tractable analytical expressions of the MEG's density and cumulative and quantile functions make it computationally feasible and particularly attractive for practical applications. Furthermore, this work highlights the relevance of the related reflected model: the reflected modified exponentiated generalized distribution. This contribution is expected to improve the statistical modeling of hydrological phenomena and provide new perspectives for future scientific investigations.

Suggested Citation

  • Thiago A. N. De Andrade & Frank Gomes‐Silva & Indranil Ghosh, 2025. "New Parametric Approach for Modeling Hydrological Data: An Alternative to the Beta, Kumaraswamy, and Simplex Models," Environmetrics, John Wiley & Sons, Ltd., vol. 36(2), March.
  • Handle: RePEc:wly:envmet:v:36:y:2025:i:2:n:e70006
    DOI: 10.1002/env.70006
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/env.70006
    Download Restriction: no

    File URL: https://libkey.io/10.1002/env.70006?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
    ---><---

    References listed on IDEAS

    as
    1. Hanlin Li & Longxia Qian & Jianhong Yang & Suzhen Dang & Mei Hong, 2023. "Parameter Estimation for Univariate Hydrological Distribution Using Improved Bootstrap with Small Samples," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1055-1082, February.
    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. Qin Gong & Bin Yin, 2024. "Statistical inference of entropy functions of generalized inverse exponential model under progressive type-II censoring test," PLOS ONE, Public Library of Science, vol. 19(9), pages 1-29, September.
    2. Kouao Laurent Kouadio & Jianxin Liu & Serge Kouamelan Kouamelan & Rong Liu, 2023. "Ensemble Learning Paradigms for Flow Rate Prediction Boosting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(11), pages 4413-4431, September.

    More about this item

    Statistics

    Access and download statistics

    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:wly:envmet:v:36:y:2025:i:2:n:e70006. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/1180-4009/ .

    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.