IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v156y2022ics0960077922000753.html
   My bibliography  Save this article

Crisis-induced intermittency in Mexican dam flows

Author

Listed:
  • García-Rojas, Blanca E.
  • Ramirez-Dámaso, Gabriel
  • Caballero, Francisco
  • Femat, Ricardo

Abstract

Historical data from 4 different dams from Mexico have been analyzed in order to investigate on the nonlinear behavior. The dams are into 4 distinct hydrological basins, which are located at the north of Mexico where dry climate displays seasonal rain. The phase plane suggests the expanding and suddenly contractions of its orbits shows intermittency. The correlation dimension is derived by careful computing of two parameters: time delay and embedding dimensions. In addition, the scaling exponent is computed as well, which reinforces the appearance of intermittency. The computation of phase plane, correlation dimension, and scaling exponent allows us to find an underlying route to chaos from each of the dam time series. The Lyapunov exponents were employed in order to strengthen the conclusion about chaotic behavior. The results suggest that the intermittency (attributable to reliefs and reservoirs and their sudden effects) is the possible mechanisms to chaos at the nonlinear behavior of dam flows; however, stochastic nature deserves additional investigation.

Suggested Citation

  • García-Rojas, Blanca E. & Ramirez-Dámaso, Gabriel & Caballero, Francisco & Femat, Ricardo, 2022. "Crisis-induced intermittency in Mexican dam flows," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
  • Handle: RePEc:eee:chsofr:v:156:y:2022:i:c:s0960077922000753
    DOI: 10.1016/j.chaos.2022.111864
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077922000753
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.chaos.2022.111864?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. Steven L. Brunton & Bingni W. Brunton & Joshua L. Proctor & Eurika Kaiser & J. Nathan Kutz, 2017. "Chaos as an intermittently forced linear system," Nature Communications, Nature, vol. 8(1), pages 1-9, December.
    2. Song, Zhijun & Jin, Wenxuan & Jiang, Guanghui & Li, Sichun & Ma, Wenqiu, 2021. "Typical and atypical multifractal systems of urban spaces—using construction land in Zhengzhou from 1988 to 2015 as an example," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    3. Baranowski, Piotr & Gos, Magdalena & Krzyszczak, Jaromir & Siwek, Krzysztof & Kieliszek, Adam & Tkaczyk, Przemysław, 2019. "Multifractality of meteorological time series for Poland on the base of MERRA-2 data," Chaos, Solitons & Fractals, Elsevier, vol. 127(C), pages 318-333.
    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. Méndez-Gordillo, Alma Rosa & Cadenas, Erasmo, 2021. "Wind speed forecasting by the extraction of the multifractal patterns of time series through the multiplicative cascade technique," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    2. Zhan, Cun & Liang, Chuan & Zhao, Lu & Jiang, Shouzheng & Niu, Kaijie & Zhang, Yaling, 2023. "Multifractal characteristics of multiscale drought in the Yellow River Basin, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    3. da Silva, Hérica Santos & Silva, José Rodrigo Santos & Stosic, Tatijana, 2020. "Multifractal analysis of air temperature in Brazil," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    4. Ali, Naseem & Cal, Raúl Bayoán, 2019. "Scale evolution, intermittency and fluctuation relations in the near-wake of a wind turbine array," Chaos, Solitons & Fractals, Elsevier, vol. 119(C), pages 215-229.
    5. Suchetana Sadhukhan & Poulomi Sadhukhan, 2022. "Sector-wise analysis of Indian stock market: Long and short-term risk and stability analysis," Papers 2210.09619, arXiv.org.
    6. Gyurhan Nedzhibov, 2024. "Delay-Embedding Spatio-Temporal Dynamic Mode Decomposition," Mathematics, MDPI, vol. 12(5), pages 1-18, March.
    7. Riccardo Colantuono & Riccardo Colantuono & Massimiliano Mazzanti & Michele Pinelli, 2023. "Aviation and the EU ETS: an overview and a data-driven approach for carbon price prediction," SEEDS Working Papers 0123, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Feb 2023.
    8. Santos, Fábio Sandro dos & Nascimento, Kerolly Kedma Felix do & Jale, Jader da Silva & Stosic, Tatijana & Marinho, Manoel H.N. & Ferreira, Tiago A.E., 2021. "Mixture distribution and multifractal analysis applied to wind speed in the Brazilian Northeast region," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    9. Gómez-Gómez, Javier & Carmona-Cabezas, Rafael & Sánchez-López, Elena & Gutiérrez de Ravé, Eduardo & Jiménez-Hornero, Francisco José, 2022. "Multifractal fluctuations of the precipitation in Spain (1960–2019)," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    10. Joseph, Annie Julie & Pournami, P.N., 2021. "Multifractal theory based breast tissue characterization for early detection of breast cancer," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    11. Wang, Jian & Shao, Wei & Kim, Junseok, 2020. "Multifractal detrended cross-correlation analysis between respiratory diseases and haze in South Korea," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    12. Soledad Le Clainche & José M. Vega, 2018. "Analyzing Nonlinear Dynamics via Data-Driven Dynamic Mode Decomposition-Like Methods," Complexity, Hindawi, vol. 2018, pages 1-21, December.
    13. Chau, Thi Tuyet Trang & Ailliot, Pierre & Monbet, Valérie, 2021. "An algorithm for non-parametric estimation in state–space models," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).
    14. Kanbur, Baris Burak & Kumtepeli, Volkan & Duan, Fei, 2020. "Thermal performance prediction of the battery surface via dynamic mode decomposition," Energy, Elsevier, vol. 201(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:eee:chsofr:v:156:y:2022:i:c:s0960077922000753. 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.