IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i4p1948-d744988.html
   My bibliography  Save this article

Resilience of a Complex Watershed under Water Variability: A Modeling Study

Author

Listed:
  • Kathleen Vazquez

    (Department of Agricultural and Biological Engineering, University of Florida, 1741 Museum Rd, Gainesville, FL 32611, USA)

  • Rachata Muneepeerakul

    (Department of Agricultural and Biological Engineering, University of Florida, 1741 Museum Rd, Gainesville, FL 32611, USA)

Abstract

Understanding how socio-ecological systems respond to environmental variability is an important step in promoting system resilience. In this paper, we asked: How do the frequency and amplitude of water availability variation affect both the social-ecological regimes present and how the system transitions between them? How do these transitions differ under flood-prone and drought-prone conditions? We modified a dynamical systems model of a complex watershed to directly link environmental variability to system-level outcomes, specifically the livelihoods present in the system. The model results suggest that flood-prone systems exhibit more drastic regime shift behavior than drought-prone systems, with abrupt shifts from the complete participation to complete abandonment of livelihood sectors. Drought-prone systems appeared to be more sensitive to the amplitude of water variability, whereas flood-prone systems exhibited more complex relationships with amplitude and frequency, with frequency playing a bigger role compared to drought-prone systems. Lower frequency variations with sufficient amplitudes exposed the system to extended periods of environmental hardship, reducing the system’s ability to recover. Our analysis also highlighted the importance of environmental stochasticity: the deterministic version of the model that assumed no stochasticity overestimated system resilience. The model and analysis offer a more systematic framework to investigate the linkages between sustainability of social-ecological systems and environmental variability. This lays the groundwork for future research in systems with significant current or predicted environmental variability due to climate change.

Suggested Citation

  • Kathleen Vazquez & Rachata Muneepeerakul, 2022. "Resilience of a Complex Watershed under Water Variability: A Modeling Study," Sustainability, MDPI, vol. 14(4), pages 1-10, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:4:p:1948-:d:744988
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/4/1948/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/4/1948/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Guttal, Vishwesha & Jayaprakash, C., 2007. "Impact of noise on bistable ecological systems," Ecological Modelling, Elsevier, vol. 201(3), pages 420-428.
    2. Kathleen Vazquez & Rachata Muneepeerakul, 2021. "Modeling Resilience and Sustainability of Water-Subsidized Systems: An Example from Northwest Costa Rica," Sustainability, MDPI, vol. 13(4), pages 1-14, 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. Zeng, Chunhua & Wang, Hua, 2012. "Noise and large time delay: Accelerated catastrophic regime shifts in ecosystems," Ecological Modelling, Elsevier, vol. 233(C), pages 52-58.
    2. Meng, Qingyan & Wang, Yejuan & Kloeden, Peter E., 2023. "The dynamical behavior of a class of stochastic vegetation models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 211(C), pages 341-367.
    3. Bian, Junhao & Ma, Zhiqin & Wang, Chunping & Huang, Tao & Zeng, Chunhua, 2024. "Early warning for spatial ecological system: Fractal dimension and deep learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    4. Mandal, Saroj Kumar & Poria, Swarup, 2023. "A study of Michaelis–Menten type harvesting effects on a population in stochastic environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).
    5. Zeng, Yujie & Liu, Dedi & Guo, Shenglian & Xiong, Lihua & Liu, Pan & Chen, Jie & Yin, Jiabo & Wu, Zhenhui & Zhou, Wan, 2023. "Assessing the effects of water resources allocation on the uncertainty propagation in the water–energy–food–society (WEFS) nexus," Agricultural Water Management, Elsevier, vol. 282(C).
    6. Hua, Mengjiao & Wu, Yu, 2022. "Transition and basin stability in a stochastic tumor growth model with immunization," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    7. Zhang, Hongxia & Xu, Wei & Han, Ping & Qiao, Yan, 2020. "Stochastic dynamic balance of a bi-stable vegetation model with pulse control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    8. Vishwesha Guttal & Srinivas Raghavendra & Nikunj Goel & Quentin Hoarau, 2016. "Lack of Critical Slowing Down Suggests that Financial Meltdowns Are Not Critical Transitions, yet Rising Variability Could Signal Systemic Risk," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-20, January.
    9. Amemiya, Takashi & Enomoto, Takatoshi & Rossberg, Axel G. & Yamamoto, Tetsuya & Inamori, Yuhei & Itoh, Kiminori, 2007. "Stability and dynamical behavior in a lake-model and implications for regime shifts in real lakes," Ecological Modelling, Elsevier, vol. 206(1), pages 54-62.
    10. Zhang, Hongxia & Xu, Wei & Guo, Qin & Han, Ping & Qiao, Yan, 2020. "First escape probability and mean first exit time for a time-delayed ecosystem driven by non-Gaussian colored noise," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    11. Zeng, Yujie & Liu, Dedi & Guo, Shenglian & Xiong, Lihua & Liu, Pan & Chen, Jie & Chen, Hua & Yin, Jiabo & Wu, Zhenhui & Zhou, Wan, 2024. "Assessment of the impacts of water resources allocation on the reliability, resilience and vulnerability of the water–energy–food–society (WEFS) nexus system," Agricultural Water Management, Elsevier, vol. 295(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:14:y:2022:i:4:p:1948-:d:744988. 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.