IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v208y2024ics0040162524004918.html
   My bibliography  Save this article

Modelling climate change-induced nonstationarity in rainfall extremes: A comprehensive approach for hydrological analysis

Author

Listed:
  • Ankush,
  • Goel, Narendra Kumar
  • Rajendran, Vinnarasi

Abstract

Climate change and global warming have induced a dynamic shift in extreme rainfall patterns, leading to nonstationary behaviour. This alteration in behaviour challenges conventional hydrologic design, which assumes stationarity and can yield misleading outcomes. This study aims to address nonstationarity by modelling distribution parameters with covariates. Utilizing a 70-year (1951–2020) high-resolution India Meteorological Department (IMD) gridded dataset, extreme annual rainfall across diverse Indian cities was extracted and modelled. Previous research and goodness-of-fit tests favour the Generalized Extreme Value (GEV) distribution for modelling extremes. This study incorporates indices like Nino3.4, dipole mode index, global and local temperature, CO2, and time to characterize nonstationarity in extreme annual rainfall, leveraging climate cycles and global warming. Performance assessment employs the Akaike information criterion, Bayesian information criterion and Likelihood ratio test, while quantile reliability is evaluated through confidence intervals (CIs). Findings reveal widespread nonstationary trends in most grid points, translating to wider CIs in estimated quantiles for chosen non-exceedance probability and covariates in fitted models. Generally, nonstationary conditions are associated with broader confidence bands in return levels, highlighting nonstationary model weaknesses compared to stationary models. However, the results showed that the rainfall extremes follow a nonstationary pattern. Hence, there is a strong need to develop nonstationary models of low uncertainty.

Suggested Citation

  • Ankush, & Goel, Narendra Kumar & Rajendran, Vinnarasi, 2024. "Modelling climate change-induced nonstationarity in rainfall extremes: A comprehensive approach for hydrological analysis," Technological Forecasting and Social Change, Elsevier, vol. 208(C).
    • Handle: RePEc:eee:tefoso:v:208:y:2024:i:c:s0040162524004918
    DOI: 10.1016/j.techfore.2024.123693
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162524004918
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2024.123693?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. Stuart G. Coles & Jonathan A. Tawn, 1996. "A Bayesian Analysis of Extreme Rainfall Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 45(4), pages 463-478, December.
    2. Wenju Cai & Simon Borlace & Matthieu Lengaigne & Peter van Rensch & Mat Collins & Gabriel Vecchi & Axel Timmermann & Agus Santoso & Michael J. McPhaden & Lixin Wu & Matthew H. England & Guojian Wang &, 2014. "Increasing frequency of extreme El Niño events due to greenhouse warming," Nature Climate Change, Nature, vol. 4(2), pages 111-116, February.
    3. A. Timmermann & J. Oberhuber & A. Bacher & M. Esch & M. Latif & E. Roeckner, 1999. "Increased El Niño frequency in a climate model forced by future greenhouse warming," Nature, Nature, vol. 398(6729), pages 694-697, April.
    4. Linyin Cheng & Amir AghaKouchak & Eric Gilleland & Richard Katz, 2014. "Non-stationary extreme value analysis in a changing climate," Climatic Change, Springer, vol. 127(2), pages 353-369, November.
    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. Ranjana Ray Chaudhuri & Prateek Sharma, 2020. "Addressing uncertainty in extreme rainfall intensity for semi-arid urban regions: case study of Delhi, India," 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. 104(3), pages 2307-2324, December.
    2. Ross Towe & Jonathan Tawn & Emma Eastoe & Rob Lamb, 2020. "Modelling the Clustering of Extreme Events for Short-Term Risk Assessment," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(1), pages 32-53, March.
    3. Savin Chand & Scott Power & Kevin Walsh & Neil Holbrook & Kathleen McInnes & Kevin Tory & Hamish Ramsay & Ron Hoeke & Anthony S. Kiem, 2023. "Climate processes and drivers in the Pacific and global warming: a review for informing Pacific planning agencies," Climatic Change, Springer, vol. 176(2), pages 1-16, February.
    4. Gilles Dufrénot & William Ginn & Marc Pourroy, 2023. "ENSO Climate Patterns on Global Economic Conditions," AMSE Working Papers 2308, Aix-Marseille School of Economics, France.
    5. Hosmay Lopez & Sang-Ki Lee & Dongmin Kim & Andrew T. Wittenberg & Sang-Wook Yeh, 2022. "Projections of faster onset and slower decay of El Niño in the 21st century," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    6. Aguilar, Arturo & Vicarelli, Marta, 2022. "El Niño and children: Medium-term effects of early-life weather shocks on cognitive and health outcomes," World Development, Elsevier, vol. 150(C).
    7. Hongxiang Yan & Hamid Moradkhani, 2016. "Toward more robust extreme flood prediction by Bayesian hierarchical and multimodeling," 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. 81(1), pages 203-225, March.
    8. Ryan Cronin & Anthony Halog, 2022. "A Unique Perspective of Materials, Practices and Structures Within the Food, Energy and Water Nexus of Australian Urban Alternative Food Networks," Circular Economy and Sustainability, Springer, vol. 2(1), pages 327-349, March.
    9. Tao Geng & Wenju Cai & Lixin Wu & Agus Santoso & Guojian Wang & Zhao Jing & Bolan Gan & Yun Yang & Shujun Li & Shengpeng Wang & Zhaohui Chen & Michael J. McPhaden, 2022. "Emergence of changing Central-Pacific and Eastern-Pacific El Niño-Southern Oscillation in a warming climate," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    10. Lan-Fen Chu & Michael McAleer & Chi-Chung Chen, 2012. "How Volatile is ENSO for Global Greenhouse Gas Emissions and the Global Economy?," Journal of Reviews on Global Economics, Lifescience Global, vol. 1, pages 1-12.
    11. Leo Kingston, S., 2024. "Time-varying parameters induced extreme events in Liénard systems and network," Chaos, Solitons & Fractals, Elsevier, vol. 186(C).
    12. Wang, Bing Xing & Ye, Zhi-Sheng, 2015. "Inference on the Weibull distribution based on record values," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 26-36.
    13. Uddameri, Venkatesh & Ghaseminejad, Ali & Hernandez, E. Annette, 2020. "A tiered stochastic framework for assessing crop yield loss risks due to water scarcity under different uncertainty levels," Agricultural Water Management, Elsevier, vol. 238(C).
    14. Christian L. E. Franzke & Marcin Czupryna, 2020. "Probabilistic assessment and projections of US weather and climate risks and economic damages," Climatic Change, Springer, vol. 158(3), pages 503-515, February.
    15. Juan Gonzalez & Daniela Rodriguez & Mariela Sued, 2013. "Threshold selection for extremes under a semiparametric model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(4), pages 481-500, November.
    16. Wei, Yu & Zhang, Jiahao & Chen, Yongfei & Wang, Yizhi, 2022. "The impacts of El Niño-southern oscillation on renewable energy stock markets: Evidence from quantile perspective," Energy, Elsevier, vol. 260(C).
    17. Yadav, Alka & Das, Sourish & Bakar, K. Shuvo & Chakraborti, Anirban, 2023. "Understanding the complex dynamics of climate change in south-west Australia using Machine Learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 627(C).
    18. Katharina Waha & John Clarke & Kavina Dayal & Mandy Freund & Craig Heady & Irene Parisi & Elisabeth Vogel, 2022. "Past and future rainfall changes in the Australian midlatitudes and implications for agriculture," Climatic Change, Springer, vol. 170(3), pages 1-21, February.
    19. Hongxiang Yan & Hamid Moradkhani, 2016. "Toward more robust extreme flood prediction by Bayesian hierarchical and multimodeling," 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. 81(1), pages 203-225, March.
    20. Hsieh, Ping-Hung, 2002. "An exploratory first step in teletraffic data modeling: evaluation of long-run performance of parameter estimators," Computational Statistics & Data Analysis, Elsevier, vol. 40(2), pages 263-283, August.

    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:tefoso:v:208:y:2024:i:c:s0040162524004918. 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: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    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.