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Investigating Spatial Extremes of Annual Daily Precipitation Using CMIP6 Multi-Model Ensembles for Sustainable Flood Risk Assessment

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  • Alaba Boluwade

    (School of Climate Change & Adaptation, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada)

  • Paul Sheridan

    (School of Mathematical and Computational Sciences, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada)

  • Upaka Rathnayake

    (Faculty of Engineering and Design, Atlantic Technological University, F91 YW50 Sligo, Ireland)

Abstract

This study investigates the spatial characteristics of daily maximum precipitation for Prince Edward Island using a max-stable process model. The ssp126, ssp245, and ssp585 climate change scenarios, indicating low/optimistic, intermediate/in-between, and worst/pessimistic emissions scenarios, respectively, were extracted from 11 global climate model ensembles. For the time periods, the reference (historical) period was from 1971 to 2000, according to the World Meteorological Organization recommendations. Other time periods considered were 2011–2040, 2041–2070, and 2071–2100 as immediate, intermediate, and far future periods, respectively. The spatial trends analysis shows a west-to-east gradient throughout the entire study area. Return levels of 25 years were predicted for all the projections using the spatial generalized extreme value model fitted to the historical period, showing that topography should be included as a covariate in the spatial extreme model. Across the 134 grid points used in the study, the predicted return level for the historical period was 94 mm. Compared with the immediate time period, there is an increase of 47%, 53%, and 50% for the low, intermediate, and worst emission scenarios, respectively. For the intermediate period, there is an increase of 43%, 59%, and 56% for the low, intermediate, and worst emission scenarios, respectively. For the far future period, there is an increase of 49%, 48%, and 84% for the low, intermediate, and worst emission scenarios, respectively. There is a systematic increase in return levels based on the different periods. This shows a high chance of increased risks of extreme events of large magnitudes for this area in the immediate future through to the far future. This study will be useful for engineers, city planners, financial officials, and policymakers tasked with infrastructure development, long-term safety protocols, and sustainability and financial risk management.

Suggested Citation

  • Alaba Boluwade & Paul Sheridan & Upaka Rathnayake, 2025. "Investigating Spatial Extremes of Annual Daily Precipitation Using CMIP6 Multi-Model Ensembles for Sustainable Flood Risk Assessment," Sustainability, MDPI, vol. 17(18), pages 1-23, September.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:18:p:8198-:d:1747446
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    References listed on IDEAS

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    1. Elmar Kriegler & Jae Edmonds & Stéphane Hallegatte & Kristie Ebi & Tom Kram & Keywan Riahi & Harald Winkler & Detlef Vuuren, 2014. "A new scenario framework for climate change research: the concept of shared climate policy assumptions," Climatic Change, Springer, vol. 122(3), pages 401-414, February.
    2. Opitz, T., 2013. "Extremal t processes: Elliptical domain of attraction and a spectral representation," Journal of Multivariate Analysis, Elsevier, vol. 122(C), pages 409-413.
    3. Brian O’Neill & Elmar Kriegler & Keywan Riahi & Kristie Ebi & Stephane Hallegatte & Timothy Carter & Ritu Mathur & Detlef Vuuren, 2014. "A new scenario framework for climate change research: the concept of shared socioeconomic pathways," Climatic Change, Springer, vol. 122(3), pages 387-400, February.
    4. Padoan, S. A. & Ribatet, M. & Sisson, S. A., 2010. "Likelihood-Based Inference for Max-Stable Processes," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 263-277.
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