Xiaoting Li, Christian Genest and Jonathan Jalbert
Article (2021)
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Open Access to the full text of this document Published Version Terms of Use: Creative Commons Attribution Non-commercial No Derivatives Download (523kB) |
Abstract
A self-exciting marked point process approach is proposed to model clustered low-flow events. It combines a self-exciting ground process designed to capture the temporal clustering behavior of extreme values and an extended Generalized Pareto mark distribution for the exceedances over a subasymptotic threshold. The model takes into account the dependence between the magnitude and occurrence time of exceedances and allows for closed-form inference on tail probabilities and large quantiles. It is used to analyze daily water levels from the Rivière des Mille Îles (Québec, Canada) and to characterize drought patterns in the Montréal area. The model is useful to generate short-term probability forecasts and to estimate the return period of major droughts. This information on the drought events is critical to water resource professionals in planning, designing, building, and managing more efficient water resource systems to hedge against the water shortage in case of extreme droughts.
Uncontrolled Keywords
Subjects: |
1600 Industrial engineering > 1600 Industrial engineering 1600 Industrial engineering > 1603 Logistics |
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Department: | Department of Mathematics and Industrial Engineering |
Funders: | Canada Research Chairs, CRSNG/NSERC, Trottier Institute for Science and Public Policy |
PolyPublie URL: | https://publications.polymtl.ca/9248/ |
Journal Title: | Environmetrics (vol. 32, no. 8) |
Publisher: | Wiley |
DOI: | 10.1002/env.2697 |
Official URL: | https://doi.org/10.1002/env.2697 |
Date Deposited: | 20 Jan 2022 16:33 |
Last Modified: | 07 Apr 2025 22:34 |
Cite in APA 7: | Li, X., Genest, C., & Jalbert, J. (2021). A self‐exciting marked point process model for drought analysis. Environmetrics, 32(8), 1-24. https://doi.org/10.1002/env.2697 |
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