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A self‐exciting marked point process model for drought analysis

Xiaoting Li, Christian Genest, Jonathan Jalbert

Article (2021)

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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

drought, extended generalized Pareto, extreme-value inference, point process

Subjects: 1600 Industrial engineering > 1600 Industrial engineering
1600 Industrial engineering > 1603 Logistics
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: 11 Nov 2022 13:58
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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