Interpolation of Precipitation Extremes on a Large Domain Toward IDF Curve Construction at Unmonitored Locations

Jonathan Jalbert (), Christian Genest () and Luc Perreault ()
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Jonathan Jalbert: Polytechnique Montréal
Christian Genest: McGill University
Luc Perreault: Institut de recherche d’Hydro-Québec

Journal of Agricultural, Biological and Environmental Statistics, 2022, vol. 27, issue 3, No 4, 486 pages

Abstract: Abstract An intensity–duration–frequency (IDF) curve describes the relationship between rainfall intensity and duration for a given return period and location. Such curves are obtained through frequency analysis of rainfall data and commonly used in infrastructure design, flood protection, water management, and urban drainage systems. However, they are typically available only in sparse locations. Data for other sites must be interpolated as the need arises. This paper describes how extreme precipitation of several durations can be interpolated to compute IDF curves on a large, sparse domain. In the absence of local data, a reconstruction of the historical meteorology is used as a covariate for interpolating extreme precipitation characteristics. This covariate is included in a hierarchical Bayesian spatial model for extreme precipitations. This model is especially well suited for a covariate gridded structure, thereby enabling fast and precise computations. As an illustration, the methodology is used to construct IDF curves over Eastern Canada. An extensive cross-validation study shows that at locations where data are available, the proposed method generally improves on the current practice of Environment and Climate Change Canada which relies on a moment-based fit of the Gumbel extreme-value distribution.

Keywords: Bayesian spatial modeling; Extreme precipitation; Gaussian random Markov field; Intensity–duration–frequency (IDF) curve (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s13253-022-00491-5

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