Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/112069
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Type: Journal article
Title: Modeling spatial dependence of rainfall extremes across multiple durations
Author: Le, P.
Leonard, M.
Westra, S.
Citation: Water Resources Research, 2018; 54(3):2233-2248
Publisher: American Geophysical Union
Issue Date: 2018
ISSN: 0043-1397
1944-7973
Statement of
Responsibility: 
Phuong Dong Le, Michael Leonard and Seth Westra
Abstract: Determining the probability of a flood event in a catchment given that another flood has occurred in a nearby catchment is useful in the design of infrastructure such as road networks that have multiple river crossings. These conditional flood probabilities can be estimated by calculating conditional probabilities of extreme rainfall and then transforming rainfall to runoff through a hydrologic model. Each catchment’s hydrological response times are unlikely to be the same, so in order to estimate these conditional probabilities one must consider the dependence of extreme rainfall both across space and across critical storm durations. To represent these types of dependence, this study proposes a new approach for combining extreme rainfall across different durations within a spatial extreme value model using max-stable process theory. This is achieved in a stepwise manner. The first step defines a set of common parameters for the marginal distributions across multiple durations. The parameters are then spatially interpolated to develop a spatial field. Storm-level dependence is represented through the max-stable process for rainfall extremes across different durations. The dependence model shows a reasonable fit between the observed pairwise extremal coefficients and the theoretical pairwise extremal coefficient function across all durations. The study demonstrates how the approach can be applied to develop conditional maps of the return period and return level across different durations.
Keywords: Max-stable process model; spatial extremes; duration dependence; linking multiple durations; conditional simulation; intensity-duration-frequency relationships
Rights: © 2018. American Geophysical Union. All Rights Reserved.
RMID: 0030086577
DOI: 10.1002/2017WR022231
Grant ID: http://purl.org/au-research/grants/arc/DP150100411
Appears in Collections:Civil and Environmental Engineering publications

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