Journal
HYDROLOGICAL PROCESSES
Volume 32, Issue 11, Pages 1635-1647Publisher
WILEY
DOI: 10.1002/hyp.11516
Keywords
Bayesian estimation; extreme precipitation; IDF curves; model selection; multiscaling
Categories
Funding
- EU's H2020 Research and Innovation Program [690462]
- H2020 Societal Challenges Programme [690462] Funding Source: H2020 Societal Challenges Programme
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Rainfall intensity-duration-frequency (IDF) curves are a standard tool in urban water resources engineering and management. They express how return levels of extreme rainfall intensity vary with duration. The simple scaling property of extreme rainfall intensity, with respect to duration, determines the form of IDF relationships. It is supposed that the annual maximum intensity follows the generalized extreme value (GEV) distribution. As well known, for simple scaling processes, the location parameter and scale parameter of the GEV distribution obey a power law with the same exponent. Although, the simple scaling hypothesis is commonly used as a suitable working assumption, the multiscaling approach provides a more general framework. We present a new IDF relationship that has been formulated on the basis of the multiscaling property. It turns out that the GEV parameters (location and scale) have a different scaling exponent. Next, we apply a Bayesian framework to estimate the multiscaling GEV model and to choose the most appropriate model. It is shown that the model performance increases when using the multiscaling approach. The new model for IDF curves reproduces the data very well and has a reasonable degree of complexity without overfitting on the data.
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