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A stochastic design rainfall generator based on copulas and mass curves

TLDR
In this paper, the concept of a copula-based secondary return period was used to generate point-scale design storms from the 105 year rainfall time series with a 10 min resolution, measured at Uccle, Belgium.
Abstract
. The use of design storms can be very useful in many hydrological and hydraulic practices. In this study, the concept of a copula-based secondary return period in combination with the concept of mass curves is used to generate point-scale design storms. The analysis is based on storms selected from the 105 year rainfall time series with a 10 min resolution, measured at Uccle, Belgium. In first instance, bivariate copulas and secondary return periods are explained, together with a focus on which couple of storm variables is of highest interest for the analysis and a discussion of how the results might be affected by the goodness-of-fit of the copula. Subsequently, the fitted copula is used to sample storms with a predefined secondary return period for which characteristic variables such as storm duration and total storm depth can be derived. In order to construct design storms with a realistic storm structure, mass curves of 1st, 2nd, 3rd and 4th quartile storms are developed. An analysis shows that the assumption of independence between the secondary return period and the internal storm structure could be made. Based on the mass curves, a technique is developed to randomly generate an intrastorm structure. The coupling of both techniques eventually results in a methodology for stochastic design storm generation. Finally, its practical usefulness for design studies is illustrated based on the generation of a set of statistically identical design storm and rainfall-runoff modelling.

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Citations
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Journal ArticleDOI

On the return period and design in a multivariate framework

TL;DR: A possible way to introduce a consistent theoretical framework for the calculation of the return period in a multi-dimensional environment, based on Copulas and the Kendall's measure is outlined.
Journal ArticleDOI

Multivariate return periods in hydrology: a critical and practical review focusing on synthetic design hydrograph estimation

TL;DR: An overview of the state of the art for estimating multivari- ate design events is given and the different approaches are compared and the design hydrograph characteristics of a 3-D phenomenon composed of annual maximum peak discharge, its volume, and duration are derived.
Journal ArticleDOI

A comparative copula‐based bivariate frequency analysis of observed and simulated storm events: A case study on Bartlett‐Lewis modeled rainfall

TL;DR: In this article, a copula-based frequency analysis of storms is proposed as a tool to evaluate differences between the return periods of several types of observed and modeled storms, which indicates a problem with the modeling of the temporal structure of rainfall by the models.
Journal ArticleDOI

Evaluation of trends and multivariate frequency analysis of droughts in three meteorological subdivisions of western India

TL;DR: In this article, the authors present an evaluation of trends and multivariate frequency analysis of droughts in three meteorological subdivisions of western India, namely, western Rajasthan, Saurashtra and Kutch and Marathwada regions.
Journal ArticleDOI

Hydraulic structures subject to bivariate hydrological loads: Return period, design, and risk assessment

TL;DR: In this article, a general, structure-based framework for the design and/or risk assessment of hydraulic structures in a bivariate environment is presented, where both the structure and the design event-based approaches are applied to the design of an idealized structure, thus exploring the differences among the methods as function of the parameters involved.
References
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Book

An Introduction to Copulas

TL;DR: This book discusses the fundamental properties of copulas and some of their primary applications, which include the study of dependence and measures of association, and the construction of families of bivariate distributions.
Journal ArticleDOI

Everything You Always Wanted to Know about Copula Modeling but Were Afraid to Ask

TL;DR: This paper presents an introduction to inference for copula models, based on rank methods, by working out in detail a small, fictitious numerical example, the various steps involved in investigating the dependence between two random variables and in modeling it using copulas.
Journal ArticleDOI

Goodness-of-fit tests for copulas: A review and a power study

TL;DR: In this paper, the authors present a critical review of the blanket test procedures and suggest new ones for goodness-of-fit testing of copula models, and describe and interpret the results of a large Monte Carlo experiment designed to assess the effect of the sample size and the strength of dependence on the level and power of blanket tests for various combinations of Copula models under the null hypothesis and the alternative.
Journal ArticleDOI

Time distribution of rainfall in heavy storms

TL;DR: Time distribution relations have been developed for heavy storms on areas ranging up to 400 square miles and presented in probability terms to provide quantitative information on interstorm variability and to provide average and extreme relations for various applications of the findings.
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