A continuous rainfall model based on vine copulas

Abstract

Abstract.Copulas have already proven their flexibility in rainfall modelling. Yet, their use is generally restricted to the description of bivariate dependence. Recently, vine copulas have been introduced, allowing multi-dimensional dependence structures to be described on the basis of a stage by stage mixing of 2-dimensional copulas. This paper explores the use of such vine copulas in order to incorporate all relevant dependences between the storm variables of interest. On the basis of such fitted vine copulas, an external storm structure is modelled. An internal storm structure is superimposed based on Huff curves, such that a continuous time series of rainfall is generated. The performance of the rainfall model is evaluated through a statistical comparison between an ensemble of synthetical rainfall series and the observed rainfall series and through the comparison of the annual maxima.

Application of logistic regression to simulate the influence of rainfall genesis on storm overflow operations: a probabilistic approach

“…Therefore, the question arises as to whether the information on the nature of precipitation (e.g., season of the year or precipitation genesis) could not find practical use in its modeling. It seems puzzling that the time course and the dynamics of the rainfall as the result of air masses advection (Vicente-Serrano et al, 2009;Alhammoud et al, 2014;Dayan et al, 2015) were not taken into account when rainfall generators were used to simulate storm overflows. The problem of the modeling of complex atmospheric phenomena is the subject of many works (Madsen et al, 1995;Paquet et al, 2006;Vicente-Serrano et al, 2009;Garavaglia et al, 2010;Abushandi and Merkel, 2011).…”

Multivariate Copula Analysis Toolbox (MvCAT): Describing dependence and underlying uncertainty using a Bayesian framework

“…While the rise of this concept dates back to the 1950s, copula gained popularity in hydrology and climatology after the early works of De Michele and Salvadori [2003], Favre et al [2004], Salvadori and De Michele [2004a,b], and De Michele et al [2005]. Multivariate methods and copulas have been used in many fields of study including finance [Rachev, 2003], insurance [Charpentier and Segers, 2007], integrated risk management [Embrechts et al, 2001], drought monitoring [Hao and AghaKouchak, 2013], flood risk analysis [Jongman et al, 2014], frequency analysis [Parent et al, 2014], rainfall simulation and analysis [Li et al, 2013;Vernieuwe et al, 2015], dependence analysis of hyetographs and hydrographs [Serinaldi and Kilsby, 2013], and extreme value analysis [Renard and Lang, 2007;Ribatet and Sedki, 2013]. Recent studies have striven to open new frontiers to address realworld problems using copulas.…”

Spatio-Temporal Synthesis of Continuous Precipitation Series Using Vine Copulas

“…Synthetic series are used for different purposes, e.g., planning and design, deriving long extended time series or regionalization of rainfall to areas without measurements. Among the existing precipitation models, the following types can be found: data driven generators (e.g., resampling [1,2] and disaggregation [3][4][5]), time series models (e.g., Markov chains [6]), and event-based methods [7][8][9], such as point and cluster process-based (e.g., Neyman-Scott [10][11][12][13], Barlett-Lewis [14][15][16][17][18][19][20]) or alternating renewal models [21][22][23][24].…”