Package mobirep

This package is associated with the article “Evaluating the efficacy of bivariate extreme modelling approaches for multi-hazard scenarios” https://nhess.copernicus.org/articles/20/2091/2020/nhess-20-2091-2020.html It includes 6 models, the joint tail KDE model derived from Cooley et al. (2019) https://doi.org/10.1007%2Fs10687-019-00348-0, the conditional extreme model initially developed by Heffernan and Tawn (2004) https://doi.org/10.1111/j.1467-9868.2004.02050.x and four copulae: Normal, Farlie-Gumbel-Morgenstern, Galambos and Gumbel.

Selection of analogous synthetic datasets from Tilloy et al. (2020)

60 bivariate datasets

SyntheticData * Import data example bivariate dataset of daily mean temperature (data from E-OBS; Cornes et al., 2018) and number of wildfire per day in Porto district (Portugal)

data(porto)

Use function Analogsel to identify which synthetic datasets from Tilloy et al. (2020) are analogs of the imput data

AnalogSel(fire01meantemp)

Model the marginal distributions of the dataset

select extreme threshold for the two margins

tr1=0.9
tr2=0.9

remove NA form the dataset

fire01meantemp=na.omit(fire01meantemp)
u=fire01meantemp

fit a GPD to both margins and create extrapolated time serie (useful to create level curves)

margins=Margins.mod(tr1,tr2,u=fire01meantemp)

Fit bivariate models to the data

jtres<-JT.KDE.ap(u2=u,pb=0.01,pobj=upobj,beta=100,
vtau=vtau,kk=kk,devplot=F,mar1=uu[,1],mar2=uu[,2],
px=pp[,1],py=pp[,2],interh=interh)

Create level curve with a given amount of point and compute the density of the curves (for most likely scenario)