Risk estimation using the joint probability method ('jointPm')

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Description

The overall impact of climate and weather related events such as flooding, wildfires and cyclones is determined by the interaction of many processes acting together. For example, coastal floods may be caused by coincident extreme rainfall and extreme storm tides, floods in confluence regions may depend on simultaneously large flows from two or more tributaries. It is challenging to perform the joint probability analysis of flood risk with multiple forcing variables, because the return period of forcing processes is not directly equivalent to the return period of floods. This package uses a bivariate integration approach to efficiently estimate risk by accounting for two forcing variables at extreme levels.

Details

Package: jointPm
Type: Package
Version: 2.3.1
Date: 2014-01-10
License: GPL (>= 2)
LazyLoad: yes

Author(s)

Feifei Zheng feifei.zheng@adelaide.edu.au, Michael Leonard michael.leonard@adelaide.edu.au, Seth Westra seth.westra@adelaide.edu.au

References

Zheng, F., S. Westra, and S. A. Sisson (2013), Quantifying the dependence between extreme rainfall and storm surge in the coastal zone, Journal of Hydrology, 505(0), 172-187.

Zheng, F., Westra S. Sisson S. and Leonard M. (2014a). Modelling the dependence between extreme rainfall and storm surge to estimate coastal flood risk, Water Resources Research, under review.

Zheng, F., Leonard M. and Westra S. (2014b). An efficient bivariate integration method for joint probability analysis of flood risk, Water Resources Research, under review.

Examples

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 library(jointPm)
 data(flood)
 px=flood$px;py=flood$py;z=flood$flood_table;prm=flood$prm;pout=flood$pout
 binteg(px,py,z,prm,pout,model="log",prob="ARI",nz=100,ninc=1000)