| jointPm-package {jointPm} | R Documentation |
Risk estimation using the joint probability method ('jointPm')
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.
Author(s)
Feifei Zheng feifeizheng@zju.edu.cn, 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(15), 172–187, doi:10.1016/j.jhydrol.2013.09.054.
Zheng, F., Westra S. Sisson S. and Leonard M. (2014). Modelling dependence between extreme rainfall and storm surge to estimate coastal flood risk, Water Resources Research, 50, pp. 2050–2071,doi:10.1002/2013WR014616
Zheng, F., Leonard M. and Westra S. (2015). Efficient joint probability analysis of flood risk, Journal of Hydroinformatics, 17(4), pp. 584–597, doi:10.2166/hydro.2015.052.
Examples
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)