jointPm-package: Risk estimation using the joint probability method...

Description Details Author(s) References Examples

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)

Example output

$p.aep
[1] 0.632120559 0.393469340 0.181269247 0.095162582 0.048770575 0.019801327
[7] 0.009950166

$p.ari
[1]   1   2   5  10  20  50 100

$zout
     Complete_dep Observed_dep Observed_dep Complete_inp
[1,]     1.318555     1.202602     1.202602     1.122552
[2,]     1.580096     1.352493     1.352493     1.238775
[3,]     1.907231     1.633757     1.632440     1.448630
[4,]     2.121605     1.886587     1.883012     1.661796
[5,]     2.416108     2.154514     2.145136     1.936741
[6,]     2.822660     2.605874     2.571522     2.355833
[7,]     3.257732     3.092154     2.984605     2.863000

$px
 [1]   0   1   2   5  10  20  50 100 200 500

$py
 [1]   0   1   2   5  10  20  50 100 200 500

$oz
        lowest astronomical tide 1 ARI 2 ARI 5 ARI 10 ARI 20 ARI 50 ARI 100 ARI
No_rain                    0.178 0.984 1.054 1.144  1.204  1.254  1.314   1.354
1 ARI                      0.739 1.307 1.369 1.450  1.501  1.541  1.584   1.614
2 ARI                      1.004 1.531 1.579 1.646  1.694  1.733  1.780   1.811
5 ARI                      1.348 1.801 1.847 1.907  1.948  1.982  2.022   2.048
10 ARI                     1.559 1.981 2.024 2.080  2.118  2.149  2.186   2.211
20 ARI                     1.842 2.246 2.289 2.345  2.383  2.415  2.453   2.479
50 ARI                     2.215 2.609 2.652 2.709  2.747  2.779  2.818   2.843
100 ARI                    2.630 3.018 3.062 3.119  3.158  3.190  3.229   3.255
200 ARI                    3.135 3.523 3.567 3.624  3.662  3.695  3.735   3.761
500 ARI                    3.975 4.358 4.403 4.460  4.500  4.533  4.573   4.600
        200 ARI 500 ARI
No_rain   1.394   1.444
1 ARI     1.644   1.683
2 ARI     1.840   1.876
5 ARI     2.073   2.104
10 ARI    2.235   2.266
20 ARI    2.504   2.536
50 ARI    2.869   2.901
100 ARI   3.281   3.313
200 ARI   3.787   3.820
500 ARI   4.626   4.659

$prm
[1] 0.9

$model
[1] "log"

$prob
[1] "ARI"

jointPm documentation built on May 1, 2019, 11:16 p.m.