PMP estimation with other methods

This example shows how to estimate 72h PMP by univariate GEV and Hershfield methods with PMP.jl and the recommended methodology by the World Meteorological Organization (WMO). The example uses observed daily precipitation data from the Montréal-Trudeau International Airport station from 1953 to 2012. To avoid solid precipitation, we only consider data from May to October.

Load required Julia packages

Before executing this tutorial, make sure to have installed the following packages:

• DataFrames.jl (for using the DataFrame type)
• PMP.jl

and import them using the following command:

julia> using DataFrames, PMP

Load required dataset

Loading the observed daily precipitations (in mm):

# Load the data
rain = PMP.dataset("rain")

Univariate GEV method

The univariate GEV method is not a WMO-recommended procedure. It estimates the return value of the rain for a given return period. In this example, we choose a 100-year return period.

pmp_gev = PMP_GEV(rain.Rain, rain.Date, 100, 24, 72)

We could estimate the 24h PMP with the following:

pmp_gev = PMP_GEV(rain.Rain, rain.Date, 100)

Hershfield method

The Hershfield method is an empirical approach using the mean and standard deviation of annual maximum precipitations and an abstracted statistic K. The fourth chapter of the WMO manual covers this estimation method in more detail. This present package does not take into consideration the size of the dataset. PMP_Hershfield function can take a chosen K in argument or calculate one with the dataset :

pmp_hershfield_k15 = PMP_Hershfield(rain.Rain, rain.Date, 15, 24, 72)
pmp_hershfield = PMP_Hershfield(rain.Rain, rain.Date, 24, 72)

We could estimate the 24h PMP with the following:

pmp_hershfield_k15 = PMP_Hershfield(rain.Rain, rain.Date, 15)
pmp_hershfield = PMP_Hershfield(rain.Rain, rain.Date)