fmev: Fitting the Metastatistical Extreme Value Distribution (MEVD)

In mevr: Fitting the Metastatistical Extreme Value Distribution MEVD

Fitting the Metastatistical Extreme Value Distribution (MEVD)

Description

Fit the MEVD distribution to rainfall observations with different estimation methods.

Usage

fmev(
  data,
  threshold = 0,
  method = c("pwm", "mle", "ls"),
  censor = FALSE,
  censor_opts = list(),
  warn = TRUE,
  left_cens = 0
)

Arguments

data	A data frame with two columns. The first column must contain dates of class Date, and the last must contain rainfall values. Negative values are disallowed, and NA values are removed with a warning.
threshold	Numeric threshold defining wet days. Values below or equal to the threshold are set to NA.
method	Character string for estimating Weibull parameters c and w. Options are probability weighted moments ("pwm"), maximum likelihood ("mle"), or least squares ("ls"). Default is "pwm" (see details).
censor	Logical. If TRUE, data is left-censored to ensure maxima follow a Weibull tail. Default is FALSE.
censor_opts	A list of censoring options: thresholds: range of quantiles. mon: start month of the block. nrtrials: number of trials. R: number of synthetic samples. See weibull_tail_test.
warn	Logical. If TRUE (default), censoring warnings are displayed.
left_cens	Numeric in [0, 1). Fraction of smallest ordinary events left-censored (see details). Default is 0. Cannot be combined with censor = TRUE.

Details

With the aim of weakening the requirement of an asymptotic assumption for the GEV distribution, a metastatistical approach was proposed by Marani and Ignaccolo (2015). The MEVD is defined in terms of the distribution of the statistical parameters describing "ordinary" daily rainfall occurrence and intensity. The MEVD accounts for the random process of event occurrence in each block and the possibly changing probability distribution of event magnitudes across different blocks, by recognizing the number of events in each block, n, and the values of the shape and scale parameters w and C of the parent Weibull distribution to be realisations of stochastic variables. The MEVD can then be written as

\begin{aligned} F = \frac{1}{T} \sum_{j=1}^T \prod_{k \in A_j} &\left( 1 - \exp\left( -\left(\frac{x}{C(j,k)}\right)^{w(j,k)} \right) \right) \end{aligned}

for w > 0 and C > 0. With T fully recorded years, yearly C and w can be estimated by fitting a Weibull distribution to the values x of this year, and n is the number of ordinary events per year, i.e. all rainfall events larger than a threshold.

If the probability distribution of daily rainfall is assumed to be time-invariant, the MEVD can be simplified to

F = [1 - exp(-x/C)^w]^n

with single values for the shape and scale parameters w and C. n is then the mean number of wet days at this location (Marra et al., 2019; Schellander et al., 2019).

As is shown e.g. Schellander et al., 2019, probability weighted moments should be preferred over maximum likelihood for the estimation of the Weibull parameters w and C. Therefore method = 'pwm' is the default.

The MEVD can also be used for sub-daily precipitation (Marra et al., 2019). In that case n has to be adapted accordingly to the 'mean number of wet events' per year.

This function returns the parameters of the fitted MEVD distribution as well as some additional fitting results and input parameters useful for further analysis.

Value

A list of class mevr with components:

c

Vector of Weibull scale parameters of the MEVD, where each component refers to one year.

w

Vector of Weibull shape parameters of the MEVD, where each component refers to one year.

n

Number of wet events per year. Wet events are defined as rainfall > threshold.

params

A named vector of the fitted parameters.

maxima

Maximum values corresponding to each year.

data

Filtered data used to fit the MEVD along with additional components useful for further analysis.

years

Vector of years as YYYY.

threshold

The chosen threshold.

method

Method used to fit the MEVD.

type

The type of distribution ("MEVD").

Author(s)

Harald Schellander, Alexander Lieb

References

Marani, M. and Ignaccolo, M. (2015) 'A metastatistical approach to rainfall extremes', Advances in Water Resources. Elsevier Ltd, 79(Supplement C), pp. 121-126. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.advwatres.2015.03.001")}.

Schellander, H., Lieb, A. and Hell, T. (2019) 'Error Structure of Metastatistical and Generalized Extreme Value Distributions for Modeling Extreme Rainfall in Austria', Earth and Space Science, 6, pp. 1616-1632. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1029/2019ea000557")}.

See Also

fsmev, ftmev

Examples

data(dailyrainfall)
fit <- fmev(dailyrainfall, method = "mle")
fit
plot(fit)

mevr documentation built on Oct. 13, 2026, 5:07 p.m.