tsEvaNanRunnigBlowTh

This function calculates the return period of low flow for a given time series
based on a threshold and window size. It uses a sliding window approach to
count the number of values below the threshold within each window, and then
calculates the return period based on the proportion of values below the
threshold. Assumes that the input data has a 7 days timestep.

Adaptation of the 'Matlab' 'tsEVA' toolbox developed by Lorenzo Mentaschi
available here:
<https://github.com/menta78/tsEva>. It contains an implementation of the
Transformed-Stationary (TS) methodology for non-stationary extreme
value Analysis (EVA) as described in Mentaschi et al. (2016) <doi:10.5194/hess-20-3527-2016>.
In synthesis this approach consists in:
(i) transforming a non-stationary time series into a
stationary one to which the stationary extreme value theory can be applied; and
(ii) reverse-transforming the result into a non-stationary extreme
value distribution.
'RtsEva' offers several options for trend estimation (mean, extremes, seasonal)
and contains multiple plotting functions displaying different aspects
of the non-stationarity of extremes.

Alois Tilloy

RtsEva

Performs the Transformed-Stationary Extreme Values Analysis

tsEvaNanRunnigBlowTh function

<dl><dt>series</dt>
<dd>The time series data.</dd>
<dt>threshold</dt>
<dd>The threshold value for low flow.</dd>
<dt>windowSize</dt>
<dd>The size of the sliding window.</dd></dl>

Arguments

Calculate the return period of low flow based on a threshold and window size — tsEvaNanRunnigBlowTh

<dl>

<dt>series</dt>
<dd>The time series data.</dd>


<dt>threshold</dt>
<dd>The threshold value for low flow.</dd>


<dt>windowSize</dt>
<dd>The size of the sliding window.</dd>

</dl>

tsEvaNanRunnigBlowTh: Calculate the return period of low flow based on a threshold and window size

Description

Usage

Value

Arguments

Examples