3.2 Check how much corrected extreme events differ from raw extreme days.

Aims of the function BEE.calc.corrections().

The function compares former binarized value to corrected binarized value. It takes as argument the two element of BEE.id.extreme_events() output. It is a quick way of testing the implications of various settings in BEE.id.extreme_events().

Usage

Arguments:

true_event_raster: The first element of the BEE.id.extreme_events() ouput, which is a spatraster binarized that inclued the corrections made by BEE.id.extreme_events().
true_event_df: The second element of the BEE.id.extreme_events() ouput, which is a list of data.table containing information about the value of each pixel before and after definition criteria are applied to distinguish isolated extreme days from extreme events.
plot: Accepted values are TRUE of FALSE. Set to “FALSE” to not display the graphs.

Warnings:

No warning implemented yet.
## Messages: A message will be sent out providing basic information on the number of days that have been reclassified as part of an extreme event, or excluded from one.

Output

The output is a data frame containing one row for each combination of date and pixel. It contains the count of corrections from 1 to 0 and from 0 to 1, which are provided in case users want to run their own tests. However, the main interest lies in the plots that are created by the function.
The first plot shows the cumulative percentage of values that have been modified amongst all non-NA values over time. This allows us to visualise whether the corrections are homogeneous over time. If so, the lines drawn should be straight. The same graph also allows you to check whether a similar percentage of non-NA values have been changed to 1 and to 0.
The second graph allows you to check whether the corrections are homogeneous across the area. It plots the studied area and shows the difference between the number extreme days that have been added and the number extreme days that have been withdrawn, for each pixel. If a pixel value is negative, it means that, over the entire time period, more extreme days have been removed than added for that pixel.

Column name	Description	Class	Unit
pixel_id	Pixel number in the spatraster	integer
original_value	Day more extreme than baseline = 1	double
	Day less extreme than baseline = 0
cleaned_value	Day belong to an exteme event = 1	double
	Day outside of an extreme event = 0
event_id	Number of the extreme event in the	integer
	time serie
duration	Duration of the period (extreme	integer	days
	event or in between 2 events)
date	Date associated to the observation	Date
ID	The event identifier *	character
delta	cleaned_value - original value	double	days
one_to_zero	Number of days changed from 1 to 0	double	days
zero_to_one	Number of days changed from 0 to 1	double	days

Examples

Load data:

library(BioExtremeEvent)
#Binarized spatraster corrected built using BEE.id.extreme_events()
file_name_1 <- system.file(file.path("extdata", "binarized_corrected_spatraster.tiff"),
                                   package = "BioExtremeEvent")
binarized_corrected_spatraster <- terra::rast(file_name_1)
#List of dataframe containning information about former and corrected binarised values, built using BEE.id.extreme_events()
file_name_2 <- system.file(file.path("extdata", "binarized_corrected_df.rds"),
                                   package = "BioExtremeEvent")
binarized_corrected_df <- readRDS(file_name_2)

See how the extreme event definition used in article 3. Identify extreme event according specific constraints. has modified the number of extreme days.

df_count_modif <- BEE.calc.corrections(true_event_raster = binarized_corrected_spatraster,
                                       true_event_df_list = binarized_corrected_df,
                                       plot = TRUE)

## Number of 0 corrected to 1: 1706
## Number of 1 corrected to 0: 22628
## Percentage of non NA values corrected from 0 to 1: 0.45318344100646
## Percentage of non NA values corrected from 1 to 0: 6.01092315538932
## 6.46410659639578 % of the non NA values have been modified.

How to save the ouputs:

write.csv(df_count_modif, file = "your_path/data/df_count_modif.csv")

Victoria Delannoy, Nicolas Loiseau, Sébastien Villéger