climetrics (version 1.0-5)

dVelocity: Distant-based Velocity of Climate Change

Description

The method is developed based on the approach used in Garcia et al. (2014) Gradiant-based velocity is also implemented in the package and is available through the gVelocity function.

Garcia, R.A., Cabeza, M., Rahbek, C. and Araújo, M.B. (2014). Multiple dimensions of climate change and their implications for biodiversity. Science, 344(6183).

Usage

dVelocity(x,...,t1, t2, ny)

Value

A single Raster layer (RasterLayer or SpatRaster depending on the input)

Arguments

x

The first input climate variable that can be a RasterStack or RasterBrick, or a raster time series

...

additional input climate variables in case of using multiple climate variables that can be entered as v1, v2, ...

t1

a chanracter or a numeric vector, specifying the index of raster layers for time 1

t2

a chanracter or a numeric vector, specifying the index of raster layers for time 2

ny

integer; specifies the number of years between time 1 and time2, if the input is Raster time series, it is not needed as it will be identified by the function

Author

Shirin Taheri; Babak Naimi

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Examples

filePath <- system.file("external/", package="climetrics") # path to the dataset folder

pr <- rast(paste0(filePath,'/precip.tif'))
tmean <- rast(paste0(filePath,'/tmean.tif'))

n <- readRDS(paste0(filePath,'/dates.rds')) # corresoinding dates

head(n) # Dates corresponds to the layers in climate variables

####################

# use rts function in the rts package to make a raster time series:

pr.t <- rts(pr,n) 
tmean.t <- rts(tmean,n)
###########################


dv <- dVelocity(pr.t,tmean.t,t1='1991/2000',t2='2010/2020')

plot(dv)