Multivariate Adaptive Constructed Analogs (MACA) is a statistical method for downscaling Global Climate Models (GCMs) from their native coarse resolution to a higher spatial resolution that captures reflects observed patterns of daily near-surface meteorology and simulated changes in GCMs experiments.
Usage
getMACA(
AOI,
varname,
timeRes = "day",
model = "CCSM4",
scenario = "rcp45",
startDate,
endDate = NULL,
verbose = FALSE,
ID = NULL,
dryrun = FALSE
)Arguments
- AOI
an sf of SpatVect point or polygon to extract data for
- varname
variable name to extract (e.g. tmin)
- timeRes
daily or monthly
- model
GCM model name generating
- scenario
A climate or modeling scenario
- startDate
a start date given as "YYYY-MM-DD" to extract data for
- endDate
an end date given as "YYYY-MM-DD" to extract data for
- verbose
Should messages be emited?
- ID
a column of unique identifiers
- dryrun
Return summary of data prior to retrieving it
Value
if AOI is polygon a list of SpatRasters, if AOI is a point then a data.frame of modeled records.
See also
Other shortcuts:
get3DEP(),
getBCCA(),
getCABCM(),
getCHIRPS(),
getDaymet(),
getGLDAS(),
getGridMET(),
getISRIC_soils(),
getLCMAP(),
getLOCA(),
getLOCA_hydro(),
getLivneh(),
getLivneh_fluxes(),
getMODIS(),
getNASADEM(),
getNLCD(),
getNLDAS(),
getPRISM(),
getTerraClim(),
getTerraClimNormals(),
getVIC(),
getWorldClim()