| Title: | Generation of ENVIREM Variables |
|---|---|
| Description: | Generation of bioclimatic rasters that are complementary to the typical 19 bioclim variables. |
| Authors: | Pascal O. Title [aut], Jordan B. Bemmels [aut], Pascal Title [cre] |
| Maintainer: | Pascal Title <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 3.0 |
| Built: | 2025-01-04 06:27:30 UTC |
| Source: | https://github.com/ptitle/envirem |
Generates thornthwaite aridity index raster.
aridityIndexThornthwaite(precipStack, PETstack, precipScale = 1)aridityIndexThornthwaite(precipStack, PETstack, precipScale = 1)
precipStack |
SpatRaster of monthly precipitation. |
PETstack |
SpatRaster of monthly potential evapotranspiration. Layer names are assumed to end in the month number. |
precipScale |
integer; scaling factor for the precipitation data, see envirem for additional details. |
Thornthwaite aridity index = 100d / n
where d = sum of monthly differences between precipitation and PET for months where precip < PET
where n = sum of monthly PET for those months
RasterLayer, unitless
Pascal Title
Thornthwaite, C.W. (1948). An approach toward a rational classification of climate. Geographical Review, 38, 55-94.
Requires rasters created with monthlyPET.
# Find example rasters rasterFiles <- list.files(system.file('extdata', package='envirem'), full.names=TRUE) env <- rast(rasterFiles) # identify the appropriate layers meantemp <- grep('mean', names(env), value=TRUE) solar <- grep('solrad', names(env), value=TRUE) maxtemp <- grep('tmax', names(env), value=TRUE) mintemp <- grep('tmin', names(env), value=TRUE) # read them in as SpatRasters meantemp <- env[[meantemp]] solar <- env[[solar]] maxtemp <- env[[maxtemp]] mintemp <- env[[mintemp]] tempRange <- abs(maxtemp - mintemp) # get monthly PET pet <- monthlyPET(meantemp, solar, tempRange) precip <- grep('prec', names(env), value=TRUE) precip <- env[[precip]] # set up naming scheme - only precip is different from default assignNames(precip = 'prec_##') aridityIndexThornthwaite(precip, pet) # set back to defaults assignNames(reset = TRUE)# Find example rasters rasterFiles <- list.files(system.file('extdata', package='envirem'), full.names=TRUE) env <- rast(rasterFiles) # identify the appropriate layers meantemp <- grep('mean', names(env), value=TRUE) solar <- grep('solrad', names(env), value=TRUE) maxtemp <- grep('tmax', names(env), value=TRUE) mintemp <- grep('tmin', names(env), value=TRUE) # read them in as SpatRasters meantemp <- env[[meantemp]] solar <- env[[solar]] maxtemp <- env[[maxtemp]] mintemp <- env[[mintemp]] tempRange <- abs(maxtemp - mintemp) # get monthly PET pet <- monthlyPET(meantemp, solar, tempRange) precip <- grep('prec', names(env), value=TRUE) precip <- env[[precip]] # set up naming scheme - only precip is different from default assignNames(precip = 'prec_##') aridityIndexThornthwaite(precip, pet) # set back to defaults assignNames(reset = TRUE)
The naming scheme for the different input variables are defined via a custom environment, which only needs to be done once.
assignNames(tmin, tmax, tmean, precip, solrad, pet, reset)assignNames(tmin, tmax, tmean, precip, solrad, pet, reset)
tmin |
naming scheme for minimum temperature |
tmax |
naming scheme for maximum temperature |
tmean |
naming scheme for mean temperature |
precip |
naming scheme for precipitation |
solrad |
naming scheme for solar radiation |
pet |
naming scheme for monthly potential evapotranspiration |
reset |
if |
The .var environment contains the naming scheme for the input variables,
and this will be queried by the various functions in this R package. The user
should use this function to define the names of the variables, up until the variable number,
and after the variable number (prefix and suffix relative to the number). This is done by
providing a template of the naming, and placing ## where the numbers would be (1:12).
For example, if your minimum temperature rasters are named as
worldclim_v2_LGM_ccsm4_minTemp_1_land.tif, then you should define the following:
"worldclim_v2_LGM_ccsm4_minTemp_##_land" for tmin.
File extensions should not be included at all (not as a suffix).
This only needs to be done once during your R session. For any variable name, if these tags are removed, and the file extension is removed, only the variable number should remain (the month number).
When using the assignNames() function, you can specify as many or as few as needed.
Variable numbers can have zero-padding. This is handled automatically.
Therefore, bio_1 or bio_01 are both fine, and nothing needs to be specified.
The default values are tmin_, tmax_, tmean_, precip_,
et_solrad_ and pet_, with no suffix.
You can use the function namingScheme() to see the current assigned values.
namingScheme() assignNames(precip = 'precip_##_5arcmin') assignNames(solrad = 'solar_##', tmin = 'minTemp##') namingScheme() # set back to default assignNames(reset = TRUE)namingScheme() assignNames(precip = 'precip_##_5arcmin') assignNames(solrad = 'solar_##', tmin = 'minTemp##') namingScheme() # set back to default assignNames(reset = TRUE)
Generate climatic moisture index.
climaticMoistureIndex(annualPrecip, PET, precipScale = 1)climaticMoistureIndex(annualPrecip, PET, precipScale = 1)
annualPrecip |
rasterLayer of annual precipitation (bioclim 12) |
PET |
rasterLayer of annual potential evapotranspiration |
precipScale |
integer; scaling factor for the precipitation data, see envirem for additional details. |
P/PET - 1 when P < PET 1 - PET/P when P >= PET
SpatRaster ranging from -1 to +1.
Pascal Title
Willmott, C. & Feddema, J. (1992). A More Rational Climatic Moisture Index. The Professional Geographer, 44, 84-88.
Vörösmarty, C.J., Douglas, E.M., Green, P.A. & Revenga, C. (2005). Geospatial Indicators of Emerging Water Stress: An Application to Africa. AMBIO: A Journal of the Human Environment, 34, 230-236.
# Find example rasters rasterFiles <- list.files(system.file('extdata', package='envirem'), full.names=TRUE) env <- rast(rasterFiles) # identify the appropriate layers meantemp <- grep('mean', names(env), value=TRUE) solar <- grep('solrad', names(env), value=TRUE) maxtemp <- grep('tmax', names(env), value=TRUE) mintemp <- grep('tmin', names(env), value=TRUE) # read them in as SpatRasters meantemp <- env[[meantemp]] solar <- env[[solar]] maxtemp <- env[[maxtemp]] mintemp <- env[[mintemp]] tempRange <- abs(maxtemp - mintemp) # get monthly PET pet <- monthlyPET(meantemp, solar, tempRange) # get mean annual PET annualPET <- sum(pet) climaticMoistureIndex(env[['bio_12']], annualPET)# Find example rasters rasterFiles <- list.files(system.file('extdata', package='envirem'), full.names=TRUE) env <- rast(rasterFiles) # identify the appropriate layers meantemp <- grep('mean', names(env), value=TRUE) solar <- grep('solrad', names(env), value=TRUE) maxtemp <- grep('tmax', names(env), value=TRUE) mintemp <- grep('tmin', names(env), value=TRUE) # read them in as SpatRasters meantemp <- env[[meantemp]] solar <- env[[solar]] maxtemp <- env[[maxtemp]] mintemp <- env[[mintemp]] tempRange <- abs(maxtemp - mintemp) # get monthly PET pet <- monthlyPET(meantemp, solar, tempRange) # get mean annual PET annualPET <- sum(pet) climaticMoistureIndex(env[['bio_12']], annualPET)
Generate Continentality index.
continentality(tmax, tmin, tempScale = 1)continentality(tmax, tmin, tempScale = 1)
tmax |
rasterLayer of average temperature of the warmest month |
tmin |
rasterLayer of average temperature of the coldest month |
tempScale |
integer; scaling factor for the temperature data, see envirem for additional details. |
continentality index = tmax - tmin
rasterLayer in units of degrees C.
Pascal Title
Rivas-Martínez, S. & Rivas-Sáenz, S. “Synoptical Worldwide Bioclimatic Classification System”. [accessed 15 February 2016]
Sayre, R., Comer, P., Warner, H. & Cress, J. (2009) A new map of standardized terrestrial ecosystems of the conterminous United States: US Geological Survey Professional Paper 1768. Reston, VA.
# Find example rasters rasterFiles <- list.files(system.file('extdata', package='envirem'), full.names=TRUE) env <- rast(rasterFiles) # identify appropriate layers tmean <- grep('tmean', names(env)) tmin <- grep('tmin', names(env)) tmax <- grep('tmax', names(env)) tmean <- env[[tmean]] tmin <- env[[tmin]] tmax <- env[[tmax]] # calculate temperature extremes temp <- otherTempExtremes(tmean, tmin, tmax) meantempWarmest <- temp[['meanTempWarmest']] meantempColdest <- temp[['meanTempColdest']] continentality(meantempWarmest, meantempColdest, tempScale = 10)# Find example rasters rasterFiles <- list.files(system.file('extdata', package='envirem'), full.names=TRUE) env <- rast(rasterFiles) # identify appropriate layers tmean <- grep('tmean', names(env)) tmin <- grep('tmin', names(env)) tmax <- grep('tmax', names(env)) tmean <- env[[tmean]] tmin <- env[[tmin]] tmax <- env[[tmax]] # calculate temperature extremes temp <- otherTempExtremes(tmean, tmin, tmax) meantempWarmest <- temp[['meanTempWarmest']] meantempColdest <- temp[['meanTempColdest']] continentality(meantempWarmest, meantempColdest, tempScale = 10)
Calculate Emberger's pluviometric quotient.
embergerQ(P, M, m, tempScale = 1, precipScale = 1)embergerQ(P, M, m, tempScale = 1, precipScale = 1)
P |
rasterLayer, total annual precipitation |
M |
rasterLayer, mean max temperature of the warmest month |
m |
rasterLayer, mean min temperature of the coldest month |
tempScale |
integer; scaling factor for the temperature data, see envirem for additional details. |
precipScale |
integer; scaling factor for the precipitation data, see envirem for additional details. |
Q = 2000 P / [(M + m + 546.4) * (M - m)]
rasterLayer in mm / degrees C
Pascal Title
Daget, P. (1977) Le bioclimat méditerranéen: analyse des formes climatiques par le système d’Emberger. Vegetatio, 34, 87–103.
# Find example rasters rasterFiles <- list.files(system.file('extdata', package='envirem'), full.names=TRUE) env <- rast(rasterFiles) embergerQ(env[['bio_12']], env[['bio_5']], env[['bio_6']], tempScale = 10)# Find example rasters rasterFiles <- list.files(system.file('extdata', package='envirem'), full.names=TRUE) env <- rast(rasterFiles) embergerQ(env[['bio_12']], env[['bio_5']], env[['bio_6']], tempScale = 10)
Generation of bioclimatic rasters that are complementary to the typical 19 bioclim variables.
Package: envirem
Type: Package
Version: 2.2
Date: 2020-06-03
License: GPL-2 | GPL-3
NOTE: Temperature rasters are now assumed by default to be in degrees C and precipitation in mm.
rasters in degrees C * 10. Worldclim v2 uses degrees C. CHELSA has several options, depending
on whether rasters are downloaded as floating point or integer. Therefore, there is an argument
tempScale to specify the units of temperature, and precipScale to define precipitation units:
For example:
If using worldclim v1 data where temperature is in degrees C * 10, specify
tempScale = 10.
If using worldclim v2 where temperature is in degrees C, specify tempScale = 1.
For CHELSA, read the documentation and carefully examine the rasters.
If a function does not have the tempScale argument, then the function is not sensitive
to the units of the input temperature rasters.
Of course, it is also perfectly acceptable to leave tempScale = 1 and precipScale = 1
and modify the input rasters yourself.
The main function for generating ENVIREM rasters is generateEnvirem.
A complete tutorial of this R package can be found at https://ptitle.github.io/envirem/.
Pascal O. Title, Jordan B. Bemmels
https://github.com/ptitle/envirem
Title, P.O., Bemmels, J.B. 2018. ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography 41:291–307.
Useful links:
Generate monthly extraterrestrial solar radiation rasters.
ETsolradRasters(rasterTemplate, year, outputDir = NULL, ...)ETsolradRasters(rasterTemplate, year, outputDir = NULL, ...)
rasterTemplate |
any rasterLayer that can be used to extract extent, resolution, projection, etc. |
year |
The year solar radiation should be calculated for. See details. |
outputDir |
destination directory for rasters, can be |
... |
additional arguments passed to |
Given the latitude values of the cells found in the raster template
and the year, monthly extraterrestrial solar radiation can be calculated,
using the palinsol R package. year = 0 corresponds to 1950.
Although the year can take on any value, it should match the time period
of the other rasters that will be used for generating ENVIREM variables.
Suggestions would be year = 40 for the present, year = -6000
for the mid Holocene, and year = -21500 for the LGM.
If you are having problems with this function and the rasterTemplate is not in long/lat, try with an unprojected long/lat raster.
If outputDir = NULL, a SpatRaster is returned. Otherwise, rasters
are written to disk in the designated directory, and nothing is returned.
Naming of the layers uses the tag specified via assignNames.
Pascal Title
J. Laskar et al., A long-term numerical solution for the insolation quantities of the Earth, Astron. Astroph., 428, 261-285 2004.
# Find example rasters rasterFiles <- list.files(system.file('extdata', package='envirem'), full.names=TRUE) env <- rast(rasterFiles) # set aside a template raster template <- env[[1]] # generate solar radiation for the present solrad <- ETsolradRasters(template, year = 40, outputDir = NULL)# Find example rasters rasterFiles <- list.files(system.file('extdata', package='envirem'), full.names=TRUE) env <- rast(rasterFiles) # set aside a template raster template <- env[[1]] # generate solar radiation for the present solrad <- ETsolradRasters(template, year = 40, outputDir = NULL)
Generates rasters from an input dataset.
generateEnvirem( masterstack, solradstack = NULL, monthPET = NULL, var, tempScale = 1, precipScale = 1 )generateEnvirem( masterstack, solradstack = NULL, monthPET = NULL, var, tempScale = 1, precipScale = 1 )
masterstack |
rasterStack containing all monthly precipitation, min temperature, max temperature, and optionally mean temperature rasters. |
solradstack |
rasterStack of monthly solar radiation, can be |
monthPET |
rasterStack of monthly potential evapotranspiration. If |
var |
vector of names of variables to generate, see Details. |
tempScale |
integer; scaling factor for the temperature data, see envirem for additional details. |
precipScale |
integer; scaling factor for the precipitation data, see envirem for additional details. |
The function verifyRasterNames should be used to
verify that the appropriate rasters are present in masterstack.
Possible variables to generate include:
annualPET
aridityIndexThornthwaite
climaticMoistureIndex
continentality
embergerQ
growingDegDays0
growingDegDays5
maxTempColdest
minTempWarmest
meanTempColdest
meanTempWarmest
monthCountByTemp10
PETColdestQuarter
PETDriestQuarter
PETseasonality
PETWarmestQuarter
PETWettestQuarter
thermicityIndex
If var = 'all', then all of the variables will be generated.
rasterStack
Pascal Title
# Find example rasters rasterFiles <- list.files(system.file('extdata', package='envirem'), full.names=TRUE) # create stack of temperature and precipitation rasters # and stack of solar radiation rasters solradFiles <- grep('solrad', rasterFiles, value=TRUE) worldclim <- rast(setdiff(rasterFiles, solradFiles)) solar <- rast(solradFiles) # set up naming scheme - only precip is different from default assignNames(precip = 'prec_##') # generate all possible envirem variables generateEnvirem(worldclim, solar, var='all', tempScale = 10) # set back to defaults assignNames(reset = TRUE)# Find example rasters rasterFiles <- list.files(system.file('extdata', package='envirem'), full.names=TRUE) # create stack of temperature and precipitation rasters # and stack of solar radiation rasters solradFiles <- grep('solrad', rasterFiles, value=TRUE) worldclim <- rast(setdiff(rasterFiles, solradFiles)) solar <- rast(solradFiles) # set up naming scheme - only precip is different from default assignNames(precip = 'prec_##') # generate all possible envirem variables generateEnvirem(worldclim, solar, var='all', tempScale = 10) # set back to defaults assignNames(reset = TRUE)
Growing degree days above some base temperature.
growingDegDays(meantempstack, baseTemp, tempScale = 1)growingDegDays(meantempstack, baseTemp, tempScale = 1)
meantempstack |
SpatRaster of mean monthly temperature in deg C |
baseTemp |
base temperature in degrees C. |
tempScale |
integer; scaling factor for the temperature data, see envirem for additional details. |
growing degree days = sum of all monthly temps greater than baseTemp, multiplied by total number of days
rasterLayer in degrees C * days.
Pascal Title
Prentice, I.C., Cramer, W., Harrison, S.P., Leemans, R., Monserud, R.A. & Solomon, A.M. (1992). A Global Biome Model Based on Plant Physiology and Dominance, Soil Properties and Climate. Journal of Biogeography, 19, 117–134.
# Find example rasters rasterFiles <- list.files(system.file('extdata', package='envirem'), full.names=TRUE) env <- rast(rasterFiles) meantemp <- env[[grep('tmean', names(env), value=TRUE)]] growingDegDays(meantemp, 10, tempScale = 10)# Find example rasters rasterFiles <- list.files(system.file('extdata', package='envirem'), full.names=TRUE) env <- rast(rasterFiles) meantemp <- env[[grep('tmean', names(env), value=TRUE)]] growingDegDays(meantemp, 10, tempScale = 10)
Number of months with mean temperature greater than some base temp.
monthCountByTemp(tempStack, minTemp = 10, tempScale = 1)monthCountByTemp(tempStack, minTemp = 10, tempScale = 1)
tempStack |
SpatRaster of monthly mean temperature in degrees C |
minTemp |
reference temperature in degrees C |
tempScale |
integer; scaling factor for the temperature data, see envirem for additional details. |
rasterLayer with values representing counts of months.
Pascal Title
Metzger, M.J., Bunce, R.G.H., Jongman, R.H.G., Sayre, R., Trabucco, A. & Zomer, R. (2013). A high-resolution bioclimate map of the world: a unifying framework for global biodiversity research and monitoring. Global Ecology and Biogeography, 22, 630-638.
# Find example rasters rasterFiles <- list.files(system.file('extdata', package='envirem'), full.names=TRUE) env <- rast(rasterFiles) # identify the appropriate layers meantemp <- grep('mean', names(env), value=TRUE) meantemp <- env[[meantemp]] monthCountByTemp(meantemp, 10, tempScale = 10)# Find example rasters rasterFiles <- list.files(system.file('extdata', package='envirem'), full.names=TRUE) env <- rast(rasterFiles) # identify the appropriate layers meantemp <- grep('mean', names(env), value=TRUE) meantemp <- env[[meantemp]] monthCountByTemp(meantemp, 10, tempScale = 10)
Monthly potential evapotranspiration
monthlyPET(Tmean, RA, TD, tempScale = 1)monthlyPET(Tmean, RA, TD, tempScale = 1)
Tmean |
SpatRaster of monthly mean temperature |
RA |
SpatRaster of monthly extraterrestrial solar radiation |
TD |
SpatRaster of monthly temperature range |
tempScale |
integer; scaling factor for the temperature data, see envirem for additional details. |
PET = 0.0023 * RA * (Tmean + 17.8) * TD ^ 0.5
SpatRaster of monthly PET in mm / month
Pascal Title
Hargreaves, G. L., Hargreaves, G. H., & Riley, J. P. (1985). Irrigation water requirements for Senegal River basin. Journal of Irrigation and Drainage Engineering, 111, 265-275.
Zomer, R.J., Trabucco, A., Bossio, D.A. & Verchot, L.V. (2008). Climate change mitigation: A spatial analysis of global land suitability for clean development mechanism afforestation and reforestation. Agriculture, Ecosystems and Environment, 126, 67-80.
Zomer, R.J., Trabucco, A., Van Straaten, O. & Bossio, D.A. (2006) Carbon, Land and Water: A Global Analysis of the Hydrologic Dimensions of Climate Change Mitigation through Afforestation/Reforestation. International Water Management Institute Research Report 101. Colombo, Sri Lanka.
# Find example rasters rasterFiles <- list.files(system.file('extdata', package='envirem'), full.names=TRUE) env <- rast(rasterFiles) # identify the appropriate layers meantemp <- grep('mean', names(env), value=TRUE) solar <- grep('solrad', names(env), value=TRUE) maxtemp <- grep('tmax', names(env), value=TRUE) mintemp <- grep('tmin', names(env), value=TRUE) # read them in as SpatRasters meantemp <- env[[meantemp]] solar <- env[[solar]] maxtemp <- env[[maxtemp]] mintemp <- env[[mintemp]] tempRange <- abs(maxtemp - mintemp) monthlyPET(meantemp, solar, tempRange, tempScale = 10)# Find example rasters rasterFiles <- list.files(system.file('extdata', package='envirem'), full.names=TRUE) env <- rast(rasterFiles) # identify the appropriate layers meantemp <- grep('mean', names(env), value=TRUE) solar <- grep('solrad', names(env), value=TRUE) maxtemp <- grep('tmax', names(env), value=TRUE) mintemp <- grep('tmin', names(env), value=TRUE) # read them in as SpatRasters meantemp <- env[[meantemp]] solar <- env[[solar]] maxtemp <- env[[maxtemp]] mintemp <- env[[mintemp]] tempRange <- abs(maxtemp - mintemp) monthlyPET(meantemp, solar, tempRange, tempScale = 10)
Lists the naming scheme, either as default, or as modified by the user.
namingScheme()namingScheme()
See assignNames.
namingScheme()namingScheme()
Generates max temp of the coldest month, min temp of the warmest month, mean temp of the coldest month, mean temp of the warmest month.
otherTempExtremes(meantempStack, mintempStack, maxtempStack)otherTempExtremes(meantempStack, mintempStack, maxtempStack)
meantempStack |
SpatRaster of monthly mean temperature |
mintempStack |
SpatRaster of monthly min temperature |
maxtempStack |
SpatRaster of monthly max temperature |
SpatRaster of maxTempColdest, minTempWarmest, meanTempColdest, meanTempWarmest, in same units as input rasters.
Pascal Title
# Find example rasters rasterFiles <- list.files(system.file('extdata', package='envirem'), full.names=TRUE) env <- rast(rasterFiles) # identify appropriate layers tmean <- grep('tmean', names(env)) tmin <- grep('tmin', names(env)) tmax <- grep('tmax', names(env)) tmean <- env[[tmean]] tmin <- env[[tmin]] tmax <- env[[tmax]] # calculate temperature extremes otherTempExtremes(tmean, tmin, tmax)# Find example rasters rasterFiles <- list.files(system.file('extdata', package='envirem'), full.names=TRUE) env <- rast(rasterFiles) # identify appropriate layers tmean <- grep('tmean', names(env)) tmin <- grep('tmin', names(env)) tmax <- grep('tmax', names(env)) tmean <- env[[tmean]] tmin <- env[[tmin]] tmax <- env[[tmax]] # calculate temperature extremes otherTempExtremes(tmean, tmin, tmax)
Takes a raster that is centered on 0 longitude (default) and recenters it on the Pacific
pacificCentric(r, crop = TRUE)pacificCentric(r, crop = TRUE)
r |
rasterLayer or SpatRaster in unprojected geographic coordinates |
crop |
logical, should raster then be cropped to longitude [100, 300] |
Cropping to [100, 300] is equivalent to [100, -60]
rasterLayer or SpatRaster
Pascal Title
# Find example rasters rasterFiles <- list.files(system.file('extdata', package='envirem'), full.names=TRUE) tmin1 <- rast(grep('tmin_1\\.', rasterFiles, value=TRUE)) pacificCentric(tmin1, crop = TRUE)# Find example rasters rasterFiles <- list.files(system.file('extdata', package='envirem'), full.names=TRUE) tmin1 <- rast(grep('tmin_1\\.', rasterFiles, value=TRUE)) pacificCentric(tmin1, crop = TRUE)
Calculates summed PET of the coldest, warmest, wettest and driest quarters.
petExtremes(PETstack, precipStack, meantempStack)petExtremes(PETstack, precipStack, meantempStack)
PETstack |
SpatRaster of monthly PET, layer names assumed to end in month numbers |
precipStack |
SpatRaster of monthly precipitation |
meantempStack |
SpatRaster of monthly mean temperature |
Generates summed monthly PET for the warmest, coldest, wettest and driest 3 consecutive months. Previous versions of the envirem package incorrectly calculated mean quarterly PET.
SpatRaster of PETColdestQuarter, PETWarmestQuarter, PETWettestQuarter, PETDriestQuarter in mm / month.
Pascal Title
Metzger, M.J., Bunce, R.G.H., Jongman, R.H.G., Sayre, R., Trabucco, A. & Zomer, R. (2013). A high-resolution bioclimate map of the world: a unifying framework for global biodiversity research and monitoring. Global Ecology and Biogeography, 22, 630-638.
# Find example rasters rasterFiles <- list.files(system.file('extdata', package='envirem'), full.names=TRUE) env <- rast(rasterFiles) # identify the appropriate layers meantemp <- grep('mean', names(env), value=TRUE) solar <- grep('solrad', names(env), value=TRUE) maxtemp <- grep('tmax', names(env), value=TRUE) mintemp <- grep('tmin', names(env), value=TRUE) precip <- grep('prec', names(env), value=TRUE) # read them in as SpatRasters meantemp <- env[[meantemp]] solar <- env[[solar]] maxtemp <- env[[maxtemp]] mintemp <- env[[mintemp]] tempRange <- abs(maxtemp - mintemp) precip <- env[[precip]] # set up naming scheme - only precip is different from default assignNames(precip = 'prec_##') # get monthly PET pet <- monthlyPET(meantemp, solar, tempRange) petExtremes(pet, precip, meantemp) # set back to defaults assignNames(reset = TRUE)# Find example rasters rasterFiles <- list.files(system.file('extdata', package='envirem'), full.names=TRUE) env <- rast(rasterFiles) # identify the appropriate layers meantemp <- grep('mean', names(env), value=TRUE) solar <- grep('solrad', names(env), value=TRUE) maxtemp <- grep('tmax', names(env), value=TRUE) mintemp <- grep('tmin', names(env), value=TRUE) precip <- grep('prec', names(env), value=TRUE) # read them in as SpatRasters meantemp <- env[[meantemp]] solar <- env[[solar]] maxtemp <- env[[maxtemp]] mintemp <- env[[mintemp]] tempRange <- abs(maxtemp - mintemp) precip <- env[[precip]] # set up naming scheme - only precip is different from default assignNames(precip = 'prec_##') # get monthly PET pet <- monthlyPET(meantemp, solar, tempRange) petExtremes(pet, precip, meantemp) # set back to defaults assignNames(reset = TRUE)
Seasonality of potential evapotranspiration
PETseasonality(PETstack)PETseasonality(PETstack)
PETstack |
SpatRaster of monthly PET rasters |
PET seasonality = 100 * standard deviation of monthly PET.
rasterLayer in mm / month
Pascal Title
Metzger, M.J., Bunce, R.G.H., Jongman, R.H.G., Sayre, R., Trabucco, A. & Zomer, R. (2013). A high-resolution bioclimate map of the world: a unifying framework for global biodiversity research and monitoring. Global Ecology and Biogeography, 22, 630-638.
# Find example rasters rasterFiles <- list.files(system.file('extdata', package='envirem'), full.names=TRUE) env <- rast(rasterFiles) # identify the appropriate layers meantemp <- grep('mean', names(env), value=TRUE) solar <- grep('solrad', names(env), value=TRUE) maxtemp <- grep('tmax', names(env), value=TRUE) mintemp <- grep('tmin', names(env), value=TRUE) # read them in as SpatRasters meantemp <- env[[meantemp]] solar <- env[[solar]] maxtemp <- env[[maxtemp]] mintemp <- env[[mintemp]] tempRange <- abs(maxtemp - mintemp) # get monthly PET pet <- monthlyPET(meantemp, solar, tempRange) PETseasonality(pet)# Find example rasters rasterFiles <- list.files(system.file('extdata', package='envirem'), full.names=TRUE) env <- rast(rasterFiles) # identify the appropriate layers meantemp <- grep('mean', names(env), value=TRUE) solar <- grep('solrad', names(env), value=TRUE) maxtemp <- grep('tmax', names(env), value=TRUE) mintemp <- grep('tmin', names(env), value=TRUE) # read them in as SpatRasters meantemp <- env[[meantemp]] solar <- env[[solar]] maxtemp <- env[[maxtemp]] mintemp <- env[[mintemp]] tempRange <- abs(maxtemp - mintemp) # get monthly PET pet <- monthlyPET(meantemp, solar, tempRange) PETseasonality(pet)
Compensated Thermicity index
thermicityIndex( annualTemp, minTemp, maxTemp, continentality, returnCompensated = TRUE, tempScale = 1 )thermicityIndex( annualTemp, minTemp, maxTemp, continentality, returnCompensated = TRUE, tempScale = 1 )
annualTemp |
rasterLayer, mean annual temperature |
minTemp |
rasterLayer, min temp of the coldest month |
maxTemp |
rasterLayer, max temp of the coldest month |
continentality |
rasterLayer, continentality index |
returnCompensated |
logical: if |
tempScale |
integer; scaling factor for the temperature data, see envirem for additional details. |
thermicity index = tempRange + minTemp + maxTemp
The compensated thermicity index incorporates corrections designed to make this metric more appropriately comparable across the globe.
rasterLayer in degrees C
Pascal Title
Rivas-Martínez, S. & Rivas-Sáenz, S. “Synoptical Worldwide Bioclimatic Classification System”. [accessed 15 February 2016]
Sayre, R., Comer, P., Warner, H. & Cress, J. (2009) A new map of standardized terrestrial ecosystems of the conterminous United States: US Geological Survey Professional Paper 1768. Reston, VA.
# Find example rasters rasterFiles <- list.files(system.file('extdata', package='envirem'), full.names=TRUE) env <- rast(rasterFiles) # identify appropriate layers tmean <- grep('tmean', names(env)) tmin <- grep('tmin', names(env)) tmax <- grep('tmax', names(env)) tmean <- env[[tmean]] tmin <- env[[tmin]] tmax <- env[[tmax]] # calculate temperature extremes temp <- otherTempExtremes(tmean, tmin, tmax) ci <- continentality(temp[['meanTempWarmest']], temp[['meanTempColdest']], tempScale = 10) # compensated thermicity index thermicityIndex(env[['bio_1']], env[['bio_6']], temp[['maxTempColdest']], ci, tempScale = 10)# Find example rasters rasterFiles <- list.files(system.file('extdata', package='envirem'), full.names=TRUE) env <- rast(rasterFiles) # identify appropriate layers tmean <- grep('tmean', names(env)) tmin <- grep('tmin', names(env)) tmax <- grep('tmax', names(env)) tmean <- env[[tmean]] tmin <- env[[tmin]] tmax <- env[[tmax]] # calculate temperature extremes temp <- otherTempExtremes(tmean, tmin, tmax) ci <- continentality(temp[['meanTempWarmest']], temp[['meanTempColdest']], tempScale = 10) # compensated thermicity index thermicityIndex(env[['bio_1']], env[['bio_6']], temp[['maxTempColdest']], ci, tempScale = 10)
Given a SpatRaster, this function will verify the naming scheme and check that all required rasters are present.
verifyRasterNames( masterstack = NULL, solradstack = NULL, monthPET = NULL, returnRasters = FALSE )verifyRasterNames( masterstack = NULL, solradstack = NULL, monthPET = NULL, returnRasters = FALSE )
masterstack |
SpatRaster containing all precipitation, min temperature, max temperature, and (optionally) mean temperature variables. |
solradstack |
SpatRaster of monthly solar radiation |
monthPET |
SpatRaster of monthly potential evapotranspiration |
returnRasters |
if |
This function checks that the following are present:
12 precipitation rasters
12 min temperature rasters
12 max temperature rasters
12 mean temperature rasters [optional]
12 solar radiation rasters
12 PET rasters [optional]
The naming scheme will be checked against the one
defined via the custom naming environment. See link{?assignNames}
for additional details.
The function can test the temp/precip SpatRaster and/or the solar radiation SpatRaster separately, or simultaneously.
Prints messages to the console if returnRasters = FALSE,
If returnRasters = TRUE, then a SpatRaster is returned. This SpatRaster
will not include rasters that were deemed unnecessary.
Pascal Title
# Find example rasters rasterFiles <- list.files(system.file('extdata', package='envirem'), full.names=TRUE) # create stack of temperature and precipitation rasters # and stack of solar radiation rasters solradFiles <- grep('solrad', rasterFiles, value=TRUE) worldclim <- rast(setdiff(rasterFiles, solradFiles)) solar <- rast(solradFiles) # modify naming names(worldclim) <- gsub('tmin_', 'minTemp', names(worldclim)) names(worldclim) <- paste0(names(worldclim), '_v1.0') names(solar) <- gsub('et_solrad_', 'solar_', names(solar)) # but don't specify this change namingScheme() # Run check verifyRasterNames(masterstack = worldclim, solradstack = solar, returnRasters = FALSE) # But if we specify our naming scheme assignNames(tmin = 'minTemp##_v1.0', tmax = 'tmax_##_v1.0', tmean = 'tmean_##_v1.0', solrad = 'solar_##', precip = 'prec_##_v1.0') namingScheme() verifyRasterNames(masterstack = worldclim, solradstack = solar, returnRasters = FALSE) # set back to defaults assignNames(reset = TRUE)# Find example rasters rasterFiles <- list.files(system.file('extdata', package='envirem'), full.names=TRUE) # create stack of temperature and precipitation rasters # and stack of solar radiation rasters solradFiles <- grep('solrad', rasterFiles, value=TRUE) worldclim <- rast(setdiff(rasterFiles, solradFiles)) solar <- rast(solradFiles) # modify naming names(worldclim) <- gsub('tmin_', 'minTemp', names(worldclim)) names(worldclim) <- paste0(names(worldclim), '_v1.0') names(solar) <- gsub('et_solrad_', 'solar_', names(solar)) # but don't specify this change namingScheme() # Run check verifyRasterNames(masterstack = worldclim, solradstack = solar, returnRasters = FALSE) # But if we specify our naming scheme assignNames(tmin = 'minTemp##_v1.0', tmax = 'tmax_##_v1.0', tmean = 'tmean_##_v1.0', solrad = 'solar_##', precip = 'prec_##_v1.0') namingScheme() verifyRasterNames(masterstack = worldclim, solradstack = solar, returnRasters = FALSE) # set back to defaults assignNames(reset = TRUE)