cliBIOMEGrid {macroBiome}

Vegetation Classifier Using the BIOME Model

Description

Calculates the values of bioclimatic indices used in the BIOME model developed by Prentice et al. (1992), and designates the biome type using these values, for a given region and year/epoch, by using the monthly time series of temperature, precipitation and relative sunshine duration, and the elevation data.

Usage

cliBIOMEGrid(
  rs.temp,
  rs.prec,
  rs.bsdf,
  rl.elv,
  sc.year = 2000,
  rl.MSMC = 150,
  aprchTEMP = c("hip", "tsi", "const"),
  aprchPREC = c("tsi", "hip", "const"),
  aprchBSDF = c("hip", "const"),
  dvTEMP = rep(0.7, 12),
  dvPREC = rep(0.7, 12),
  verbose = FALSE,
  filename = "",
  ...
)

Arguments

rs.temp

multi-layer Raster*/SpatRaster object with one-year time series of monthly mean air temperature (in °C)

rs.prec

multi-layer Raster*/SpatRaster object with one-year time series of monthly precipitation sum (in mm)

rs.bsdf

multi-layer Raster*/SpatRaster object with one-year time series of monthly mean relative sunshine duration (dimensionless)

rl.elv

single-layer Raster*/SpatRaster object with the elevation values (in meters above sea level)

sc.year

'numeric' scalar with the value of the year (using astronomical year numbering)

rl.MSMC

'numeric' scalar or single-layer Raster*/SpatRaster object with the value/values of the maximum soil moisture capacity (aka 'bucket size') (in mm)

aprchTEMP

'character' vector of length 1 that indicates the scheme used to generate daily values of the daily mean air temperature for a specific year. Valid values are as follows:
(a) 'hip' - this scheme applies the mean-preserving 'harmonic' interpolation method of Epstein (1991) to the values of monthly mean air temperature in order to generate daily values;
(b) 'tsi' - this scheme uses an iterative interpolation technique (Lüdeke et al. 1994) to time series of the monthly mean air temperature, in order to generate a synthetic time series of the selected meteorological variable at a temporal resolution that is higher than the daily scale; finally, this synthetic time series is upscaled to a daily resolution;
(c) 'const' - this scheme is assumed that values of the daily mean air temperature are constant within each month.

aprchPREC

'character' vector of length 1 that indicates the scheme to generate daily values of the daily precipitation sum. Valid values are as follows:
(a) 'tsi' - this scheme uses an iterative interpolation technique (Lüdeke et al. 1994) to time series of the monthly mean precipitation intensity, in order to generate a synthetic time series of the selected meteorological variable at a temporal resolution that is higher than the daily scale; finally, this synthetic time series is upscaled to a daily resolution;
(b) 'hip' - this scheme applies the mean-preserving 'harmonic' interpolation method of Epstein (1991) to the values of monthly mean precipitation intensity in order to generate daily values;
(c) 'const' - this scheme is assumed that values of the daily precipitation sum are constant within each month (the monthly precipitation sum is divided equally across each day of the month).

aprchBSDF

'character' vector of length 1 that indicates the scheme used to generate daily values of the daily fractional sunshine duration for a specific year. Valid values are as follows:
(a) 'hip' - this scheme applies the mean-preserving 'harmonic' interpolation method of Epstein (1991) to the values of monthly mean relative sunshine duration in order to generate daily values;
(b) 'const' - this scheme is assumed that values of the daily relative sunshine duration are constant within each month.

dvTEMP

'numeric' vector of length 12 with monthly values of the damping variable for the air temperature data.

dvPREC

'numeric' vector of length 12 with monthly values of the damping variable for the precipitation data.

verbose

'logical' scalar that indicates whether or not values of the bioclimatic indices used should be added to the output.

filename

output filename

...

additional arguments passed on to writeRaster

Details

See cliBIOMEPoints.

Value

Depending on the setting, a SpatRaster object with one or more layers where the numeric integers encoding the biome type are stored at the last layer, while the additional layers contain the values of bioclimatic indices used. The meaning of integers is given in the data frame vegClsNumCodes. If verbose = FALSE, the return object is a single-layer SpatRaster object with numeric integers encoding the biome type.

Note

The objects 'rs.temp', 'rs.prec' and 'rs.bsdf' must be 12-layer Raster*/SpatRaster objects, while the object 'rl.elv' has to be a single-layer Raster*/SpatRaster object. The object 'rl.MSMC' must be either a single positive number (a universal bucket size) or a single-layer Raster*/SpatRaster object (a regionally-specified bucket size). These Raster*/SpatRaster objects must have the same bounding box, projection, and resolution. The object 'sc.year' has to be a single integer number.

References

Epstein ES (1991) On Obtaining Daily Climatological Values from Monthly Means. J Clim 4(3):365–368. doi:10.1175/1520-0442(1991)004<0365:OODCVF>2.0.CO;2

Lüdeke MKB, Badeck FW, Otto RD, Häger C, Dönges S, Kindermann J, Würth G, Lang T, Jäkel U, Klaudius A, Ramge P, Habermehl S, Kohlmaier GH (1994) The Frankfurt Biosphere Model: A global process-oriented model of seasonal and long-term CO2 exchange between terrestrial ecosystems and the atmosphere. I. Model description and illustrative results for cold deciduous and boreal forests. Clim Res 4(2):143-166. doi:10.3354/cr004143

Prentice IC, Cramer W, Harrison SP, Leemans R, Monserud RA, Solomon AM (1992) A Global Biome Model Based on Plant Physiology and Dominance, Soil Properties and Climate. J Biogeogr 19(2):117–134. doi:10.2307/2845499

Examples

library(raster)

# Loading mandatory data for the Example 'Climate Normal Grid'
data(inp_exClnrGrid)
inp_exClnrGrid <- lapply(inp_exClnrGrid, crop, extent(20.15, 20.25, 46.25, 46.35))

# Designate the biome type (using the related bioclimatic indices), with default settings,
# at a grid cell near Szeged, Hungary (46.3N, 20.2E) (for the normal period 1981-2010)
with(inp_exClnrGrid, {
year <- trunc(mean(seq(1981, 2010)))
rs.BIOME <- cliBIOMEGrid(temp, prec, bsdf, elv, sc.year = year, verbose = TRUE)
rs.BIOME
})