cliBIOMEPoints {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 geographical location (latitude and elevation) and year/epoch, by using the monthly time series of temperature, precipitation and relative sunshine duration.

Usage

cliBIOMEPoints(
  temp,
  prec,
  bsdf,
  lat,
  elv,
  year = 2000,
  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
)

Arguments

temp

'numeric' R object with one-year time series of monthly mean air temperature (in °C)

prec

'numeric' R object with one-year time series of monthly precipitation sum (in mm)

bsdf

'numeric' R object with one-year time series of monthly mean relative sunshine duration (dimensionless)

lat

'numeric' vector with the latitude coordinates (in decimal degrees)

elv

'numeric' vector with the elevation values (in meters above sea level)

year

'numeric' vector with values of the year (using astronomical year numbering)

MSMC

'numeric' vector with 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.

Details

To classify vegetation, the BIOME model developed by Prentice et al. (1992) uses the values of the following 5 bioclimatic indices:

• tc: Mean Temperature of the Coldest Month (in °C)

• tw: Mean Temperature of the Warmest Month (in °C)

• gdd5: Growing Degree-Days on 5°C base (in °C day)

• gdd0: Growing Degree-Days on 0°C base (in °C day)

• ptc: Priestley–Taylor Coefficient (dimensionless)

For details about calculating bioclimatic indices, see the function cliBioCliIdxPoints. The Priestley–Taylor coefficient (ptc, dimensionless) is exceptional because its computation requires a simulation of evapotranspiration at daily time step via the implementation of the SPLASH algorithm (Davis et al. 2017) (see dlyEngWtrFluxPoints). The application of the SPLASH algorithm requires, among other things, one-year time series of the climate variables at daily scale, which are generated from average monthly values using the function dlyWeaGenPoints.
The designation of the biome type is implemented as a two-step procedure. First, the presence of each plant functional type (PFT) is estimated under the given climatic conditions. Subsequently, the biome type is designated by combining PFTs occurring at the maximal dominance level with each other (see Table 5 in Prentice et al. (1992)). Each PFT is described by constraints of bioclimatic variables associated with their climatic tolerances and requirements (see Table 1 in Prentice et al. (1992)). In the initial version of the BIOME model, a total of 17 biome types are distinguished (see vegClsNumCodes).

Value

Depending on the setting, a data frame with one or more columns where the biome types are stored in the last (character) column, while the additional columns contain the values of bioclimatic indices used. The abbreviations of biome types can be found in the data frame vegClsNumCodes. If verbose = FALSE, the return object is a one-column data frame with the biome types.

Note

As with any function with a point mode, a set of basic input data is defined here. In this case, they are as follows: 'temp' (one-year time series of monthly mean air temperature), 'prec' (one-year time series of monthly precipitation sum), and 'bsdf' (one-year time series of monthly mean relative sunshine duration). The objects 'temp', 'prec' and 'bsdf' must be either vectors of length 12 or 12-column matrices. The first dimensions of these matrices have to be the same length. The function automatically converts vectors into single-row matrices during the error handling, and then uses these matrices. The first dimensions of these matrices determines the number of rows in the result matrix. In the case of arguments that do not affect the course of the calculation procedure or the structure of the return object, scalar values (i.e., 'numeric' vector of length 1) may also be allowed. In this case, they are as follows: 'lat' (latitude coordinates in decimal degrees), 'elv' (elevation in meters above sea level), 'year' (year using astronomical year numbering), and 'MSMC' ('bucket size' in mm). These scalars are converted to vectors by the function during the error handling, and these vectors are applied in the further calculations. If these data are stored in vectors of length at least 2, their length must be the same size of first dimension of the matrices containing the basic data.

References

Davis TW, Prentice IC, Stocker BD, Thomas RT, Whitley RJ, Wang H, Evans BJ, Gallego-Sala AV, Sykes MT, Cramer W (2017) Simple process-led algorithms for simulating habitats (SPLASH v.1.0): robust indices of radiation, evapotranspiration and plant-available moisture. Geosci Model Dev 10(2):689–708. doi:10.5194/gmd-10-689-2017

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

# Loading mandatory data for the Example 'Points'
data(inp_exPoints)
data(vegClsNumCodes)

# Designate the biome type (using the related biolcimatic indices), with default settings,
# at a grid cell near Szeged, Hungary (46.3N, 20.2E) (for the normal period 1981-2010)
with(inp_exPoints, {
year <- trunc(mean(seq(1981, 2010)))
BIOME <- cliBIOMEPoints(colMeans(temp), colMeans(prec), colMeans(bsdf), lat, elv,
    year = year, verbose = TRUE)
numCode <- which(sapply(vegClsNumCodes$Code.BIOME, identical, BIOME[, "vegCls"]))
cbind(BIOME[,-c(6)], vegClsNumCodes[numCode, c("Name.BIOME", "Code.BIOME")])
})