R: Forest-Steppe Models

cliForestSteppePoints {macroBiome}

R Documentation

Forest-Steppe Models

Description

Calculates the values of bioclimatic indices used in forest-steppe models with different theoretical backgrounds, and estimates the presence/absence of 'forest-steppe' ecotone, for a given geographical location (latitude and elevation) and year/epoch, by using the monthly time series of climate variables.

Usage

cliForestSteppePoints(
  temp,
  prec,
  bsdf = NULL,
  lat = NULL,
  elv = NULL,
  year = 2000,
  aprchTEMP = c("hip", "tsi", "const"),
  aprchBSDF = c("hip", "const"),
  dvTEMP = 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)
`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.
`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.
`verbose`	'logical' scalar that indicates whether or not values of the bioclimatic indices used should be added to the output.

Details

Here, three forest-steppe models with different theoretical backgrounds are implemented:

fsp_hlz: A modified variant of the widely used Holdridge life zone (HLZ) system (see for the basic concept Holdridge 1947, 1967; for a proposed variant Szelepcsényi et al. 2014).
fsp_fai: A clarified version of the forestry climate classification (see for the basic concept Führer et al. 2011; for a proposed variant Mátyás et al. 2018)
fsp_svm: An initial version of the Siberian Vegetation Model (see Monserud et al. 1993)

The HLZ system classifies the vegetation type based on the distance from the ideal (theoretical) point in the 3-dimensional space of the following bioclimatic indices:

abt: Mean Annual Biotemperature (Eq 1 in Szelepcsényi et al. (2014); in °C)
tap: Total Annual Precipitation (in mm)
per: Potential Evapotranspiration Ratio (Eq 4 in Szelepcsényi et al. (2014); dimensionless)

The plotting of thresholds of the above-mentioned bioclimatic indices in the HLZ chart leads to emerge a set of hexagons and triangles. The hexagons indicate the so-called core HLZ types, while the so-called transitional HLZ types are circumscribed by equilateral triangles in the HLZ chart (see Szelepcsényi et al. 2014). However, in contrast to this study, here, the transitional types are defined as separate zones designated by the centres of the triangles. As a result, hexagons appear around the triangles in the HLZ chart, and in parallel, the size of the hexagons denoting the core types also decreases. Thus, the size of the core and transitional types are the same in this approach. During the classification, all forest-steppe types designated by Szelepcsényi et al. (2014) (and redefined by us) are aggregated into one class.
The forestry climate classification developed by Führer et al. (2011) was reworked by Mátyás et al. (2018). In the context of assessing the effects of future climate change, the 'forest-steppe' climate class was introduced in the model. In the work of Mátyás et al. (2018), this type is characterized by the Forestry Aridity Index (fai, dimensionless) values between 7.25 and 8. This definition is used here.
The Siberian Vegetation Model (Monserud et al. 1993) defines numerous types of forest-steppe on the basis of values of the Growing Degree-Days above 5°C (gdd5, in °C day), the Budyko's Dryness Index (bdi, dimensionless), and the Condrad's Continentality Index (cci, in per cent). Here, all such ecotone types are aggregated into one class, in order to estimate the presence/absence of the ‘forest-steppe’ ecotone.

Value

Depending on the setting, a data frame with three or more columns where the presence/absence data are stored in the last three columns labelled 'fsp_hlz', 'fsp_fai' and 'fsp_svm', while the additional columns contain the values of bioclimatic indices used. If verbose = FALSE, the return object is a two- or three-column data frame with the presence/absence data, depending on the available data.

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), and 'year' (year using astronomical year numbering). 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

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

Führer E, Horváth L, Jagodics A, Machon A, Szabados I (2011) Application of a new aridity index in Hungarian forestry practice. Időjárás 115(3):205–216

Holdridge LR (1947) Determination of World Plant Formations From Simple Climatic Data. Science 105(2727):367–368. doi:10.1126/science.105.2727.367

Holdridge LR (1967) Life zone ecology. Tropical Science Center, San Jose, Costa Rica

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

Mátyás Cs, Berki I, Bidló A, Csóka Gy, Czimber K, Führer E, Gálos B, Gribovszki Z, Illés G, Hirka A, Somogyi Z (2018) Sustainability of Forest Cover under Climate Change on the Temperate-Continental Xeric Limits. Forests 9(8):489. doi:10.3390/f9080489

Monserud RA, Denissenko OV, Tchebakova NM (1993) Comparison of Siberian paleovegetation to current and future vegetation under climate change. Clim Res 3(3):143–159. doi:10.3354/cr003143

Szelepcsényi Z, Breuer H, Sümegi P (2014) The climate of Carpathian Region in the 20th century based on the original and modified Holdridge life zone system. Cent Eur J Geosci 6(3):293–307. doi:10.2478/s13533-012-0189-5

Examples

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

# Predict the 'forest-steppe' ecotone (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_exPoints, {
year <- trunc(mean(seq(1981, 2010)))
fsp <- cliForestSteppePoints(colMeans(temp), colMeans(prec), colMeans(bsdf), lat, elv,
    year = year, verbose = TRUE)
fsp
})

[Package macroBiome version 0.4.0 Index]