The vegetation period, or growing season, is the period of the year when the weather conditions are sufficient for plants to grow. This package provides methods to calculate climatological or thermal growing seasons solely based on daily mean temperatures and the day of the year (DOY). Because of their simplicity, they are commonly used in plant growth models and climate change impact assessments.
The concept of a temperature driven vegetation period holds mostly for the temperate climate zone. At lower latitudes, other factors such as precipitation and evaporation can be more decisive. Some methods such as GSL of ETCCDI
are employed globally (with a half year shift in the southern hemisphere). Others have a smaller area of application as they have been parameterized with local to regional observations. However, the methods Menzel
and vonWilpert
are used throughout Germany.
The package also includes functions for downloading open meteo data from Germany's National Meteorological Service (Deutscher Wetterdienst, DWD).
The stable version can be installed from CRAN
install.packages("vegperiod")
and the development version is available from Github using the package remotes
remotes::install_github("rnuske/vegperiod")
Vegetation periods are calculated using the function vegperiod()
. One has to choose at least a start and an end method. Some methods require additional arguments, such as 'Menzel' which needs 'species'.
data(goe)
vegperiod(dates=goe$date, Tavg=goe$t,
start.method="Menzel", end.method="vonWilpert",
species="Picea abies (frueh)", est.prev=3)
Common methods for determining the onset and end of thermal vegetation periods are provided, for details see next sections and documentation. Suggestions or contributions of additional methods are always welcome. Popular choices with regard to forest trees in Germany are Menzel
and vonWilpert
. Climate change impact studies at NW-FVA are frequently conducted using Menzel
with "Picea abies (frueh)" and NuskeAlbert
for all tree species; with tree species specifics accounted for in subsequent statistical models.
Germany's National Meteorological Service offers open meteo data in its Climate Data Center.
The files are organized in deep folder structures and end with an arcane/legacy EOF character.
The Function read.DWDdata()
deals with all of that and returns a data.frame
. Beware there might be missing values and inhomogeneities.
Note: Downloading 'historical' data from DWD with read.DWDdata()
requires the package 'curl'.
Implementations of further start and end methods or download functions are more than welcome! Please suggest suitable candidates via issue or pull request.
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