Collection of common methods to determine growing season length in a simple manner. Start and end dates of the vegetation periods are calculated solely based on daily mean temperatures and the day of the year.
The vegetation period, or growing season, is the period of the year when the weather conditions are sufficient for plants to grow. This package collects climatological or thermal growing seasons that can be calculated from 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.
vonWilpert are used throughout Germany.
The package also includes functions for downloading open meteo data from Germany's National Meteorological Service (Deutscher Wetterdienst, DWD).
A development version of the package
vegperiod can be installed from Github using the package devtools.
Vegetation periods a calculated using the function
vegperiod(). One has to choose at least a start and end method. Some methods, such as 'Menzel', require additional arguments.
data(goe)vegperiod(dates=goe$date, Tavg=goe$t,start.method="Menzel", end.method="vonWilpert",species="Picea abies (frueh)", est.prev=5)
Some common methods for calculating the onset and end of vegetation periods are already implemented. Popular choices with regard to forest trees in Germany are 'Menzel' and 'vonWilpert'.
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.
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'.
Further start and end methods or download functions are more than welcome!