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Determine vegetation period

Source: R/vegperiod.R
vegperiod.Rd

Calculate start and end date of vegetation periods based on daily average air temperature and the day of the year (DOY). The sum of day degrees within the vegetation period is included for convenience.

Usage

vegperiod(
  dates,
  Tavg,
  start.method,
  end.method,
  Tsum.out = FALSE,
  Tsum.crit = 0,
  species = NULL,
  est.prev = 0,
  check.data = TRUE
)

Arguments

dates

vector of calendar dates (objects of class Date or something understood by as.Date()). Must contain entire years if est.prev > 0 else the first year may comprise only November and December.

Tavg

vector of daily average air temperatures in degree Celsius. Same length as dates.

start.method

name of method to use for vegetation start. One of "Menzel" (needs additional argument species, see below), "StdMeteo" resp. "ETCCDI", "Ribes uva-crispa". Can be abbreviated (partial matching). For further discussion see Details.

end.method

name of method to use for vegetation end. One of "vonWilpert", "LWF-BROOK90", "NuskeAlbert" and "StdMeteo" resp. "ETCCDI". Can be abbreviated (partial matching). For further discussion see Details.

Tsum.out

boolean. Return the sum of daily mean temperatures above Tsum.crit within vegetation period, also known as growing day degrees.

Tsum.crit

threshold for sum of day degrees. Only daily mean temperatures > Tsum.crit will be tallied. The default of 0 prevents negative daily temperatures from reducing the sum. Climate change studies often use a threshold of 5.

species

name of tree species (required if start.method="Menzel" ignored otherwise).

Must be one of "Larix decidua", "Picea abies (frueh)", "Picea abies (spaet)", "Picea abies (noerdl.)", "Picea omorika", "Pinus sylvestris", "Betula pubescens", "Quercus robur", "Quercus petraea", "Fagus sylvatica".

est.prev

number of years to estimate previous year's chill days for the first year (required if start.method="Menzel" ignored otherwise).

Menzel requires the number of chill days of previous November and December. If est.prev = 0 the first year is used to get the previous year's chill days and dropped afterwards. Thus, a year from the time series is lost. To avoid losing a year, est.prev = n estimates the previous year's chill days for the first year from the average of n first years of the time series.

check.data

Performs plausibility checks on the temperature data to ensure that the temperatures have not been multiplied by ten. Plausible range is -35 to +40°C.

Value

A data.frame with year and DOY of start and end day of vegetation period. If Tsum.out=TRUE, the data.frame contains an additional column with the sum of day degrees within vegetation periods.

Details

Common methods for determining the onset and end of thermal vegetation periods are provided, for details see next sections. 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.

Start methods:

The method Menzel implements the algorithm described in Menzel (1997). The method is parameterized for 10 common tree species. It needs previous year's chill days. ETCCDI resp. StdMeteo is a simple threshold based procedure as defined by the Expert Team on Climate Change Detection and Indices (cf. ETCCDI 2009, Frich et al. 2002, Zhang et al. 2011) leading to quite early vegetation starts. This method is widely used in climate change studies. The method Ribes uva-crispa is based on leaf-out of gooseberry (Janssen 2009). It was developed by the Germany's National Meteorological Service (Deutscher Wetterdienst, DWD) and is more robust against early starts than common simple meteorological procedures.

End methods:

The end method vonWilpert is based on von Wilpert (1990). It was originally developed for Picea abies in the Black Forest but is commonly used for all tree species throughout Germany. As usual, the rules regarding the soilmatrix are neglected in this implementation. LWF-BROOK90 is -for the sake of convenience- a reimplementation of the LWF-BROOK90 VBA (version 3.4) variant of "vonWilpert" (Hammel and Kennel 2001). Their interpretation of von Wilpert (1990) and the somewhat lower precision of VBA was mimicked. NuskeAlbert provide a very simple method which is inspired by standard climatological procedures but employs a 7 day moving average and a 5 °C threshold (cf. Walther and Linderholm 2006). ETCCDI resp. StdMeteo is a simple threshold based procedure as defined by the Expert Team on Climate Change Detection and Indices (cf. ETCCDI 2009, Frich et al. 2002, Zhang et al. 2011) leading to quite late vegetation ends.

References

ETCCDI (2009) Climate Change Indices: Definitions of the 27 core indices. http://etccdi.pacificclimate.org/list_27_indices.shtml

Frich, P., Alexander, L., Della-Marta, P., Gleason, B., Haylock, M., Klein Tank, A. and Peterson, T. (2002) Observed coherent changes in climatic extremes during the second half of the twentieth century. Climate Research, 19, 193--212. doi:10.3354/cr019193 .

Hammel, K. and Kennel, M. (2001) Charakterisierung und Analyse der Wasserverfügbarkeit und des Wasserhaushalts von Waldstandorten in Bayern mit dem Simulationsmodell BROOK90. Forstliche Forschungsberichte München.

Janssen, W. (2009) Definition des Vegetationsanfanges. Internal Report, Deutscher Wetterdienst, Abteilung Agrarmeteorologie.

Menzel, A. (1997) Phänologie von Waldbäumen unter sich ändernden Klimabedingungen - Auswertung der Beobachtungen in den Internationalen Phänologischen Gärten und Möglichkeiten der Modellierung von Phänodaten. Forstliche Forschungsberichte München.

von Wilpert, K. (1990) Die Jahrringstruktur von Fichten in Abhängigkeit vom Bodenwasserhaushalt auf Pseudogley und Parabraunerde: Ein Methodenkonzept zur Erfassung standortsspezifischer Wasserstreßdispostion. Freiburger Bodenkundliche Abhandlungen.

Walther, A. and Linderholm, H. W. (2006) A comparison of growing season indices for the Greater Baltic Area. International Journal of Biometeorology, 51(2), 107--118. doi:10.1007/s00484-006-0048-5 .

Zhang, X., Alexander, L., Hegerl, G. C., Jones, P., Tank, A. K., Peterson, T. C., Trewin, B. and Zwiers, F. W. (2011) Indices for monitoring changes in extremes based on daily temperature and precipitation data. Wiley Interdisciplinary Reviews: Climate Change, 2(6), 851--870. doi:10.1002/wcc.147 .

Examples

data(goe)
vegperiod(dates=goe$date, Tavg=goe$t,
          start.method="Menzel", end.method="vonWilpert",
          species="Picea abies (frueh)", est.prev=5)
#>    year start end
#> 1  2001   119 274
#> 2  2002   127 279
#> 3  2003   125 279
#> 4  2004   116 279
#> 5  2005   124 279
#> 6  2006   126 271
#> 7  2007   124 279
#> 8  2008   125 279
#> 9  2009   115 279
#> 10 2010   116 279

# take chill days from first year, which is then dropped
vegperiod(dates=goe$date, Tavg=goe$t, start="Menzel", end="vonWilpert",
          species="Picea abies (frueh)", est.prev=0)
#>   year start end
#> 1 2002   127 279
#> 2 2003   125 279
#> 3 2004   116 279
#> 4 2005   124 279
#> 5 2006   126 271
#> 6 2007   124 279
#> 7 2008   125 279
#> 8 2009   115 279
#> 9 2010   116 279

# add column with sum of day degrees in vegetation periods
vegperiod(dates=goe$date, Tavg=goe$t, Tsum.out=TRUE,
          start="StdMeteo", end="StdMeteo")
#>    year start end   Tsum
#> 1  2001    34 347 3474.7
#> 2  2002    36 329 3273.8
#> 3  2003    65 323 3273.7
#> 4  2004    38 333 3361.4
#> 5  2005    69 327 3229.4
#> 6  2006    18 308 2891.6
#> 7  2007    84 354 3216.4
#> 8  2008    64 322 3273.7
#> 9  2009    48 321 3195.9
#> 10 2010    88 356 3359.8