rainy_season | R Documentation |
Function calculates the yearly start and end dates of the rainy season from a precipitation time series based on a statistical approach as described by Gerstengarbe & Werner (1999).
rainy_season(prec_ts = NULL, dry_season = NULL, nodata = NA)
prec_ts |
Daily precipitation time series object of class |
dry_season |
Day of year in the expected centre of dry season. This is the starting point of the stastical procedure to look for the next rainy season in the data set. Should be set approximately (+- 1 month) to the centre of the dry season. |
nodata |
No-data value in the time series. Default: |
data.frame
of rainy season start and end days in input format for
the hydrological model WASA.
Columns are: station ID (column names of input xts
object), year, start
day of rainy season (negative values refer to the previous year), day of year
the climax of the vegetation season is reached, end day of rainy season,
last day of transition period from rainy to dry season (values greater than
365/366 refer to the next year).
The first rainy season that can be identified by the function is the one
that starts after the first dry season in the given time series. This means,
for instance, that if the time series starts on Jan 1st 1978 and dry_season
is set to 243 (conditions for NE Brazil), the first detectable rainy season
is the one starting around Dec 78 to Feb 79. To get also the rainy season
for year 1978 for this example, 365 daily values can be artifically inserted
at the beginning of the time series, representing the hypothetical year 1977.
Just copy the data of 1978.
Tobias Pilz tpilz@uni-potsdam.de
lumpR package introduction with literature study and sensitivity analysis:
Pilz, T.; Francke, T.; Bronstert, A. (2017): lumpR 2.0.0: an R package facilitating
landscape discretisation for hillslope-based hydrological models.
Geosci. Model Dev., 10, 3001-3023, doi: 10.5194/gmd-10-3001-2017
Function uses the FORTRAN 77 code developed by Gerstengarbe et al.. The algorithm is described in:
Gerstengarbe & Werner (1999): Estimation of the beginning and end of recurrent events within a climate regime. Climate Research, 11(2), 97-107.
Function further used by A. Guentner to simulate vegetation dynamics within the hydrological model WASA:
Guentner, A. (2002): Large-scale hydrological modelling in the semi-arid North-East of Brazil. PIK Report 77, Potsdam Institute for Climate Impact Research, Potsdam, Germany.
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