Description Author(s) Examples
Rhine flood seasonality analysis. PhD project by Berry Boessenkool. The main functions are introduced in the examples below.
Berry Boessenkool, berry-b@gmx.de, 2017
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | # qdoyCompute aggregates streamflow values per day of the year (doy)
# The first examples require the original data to be in seasFolder.
# Unfortunately, we are not allowed to make it publicly available.
# Please contact berry-b@gmx.de if you need the raw data.
load(seasFolder("data/dismeta.Rdata"))
dd <- selectDates(1990,2010, df=dis)[,c("date","Koeln")]
qdoy <- qdoyCompute("date", "Koeln", data=dd, shift=117) # 2 secs
str(qdoy)
# Visualize the result:
qdoyVis(qdoy, shift=117)
qdoyVis(qdoy, main="Cologne 1990-2010", RPs=50, cols=4, ylim=c(2e3,10e3), shift=117, lab=0)
qdoyVis(qdoy, dist="empirical", RPs=50, cols=3, add=TRUE, lab=0)
legend("topright", c("empirical", "gev"), col=3:4, lwd=3)
# qdoyPeriods computes the aggregates for separate periods.
# This procedure was applied to 55 stations at large streams and rivers.
# (see source code rfs-package.R)
# The result is stored in this package, see ?seas
# Elements from that list can be visualized as follows:
qdoyVisPeriods("Rekingen")
# You can visualize your own data split up in periods with
qdoy <- qdoyPeriods("Koeln") # 3x3 seconds
str(qdoy)
qdoyVisPeriods("Koeln", list(Koeln=qdoy))
# By default, the package dataset "seas" will be used:
qdoyVisPeriods("Mainz")
qdoyVisPeriods("Mainz", sd=3) # for smoothing
qdoyVisPeriods("Oberriet_Blatten") # can handle NAs
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