Installing the latest stable version (from CRAN):
install.packages("hydroTSM")
\noindent Alternatively, you can also try the under-development version (from Github):
if (!require(devtools)) install.packages("devtools")
library(devtools)
install_github("hzambran/hydroTSM")
library(hydroTSM)
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
## Loading required package: xts
data(SanMartinoPPts)
x <- window(SanMartinoPPts, start=as.Date("1985-01-01"))
( m <- daily2monthly(x, FUN=sum) )
## 1985-01-01 1985-02-01 1985-03-01 1985-04-01 1985-05-01 1985-06-01
## 141.2 7.0 140.6 72.0 175.6 131.4
## 1985-07-01 1985-08-01 1985-09-01 1985-10-01 1985-11-01 1985-12-01
## 85.4 159.4 27.2 58.4 101.8 54.8
## 1986-01-01 1986-02-01 1986-03-01 1986-04-01 1986-05-01 1986-06-01
## 75.8 131.6 59.6 237.8 108.2 144.8
## 1986-07-01 1986-08-01 1986-09-01 1986-10-01 1986-11-01 1986-12-01
## 81.2 141.0 69.8 38.2 44.4 20.4
## 1987-01-01 1987-02-01 1987-03-01 1987-04-01 1987-05-01 1987-06-01
## 46.8 111.0 45.6 98.4 212.0 153.8
## 1987-07-01 1987-08-01 1987-09-01 1987-10-01 1987-11-01 1987-12-01
## 221.8 175.0 90.6 278.8 164.8 29.8
## 1988-01-01 1988-02-01 1988-03-01 1988-04-01 1988-05-01 1988-06-01
## 118.0 49.8 22.4 100.6 187.4 193.0
## 1988-07-01 1988-08-01 1988-09-01 1988-10-01 1988-11-01 1988-12-01
## 120.4 149.2 61.2 136.4 10.0 59.4
## 1989-01-01 1989-02-01 1989-03-01 1989-04-01 1989-05-01 1989-06-01
## 0.0 152.6 46.2 365.4 77.4 241.6
## 1989-07-01 1989-08-01 1989-09-01 1989-10-01 1989-11-01 1989-12-01
## 302.8 114.4 65.4 12.8 145.0 110.6
## 1990-01-01 1990-02-01 1990-03-01 1990-04-01 1990-05-01 1990-06-01
## 51.6 12.4 65.8 127.0 74.4 175.0
## 1990-07-01 1990-08-01 1990-09-01 1990-10-01 1990-11-01 1990-12-01
## 143.8 90.8 106.0 153.0 326.6 106.0
dates <- time(x)
( nyears <- yip(from=start(x), to=end(x), out.type="nmbr" ) )
## [1] 6
1) Summary statistics
smry(x)
## Index x
## Min. 1985-01-01 0.0000
## 1st Qu. 1986-07-02 0.0000
## Median 1988-01-01 0.0000
## Mean 1988-01-01 3.7470
## 3rd Qu. 1989-07-01 2.6000
## Max. 1990-12-31 122.0000
## IQR <NA> 2.6000
## sd <NA> 10.0428
## cv <NA> 2.6800
## Skewness <NA> 5.3512
## Kurtosis <NA> 39.1619
## NA's <NA> 0.0000
## n <NA> 2191.0000
hydroplot(x, var.type="Precipitation", main="at San Martino",
pfreq = "dm", from="1987-01-01")
2) Amount of days with information (not \texttt{NA}) per year
dwi(x)
## 1985 1986 1987 1988 1989 1990
## 365 365 365 366 365 365
3) Amount of days with information (not \texttt{NA}) per month per year
dwi(x, out.unit="mpy")
## Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
## 1985 31 28 31 30 31 30 31 31 30 31 30 31
## 1986 31 28 31 30 31 30 31 31 30 31 30 31
## 1987 31 28 31 30 31 30 31 31 30 31 30 31
## 1988 31 29 31 30 31 30 31 31 30 31 30 31
## 1989 31 28 31 30 31 30 31 31 30 31 30 31
## 1990 31 28 31 30 31 30 31 31 30 31 30 31
4) Plotting the monthly precipitation values for each year, useful for identifying dry/wet months.
# Daily zoo to monthly zoo
m <- daily2monthly(x, FUN=sum, na.rm=TRUE)
# Creating a matrix with monthly values per year in each column
M <- matrix(m, ncol=12, byrow=TRUE)
colnames(M) <- month.abb
rownames(M) <- unique(format(time(m), "%Y"))
# Plotting the monthly precipitation values
require(lattice)
## Loading required package: lattice
print(matrixplot(M, ColorRamp="Precipitation",
main="Monthly precipitation at San Martino st., [mm/month]"))
Annual values of precipitation
daily2annual(x, FUN=sum, na.rm=TRUE)
## 1985-01-01 1986-01-01 1987-01-01 1988-01-01 1989-01-01 1990-01-01
## 1154.8 1152.8 1628.4 1207.8 1634.2 1432.4
Average annual precipitation
Obvious way:
mean( daily2annual(x, FUN=sum, na.rm=TRUE) )
## [1] 1368.4
Another way (more useful for streamflows, where FUN=mean):
The function annualfunction applies FUN twice over x:
( i) firstly, over all the elements of x belonging to the same year, in order to obtain the corresponding annual values, and
(ii) secondly, over all the annual values of x previously obtained, in order to obtain a single annual value.
annualfunction(x, FUN=sum, na.rm=TRUE) / nyears
## value
## 1368.4
Median of the monthly values at station 'x'. Not needed, just for looking at these values in the boxplot.
monthlyfunction(m, FUN=median, na.rm=TRUE)
## Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
## 63.7 80.4 52.9 113.8 141.9 164.4 132.1 145.1 67.6 97.4 123.4 57.1
Vector with the three-letter abbreviations for the month names
cmonth <- format(time(m), "%b")
Creating ordered monthly factors
months <- factor(cmonth, levels=unique(cmonth), ordered=TRUE)
Boxplot of the monthly values
boxplot( coredata(m) ~ months, col="lightblue", main="Monthly Precipitation",
ylab="Precipitation, [mm]", xlab="Month")
Average seasonal values of precipitation
seasonalfunction(x, FUN=sum, na.rm=TRUE) / nyears
## DJF MAM JJA SON
## 213.1333 369.4000 470.8000 315.0667
Extracting the seasonal values for each year
( DJF <- dm2seasonal(x, season="DJF", FUN=sum) )
## 1985 1986 1987 1988 1989 1990
## 148.2 262.2 178.2 197.6 212.0 174.6
( MAM <- dm2seasonal(m, season="MAM", FUN=sum) )
## 1985 1986 1987 1988 1989 1990
## 388.2 405.6 356.0 310.4 489.0 267.2
( JJA <- dm2seasonal(m, season="JJA", FUN=sum) )
## 1985 1986 1987 1988 1989 1990
## 376.2 367.0 550.6 462.6 658.8 409.6
( SON <- dm2seasonal(m, season="SON", FUN=sum) )
## 1985 1986 1987 1988 1989 1990
## 187.4 152.4 534.2 207.6 223.2 585.6
Plotting the time evolution of the seasonal precipitation values
hydroplot(x, pfreq="seasonal", FUN=sum, stype="default")
Common steps for the analysis of this section:
Loading daily precipitation data at the station San Martino di Castrozza, Trento Province, Italy, with data from 01/Jan/1921 to 31/Dec/1990.
data(SanMartinoPPts)
Selecting only a three-year time slice for the analysis
x <- window(SanMartinoPPts, start=as.Date("1988-01-01"))
Plotting the selected time series
hydroplot(x, ptype="ts", pfreq="o", var.unit="mm")
Counting and plotting the number of days in the period where precipitation is > 10 [mm]
( R10mm <- length( x[x>10] ) )
## [1] 127
wet.index <- which(x >= 1)
( PRwn95 <- quantile(x[wet.index], probs=0.95, na.rm=TRUE) )
## 95%
## 39.75
Note 1: this computation was carried out for the three-year time period 1988-1990, not the 30-year period 1961-1990 commonly used.
Note 2: missing values are removed from the computation.
(very.wet.index <- which(x >= PRwn95))
## [1] 30 92 234 287 422 423 461 550 551 674 676 719 939 950
## [15] 998 1058 1061 1075
( R95p <- sum(x[very.wet.index]) )
## [1] 1196.4
Note 3: this computation was carried out for the three-year time period 1988-1990, not the 30-year period 1961-1990 commonly used
Computing the 5-day total (accumulated) precipitation \newline
x.5max <- rollapply(data=x, width=5, FUN=sum, fill=NA, partial= TRUE,
align="center")
hydroplot(x.5max, ptype="ts+boxplot", pfreq="o", var.unit="mm")
## [Note: pfreq='o' => ptype has been changed to 'ts']
Maximum annual value of 5-day total precipitation
(x.5max.annual <- daily2annual(x.5max, FUN=max, na.rm=TRUE))
## 1988-01-01 1989-01-01 1990-01-01
## 113.2 170.8 237.2
Note 1: for this computation, a moving window centred in the current day is used. If the user wants the 5-day total precipitation accumulated in the 4 days before the current day + the precipitation in the current day, the user have to modify the moving window.\newline
Note 2: For the first two and last two values, the width of the window is adapted to ignore values not within the time series
Since v0.5-0, hydroTSM includes a function to plot a climograph, considering not only precipitation but air temperature data as well:
# Loading daily ts of precipitation, maximum and minimum temperature
data(MaquehueTemuco)
# extracting individual ts of precipitation, maximum and minimum temperature
pcp <- MaquehueTemuco[, 1]
tmx <- MaquehueTemuco[, 2]
tmn <- MaquehueTemuco[, 3]
# Plotting the climograph
m <- climograph(pcp=pcp, tmx=tmx, tmn=tmn, na.rm=TRUE)
This tutorial was built under:
## [1] "x86_64-pc-linux-gnu (64-bit)"
## [1] "R Under development (unstable) (2020-03-10 r77920)"
## [1] "hydroTSM 0.5-3"
In order to make easier the use of \texttt{hydroTSM} for users not familiar with R, in this section a minimal set of information is provided to guide the user in the R world.
?read.table, ?write.table: allow the user to read/write a file (in $~$table format) and create a data frame from it. Related functions are ?read.csv, ?write.csv, ?read.csv2, ?write.csv2.
foreign: read data stored in several R-external formats (dBase, Minitab, S, SAS, SPSS, Stata, Systat, Weka, …)
?zoo::read.zoo, ?zoo::write.zoo: functions for reading and writing time series from/to text files, respectively.