├── .Rapp.history ├── man ├── .Rapp.history ├── recode.Rd ├── get.days.Rd ├── ann.cv.Rd ├── Acheron.Rd ├── monthly.cv.Rd ├── daily.cv.Rd ├── Cooper.Rd ├── CTF.Rd ├── hydrostats-package.Rd ├── four.digit.year.Rd ├── ts.format.Rd ├── seasonality.Rd ├── flood.length.max.Rd ├── hydro.year.Rd ├── day.dist.Rd ├── low.spell.lengths.Rd ├── high.spell.lengths.Rd ├── baseflows.Rd ├── partial.series.Rd ├── Colwells.Rd ├── low.spells.Rd └── high.spells.Rd ├── .Rbuildignore ├── .DS_Store ├── .gitignore ├── R ├── daily.cv.R ├── four.digit.year.R ├── ts.format.R ├── get.days.R ├── ann.cv.R ├── monthly.cv.R ├── recode.R ├── flood.length.max.R ├── day.dist.R ├── CTF.R ├── hydro.year.R ├── seasonality.R ├── low.spell.lengths.R ├── baseflows.R ├── high.spell.lengths.R ├── Colwells.R ├── partial.series.R ├── low.spells.R └── high.spells.R ├── hydrostats.Rproj ├── NAMESPACE ├── DESCRIPTION ├── README.md ├── NEWS └── data └── cooper.txt /.Rapp.history: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /man/.Rapp.history: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /.Rbuildignore: -------------------------------------------------------------------------------- 1 | ^.*\.Rproj$ 2 | ^\.Rproj\.user$ 3 | -------------------------------------------------------------------------------- /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nickbond/hydrostats/HEAD/.DS_Store -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .Rproj.user 2 | .Rhistory 3 | .RData 4 | .DS_Store 5 | .Rapp.history 6 | .Rprofile 7 | 8 | -------------------------------------------------------------------------------- /R/daily.cv.R: -------------------------------------------------------------------------------- 1 | daily.cv <- function(flow.ts) { 2 | 3 | daily.cv <- (sd(flow.ts$Q, na.rm = T)/mean(flow.ts$Q, na.rm = T)) * 100 4 | 5 | return(data.frame(daily.cv = daily.cv)) 6 | } 7 | -------------------------------------------------------------------------------- /R/four.digit.year.R: -------------------------------------------------------------------------------- 1 | four.digit.year <- function(x, year = 1968) { 2 | n <- as.numeric(strftime(x, format = "%y"))%%100 3 | Y <- ifelse(n > year%%100, 1900 + n, 2000 + n) 4 | return(Y) 5 | } 6 | -------------------------------------------------------------------------------- /R/ts.format.R: -------------------------------------------------------------------------------- 1 | ts.format <- function(x, format = "%d/%m/%Y", cols = c(1, 2)) { 2 | names(x)[cols] <- c("Date", "Q") 3 | x[["Date"]] <- strptime(x[["Date"]], format = format) 4 | x[["Date"]] <- as.POSIXct(x[["Date"]]) 5 | return(x) 6 | } 7 | -------------------------------------------------------------------------------- /R/get.days.R: -------------------------------------------------------------------------------- 1 | get.days <- function(year) { 2 | days <- c() 3 | d1 <- as.Date(paste(year, "-01-01", sep = "")) 4 | d2 <- as.Date(paste(year, "-12-31", sep = "")) 5 | day <- difftime(d2, d1) + 1 6 | days <- rbind(day, days) 7 | return(as.vector(days)) 8 | } 9 | -------------------------------------------------------------------------------- /R/ann.cv.R: -------------------------------------------------------------------------------- 1 | ann.cv <- function(flow.ts) { 2 | flow.ts$year <- strftime(flow.ts$Date, format = "%Y") 3 | ann.stats <- aggregate(flow.ts["Q"], flow.ts["year"], mean, na.rm = T) 4 | 5 | 6 | out <- sd(ann.stats$Q, na.rm = T)/mean(ann.stats$Q, na.rm = T) * 100 7 | 8 | return(data.frame(ann.cv = out)) 9 | } 10 | -------------------------------------------------------------------------------- /R/monthly.cv.R: -------------------------------------------------------------------------------- 1 | monthly.cv <- function(flow.ts) { 2 | month.means <- aggregate(flow.ts[, "Q"], by = list(month = strftime(flow.ts[["Date"]], format = "%m")), mean, na.rm = T) 3 | month.sd <- sd(month.means[, "x"], na.rm = T) 4 | 5 | monthly.cv <- (month.sd/mean(month.means[, "x"])) * 100 6 | 7 | return(data.frame(monthly.cv = monthly.cv)) 8 | } 9 | -------------------------------------------------------------------------------- /hydrostats.Rproj: -------------------------------------------------------------------------------- 1 | Version: 1.0 2 | 3 | RestoreWorkspace: Default 4 | SaveWorkspace: Default 5 | AlwaysSaveHistory: Default 6 | 7 | EnableCodeIndexing: Yes 8 | UseSpacesForTab: No 9 | NumSpacesForTab: 2 10 | Encoding: UTF-8 11 | 12 | RnwWeave: Sweave 13 | LaTeX: pdfLaTeX 14 | 15 | BuildType: Package 16 | PackageUseDevtools: Yes 17 | PackageInstallArgs: --no-multiarch --with-keep.source 18 | PackageCheckArgs: --as-cran 19 | -------------------------------------------------------------------------------- /NAMESPACE: -------------------------------------------------------------------------------- 1 | export(ann.cv, 2 | baseflows, 3 | CTF, 4 | Colwells, 5 | daily.cv, 6 | day.dist, 7 | flood.length.max, 8 | four.digit.year, 9 | get.days, 10 | high.spell.lengths, 11 | high.spells, 12 | hydro.year, 13 | low.spells, 14 | low.spell.lengths, 15 | monthly.cv, 16 | partial.series, 17 | recode, 18 | seasonality, 19 | ts.format) 20 | import(stats, 21 | utils, 22 | graphics) 23 | -------------------------------------------------------------------------------- /DESCRIPTION: -------------------------------------------------------------------------------- 1 | Package: hydrostats 2 | Type: Package 3 | Title: Hydrologic Indices for Daily Time Series Data 4 | Version: 0.2.9 5 | Date: 2022-05-31 6 | Author: Nick Bond 7 | Maintainer: Nick Bond 8 | Description: Calculates a suite of hydrologic indices for daily time series data that are widely used in hydrology and stream ecology. 9 | License: GPL (>= 2) 10 | URL: https://github.com/nickbond/hydrostats 11 | Suggests: dplyr, plyr 12 | Imports: stats, utils, graphics 13 | RoxygenNote: 7.1.1 14 | -------------------------------------------------------------------------------- /R/recode.R: -------------------------------------------------------------------------------- 1 | recode <- function(x, oldvalue, newvalue) { 2 | 3 | # convert any factors to characters 4 | 5 | if (is.factor(x)) 6 | x <- as.character(x) 7 | if (is.factor(oldvalue)) 8 | oldvalue <- as.character(oldvalue) 9 | if (is.factor(newvalue)) 10 | newvalue <- as.character(newvalue) 11 | 12 | # create the return vector 13 | 14 | newvec <- x 15 | 16 | # put recoded values into the correct position in the return vector 17 | 18 | for (i in unique(oldvalue)) newvec[x == i] <- newvalue[oldvalue == i] 19 | 20 | return(newvec) 21 | 22 | } 23 | -------------------------------------------------------------------------------- /man/recode.Rd: -------------------------------------------------------------------------------- 1 | \name{recode} 2 | \alias{recode} 3 | \title{recode} 4 | \description{ 5 | Recodes values in a vector based on original and new values provided as two vectors. 6 | } 7 | \usage{ 8 | recode(x, oldvalue, newvalue)} 9 | \arguments{ 10 | \item{x}{ 11 | A vector with values to be replaced.} 12 | \item{oldvalue}{ 13 | A vector of original values to be recoded. 14 | } 15 | \item{newvalue}{ 16 | A vector of replacement values of the same length as oldvalue. 17 | } 18 | } 19 | 20 | \value{ 21 | A vector of same length as input. 22 | } 23 | 24 | 25 | \author{ 26 | Nick Bond 27 | } 28 | \examples{ 29 | x<-seq(1:10) 30 | recode(x, c(1,5,10), c(-1,-5,-10)) 31 | } 32 | -------------------------------------------------------------------------------- /man/get.days.Rd: -------------------------------------------------------------------------------- 1 | \name{get.days} 2 | \alias{get.days} 3 | %- Also NEED an '\alias' for EACH other topic documented here. 4 | \title{ 5 | Function to determine the number of days 365 or 366 in a given year. 6 | } 7 | \description{ 8 | A helper function for circular statistic functions. Determines the number of days in any given year (i.e. 365 or 366)} 9 | \usage{ 10 | get.days(year) 11 | } 12 | %- maybe also 'usage' for other objects documented here. 13 | \arguments{ 14 | \item{year}{ 15 | A vector of years in numeric format} 16 | } 17 | 18 | \value{ 19 | A vector containing the number of days in each year in the input vector 20 | } 21 | 22 | \author{ 23 | Nick Bond 24 | } 25 | 26 | \examples{ 27 | years<-c("1968","1975","1983","1990","2004") 28 | get.days(years) 29 | 30 | } -------------------------------------------------------------------------------- /man/ann.cv.Rd: -------------------------------------------------------------------------------- 1 | \name{ann.cv} 2 | \alias{ann.cv} 3 | \title{ 4 | Calculate interannual coefficient of variation} 5 | \description{ 6 | This function takes a daily time series and returns the coefficient of variation of mean annual flow expressed as a percentage. 7 | 8 | i.e. (sd/mean)*100 9 | 10 | Missing values are ignored. 11 | } 12 | \usage{ 13 | ann.cv(flow.ts) 14 | } 15 | \arguments{ 16 | \item{flow.ts}{Dataframe with date and discharge data in columns named "Date" and "Q" respectively. Date must be in POSIX format (see ts.format). Missing values are ignored. 17 | } 18 | } 19 | \value{ 20 | A dataframe with one column (ann.cv). 21 | } 22 | 23 | \author{ 24 | Nick Bond 25 | } 26 | 27 | \examples{ 28 | data(Cooper) 29 | cooper<-ts.format(Cooper) 30 | ann.cv(Cooper) 31 | 32 | } 33 | -------------------------------------------------------------------------------- /man/Acheron.Rd: -------------------------------------------------------------------------------- 1 | \name{Acheron} 2 | \docType{data} 3 | \alias{Acheron} 4 | \title{Acheron River flow data} 5 | \description{Daily discharge time series in megalitres per day (ML/day) for the Acheron River @ Taggerty (Gauge No. 405209), Victoria, Australia, from 1971-2000.} 6 | \usage{ 7 | data(Acheron) 8 | } 9 | \format{ 10 | A data frame with 10944 observations (from 1971-2000) on 2 variables. 11 | 12 | [,'Date'] date (format dd/mm/yy) 13 | [,'Q'] discharge (ML/day) 14 | } 15 | 16 | \source{ 17 | 18 | Data provided by the State of Victoria, Department of Environment and Primary Industries, under Creative Commons Licence 3.0. 19 | 20 | } 21 | \examples{ 22 | data(Acheron) 23 | Acheron<-ts.format(Acheron) 24 | plot(Acheron[,"Date"],Acheron[,"Q"],type="l", xlab="Date",ylab="Discharge (ML/day)") 25 | 26 | 27 | } 28 | -------------------------------------------------------------------------------- /man/monthly.cv.Rd: -------------------------------------------------------------------------------- 1 | \name{monthly.cv} 2 | \alias{monthly.cv} 3 | %- Also NEED an '\alias' for EACH other topic documented here. 4 | \title{ 5 | Calculate monthly coefficient of variation 6 | } 7 | \description{ 8 | This function takes a daily time series and returns the coefficient of variation of mean monthly flow expressed as a percentage.} 9 | \usage{ 10 | monthly.cv(flow.ts) 11 | } 12 | %- maybe also 'usage' for other objects documented here. 13 | \arguments{ 14 | \item{flow.ts}{Dataframe with date and discharge data in columns named "Date" and "Q" respectively. Date must be in POSIX format (see ts.format). Missing values are ignored.} 15 | } 16 | 17 | \value{ 18 | a dataframe with 1 column (monthly.cv) 19 | } 20 | 21 | \author{ 22 | Nick Bond 23 | } 24 | 25 | \examples{ 26 | data(Cooper) 27 | Cooper<-ts.format(Cooper) 28 | ann.cv(Cooper) 29 | } 30 | -------------------------------------------------------------------------------- /R/flood.length.max.R: -------------------------------------------------------------------------------- 1 | flood.length.max <- function(flow.ts, threshold, ind.days = 5) { 2 | 3 | high.flows <- ifelse(flow.ts[["Q"]] > threshold, 1, 0) 4 | 5 | high.flow.runs <- rle(high.flows) 6 | too.short <- which(high.flow.runs$lengths < ind.days & high.flow.runs$values == 0) 7 | spell.factor <- rep(seq_along(high.flow.runs$lengths), times = high.flow.runs$lengths) 8 | add.to.spell <- which(spell.factor %in% too.short) 9 | high.flows[add.to.spell] <- 1 10 | 11 | 12 | high.flow.runs <- rle(high.flows) 13 | if (length(which(high.flow.runs$values == 1)) == 0) { 14 | 15 | max.duration <- 0 16 | } else { 17 | max.duration <- max(high.flow.runs$lengths[which(high.flow.runs$values == 1)], na.rm = T) 18 | } 19 | 20 | max.duration <- data.frame(max.duration = max.duration) 21 | 22 | return(max.duration) 23 | } 24 | -------------------------------------------------------------------------------- /man/daily.cv.Rd: -------------------------------------------------------------------------------- 1 | \name{daily.cv} 2 | \alias{daily.cv} 3 | %- Also NEED an '\alias' for EACH other topic documented here. 4 | \title{ 5 | Calculate daily cofficient of variation 6 | } 7 | \description{ 8 | This function takes a daily time series and returns the coefficient of variation of daily flows expressed as a percentage. 9 | 10 | i.e. (sd/mean)*100 11 | 12 | Missing values are ignored. 13 | } 14 | \usage{ 15 | daily.cv(flow.ts) 16 | } 17 | %- maybe also 'usage' for other objects documented here. 18 | \arguments{ 19 | \item{flow.ts}{Dataframe with date and discharge data in columns named "Date" and "Q" respectively. Date must be in POSIX format (see ts.format)} 20 | } 21 | \value{ 22 | A dataframe with one column (daily.cv). 23 | } 24 | 25 | \author{ 26 | Nick Bond 27 | } 28 | 29 | \examples{ 30 | data(Cooper) 31 | Cooper<-ts.format(Cooper) 32 | 33 | daily.cv(Cooper) 34 | 35 | } 36 | -------------------------------------------------------------------------------- /R/day.dist.R: -------------------------------------------------------------------------------- 1 | day.dist <- function(Dates = NULL, days = NULL, years = NULL) { 2 | 3 | if (any(class(Dates) == "POSIXct") | any(class(Dates) == "Date") == T) { 4 | 5 | days <- as.numeric(strftime(Dates, format = "%j")) 6 | years <- as.numeric(strftime(Dates, format = "%Y")) 7 | 8 | } else { 9 | days <- days 10 | years <- years 11 | } 12 | 13 | 14 | year.length <- get.days(years) 15 | theta <- c() 16 | adjust <- 0.5 * ((2 * pi)/365) 17 | theta <- days * (2 * pi/year.length) - adjust 18 | x <- mean(cos(theta)) 19 | y <- mean(sin(theta)) 20 | m.theta <- atan2(y, x) 21 | m.deg <- (m.theta * 365/(2 * pi)) + 0.5 22 | if (m.deg < 0) 23 | m.deg <- m.deg + 365 24 | 25 | 26 | r <- sqrt((x^2) + (y^2)) 27 | sd.rad <- sqrt(-2 * log(r)) 28 | sd.deg <- sd.rad * (365/(2 * pi)) 29 | 30 | 31 | data.frame(mean.doy = round(m.deg, 0), sd.doy = round(sd.deg, 0)) 32 | 33 | } 34 | -------------------------------------------------------------------------------- /man/Cooper.Rd: -------------------------------------------------------------------------------- 1 | \name{Cooper} 2 | \docType{data} 3 | \alias{Cooper} 4 | %- Also NEED an '\alias' for EACH other topic documented here. 5 | \title{ 6 | Cooper Creek flow data 7 | } 8 | \description{ 9 | Daily discharge time series in megalitres per day (ML/day) for Coopers Creek @ Currareva (Gauge No. 003101), Qld, Australia, from 1967-1987. 10 | 11 | } 12 | \usage{ 13 | data(Cooper) 14 | } 15 | %- maybe also 'usage' for other objects documented here. 16 | 17 | \format{ 18 | A data frame with 7670 observations (from 1967-1987) on 2 variables. 19 | 20 | [,'Date'] date (format dd/mm/yy) 21 | [,'Q'] discharge (ML/day) 22 | } 23 | 24 | \source{ 25 | Data provided by the State of Queensland, Department of Natural Resources and Mines, under creative commons licence agreement. Details available at http://watermonitoring.dnrm.qld.gov.au/wini/copyright.htm 26 | 27 | } 28 | \examples{ 29 | data(Cooper) 30 | Cooper<-ts.format(Cooper) 31 | plot(Cooper[, "Date"],Cooper[, "Q"],type="l", xlab="Date",ylab="Discharge (ML/day)") 32 | 33 | 34 | } 35 | -------------------------------------------------------------------------------- /R/CTF.R: -------------------------------------------------------------------------------- 1 | CTF <- function(flow.ts, threshold = 0.1) { 2 | 3 | flow.ts <- na.omit(flow.ts) 4 | 5 | if (length(flow.ts[["Q"]]) == 0 | sum(flow.ts[["Q"]]) == 0) { 6 | 7 | return(data.frame(p.CTF = 1, avg.CTF = Inf, med.CTF = Inf, min.CTF = Inf, max.CTF = Inf)) 8 | } 9 | 10 | 11 | if (sum(flow.ts[["Q"]] <= threshold) < 1) { 12 | 13 | return(data.frame(p.CTF = 0, avg.CTF = 0, med.CTF = 0, min.CTF = 0, max.CTF = 0)) 14 | } else { 15 | 16 | CTF.ts <- ifelse(flow.ts[["Q"]] > threshold, 1, 0) 17 | 18 | CTF.runs <- rle(CTF.ts) 19 | p.CTF <- sum(CTF.ts == 0)/length(CTF.ts) 20 | avg.CTF <- mean(CTF.runs$lengths[which(CTF.runs$values == 0)], na.rm = T) 21 | med.CTF <- median(CTF.runs$lengths[which(CTF.runs$values == 0)], na.rm = T) 22 | min.CTF <- min(CTF.runs$lengths[which(CTF.runs$values == 0)], na.rm = T) 23 | max.CTF <- max(CTF.runs$lengths[which(CTF.runs$values == 0)], na.rm = T) 24 | 25 | 26 | } 27 | return(data.frame(p.CTF = p.CTF, avg.CTF = avg.CTF, med.CTF = med.CTF, min.CTF = min.CTF, max.CTF = max.CTF)) 28 | 29 | 30 | } 31 | -------------------------------------------------------------------------------- /man/CTF.Rd: -------------------------------------------------------------------------------- 1 | \name{CTF} 2 | \alias{CTF} 3 | %- Also NEED an '\alias' for EACH other topic documented here. 4 | \title{ 5 | Cease-to-flow (CTF) spell statistics 6 | } 7 | \description{ 8 | Calculates summary statistics describing cease-to-flow spell characteristics.} 9 | \usage{ 10 | CTF(flow.ts, threshold = 0.1) 11 | } 12 | %- maybe also 'usage' for other objects documented here. 13 | \arguments{ 14 | \item{flow.ts}{ 15 | Dataframe with date and discharge data in columns named "Date" and "Q" respectively. Date must be in POSIX format (see ts.format).} 16 | \item{threshold}{ 17 | values below this threshold (default 0.1) are treated as zero for the purpose of defining cease to flow spells to account for the fact that cease to flow levels are poorly defined for many gauging sites.} 18 | } 19 | 20 | \value{ 21 | A dataframe with 5 columns (see below). 22 | 23 | \item{p.CTF }{Proportion time cease to flows occur} 24 | \item{avg.CTF }{Average cease-to-flow spell duration} 25 | \item{med.CTF }{Median cease-to-flow spell duration} 26 | \item{min.CTF }{Minimum cease-to-flow spell duration} 27 | \item{max.CTF }{Maximum cease-to-flow spell duration} 28 | 29 | } 30 | 31 | \author{ 32 | Nick Bond 33 | } 34 | 35 | \examples{ 36 | data(Cooper) 37 | Cooper<-ts.format(Cooper) 38 | 39 | CTF(Cooper) 40 | CTF(Cooper, threshold=0) 41 | 42 | } 43 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | #Hydrostats 2 | 3 | hydrostats is a set of tools to compute streamflow indices commonly used in hydrology and ecology such as: 4 | - basic summary statistics 5 | - high spells (magnitude, frequency, duration, timing etc.) 6 | - low spells 7 | - cease-to-flow spells 8 | - Colwell's indices 9 | - partial series 10 | - baseflow components 11 | 12 | 13 | Most of the functions can be used with the packages plyr and dplyr to return a dataframe of indices based on an additional grouping variable. This allows the analysis (for example) of data from multiple gauges simultaneously, without the need to write a for loop. While most functions return a single row dataframe, several functions will instead return a dataframe of individual spell characteristics for use in subsequent analysis and plotting. 14 | 15 | #Installation 16 | To install the latest development version run the following code: 17 | 18 | Current version on github and CRAN is 0.2.9. 19 | 20 | # install dependencies/suggests: 21 | # devtools required only to install from Github; 22 | # plyr required for examples 23 | install.packages(c("devtools", "plyr")) 24 | 25 | #install pacakge 26 | devtools::install_github("nickbond/hydrostats") 27 | 28 | # Remove the package zip after installation 29 | unlink("hydrostats.zip") 30 | 31 | 32 | #Developer 33 | Nick Bond n.bond@latrobe.edu.au 34 | -------------------------------------------------------------------------------- /man/hydrostats-package.Rd: -------------------------------------------------------------------------------- 1 | \name{hydrostats} 2 | \alias{hydrostats} 3 | \alias{hydrostats} 4 | \docType{package} 5 | \title{ 6 | calculate hydrologic statistics 7 | } 8 | \description{ 9 | Calculate a range of hydrologic statistics based on daily time series data and which are widely used in hydrology and ecological applications. 10 | } 11 | \details{ 12 | \tabular{ll}{ 13 | Package: \tab hydrostats\cr 14 | Type: \tab Package\cr 15 | Version: \tab 0.2.4\cr 16 | Date: \tab 2015-10-16\cr 17 | License: \tab GPL (>= 2) \cr 18 | } 19 | 20 | Data must be provided as a dataframe in which the date is in POSIXct format. The function \code{\link[hydrostats:ts.format]{ts.format}} can be used to specify the Date and discharge columns (named Date and Q respectively) in a dataframe, and convert dates to POSIXct format. The date and discharge data must be in columns labelled "Date" and "Q" for the functions to work. 21 | 22 | Includes several sample datasets.\cr 23 | data(Cooper) - Flow data for Coopers Creek, Australia. Gauge 003101@Currareva \cr 24 | data(Acheron) - Flow data for Acheron River, Australia, Gauge 405209@Taggerty 25 | 26 | } 27 | \author{ 28 | Nick Bond 29 | Maintainer: Nick Bond 30 | } 31 | 32 | \examples{ 33 | data(Acheron) 34 | Acheron<-ts.format(Acheron) 35 | with(Acheron, plot(Q~Date)) 36 | 37 | high.spell.lengths(Acheron, threshold=50000) 38 | 39 | 40 | } 41 | -------------------------------------------------------------------------------- /man/four.digit.year.Rd: -------------------------------------------------------------------------------- 1 | \name{four.digit.year} 2 | \alias{four.digit.year} 3 | %- Also NEED an '\alias' for EACH other topic documented here. 4 | \title{ 5 | Correct conversion of years to four digit format} 6 | \description{ 7 | Converts from two to four digit representation of years correcting the century for years earlier than that specified. Addresses the fact that under POSIX specifications, values 00 to 68 are prefixed by 20 and 69 to 99 by 19 when converting from two digit years, which can affectlonger time series and older data sets. } 8 | \usage{ 9 | four.digit.year(x, year=1968) 10 | } 11 | %- maybe also 'usage' for other objects documented here. 12 | \arguments{ 13 | \item{x}{ 14 | A vector of POSIXct dates, presumably with some years (often those earlier than 1969) assigned to the wrong century.} 15 | \item{year}{ 16 | The year (in four digit format) indicating the cutoff for setting the century to 1900's or 2000's. 17 | } 18 | } 19 | 20 | \value{ 21 | A vector of same length as input with years in four digit format. 22 | } 23 | 24 | \author{ 25 | Nick Bond 26 | } 27 | 28 | \seealso{ 29 | \code{\link{strptime}}. 30 | %% ~~objects to See Also as \code{\link{help}}, ~~~ 31 | } 32 | 33 | \examples{ 34 | x <- as.POSIXct(c("01/01/43","01/01/68","01/01/69","01/01/99","01/01/04"), format="\%d/\%m/\%y") 35 | x 36 | four.digit.year(x, year=1968) 37 | four.digit.year(x, year=1942) 38 | } 39 | -------------------------------------------------------------------------------- /R/hydro.year.R: -------------------------------------------------------------------------------- 1 | hydro.year <- function(flow.ts, hydro.year = "hydro", year.only = FALSE) { 2 | 3 | begin <- head(flow.ts[["Date"]], 1) 4 | finish <- tail(flow.ts[["Date"]], 1) 5 | 6 | if (hydro.year == "hydro") { 7 | month.runs <- c() 8 | month.means <- aggregate(flow.ts["Q"], by = list(month = strftime(flow.ts[["Date"]], format = "%m")), sum, na.rm = T) 9 | month.runs[1] <- sum(month.means[1:6, "Q"]) 10 | month.runs[2] <- sum(month.means[2:7, "Q"]) 11 | month.runs[3] <- sum(month.means[3:8, "Q"]) 12 | month.runs[4] <- sum(month.means[4:9, "Q"]) 13 | month.runs[5] <- sum(month.means[5:10, "Q"]) 14 | month.runs[6] <- sum(month.means[6:11, "Q"]) 15 | month.runs[7] <- sum(month.means[7:12, "Q"]) 16 | month.runs[8] <- sum(month.means[c(8:12, 1), "Q"]) 17 | month.runs[9] <- sum(month.means[c(9:12, 1:2), "Q"]) 18 | month.runs[10] <- sum(month.means[c(10:12, 1:3), "Q"]) 19 | month.runs[11] <- sum(month.means[c(11:12, 1:4), "Q"]) 20 | month.runs[12] <- sum(month.means[c(12, 1:5), "Q"]) 21 | 22 | alt.month <- which.min(month.runs) 23 | hydro.year <- c() 24 | hydro.year <- ifelse(as.numeric(strftime(flow.ts[["Date"]], format = "%m")) < alt.month, as.numeric(strftime(flow.ts[["Date"]], format = "%Y")) - 1, as.numeric(strftime(flow.ts[["Date"]], 25 | format = "%Y"))) 26 | } 27 | if (year.only == TRUE) { 28 | return(hydro.year) 29 | } else { 30 | flow.ts <- data.frame(flow.ts, hydro.year = hydro.year) 31 | return(flow.ts) 32 | } 33 | 34 | } 35 | -------------------------------------------------------------------------------- /man/ts.format.Rd: -------------------------------------------------------------------------------- 1 | \name{ts.format} 2 | \alias{ts.format} 3 | %- Also NEED an '\alias' for EACH other topic documented here. 4 | \title{ 5 | Format dates as POSIXct 6 | } 7 | \description{ 8 | Converts dates from class character (format dd/mm/yyyy or other as specified) into class POSIXct and renames columns for use with other functions in the hydrostats package.} 9 | \usage{ 10 | ts.format(x, format="\%d/\%m/\%Y", cols=c(1,2)) 11 | } 12 | %- maybe also 'usage' for other objects documented here. 13 | \arguments{ 14 | \item{x}{ 15 | Dataframe including date and discharge data. Dates are assumed to be of class character (see format). The columns containing date and discharge data are required (defaults to renaming columns 1 and 2 to Date and Q respectively if no other columns are specified (see cols)).} 16 | \item{format}{ 17 | Format of dates in existing data frame.} 18 | \item{cols}{ 19 | A vector of column indices for the date and discharge data. Used to rename columns.} 20 | } 21 | 22 | \details{ 23 | Default assumes the date is of class character and in the first column, with discharge in the second column of the data frame. These columns can be specified if the defaults are not appropriate. The date and discharge columns are renamed to 'Date' and 'Q' respectively. For more flexibility in formatting dates/times see the lubridate package.} 24 | \value{ 25 | A dataframe with the dates formatted as POSIXct and named columns Date and Q. 26 | %% ~Describe the value returned 27 | %% If it is a LIST, use 28 | %% \item{comp1 }{Description of 'comp1'} 29 | %% \item{comp2 }{Description of 'comp2'} 30 | %% ... 31 | } 32 | \author{ 33 | Nick Bond 34 | } 35 | \examples{ 36 | data(Cooper) 37 | Cooper<-ts.format(Cooper) 38 | } 39 | -------------------------------------------------------------------------------- /man/seasonality.Rd: -------------------------------------------------------------------------------- 1 | \name{seasonality} 2 | \alias{seasonality} 3 | %- Also NEED an '\alias' for EACH other topic documented here. 4 | \title{ 5 | Calculate seasonality of discharge 6 | } 7 | \description{ 8 | Returns statistics decribing seasonal variation in runoff. } 9 | \usage{ 10 | seasonality(flow.ts, monthly.range = FALSE) 11 | } 12 | %- maybe also 'usage' for other objects documented here. 13 | \arguments{ 14 | \item{flow.ts}{ 15 | Dataframe with date and discharge data in columns named "Date" and "Q" respectively. Date must be in POSIX format (see ts.format). Missing values are ignored. 16 | } 17 | \item{monthly.range}{ 18 | logical. If FALSE (default), the function returns the percentage of runoff occurring during the average driest 6 month period (as defined across all years). If true, additional statistics describing cumulative average monthly flows, the range between the runoff in the wettest and driest months, and the average number of months between the wettest and driest periods of runoff.} 19 | } 20 | 21 | \value{ 22 | If monthly.range=FALSE (default) the function returns a dataframe with one column with the percentage of annual runoff delivered during the average driest 6 month period. 23 | 24 | If monthly.range=TRUE, the function returns a list with the following elements: 25 | 26 | \item{seasonality}{The percentage of annual runoff delivered during the driest 6 months} 27 | \item{monthly.means}{Average flow in each month of the year} 28 | \item{avg.ann.month.range}{Average difference between the monthly minimum and maximum} 29 | \item{max.min.time.dif}{Average number of months between the highest and lowest monthly runoff} 30 | } 31 | 32 | \author{ 33 | Nick Bond 34 | } 35 | 36 | \examples{ 37 | 38 | data(Cooper) 39 | Cooper<-ts.format(Cooper) 40 | 41 | seasonality(Cooper, monthly.range=TRUE) 42 | 43 | } 44 | -------------------------------------------------------------------------------- /man/flood.length.max.Rd: -------------------------------------------------------------------------------- 1 | \name{flood.length.max} 2 | \alias{flood.length.max} 3 | %- Also NEED an '\alias' for EACH other topic documented here. 4 | \title{ 5 | Calculates the maximum flood length in a time series. 6 | } 7 | \description{ 8 | Calculates the maximum flood length above a user defined threshold in a time series. Used with ddply (from the plyr package) it can be used to return a vector of maximum flood lengths for multiple gauges or for multiple years (see examples). Alternatively, the function \code{\link[hydrostats:high.spell.lengths]{high.spell.lengths}} can be used to return the length of all events above a threshold.} 9 | \usage{ 10 | flood.length.max(flow.ts, threshold, ind.days = 5) 11 | } 12 | %- maybe also 'usage' for other objects documented here. 13 | \arguments{ 14 | \item{flow.ts}{ 15 | Dataframe with date and discharge data in columns named "Date" and "Q" respectively. Date must be in POSIX format (see ts.format) 16 | } 17 | \item{threshold}{ 18 | A user supplied threshold for defining spells. This would typically be derived from hydraulic models or similar knowledge pertaining to a gauge site} 19 | \item{ind.days}{ 20 | Periods between spells of less than ind.days (default 5) are considered to be 'in spell' for the purpose of further calculations. A value of 0 means spells 1 day apart are considered indpedendent} 21 | } 22 | 23 | \value{ 24 | A dataframe with one column (flood.length.max). 25 | } 26 | \author{ 27 | Nick Bond 28 | } 29 | \examples{ 30 | 31 | data(Cooper) 32 | Cooper<-ts.format(Cooper) 33 | 34 | flood.length.max(Cooper, threshold = 50000, ind.days = 5) 35 | 36 | # Return annual maximum flood length based on calendar year using ddply (from plyr package) 37 | require(plyr) 38 | Cooper$Year=format(Cooper$Date, format="\%Y") 39 | ddply(Cooper, .(Year), flood.length.max, threshold = 50000) 40 | 41 | require(dplyr) 42 | Cooper \%>\% 43 | dplyr::group_by(Year) \%>\% 44 | dplyr::do(flood.length.max(., threshold = 50000)) 45 | 46 | 47 | # Based on hydrologic year. 48 | Cooper<-hydro.year(Cooper) 49 | plyr::ddply(Cooper, .(Year), flood.length.max, threshold = 50000) 50 | 51 | } 52 | -------------------------------------------------------------------------------- /man/hydro.year.Rd: -------------------------------------------------------------------------------- 1 | \name{hydro.year} 2 | \alias{hydro.year} 3 | %- Also NEED an '\alias' for EACH other topic documented here. 4 | \title{ 5 | Determine hydrologic year for the purposes of deriving spell characteristics 6 | } 7 | \description{ 8 | Defines a hydrologic year to minimise the risk that defined spells are interrupted by transitions between calendar years. The function can be called by several other functions in the hydrostats package (e.g. \code{\link{high.spells}}, \code{\link{low.spells}}, \code{\link{high.spell.lengths}}. 9 | } 10 | \usage{ 11 | hydro.year(flow.ts, hydro.year = "hydro", year.only=FALSE) 12 | } 13 | %- maybe also 'usage' for other objects documented here. 14 | \arguments{ 15 | \item{flow.ts}{ 16 | Dataframe with date and discharge data in columns named "Date" and "Q" respectively. Date must be in POSIX format (see ts.format).} 17 | \item{hydro.year}{ 18 | hydro.year="hydro" calculates the hydrologic year and returns a dataframe with an additional column indiating the hydrologic year to which each observation belongs. The hydrologic year is defined as starting in the first month of the average driest 6 month period across all years. This maximises the likelihood that low-flow and high-flow spells will be contained within a rolling 12 month period. 19 | 20 | Other options may be added in the future. 21 | 22 | } 23 | \item{year.only}{ 24 | logical. If FALSE (default), a column indicating the hydrologic year of each record is added to the original data.frame. If TRUE, a vector indicating the hydrologic year of each record is returned. 25 | } 26 | } 27 | 28 | \value{ 29 | %% ~Describe the value returned 30 | If year.only=FALSE (default), the function returns the original dataframe with an added column "hydro.year" indicating the hydrologic year to which each case belongs. Otherwise, if year.only=TRUE, a vector of hydrologic years is returned. 31 | } 32 | 33 | \author{ 34 | Nick Bond 35 | } 36 | \seealso{ 37 | \code{\link{high.spells}}, \code{\link{low.spells}}. 38 | } 39 | \examples{ 40 | data(Cooper) 41 | Cooper<-ts.format(Cooper) 42 | 43 | head(hydro.year(Cooper)) 44 | head(hydro.year(Cooper, year.only=TRUE)) 45 | } 46 | -------------------------------------------------------------------------------- /man/day.dist.Rd: -------------------------------------------------------------------------------- 1 | \name{day.dist} 2 | \alias{day.dist} 3 | %- Also NEED an '\alias' for EACH other topic documented here. 4 | \title{ 5 | Calculate average timing of events (doy) 6 | } 7 | \description{ 8 | Calculates the mean and standard deviation of the timing of annual events. Given a dataframe consisting of years in column one and a day of year (0-365 [366 for leap years]) in column two, day.dist returns the mean day of the year (doy) and standard deviation of days around the mean. 9 | 10 | Circular statistics are used to account for the proximity of days close to the start and the end of the year (i.e. numbers close to 0 and 365), which would notionally have a mean of approximiately 182 (see Bayliss and Jones (1993)). The mean that is returned can be interpreted as a calendar day, and data that are strongly directional will have a standard deviation close to zero. 11 | } 12 | \usage{ 13 | day.dist(Dates, days, years) 14 | } 15 | %- maybe also 'usage' for other objects documented here. 16 | \arguments{ 17 | \item{Dates}{ 18 | A vector of POSIX dates from which days and years are extracted. If Dates are not provided, days and years must be } 19 | 20 | \item{days}{ 21 | A vector of days in numeric format. Not required if POSIXct dates are provided} 22 | 23 | \item{years}{ 24 | A vector of years in numeric format. Not required if POSIXct dates are provided} 25 | } 26 | 27 | \value{ 28 | A dataframe with two columns. 29 | \item{mean.doy }{mean day of year events occur on} 30 | \item{sd.doy }{standard deviation indicating the spread of event timing} 31 | 32 | } 33 | \references{ 34 | Bayliss, A. C., Jones, R. C. (1993) Peaks-over-threshold flood database: Summary statistics and seasonality. Institute of Hydrology. Wallingford, UK. 35 | } 36 | \author{ 37 | Nick Bond 38 | } 39 | \examples{ 40 | days<-c(366,1,365,1,366) 41 | years<-c("1968","1975","1983","1990","2004") 42 | day.dist(days=days, years=years) 43 | 44 | days<-c(170,180,1,365,170) 45 | day.dist(days=days, years=years) 46 | 47 | dates<-c("1968-06-18", "1975-06-29", "1983-01-01", "1990-12-31", "2004-06-18") 48 | dates<-as.POSIXct(dates, format = "\%Y-\%m-\%d", tz="") 49 | day.dist(Dates=dates) 50 | 51 | } 52 | -------------------------------------------------------------------------------- /R/seasonality.R: -------------------------------------------------------------------------------- 1 | seasonality <- function(flow.ts, monthly.range = FALSE) { 2 | 3 | 4 | month.runs <- c() 5 | month.means <- aggregate(flow.ts["Q"], by = list(month = strftime(flow.ts[["Date"]], format = "%m")), mean, na.rm = T) 6 | month.runs[1] <- sum(month.means[1:6, "Q"], na.rm = T) 7 | month.runs[2] <- sum(month.means[2:7, "Q"], na.rm = T) 8 | month.runs[3] <- sum(month.means[3:8, "Q"], na.rm = T) 9 | month.runs[4] <- sum(month.means[4:9, "Q"], na.rm = T) 10 | month.runs[5] <- sum(month.means[5:10, "Q"], na.rm = T) 11 | month.runs[6] <- sum(month.means[6:11, "Q"], na.rm = T) 12 | month.runs[7] <- sum(month.means[7:12, "Q"], na.rm = T) 13 | month.runs[8] <- sum(month.means[c(8:12, 1), "Q"], na.rm = T) 14 | month.runs[9] <- sum(month.means[c(9:12, 1:2), "Q"], na.rm = T) 15 | month.runs[10] <- sum(month.means[c(10:12, 1:3), "Q"], na.rm = T) 16 | month.runs[11] <- sum(month.means[c(11:12, 1:4), "Q"], na.rm = T) 17 | month.runs[12] <- sum(month.means[c(12, 1:5), "Q"], na.rm = T) 18 | 19 | month.runs.sort <- sort(month.runs) 20 | low.6.months <- sum(month.runs.sort[1:6], na.rm=TRUE) 21 | out <- low.6.months/sum(month.runs, na.rm=TRUE) * 100 22 | 23 | 24 | if (monthly.range == TRUE) { 25 | flow.ts[["Year"]] <- as.factor(strftime(flow.ts[["Date"]], format = "%Y")) 26 | flow.ts[["month"]] <- as.factor(strftime(flow.ts[["Date"]], format = "%m")) 27 | month.year.means <- tapply(flow.ts[["Q"]], flow.ts[c("Year", "month")], sum) 28 | month.year.means <- na.omit(month.year.means) 29 | monthly.means <- apply(month.year.means, 2, mean, na.rm = T) 30 | month.range <- apply(month.year.means, 1, range, na.rm = T) 31 | min.months <- apply(month.year.means, 1, which.min) 32 | max.months <- apply(month.year.means, 1, which.max) 33 | av.ann.month.range <- mean(month.range[2, ] - month.range[1, ]) 34 | month.difs <- factor(as.character(abs(max.months - min.months))) 35 | month.difs <- as.factor(hydrostats::recode(month.difs, oldvalue = c("11", "10", "9", "8", "7"), newvalue = c("1", "2", "3", "4", "5"))) 36 | 37 | return(list(seasonality = out, monthly.means, avg.ann.month.range = av.ann.month.range, max.min.time.dif = round(mean(as.numeric(levels(month.difs)[month.difs])), 0))) 38 | 39 | } else { 40 | return(out) 41 | } 42 | } 43 | -------------------------------------------------------------------------------- /man/low.spell.lengths.Rd: -------------------------------------------------------------------------------- 1 | \name{low.spell.lengths} 2 | \alias{low.spell.lengths} 3 | %- Also NEED an '\alias' for EACH other topic documented here. 4 | \title{ 5 | Calculate the length of all low flow spells 6 | } 7 | \description{ 8 | Returns the length (and start date) of all flow spells above (or below) a given percentile or user defined threshold. 9 | 10 | Independence criteria allow short periods below the spell threshold to be ignored and flows below a threshold (e.g. zero flows) can be ignored when calculating percentile flows (useful in ephemeral rivers). 11 | } 12 | \usage{ 13 | low.spell.lengths(flow.ts, quant = 0.1, threshold, 14 | ind.days = 5, ignore.zeros = F, ctf.threshold = 0.1, inter.spell=FALSE) 15 | } 16 | 17 | \arguments{ 18 | \item{flow.ts}{ 19 | Dataframe with date and discharge data in columns named "Date" and "Q" respectively. Date must be in POSIX format (see ts.format). 20 | } 21 | \item{quant}{ 22 | Percentile/quantile to use for defining event magnitude (default 0.9). A value of 0.9 is the upper 90th percentile (i.e. a volume exceeded 10\% of the time).} 23 | \item{threshold}{ 24 | A user supplied threshold for defining spells. This would typically be derived from hydraulic models or similar knowledge pertaining to a gauge site. 25 | } 26 | \item{ind.days}{ 27 | Periods between spells of less than ind.days (default 5) are considered to be 'in spell' for the purpose of further calculations. A value of 0 means spells 1 day apart are considered indpedendent.} 28 | \item{ignore.zeros}{ 29 | logical. If TRUE, days below a user defined cease-to-flow threshold (default 0.1) will be excluded when estimating the spell threshold for a given percentile. This is primarily of interest in highly ephemeral rivers, where flow may only occur for a small fraction of the time. In such cases, the inclusion of zeros will skew estimates of low flow events downwards, which may be undesirable.} 30 | \item{ctf.threshold}{ 31 | values below this threshold are treated as zero for the purpose of percentile based calculations (see ignore zeros).} 32 | \item{inter.spell}{ 33 | logical. If TRUE, the function returns the spell lengths and start dates for periods above (rather than below) the defined threshold. 34 | } 35 | } 36 | 37 | \value{ 38 | Returns a dataframe of spell lengths and their associated starting dates. 39 | 40 | Note that spells will always end at NAs. 41 | } 42 | 43 | \author{ 44 | Nick Bond 45 | } 46 | \examples{ 47 | data(Cooper) 48 | Cooper<-ts.format(Cooper) 49 | 50 | low.spell.lengths(Cooper, threshold=50000) 51 | 52 | } 53 | -------------------------------------------------------------------------------- /man/high.spell.lengths.Rd: -------------------------------------------------------------------------------- 1 | \name{high.spell.lengths} 2 | \alias{high.spell.lengths} 3 | %- Also NEED an '\alias' for EACH other topic documented here. 4 | \title{ 5 | Calculate the length of all high flow spells 6 | } 7 | \description{ 8 | Returns the length (and start date) of all flow spells above (or below) a given percentile or user defined threshold. 9 | 10 | Independence criteria allow short periods below the spell threshold to be ignored and flows below a threshold (e.g. zero flows) can be ignored when calculating percentile flows (useful in ephemeral rivers). 11 | } 12 | \usage{ 13 | high.spell.lengths(flow.ts, quant = 0.9, threshold, 14 | ind.days = 5, ignore.zeros = T, ctf.threshold = 0.1, inter.flood=FALSE) 15 | } 16 | 17 | \arguments{ 18 | \item{flow.ts}{ 19 | Dataframe with date and discharge data in columns named "Date" and "Q" respectively. Date must be in POSIX format (see ts.format). 20 | } 21 | \item{quant}{ 22 | Percentile/quantile to use for defining event magnitude (default 0.9). A value of 0.9 is the upper 90th percentile (i.e. a volume exceeded 10\% of the time).} 23 | \item{threshold}{ 24 | A user supplied threshold for defining spells. This would typically be derived from hydraulic models or similar knowledge pertaining to a gauge site. 25 | } 26 | \item{ind.days}{ 27 | Periods between spells of less than ind.days (default 5) are considered to be 'in spell' for the purpose of further calculations. A value of 0 means spells 1 day apart are considered indpedendent.} 28 | \item{ignore.zeros}{ 29 | logical. If TRUE, days below a user defined cease-to-flow threshold (default 0.1) will be excluded when estimating the spell threshold for a given percentile. This is primarily of interest in highly ephemeral rivers, where flow may only occur for a small fraction of the time. In such cases, the inclusion of zeros will skew estimates of high flow events downwards, which may be undesirable.} 30 | \item{ctf.threshold}{ 31 | values below this threshold are treated as zero for the purpose of percentile based calculations (see ignore zeros).} 32 | \item{inter.flood}{ 33 | logical. If TRUE, the function returns the spell lengths and start dates for periods below (rather than above) the defined threshold. 34 | } 35 | } 36 | 37 | \value{ 38 | Returns a dataframe of spell lengths and their associated starting dates. 39 | 40 | Note that spells will always end at NAs. 41 | } 42 | 43 | \author{ 44 | Nick Bond 45 | } 46 | \examples{ 47 | data(Cooper) 48 | Cooper<-ts.format(Cooper) 49 | 50 | high.spell.lengths(Cooper, threshold=50000) 51 | 52 | } 53 | -------------------------------------------------------------------------------- /R/low.spell.lengths.R: -------------------------------------------------------------------------------- 1 | low.spell.lengths <- function(flow.ts, quant = 0.1, threshold = NULL, ind.days = 5, ignore.zeros = F, ctf.threshold = 0.1, inter.spell = FALSE) { 2 | 3 | record.year <- strftime(flow.ts[["Date"]], format = "%Y") 4 | flow.ts <- data.frame(flow.ts, year = record.year) 5 | #make missing Q values 0 so that the correct start and end dates are returned. 6 | flow.ts[is.na(flow.ts[["Q"]]), "Q"] <- 0 7 | n.years <- nlevels(as.factor(record.year)) 8 | 9 | 10 | if (!is.null(threshold)) { 11 | 12 | 13 | flow.threshold <- threshold 14 | # names(flow.threshold)<-NULL #normallyhide 15 | 16 | } else { 17 | 18 | 19 | if (ignore.zeros == T) { 20 | 21 | flow.threshold <- quantile(flow.ts[which(flow.ts[, "Q"] > ctf.threshold), "Q"], quant, na.rm = T) 22 | names(flow.threshold) <- NULL 23 | } else { 24 | flow.threshold <- quantile(flow.ts[, "Q"], quant, na.rm = T) 25 | names(flow.threshold) <- NULL 26 | } 27 | 28 | } 29 | 30 | 31 | 32 | 33 | 34 | low.flows <- ifelse(flow.ts[, "Q"] < flow.threshold, 1, 0) 35 | 36 | if (ind.days > 0) { 37 | 38 | low.flow.runs <- rle(low.flows) 39 | too.short <- which(low.flow.runs$lengths < ind.days & low.flow.runs$values == 0) 40 | spell.factor <- rep(seq_along(low.flow.runs$lengths), times = low.flow.runs$lengths) 41 | add.to.spell <- which(spell.factor %in% too.short) 42 | low.flows[add.to.spell] <- 1 43 | } 44 | 45 | 46 | low.flow.runs <- rle(low.flows) 47 | # low.flow.runs<-low.flow.runs[which(low.flow.runs$values==1)] 48 | 49 | good.low.flow.runs <- which(!is.na(low.flow.runs$values)) 50 | flow.runs.values <- low.flow.runs$values[good.low.flow.runs] 51 | flow.runs.lengths <- low.flow.runs$lengths[good.low.flow.runs] 52 | spell.starts <- c(1, cumsum(head(flow.runs.lengths, -1)) + 1) 53 | spell.lengths <- data.frame(flow.runs.values, start.date = flow.ts[spell.starts, "Date"], spell.length = flow.runs.lengths) 54 | 55 | if (inter.spell == TRUE) { 56 | 57 | spell.lengths <- subset(spell.lengths, flow.runs.values == 0, select = names(spell.lengths) %in% c("start.date", "spell.length")) 58 | 59 | 60 | } else { 61 | spell.lengths <- subset(spell.lengths, flow.runs.values == 1, select = names(spell.lengths) %in% c("start.date", "spell.length")) 62 | 63 | } 64 | if (nrow(spell.lengths) == 0) { 65 | spell.lengths <- data.frame(start.date = NA, spell.length = NA) 66 | 67 | } 68 | 69 | return(spell.lengths) 70 | } 71 | -------------------------------------------------------------------------------- /R/baseflows.R: -------------------------------------------------------------------------------- 1 | baseflows <- function(flow.ts, a = 0.975, n.reflected = 30, ts = "mean") { 2 | 3 | full.flow.ts <- flow.ts[c("Date", "Q")] 4 | record.year <- strftime(full.flow.ts[["Date"]], format = "%Y") 5 | n.years <- length(levels(factor(record.year))) 6 | 7 | red.flow.ts <- full.flow.ts[complete.cases(full.flow.ts[["Date"]], full.flow.ts[["Q"]]), ] 8 | 9 | Date <- red.flow.ts[[1]] 10 | Q <- red.flow.ts[[2]] 11 | 12 | seq.length <- length(Q) 13 | 14 | Q <- c(Q[n.reflected:1], Q, Q[seq.length:(seq.length-n.reflected)]) 15 | 16 | lh3 <- function(Q, a) { 17 | 18 | qb1 <- lh(Q, a) 19 | qb2 <- lh(rev(qb1), a) 20 | qb3 <- lh(rev(qb2), a) 21 | 22 | return(qb3) 23 | 24 | } 25 | 26 | 27 | lh <- function(Q, a) { 28 | 29 | qf <- rep(Q[1], length(Q)) 30 | qb <- rep(Q[1], length(Q)) 31 | 32 | qf[1] <- (Q[2] - Q[1]) * ((1 + a)/2) 33 | 34 | for (i in 2:length(Q)) { 35 | 36 | qf[i] <- a * qf[i - 1] + ((Q[i] - Q[i - 1]) * ((1 + a)/2)) 37 | 38 | if (qf[i] <= 0) { 39 | qf[i] <- 0 40 | 41 | } 42 | } 43 | 44 | for (i in 1:length(Q)) { 45 | 46 | if (qf[i] <= 0) { 47 | qb[i] <- Q[i] 48 | } else if (Q[i] > qf[i]) { 49 | qb[i] <- Q[i] - qf[i] 50 | } else qb[i] <- 0 51 | 52 | } 53 | return(qb) 54 | 55 | } 56 | 57 | 58 | 59 | bf <- lh3(Q, a) 60 | bf <- bf[-c(1:n.reflected, (length(bf)-n.reflected):length(bf))] 61 | Q <- Q[-c(1:n.reflected, (length(bf)-n.reflected):length(bf))] 62 | bfi <- ifelse(Q == 0, 0, bf/Q) 63 | 64 | 65 | out <- data.frame(Date, bf, bfi) 66 | out <- merge(full.flow.ts, out, by = "Date", all.x = T) 67 | names(out) <- c("Date", "Q", "bf", "bfi") 68 | 69 | if (ts == "daily") { 70 | return(out) 71 | } else { 72 | 73 | if (ts == "annual") { 74 | a.obs <- aggregate(full.flow.ts$Q, by = list(year = strftime(full.flow.ts[["Date"]], format = "%Y")), function(x) { 75 | sum(!is.na(x)) 76 | }) 77 | names(a.obs) <- c("year", "no.obs") 78 | a.bf <- aggregate(out[2:4], by = list(year = strftime(out[["Date"]], format = "%Y")), mean, na.rm = T) 79 | out <- merge(a.obs, a.bf, by = "year", all.x = T) 80 | 81 | return(out) 82 | 83 | } 84 | 85 | 86 | if (ts == "mean") { 87 | all.days <- nrow(full.flow.ts) 88 | obs <- nrow(red.flow.ts) 89 | prop.obs = obs/all.days 90 | 91 | return(data.frame(n.years = n.years, prop.obs = prop.obs, MDF = mean(out[, 2], na.rm = T), Q50 = median(out[, 2], na.rm = T), mean.bf = mean(out[, 3], na.rm = T), mean.bfi = mean(out[, 92 | 3], na.rm = T)/mean(out[, 2], na.rm = T))) 93 | 94 | } 95 | } 96 | } 97 | -------------------------------------------------------------------------------- /R/high.spell.lengths.R: -------------------------------------------------------------------------------- 1 | high.spell.lengths <- function(flow.ts, quant = 0.9, threshold = NULL, ind.days = 5, ignore.zeros = T, ctf.threshold = 0.1, inter.flood = FALSE) { 2 | 3 | record.year <- strftime(flow.ts[["Date"]], format = "%Y") 4 | flow.ts <- data.frame(flow.ts, year = record.year) 5 | #make missing Q values 0 so that the correct start and end dates are returned. 6 | flow.ts[is.na(flow.ts[["Q"]]), "Q"] <- 0 7 | n.years <- nlevels(as.factor(record.year)) 8 | 9 | 10 | if (!is.null(threshold)) { 11 | 12 | 13 | flow.threshold <- threshold 14 | # names(flow.threshold)<-NULL #normallyhide 15 | 16 | } else { 17 | 18 | 19 | if (ignore.zeros == T) { 20 | 21 | flow.threshold <- quantile(flow.ts[which(flow.ts[, "Q"] > ctf.threshold), "Q"], quant, na.rm = T) 22 | names(flow.threshold) <- NULL 23 | } else { 24 | flow.threshold <- quantile(flow.ts[, "Q"], quant, na.rm = T) 25 | names(flow.threshold) <- NULL 26 | } 27 | 28 | } 29 | 30 | 31 | 32 | 33 | 34 | high.flows <- ifelse(flow.ts[, "Q"] > flow.threshold, 1, 0) 35 | 36 | if (ind.days > 0) { 37 | 38 | high.flow.runs <- rle(high.flows) 39 | too.short <- which(high.flow.runs$lengths < ind.days & high.flow.runs$values == 0) 40 | spell.factor <- rep(seq_along(high.flow.runs$lengths), times = high.flow.runs$lengths) 41 | add.to.spell <- which(spell.factor %in% too.short) 42 | high.flows[add.to.spell] <- 1 43 | } 44 | 45 | 46 | high.flow.runs <- rle(high.flows) 47 | # high.flow.runs<-high.flow.runs[which(high.flow.runs$values==1)] 48 | 49 | good.high.flow.runs <- which(!is.na(high.flow.runs$values)) 50 | flow.runs.values <- high.flow.runs$values[good.high.flow.runs] 51 | flow.runs.lengths <- high.flow.runs$lengths[good.high.flow.runs] 52 | spell.starts <- c(1, cumsum(head(flow.runs.lengths, -1)) + 1) 53 | spell.lengths <- data.frame(flow.runs.values, start.date = flow.ts[spell.starts, "Date"], spell.length = flow.runs.lengths) 54 | 55 | if (inter.flood == TRUE) { 56 | 57 | spell.lengths <- subset(spell.lengths, flow.runs.values == 0, select = names(spell.lengths) %in% c("start.date", "spell.length")) 58 | 59 | 60 | } else { 61 | spell.lengths <- subset(spell.lengths, flow.runs.values == 1, select = names(spell.lengths) %in% c("start.date", "spell.length")) 62 | 63 | } 64 | if (nrow(spell.lengths) == 0) { 65 | spell.lengths <- data.frame(start.date = NA, spell.length = NA) 66 | 67 | } 68 | 69 | return(spell.lengths) 70 | } 71 | -------------------------------------------------------------------------------- /man/baseflows.Rd: -------------------------------------------------------------------------------- 1 | \name{baseflows} 2 | \alias{baseflows} 3 | 4 | \title{ 5 | Measures of central tendency and baseflow via the Lynne-Hollick (LH) baseflow filter 6 | } 7 | \description{ 8 | Calculate measure of central tendency and baseflow indices using the Lynne-Hollick filter 9 | } 10 | \usage{ 11 | baseflows(flow.ts, a, n.reflected = 30, ts = "mean") 12 | } 13 | 14 | \arguments{ 15 | \item{flow.ts}{ 16 | Dataframe with date and discharge data in columns named "Date" and "Q" respectively. Date must be in POSIX format (see ts.format). Missing values are ignored. 17 | } 18 | \item{a}{ 19 | The alpha value used in the Lynne-Hollick filter for digital baseflow separation. Default value is 0.975 20 | } 21 | \item{n.reflected}{ 22 | The number of days that are reflected at the start and end of the series to provide a burn in for the digital filter. Default value is 30. (See Ladson et al. 2013). 23 | } 24 | \item{ts}{ 25 | ts="mean" returns means for the entire time series \cr 26 | ts="annual" returns annual averages. Note this function does not currently use hydrologic years even when defined\cr 27 | ts="daily" returns complete series} 28 | } 29 | \details{ 30 | Technically the LH filter cannot be calculated where there are missing data. Here the function removes missing values and is applied to a concatenated version of the time series. Missing dates are reinserted after the filter has been applied for the purpose of returning annual or daily series. The function further reports the number of missing values leaving the user to decide on the reliability of the baseflow estimates. 31 | } 32 | \value{ 33 | A dataframe. See below for details. The original dataframe with appended columns "bf" and "bfi". See ts="annual" for details. 34 | 35 | 36 | ts="mean" 37 | \item{n.years }{The number of years of record in the series} 38 | \item{prop.obs }{proportion of non-missing observations} 39 | \item{MDF }{mean daily flow} 40 | \item{Q50 }{median daily flow} 41 | \item{mean.bf }{mean baseflow volume} 42 | \item{mean.bfi }{mean baseflow index} 43 | 44 | ts="annual" 45 | \item{year}{the record year} 46 | \item{no.obs}{no of observations in each year} 47 | \item{Q}{mean daily flow in each year} 48 | \item{bf}{mean baseflow volume in each year} 49 | \item{bfi}{baseflow index for each year} 50 | 51 | ts="daily" 52 | \item{bf}{baseflow index for each observation} 53 | \item{bfi}{baseflow index associated with each observation} 54 | } 55 | \references{ 56 | Ladson, A. R., R. Brown, B. Neal and R. Nathan (2013) A standard approach to baseflow separation using the Lyne and Hollick filter. Australian Journal of Water Resources 17(1): 173-18 57 | 58 | Lynne, V., Hollick, M. (1979) Stochastic time-variable rainfall-runoff modelling. In: pp. 89-93 Institute of Engineers Australia National Conference. Perth. 59 | } 60 | \author{ 61 | Nick Bond 62 | } 63 | 64 | \examples{ 65 | data(Acheron) 66 | Acheron<-ts.format(Acheron) 67 | 68 | baseflows(Acheron,a=0.975, ts="mean") 69 | baseflows(Acheron,a=0.975, ts="annual") 70 | head(baseflows(Acheron,a=0.975, ts="daily")) 71 | 72 | 73 | } 74 | -------------------------------------------------------------------------------- /man/partial.series.Rd: -------------------------------------------------------------------------------- 1 | \name{partial.series} 2 | \alias{partial.series} 3 | %- Also NEED an '\alias' for EACH other topic documented here. 4 | \title{ 5 | Partial and annual exceedence series. 6 | } 7 | \description{ 8 | Returns a partial or annual exceedence series (ari=1) based on a user defined recurrence interval (ari). 9 | 10 | For analyses based on a defined threshold (rather than recurrence interval) use \code{\link[hydrostats:high.spells]{high.spells}} instead. 11 | } 12 | 13 | \usage{ 14 | partial.series(flow.ts, ari = 2, ind.days = 7, duration = T, plot = F, volume = T, 15 | series = FALSE) 16 | } 17 | %- maybe also 'usage' for other objects documented here. 18 | \arguments{ 19 | \item{flow.ts}{Dataframe with date and discharge data in columns named "Date" and "Q" respectively. Date must be in POSIX format (see ts.format) 20 | } 21 | \item{ari}{ 22 | The desired average return interval. As a partial series, an ari of 1 will return statistics for the n largest floods in n years of record (also referred to as the annual exceedence series). The annual maximum series can be derived from high.spells with annual.stats=T} 23 | \item{ind.days}{ 24 | Spells of less than ind.days (default 7) are considered to be non-independent, and only the larger of the two spells is included in the results. This behaviour differs from high.spells, where periods below the determined spell threshold of less than the independence period are infilled for the purposes of determining spell duration. This behaviour may change in the future} 25 | \item{duration}{ 26 | logical. If TRUE (default), statistics describing the duration of events are returned} 27 | \item{plot}{ 28 | logical. If TRUE a plot is returned showing the events included in the partial series} 29 | \item{volume}{ 30 | logical. If TRUE, statistics are returned describing the volume of spells (above the spell threshold)} 31 | \item{series}{ 32 | logical. If TRUE, the partial series is returned. If FALSE (default), only the indices describing the partial series are returned} 33 | } 34 | 35 | \value{ 36 | A list or dataframe dependening on whether series = TRUE. If TRUE, a list is returned (see below). If FALSE a dataframe is returned with all indices but without the actual partial series (p.series). 37 | 38 | %% ~Describe the value returned 39 | %% If it is a LIST, use 40 | Indices 41 | \item{p.series }{A dataframe containing an ordered partial series\cr showing the timing and peak magnitude of events}. 42 | \item{n.years }{Number of (almost) complete years in the series. Years with fewer than 350 non-missing values are ignored}. 43 | \item{n.events }{Number of events in the partial series}{} 44 | \item{flow.threshold }{the peak volume of the smallest event include in the series}{} 45 | \item{avg.duration }{the average duration of events in the series}{} 46 | \item{max.duration }{the maximum duration of events in the series}{} 47 | \item{med.spell.volume }{the median volume (above the threshold) of events in the series}{} 48 | 49 | } 50 | 51 | \author{ 52 | Nick Bond 53 | } 54 | \examples{ 55 | data(Cooper) 56 | Cooper<-ts.format(Cooper) 57 | 58 | partial.series(Cooper,ari=2) 59 | partial.series(Cooper, ari=5, plot=TRUE, ind.days=2) 60 | partial.series(Cooper, ari=5, plot=TRUE, ind.days=10) 61 | } 62 | -------------------------------------------------------------------------------- /R/Colwells.R: -------------------------------------------------------------------------------- 1 | Colwells <- function(flow.ts, fn = "mean", boundaries = "transform", s = 11, base.binning = 2, from = 0.5, by = 0.25, base.entropy = 2, indices.only = FALSE) { 2 | fn <- match.fun(fn) 3 | 4 | 5 | # fn<-summary.stat 6 | 7 | flow.ts$month <- factor(strftime(flow.ts[, "Date"], format = "%m")) 8 | flow.ts$year <- factor(strftime(flow.ts[, "Date"], format = "%Y")) 9 | 10 | flow.ts.monthly <- aggregate(Q ~ month + year, flow.ts, fn, na.rm = TRUE) 11 | 12 | 13 | 14 | if (boundaries == "transform") { 15 | 16 | flow.ts.monthly$Q <- log10(flow.ts.monthly$Q + 1) 17 | flow.ts.monthly$flow.class <- cut(flow.ts.monthly$Q, s, right = FALSE, include.lowest = TRUE) 18 | flow.table <- with(flow.ts.monthly, table(flow.class, month)) 19 | pbreaks <- "see Table" 20 | 21 | 22 | } else { 23 | 24 | if (boundaries == "log_class_size") { 25 | 26 | breaks <- base.binning^(seq(1:s) - 2) 27 | breaks <- c(-Inf, breaks, Inf) 28 | pbreaks <- breaks 29 | flow.ts.monthly$flow.class <- cut(flow.ts.monthly$Q, breaks) 30 | flow.table <- with(flow.ts.monthly, table(flow.class, month)) 31 | 32 | } else { 33 | 34 | if (boundaries == "weighted_log_class_size") { 35 | low_exp <- ceiling(0 - (s - 1)/2) 36 | breaks <- base.binning^seq(low_exp, length.out = s - 1) 37 | breaks <- c(-Inf, breaks, Inf) 38 | pbreaks <- breaks 39 | breaks <- fn(flow.ts.monthly$Q) * breaks 40 | 41 | 42 | flow.ts.monthly$flow.class <- cut(flow.ts.monthly$Q, breaks, right = FALSE, include.lowest = TRUE) 43 | flow.table <- with(flow.ts.monthly, table(flow.class, month)) 44 | 45 | 46 | } else { 47 | 48 | if (boundaries == "equal") { 49 | flow.ts.monthly$flow.class <- cut(flow.ts.monthly$Q, s, right = FALSE, include.lowest = TRUE) 50 | flow.table <- with(flow.ts.monthly, table(flow.class, month)) 51 | pbreaks <- "see Table" 52 | } else { 53 | 54 | if (boundaries == "Gan") { 55 | Q <- fn(flow.ts.monthly$Q, na.rm = TRUE) 56 | breaks <- seq(from = from, by = by, length.out = s - 1) 57 | pbreaks <- breaks 58 | breaks <- Q * breaks 59 | breaks <- c(-Inf, breaks, Inf) 60 | flow.ts.monthly$flow.class <- cut(flow.ts.monthly$Q, breaks, right = FALSE, include.lowest = TRUE) 61 | flow.table <- with(flow.ts.monthly, table(flow.class, month)) 62 | } 63 | } 64 | } 65 | } 66 | } 67 | # X<-margin.table(flow.table, 2) 68 | X <- apply(flow.table, 2, sum, na.rm = T) 69 | # Y<-margin.table(flow.table, 1) 70 | Y <- apply(flow.table, 1, sum, na.rm = T) 71 | Z <- sum(flow.table, na.rm = TRUE) 72 | 73 | HX <- -1 * sum((X/Z) * log(X/Z, base = base.entropy), na.rm = TRUE) 74 | HY <- -1 * sum((Y/Z) * log(Y/Z, base = base.entropy), na.rm = TRUE) 75 | HXY <- -1 * sum((flow.table/Z) * log(flow.table/Z, base = base.entropy), na.rm = TRUE) 76 | 77 | P <- round(1 - (HXY - HX)/log(s, base = base.binning), 2) 78 | C <- round(1 - HY/log(s, base = base.binning), 2) 79 | M <- round((HX + HY - HXY)/log(s, base = base.binning), 2) 80 | 81 | 82 | if (indices.only == TRUE) { 83 | 84 | return(data.frame(P = P, C = C, M = M, CP = C/P, MP = M/P)) 85 | 86 | } else { 87 | 88 | return(list(breaks = pbreaks, flow.table = flow.table, P = P, C = C, M = M, CP = C/P, MP = M/P)) 89 | } 90 | } 91 | -------------------------------------------------------------------------------- /NEWS: -------------------------------------------------------------------------------- 1 | hydrostats 0.2.8 2 | ---------------------------------------------------------------- 3 | BUG FIXES AND MINOR IMPROVEMENTS 4 | *Addressed a coding issue in the seasonaility function to deal with an occassional error. Returned results unaffected. 5 | 6 | hydrostats 0.2.7 7 | ---------------------------------------------------------------- 8 | BUG FIXES AND MINOR IMPROVEMENTS 9 | *Addressed codign issue that occassionally threw an error when using the CTF function with dplyr 'do' or purrr. Returned results unaffected. 10 | 11 | hydrostats 0.2.6 12 | ---------------------------------------------------------------- 13 | BUG FIXES AND MINOR IMPROVEMENTS 14 | *Made a minor change to the baseflow function so that when the Lynn Hollick filter is used to estimate quickflow at each time step, any values < 0 are set to 0 immediately, before calculating the next quickflow value (Thanks to Leo-Juhani Meriö). 15 | 16 | *Fixed a bug in the high.spell and low.spell functions, so that the SD of the timing of max and min annual flows, respectively, are still calculated in situations where the max/min value occurs on multiple days. 17 | 18 | 19 | hydrostats 0.2.5 20 | ---------------------------------------------------------------- 21 | BUG FIXES AND MINOR IMPROVEMENTS 22 | *The high.spells function was fixed to correct an error in the calculation of summary statistics for peaks (mean and sd). Formerly these included all flows above the threshold. (Thanks to Scott Hardie for picking this up). 23 | 24 | *The Colwells function was modified to use log base 2 for the entropy calculations. This value can now be set by the user. (Thanks to Benedikt Heudorfer for modifying the code to include this functionality). 25 | 26 | *A typo was corrected in the monthly.cv function help file to allow the examples to run correctly. (Thanks to Sunny Yu). 27 | 28 | *A low.spell.lengths function was added to provide the ability to calculate low spell start date and duration with the same flexibility as for high spells (including independence criteria). This extends the functionality of using high.spell.lengths with inter.flood=TRUE. (Thanks to Tim Haeusler). 29 | ---------------------------------------------------------------- 30 | 31 | 32 | hydrostats 0.2.4 33 | ---------------------------------------------------------------- 34 | BUG FIXES AND MINOR IMPROVEMENTS 35 | * The high.spell.lengths function was fixed so that missing values do not result in incorrect start dates being returned (Thanks to Bastian Pöschl). 36 | 37 | * Fixed a typo in the help for daily.cv (Thanks to Tony Ladson). 38 | 39 | * The baseflows function now reflects the start and end values from the time-series (n.reflected) before calculating the baseflow component to reduce sensitivity to the starting conditions (as recommended by Ladson, A. R., R. Brown, B. Neal and R. Nathan (2013). “A standard approach to baseflow separation using the Lyne and Hollick filter.” Australian Journal of Water Resources 17(1): 173-180.). 40 | 41 | * corrected year count in the baseflow function. 42 | 43 | hydrostats 0.2.3 44 | ---------------------------------------------------------------- 45 | 46 | BUG FIXES AND MINOR IMPROVEMENTS 47 | * Modified a number of functions (including partial.series and baseflow) to prevent an error when used with group_by and 'do' 48 | from the dplyr package (useful for analysing multiple flow series simultaneously). 49 | 50 | * added minimum and median spell duration to the results returned by 51 | the low.spells and high.spells functions. 52 | 53 | * added number of years and ARI to the partial series output. 54 | 55 | * fixed an error in high.spells and low.spells, which could produce an error in the duration statistics for annual series. 56 | 57 | 58 | hydrostats 0.2.2 59 | ---------------------------------------------------------------- 60 | 61 | BUG FIXES AND MINOR IMPROVEMENTS 62 | * Modified the code for high.spells and low.spells to prevent an error when used with the 'do' 63 | function in dplyr, which allows the use of arbitrary functions 64 | with the group_by function. 65 | 66 | 67 | 68 | 69 | hydrostats 0.2.1 70 | ---------------------------------------------------------------- 71 | 72 | BUG FIXES AND MINOR IMPROVEMENTS 73 | 74 | * fixed a bug in baseflow function that caused issues when date and 75 | discharge data were in columns other than columns 1 and 2. 76 | 77 | * added to Colwells function the ability to output a dataframe of the 78 | indices only. 79 | 80 | * shifted the reporting of record length from high.spells to baseflows 81 | function so that if outputs are combined in a dataframe, record 82 | length is now the first variable if baseflows is the first function run. 83 | 84 | * various corrections/edits to the manual pages. 85 | 86 | -------------------------------------------------------------------------------- /R/partial.series.R: -------------------------------------------------------------------------------- 1 | partial.series <- function(flow.ts, ari = 2, ind.days = 7, duration = T, plot = F, volume = T, series = FALSE) { 2 | gauge <- deparse(substitute(flow.ts)) 3 | 4 | record.year <- strftime(flow.ts[["Date"]], format = "%Y") 5 | flow.ts <- data.frame(flow.ts, hydro.year = record.year) 6 | flow.ts.comp <- na.omit(flow.ts) 7 | n.days <- tapply(flow.ts.comp[["Q"]], flow.ts.comp[["hydro.year"]], length) 8 | n.most.days <- which(n.days >= 350) 9 | flow.ts.comp <- flow.ts.comp[which(flow.ts.comp[["hydro.year"]] %in% names(n.most.days)), ] 10 | flow.ts.comp[["hydro.year"]] <- factor(flow.ts.comp[["hydro.year"]]) 11 | record.year <- flow.ts.comp[["hydro.year"]] 12 | 13 | n.years <- length(n.most.days) 14 | 15 | 16 | if (ari > n.years) { 17 | print("warning(time-series is shorter than ari. Please provide a smaller ari") 18 | if (volume == FALSE) { 19 | return(data.frame(ari = ari, n.years = n.years, n.events = NA, flow.threshold = NA, avg.duration = NA, max.duration = NA)) 20 | } else { 21 | return(data.frame(ari = ari, n.years = n.years, n.events = NA, flow.threshold = NA, avg.duration = NA, max.duration = NA, med.spell.volume = NA)) 22 | } 23 | 24 | 25 | } 26 | n.events <- data.frame(n.events = ceiling(n.years/ari)) 27 | p.series <- vector("list", length = n.events$n.events) 28 | 29 | rising <- data.frame(rising = flow.ts[2:nrow(flow.ts), "Q"] - flow.ts[1:nrow(flow.ts) - 1, "Q"]) 30 | falling <- data.frame(falling = flow.ts[3:nrow(flow.ts), "Q"] - flow.ts[2:nrow(flow.ts) - 2, "Q"]) 31 | 32 | peak.search <- data.frame(flow.ts, rising = c(NA, rising[["rising"]]), falling = c(falling[["falling"]], NA, NA)) 33 | peaks <- flow.ts[which(peak.search[["rising"]] > 0 & peak.search[["falling"]] < 0), ] 34 | 35 | peaks.ord <- peaks[order(peaks[["Q"]], decreasing = T), ] 36 | 37 | p.series[[1]] <- data.frame(peaks.ord[1, ]) 38 | 39 | i = 1 40 | 41 | while (i < n.events$n.events) { 42 | i <- i + 1 43 | dif.time.test <- difftime(peaks.ord$Date[1], peaks.ord$Date) 44 | 45 | peaks.ord <- peaks.ord[which(abs(dif.time.test) > (ind.days * 24 * 60 * 60)), ] 46 | 47 | p.series[[i]] <- data.frame(peaks.ord[1, ]) 48 | 49 | if (is.na(peaks.ord[1, "Q"]) == T) 50 | NA 51 | } 52 | p.series <- do.call("rbind", p.series) 53 | 54 | p.series$event.rank <- seq(1:nrow(p.series)) 55 | 56 | flow.threshold <- tail(p.series[["Q"]], 1) 57 | 58 | if (plot == TRUE) { 59 | plot(flow.ts[["Date"]], flow.ts[["Q"]], type = "l", main = gauge, xlab = "Date", ylab = "Q") 60 | 61 | points(p.series$Date, p.series$Q, col = "red", cex = 0.25) 62 | abline(h = (tail(p.series["Q"], 1) - 1)) 63 | } 64 | 65 | high.flows <- ifelse(flow.ts[["Q"]] >= flow.threshold, 1, 0) 66 | high.flow.runs <- rle(high.flows) 67 | 68 | if (duration == TRUE) { 69 | avg.duration <- mean(high.flow.runs$lengths[which(high.flow.runs$values == 1)], na.rm = T) 70 | max.duration <- max(high.flow.runs$lengths[which(high.flow.runs$values == 1)], na.rm = T) 71 | } 72 | if (volume == TRUE) { 73 | spell.factor <- rep(seq_along(high.flow.runs$lengths), times = high.flow.runs$lengths) 74 | spells <- split(flow.ts[["Q"]], spell.factor) 75 | spell.volumes <- flow.ts[["Q"]] 76 | spell.volumes <- sapply(spells, sum) 77 | spell.volumes.below.threshold <- sapply(spells, length) * flow.threshold 78 | spell.volumes <- spell.volumes[which(high.flow.runs$values == 1)] - spell.volumes.below.threshold[which(high.flow.runs$values == 1)] 79 | 80 | 81 | if (series == TRUE) { 82 | return(list(p.series = p.series, ari = ari, n.years = n.years, n.events = n.events, flow.threshold = flow.threshold, avg.duration = avg.duration, max.duration = max.duration, med.spell.volume = median(spell.volumes))) 83 | } else { 84 | return(data.frame(ari = ari, n.years = n.years, n.events = n.events, flow.threshold = flow.threshold, avg.duration = avg.duration, max.duration = max.duration, med.spell.volume = median(spell.volumes))) 85 | } 86 | } else { 87 | if (series == TRUE) { 88 | return(list(p.series = p.series, ari = ari, n.years = n.years, n.events = n.events, flow.threshold = flow.threshold, avg.duration = avg.duration, max.duration = max.duration)) 89 | } else { 90 | return(data.frame(ari = ari, n.years = n.years, n.events = n.events, flow.threshold = flow.threshold, avg.duration = avg.duration, max.duration = max.duration)) 91 | } 92 | } 93 | } 94 | -------------------------------------------------------------------------------- /man/Colwells.Rd: -------------------------------------------------------------------------------- 1 | \name{Colwells} 2 | \alias{Colwells} 3 | 4 | \title{ 5 | Colwells indices } 6 | \description{ 7 | Calculates Colwell's (1974), which provide a measure of the seasonal predictability of environmental phenomena. Defined in terms of Predictability (P), Constancy (C) and Contingency (M). For detailed information on the calculation and description of Colwell's indices refer to (Colwell, 1974).} 8 | \usage{ 9 | Colwells(flow.ts, fn = "mean", 10 | boundaries = "transform", s = 11, 11 | base.binning = 2, from = 0.5, by = 0.25, base.entropy=2, indices.only=FALSE) 12 | } 13 | %- maybe also 'usage' for other objects documented here. 14 | \arguments{ 15 | \item{flow.ts}{ 16 | Dataframe with date and discharge data in columns named "Date" and "Q" respectively. Date must be in POSIX format (see ts.format). Missing values are ignored. 17 | } 18 | \item{fn}{ 19 | The function used to summarise daily data (default mean) and for scaling break points when binning data. Can also use median, min, max.} 20 | \item{boundaries}{ 21 | The method used to define break points when binning data. 22 | 23 | boundaries="equal" splits the data into s equal sized bins 24 | 25 | boundaries="transform" (default) first applies a log10(x+1) transformation and then splits the data into s equal size bins 26 | 27 | boundaries="log_class_size" generates breaks based on logarithmic scale (default base 2) with a roughly equal number of bins above and below 1 28 | 29 | boundaries="weighted_log_class_size" generates breaks based on logarithmic scale (default base 2) * mean (or other summary statistic) of the variable. A roughly equal number of bins occur above and below the mean (or other summary statistic) 30 | 31 | boundaries="Gan" creates bins that match those of Gan et al. (1991). Requires: from (default 0.25), by (default 0.25) and s (number of bins) 32 | } 33 | 34 | \item{s}{ 35 | The number of classes the flow data is broken into (default 11) 36 | } 37 | 38 | \item{base.binning}{ 39 | The base integer for defining classes when using the "log_class_size" or "weighted_log_class_size" boundaries 40 | } 41 | 42 | \item{from}{ 43 | The lowest break point for defining classes when using the "Gan" boundaries (default 0.25) 44 | } 45 | 46 | \item{by}{ 47 | The bin width when using the "Gan" boundaries (default 0.25) 48 | } 49 | 50 | \item{base.entropy}{ 51 | The base integer used for the entropy calculations (default=2) 52 | } 53 | \item{indices.only}{ 54 | Logical. If FALSE (default), the function returns a list of length 3, including the breaks, a table of frequencies, and Colwell's indices. If TRUE, the function returns just a dataframe of indices, useful for combining output with that from other functions.} 55 | } 56 | \details{ 57 | Predictability measures how tightly an event is linked to a season; Constancy measures how uniformly the event occurs through all seasons, and Contingency measures the repeatability of season patterns. Predictability is the sum of Constancy and Contingency, and reflects the likelihood of being able to predict a flow occurrence. It is maximized when the flow is constant throughout the year (Constancy Maximised), or if the pattern of high or low flow occurrence is repeated across all years (Contingency maximized). 58 | } 59 | \value{ 60 | A list or dataframe (see above). 61 | 62 | \item{breaks }{shows the break points used between classes. Not returned for all boundary options. The upper and lower classes are always open even if -Inf/Inf are not shown.} 63 | \item{flow.table }{Table showing the number of times the monthly flows fall into each flow class in each month. Useful for examining the results of different binning techniques.} 64 | \item{P }{Predictability} 65 | \item{C }{Constancy} 66 | \item{M }{Contingency} 67 | \item{CP }{C/P} 68 | \item{MP }{M/P} 69 | 70 | } 71 | \references{ 72 | Colwell, R.K. 1974. Predictability, constancy, and contingency of periodic phenomena. Ecology 55(5): 1148-53. 73 | 74 | Gan, K.C., McMahon, T.A., and Finlayson, B.L. 1991. Analysis of periodicity in streamflow and rainfall data by Colwell's indices. Journal of Hydrology 123(1-2): 105-18. 75 | } 76 | \author{ 77 | Nick Bond 78 | } 79 | \examples{ 80 | data(Cooper) 81 | Cooper<-ts.format(Cooper) 82 | 83 | Colwells(Cooper, s=5) 84 | Colwells(Cooper, boundaries="equal", s=11) 85 | Colwells(Cooper, boundaries="log_class_size", s=11) 86 | Colwells(Cooper, boundaries="weighted_log_class_size", s=11) 87 | 88 | Colwells(Cooper, boundaries="Gan", from=1,by=1, s=4) 89 | Colwells(Cooper, boundaries="Gan", from=0.25,by=0.25, s=9) 90 | 91 | Colwells(Cooper, boundaries="Gan", from=0.25,by=0.25, s=9, indices.only=TRUE) 92 | 93 | 94 | %Calculate indices for two gauges (Acheron River and Cooper Creek) simultaneously using the plyr package. 95 | require(plyr) 96 | data(Acheron) 97 | Acheron<-ts.format(Acheron) 98 | flow.ts<-rbind(data.frame(River="Acheron", Acheron), data.frame(River="Cooper", Cooper)) 99 | 100 | ddply(flow.ts, .(River), function(x) 101 | Colwells(x, boundaries="weighted_log_class_size", s=11, indices.only=TRUE)) 102 | } 103 | -------------------------------------------------------------------------------- /man/low.spells.Rd: -------------------------------------------------------------------------------- 1 | \name{low.spells} 2 | \alias{low.spells} 3 | %- Also NEED an '\alias' for EACH other topic documented here. 4 | \title{ 5 | Calculate low flow spell statistics 6 | } 7 | \description{ 8 | Calculates a suite of statistics describing low-flow spell characteristics, such as the timing, frequency and duration of events below a threshold. The event threshold can be defined as a flow quantile (e.g. the 10th percentile, which is flows exceeded 90\% of the time, corresponding to Q10) or a specific threshold volume (e.g. ML/day). 9 | 10 | For the purpose of deriving annual low-flow spell statistics, the function can also be applied based on the hydrologic year. This is advisable where the low flow season spans calendar years, such that prolonged spells may be split at the transition from one calendar year to the next. This first requires the time series be processed using the hydro.year function. This adds an additional column indicating the hydrologic year to which each row belongs, which is used for deriving annual spell characteristics. 11 | 12 | It is possible for there to be multiple days with the same low flow value (especially zero flows). In estimating the average timing (and sd of timing) of minimum flows, the function calculates the average DOY (day of year) of minimum flows in each year first, before calculating the average across years. Circular functions are used to address the proximity between days toward the beginning and end of the year. 13 | 14 | Missing values are allowed for convenience (NA's are removed and the time-series is concatenated before functions are applied), but of course may lead to biased results. For the purpose of the annual statistics years with fewer than 350 days of available record are ignored. 15 | 16 | When used with ddply to compute outputs for multiple gauges or time periods simultaneously, results, icluding graphs are produced for each factor level, including graphs. Note however that the funtion will return warnings if annual stats are calculated when year is used as a factor. 17 | } 18 | \usage{ 19 | low.spells(flow.ts, quant = 0.1, threshold=NULL, 20 | duration = T, volume = T, plot = T, ann.stats = T, ann.stats.only = F, 21 | hydro.year=FALSE) 22 | } 23 | \arguments{ 24 | \item{flow.ts}{ 25 | Dataframe with date and discharge data in columns named "Date" and "Q" respectively. Date must be in POSIX format (see ts.format). 26 | 27 | If a third column exists then this is assumed to provide a vector of years for the purpose of calculating annual spell statistics based on a predetermined hydrologic year.} 28 | \item{quant}{ 29 | Percentile/quantile to use for defining event magnitude (default 0.1). A value of 0.1 is the lower 10th percentile (i.e. a volume exceeded 90\% of the time).} 30 | \item{threshold}{ 31 | A user supplied threshold for defining spells. This would typically be derived from hydraulic models or similar knowledge pertaining to a gauge site. 32 | } 33 | \item{duration}{ 34 | logical. Should statistics describing spell duration be returned?} 35 | \item{volume}{ 36 | logical. Should statistics describing spell volumes be returned?} 37 | \item{plot}{ 38 | logical. Should the time-series be plotted? Data points considered 'within spell' are identifed using red circles and the threshold is identified with a horizontal line.} 39 | \item{ann.stats}{ 40 | logical. If TRUE, the function returns results describing the annual series (i.e. the characteristics of the spells associated with the lowest annual daily flow). The duration of each annual low spell is defined as the number of days below the smallest annual minimum for the lowest (and longest) low flow event in each year.} 41 | \item{ann.stats.only}{ 42 | logical. If TRUE, statistics describing the annual series only are returned.} 43 | \item{hydro.year}{ 44 | logical. If TRUE, each record is first assigned to a hydrologic year based on the timing of minimum flows. See \code{\link{hydro.year}} for further details.} 45 | } 46 | 47 | \value{ 48 | A dataframe with the following columns. 49 | 50 | low flow indices 51 | \item{low.spell.threshold }{The low spell threshold applied in the analysis} 52 | \item{min.low.spell.duration }{Minimum duration of spell events} 53 | \item{avg.low.spell.duration }{Average duration of spell events} 54 | \item{med.low.spell.duration }{Median duration of spell events} 55 | \item{max.low.duration }{Maximum duration of spell events} 56 | \item{low.spell.freq}{The frequency of spell events (no. per year)} 57 | 58 | Annual low flow statistics 59 | \item{avg.min.ann}{The average annual minimum flow} 60 | \item{cv.min.ann}{The coefficient of variation of annual minimum flows} 61 | \item{ann.min.timing}{The average day of the year (0-366) on which the minimum flow(s) occur.} 62 | \item{ann.min.timing.sd}{circular standard deviation of the average timing of annual minimum flows} 63 | \item{ann.min.min.dur}{Minimum duration of the annual maximum spells (always equal to 1)} 64 | \item{ann.min.avg.dur}{Average duration of the annual maximum spells} 65 | \item{ann.min.max.dur}{Maximum duration of the annual maximum spells} 66 | } 67 | 68 | \author{ 69 | Nick Bond 70 | } 71 | 72 | \examples{ 73 | 74 | data(Cooper) 75 | Cooper<-ts.format(Cooper) 76 | 77 | low.spells(Cooper, quant=0.1) 78 | 79 | low.spells(Cooper, quant=0.1, hydro.year=TRUE) 80 | 81 | #generate results for each year 82 | Cooper$year<-strftime(Cooper$Date, format="\%Y") 83 | 84 | require(plyr) 85 | ddply(Cooper, .(year), function(x) low.spells(x, threshold=20, ann.stats=FALSE)) 86 | 87 | #generate seperate results prior to 1980. 88 | Cooper$time.period<-ifelse(Cooper$year<1980,"pre_1980","post_1980") 89 | ddply(Cooper, .(time.period), function(x) low.spells(x, threshold=20, ann.stats=FALSE)) 90 | 91 | } 92 | -------------------------------------------------------------------------------- /man/high.spells.Rd: -------------------------------------------------------------------------------- 1 | \name{high.spells} 2 | \alias{high.spells} 3 | %- Also NEED an '\alias' for EACH other topic documented here. 4 | \title{ 5 | Calculates high flow spell statistics 6 | } 7 | \description{ 8 | Calculates a suite of statistics describing flood characteristics, such as the timing, frequency and duration of events. The event threshold can be defined as a flow quantile (e.g. upper 90th percentile [default]) or a specific threshold volume (e.g. ML/day). 9 | 10 | For the purpose of deriving annual flood statistics, the function can also be applied based on the hydrologic year. This is advisable where the high flow season spans years, such that prolonged spells may span years. Setting the parameter hydro.year=TRUE uses the hydro.year function to determine the appropriate hydrologic year for each record, which is then used for deriving annual spell characteristics. 11 | 12 | It is possible for there to be multiple days with the same annual maximum flow value (although less likely than for low flows). In estimating the average timing (and sd of timing) of minimum flows, the function calculates the average day of year (DOY) of minimum flows in each year first, before calculating the average across years. Circular functions are used to address the proximity between days toward the beginning and end of the year. 13 | 14 | Missing values are allowed for convenience (NA's are removed and the time-series is concatenated before functions are applied), but may lead to biased results. For the purpose of the annual statistics years with fewer than 350 days of available record are ignored. 15 | 16 | When used with ddply to compute outputs for multiple gauges or time periods simultaneously, results, icluding graphs are produced for each factor level, including graphs. Note the funtion will return warnings if annual stats are calculated when year is used as a factor. 17 | 18 | } 19 | \usage{ 20 | high.spells(flow.ts, quant = 0.9, threshold = NULL, 21 | ind.days = 5, duration = TRUE, volume = TRUE, plot = TRUE, ignore.zeros = FALSE, 22 | ctf.threshold = 0.1, ann.stats = TRUE, ann.stats.only = FALSE, inter.flood = FALSE, 23 | hydro.year=FALSE) 24 | } 25 | %- maybe also 'usage' for other objects documented here. 26 | \arguments{ 27 | \item{flow.ts}{Dataframe with date and discharge data in columns named "Date" and "Q" respectively. Date must be in POSIX format (see ts.format). 28 | 29 | If a third column exists then this is assumed to provide a vector of years for the purpose of calculating annual spell statistics based on a predetermined hydrologic year. 30 | } 31 | \item{quant}{Percentile/quantile to use for defining event magnitude (default 0.9). A value of 0.9 is the upper 90th percentile (i.e. a volume exceeded 10\% of the time), and corresponds to Q90. 32 | } 33 | \item{threshold}{ 34 | A user supplied threshold for defining spells. This would typically be derived from hydraulic models or similar knowledge pertaining to a gauge site. 35 | } 36 | \item{ind.days}{ 37 | Periods between spells of less than ind.days (default 5) are considered to be 'in spell' for the purpose of further calculations. A value of 0 means spells 1 day apart are considered indpedendent.} 38 | \item{duration}{ 39 | logical. Should statistics describing spell duration be returned?} 40 | \item{volume}{ 41 | logical. Should statistics describing spell volumes be returned? Note that for days considered 'in-spell', the returned values have the threshold volume subtracted first, and hence reflect the amount of water that was flowing past the gauge above threshold. This is most useful in water planning scenarios.} 42 | \item{plot}{ 43 | logical. Should the time-series be plotted? Data points considered 'within spell' are identifed using red circles and the threshold is identified with a horizontal line.} 44 | \item{ignore.zeros}{ 45 | logical. If TRUE, days below a user defined cease-to-flow threshold (default 0.1) will be excluded when estimating the spell threshold for a given percentile. This is primarily of interest in highly ephemeral rivers, where flow may only occur for a small fraction of the time. In such cases, the inclusion of zeros will skew estimates of high flow events downwards, which may be undesirable. 46 | } 47 | \item{ctf.threshold}{ 48 | values below this threshold are treated as zero for the purpose of percentile based calculations (see ignore zeros). 49 | } 50 | \item{ann.stats}{ 51 | logical. If TRUE, the function returns results describing the annual maximum series (i.e. that describing the characteristics of the largest flood event in each year of the time-series). The duration of each annual high.spell is defined as the number of days above the smallest annual maximum for the largest (and longest) high.spell event in each year. 52 | } 53 | \item{ann.stats.only}{ 54 | logical. If TRUE, statistics describing the annual series only are returned. } 55 | 56 | \item{inter.flood}{ 57 | logical. If TRUE, statistics describing inter-flood spell characteristics are reported.} 58 | \item{hydro.year}{ 59 | logical. If TRUE, each record is first assigned to a hydrologic year based on the timing of minimum flows. See \code{\link{hydro.year}} for further details. 60 | } 61 | } 62 | 63 | \value{ 64 | A dataframe with the following columns. 65 | 66 | flood indices 67 | \item{high.spell.threshold }{The high spell threshold applied in the analysis)} 68 | \item{n.events }{The number of events in the series greater than or equal to the high.spell.threshold} 69 | \item{spell.freq}{The frequency of spell events (no. per year)} 70 | \item{ari}{Average Recurrence Interval of events in years (1/spell.freq)} 71 | \item{min.high.spell.dur}{Minimum duration of spell events} 72 | \item{avg.high.spell.dur}{Average duration of spell events} 73 | \item{med.high.spell.dur}{Median duration of spell events} 74 | \item{max.high.spell.dur}{Maximum duration of spell events} 75 | \item{avg.spell.volume}{Average spell volume (volumes above the threshold only)} 76 | \item{avg.spell.peak}{Average spell peak} 77 | \item{sd.spell.peak}{Standard deviation of spell speaks} 78 | \item{avg.rise}{Average absolute rate of daily rise during spell events} 79 | \item{avg.fall}{Average absolute rate of daily fall during spell events} 80 | 81 | interflood indices 82 | \item{average.interval}{The average time between spells (years)} 83 | \item{min.interval}{The mininum time between spells (years)} 84 | \item{max.interval}{The maximum time between spells (years)} 85 | 86 | Annual flood statistics 87 | \item{avg.max.ann}{The average annual maximum flow} 88 | \item{cv.max.ann}{The coefficient of variation of annual maximum flows} 89 | \item{flood.skewness}{The average annual maximum / mean daily flow} 90 | \item{ann.max.timing}{The average day of the year (0-366) on which maximum flows occur} 91 | \item{ann.max.timing.sd}{circular standard deviation of the average timing of annual maximum flows} 92 | \item{ann.max.min.dur}{Minimum duration of the annual maximum spells (always equal to 1)} 93 | \item{ann.max.avg.dur}{Average duration of the annual maximum spells} 94 | \item{ann.max.max.dur}{Maximum duration of the annual maximum spells} 95 | \item{ann.max.cv.dur}{The coefficient of variation of the duration of annual maximum spells} 96 | 97 | } 98 | 99 | \author{ 100 | Nick Bond 101 | } 102 | 103 | 104 | \examples{ 105 | 106 | data(Cooper) 107 | Cooper<-ts.format(Cooper) 108 | 109 | high.spells(Cooper, quant=0.9) 110 | 111 | high.spells(Cooper, quant=0.9, ann.stats=FALSE, plot=FALSE) 112 | 113 | high.spells(Cooper, quant=0.9, ann.stats=FALSE, ignore.zeros=TRUE) 114 | 115 | high.spells(Cooper, quant=0.9, ann.stats=FALSE, ignore.zeros=TRUE, hydro.year=TRUE) 116 | 117 | %generate results for each year 118 | require(plyr) 119 | Cooper$year<-strftime(Cooper$Date, format="\%Y") 120 | ddply(Cooper, .(year), function(x) high.spells(x, ann.stats=FALSE)) 121 | 122 | %generate seperate results before/after 1980. 123 | Cooper$time.period <- ifelse(Cooper$year<1980,"pre_1980","post_1980") 124 | 125 | ddply(Cooper, .(time.period), function(x) high.spells(x, ann.stats=FALSE)) 126 | 127 | } 128 | -------------------------------------------------------------------------------- /R/low.spells.R: -------------------------------------------------------------------------------- 1 | low.spells <- function(flow.ts, quant = 0.1, threshold = NULL, duration = T, volume = T, plot = T, ann.stats = T, ann.stats.only = F, hydro.year = FALSE) { 2 | gauge <- deparse(substitute(flow.ts)) 3 | 4 | if (hydro.year == TRUE) { 5 | print("Returning results based on hydrologic year") 6 | flow.ts <- hydro.year(flow.ts, year = "hydro") 7 | record.year <- flow.ts[["hydro.year"]] 8 | } else { 9 | record.year <- strftime(flow.ts[["Date"]], format = "%Y") 10 | flow.ts <- data.frame(flow.ts, hydro.year = record.year) 11 | } 12 | 13 | n.years <- nlevels(as.factor(record.year)) 14 | 15 | if (ann.stats == T) { 16 | # calculate annual minimum flow 17 | flow.ts.comp <- na.omit(flow.ts) 18 | 19 | n.days <- tapply(flow.ts.comp[["Q"]], flow.ts.comp[["hydro.year"]], length) 20 | n.most.days <- which(n.days >= 350) 21 | if (length(n.most.days) == 0) { 22 | ann.mins.mean <- NA 23 | cv.min.ann <- NA 24 | avg.min.day <- data.frame(mean.doy = NA, sd.doy = NA) 25 | min.min.ann.duration <- NA 26 | max.min.ann.duration <- NA 27 | avg.min.ann.duration <- NA 28 | 29 | 30 | } else { 31 | 32 | 33 | flow.ts.comp <- flow.ts.comp[which(flow.ts.comp[["hydro.year"]] %in% names(n.most.days)), ] 34 | flow.ts.comp[["hydro.year"]] <- factor(flow.ts.comp[["hydro.year"]]) 35 | record.year <- flow.ts.comp[["hydro.year"]] 36 | n.years <- nlevels(as.factor(record.year)) 37 | 38 | 39 | ann.mins <- aggregate(flow.ts.comp["Q"], flow.ts.comp["hydro.year"], min, na.rm = T) 40 | 41 | ann.min.days <- merge(ann.mins, flow.ts.comp) 42 | 43 | 44 | ann.mins.mean <- mean(ann.mins[["Q"]], na.rm = T) 45 | ann.mins.sd <- sd(ann.mins[["Q"]], na.rm = T) 46 | 47 | 48 | avg.ann.min.days <- aggregate(ann.min.days["Date"], ann.min.days["hydro.year"], function(x) t(day.dist(x)[["mean.doy"]])) 49 | 50 | avg.min.day <- day.dist(days = avg.ann.min.days[["Date"]], years = avg.ann.min.days[["hydro.year"]]) 51 | 52 | min.ann.flow.threshold <- max(ann.mins$Q, na.rm = T) 53 | 54 | ann.min.spells <- ifelse(flow.ts.comp[["Q"]] <= min.ann.flow.threshold, 1, 0) 55 | # ann.max.spell.runs <- rle(ann.max.spells) now done seperately for each year below. 56 | 57 | ann.runs <- tapply(ann.min.spells, flow.ts.comp[["hydro.year"]], rle) 58 | ann.runs.cum <- lapply(ann.runs, function(x) cumsum(x$lengths)) 59 | 60 | 61 | ann.min.index.all.events <- tapply(flow.ts.comp[["Q"]], flow.ts.comp[["hydro.year"]], function(x) which(x == min(x))) 62 | 63 | if (max(sapply(ann.min.index.all.events, length)) > 1) { 64 | ann.min.event.rles <- lapply(ann.min.index.all.events, function(x) rle(diff(x))) 65 | idx <- !(sapply(ann.min.event.rles, function(x) length(x$lengths))) 66 | ann.min.event.rles[idx] <- lapply(ann.min.event.rles[idx], function(x) { 67 | x$lengths <- 0 68 | x$values <- 1 69 | x 70 | }) 71 | 72 | ann.min.events.max.dur <- unlist(lapply(ann.min.event.rles, function(x) max(x$lengths[which(x$values == 1)]) + 1)) #add 1 because diff function returns index-1 73 | ann.min.events.max.dur <- ann.min.events.max.dur[is.finite(ann.min.events.max.dur)] 74 | } else { 75 | ann.min.rle.event.indices <- sapply(ann.runs, function(x) which(x$values == 1)) 76 | 77 | ann.min.rle.event.indices <- mapply(function(x, y) x[y], ann.runs.cum, ann.min.rle.event.indices) 78 | ann.min.rle.run.indices <- lapply(ann.runs, function(x) which(x$values == 1)) 79 | 80 | smallest.event.index <- mapply(function(x, y) which.min(abs(x - y)), ann.min.index.all.events, ann.min.rle.event.indices) 81 | final.event.index <- mapply(function(x, y) x[y], ann.min.rle.run.indices, smallest.event.index) 82 | 83 | ann.min.events.max.dur <- unlist(mapply(function(x, y) x$lengths[y], ann.runs, final.event.index, SIMPLIFY = TRUE)) 84 | 85 | } 86 | 87 | 88 | min.min.ann.duration <- min(ann.min.events.max.dur, na.rm = T) 89 | avg.min.ann.duration <- mean(ann.min.events.max.dur, na.rm = T) 90 | max.min.ann.duration <- max(ann.min.events.max.dur, na.rm = T) 91 | 92 | cv.min.ann <- (ann.mins.sd/ann.mins.mean) * 100 93 | } 94 | } 95 | 96 | if (ann.stats.only == T) { 97 | 98 | 99 | 100 | return(data.frame(avg.min.ann = ann.mins.mean, cv.min.ann = cv.min.ann, ann.min.timing = avg.min.day[, "mean.doy"], ann.min.timing.sd = avg.min.day[, "sd.doy"], ann.min.min.dur = min.min.ann.duration, 101 | ann.min.avg.dur = avg.min.ann.duration, ann.min.max.dur = max.min.ann.duration)) 102 | 103 | } else { 104 | 105 | if (!is.null(threshold)) { 106 | 107 | 108 | flow.threshold <- threshold 109 | 110 | } else { 111 | 112 | 113 | # average spell characteristics 114 | flow.threshold <- quantile(flow.ts[, "Q"], quant, na.rm = T) 115 | names(flow.threshold) <- NULL 116 | 117 | } 118 | low.flows <- ifelse(flow.ts[, "Q"] <= flow.threshold, 1, 0) 119 | low.flow.av <- mean(flow.ts[which(low.flows == 1), "Q"]) 120 | low.flow.sd <- sd(flow.ts[which(low.flows == 1), "Q"]) 121 | 122 | 123 | low.flow.runs <- rle(low.flows) 124 | 125 | 126 | low.spell.days <- as.numeric(strftime(flow.ts[which(low.flows == 1), "Date"], format = "%j")) 127 | 128 | good.low.flow.runs <- which(!is.na(low.flow.runs$values)) 129 | flow.runs.values <- low.flow.runs$values[good.low.flow.runs] 130 | flow.runs.lengths <- low.flow.runs$lengths[good.low.flow.runs] 131 | 132 | low.spell.frequency <- length(flow.runs.values[flow.runs.values == 1])/n.years 133 | 134 | 135 | 136 | 137 | 138 | 139 | if (duration == TRUE) { 140 | min.duration <- min(low.flow.runs$lengths[which(low.flow.runs$values == 1)], na.rm = T) 141 | avg.duration <- mean(low.flow.runs$lengths[which(low.flow.runs$values == 1)], na.rm = T) 142 | med.duration <- median(low.flow.runs$lengths[which(low.flow.runs$values == 1)], na.rm = T) 143 | max.duration <- max(low.flow.runs$lengths[which(low.flow.runs$values == 1)], na.rm = T) 144 | sd.duration <- sd(low.flow.runs$lengths[which(low.flow.runs$values == 1)], na.rm = T) 145 | cv.duration <- sd.duration/avg.duration 146 | } 147 | 148 | if (volume == TRUE) { 149 | spell.factor <- rep(seq_along(low.flow.runs$lengths), times = low.flow.runs$lengths) 150 | spells <- split(flow.ts[, "Q"], spell.factor) 151 | spell.volumes <- flow.ts[, "Q"] 152 | spell.volumes <- sapply(spells, sum) 153 | spell.volumes <- spell.volumes[which(low.flow.runs$values == 1)] 154 | 155 | } 156 | 157 | 158 | if (plot == TRUE) { 159 | plot(flow.ts[, "Date"], flow.ts[, "Q"], type = "l", main = gauge, xlab = "Date", ylab = "Q") 160 | 161 | points(flow.ts[which(low.flows == 1), "Date"], flow.ts[which(low.flows == 1), "Q"], col = "red", cex = 0.25) 162 | 163 | abline(h = flow.threshold) 164 | } 165 | 166 | 167 | } 168 | 169 | if (ann.stats == F) { 170 | return(data.frame(low.spell.threshold = flow.threshold, min.low.spell.duration = min.duration, avg.low.spell.duration = avg.duration, med.low.spell.duration = med.duration, max.low.duration = max.duration, 171 | low.spell.freq = low.spell.frequency)) 172 | 173 | } else { 174 | 175 | return(data.frame(low.spell.threshold = flow.threshold, min.low.spell.duration = min.duration, avg.low.spell.duration = avg.duration, med.low.spell.duration = med.duration, max.low.duration = max.duration, 176 | low.spell.freq = low.spell.frequency, avg.min.ann = ann.mins.mean, cv.min.ann = cv.min.ann, ann.min.timing = avg.min.day[, "mean.doy"], ann.min.timing.sd = avg.min.day[, "sd.doy"], 177 | ann.min.min.dur = min.min.ann.duration, ann.min.avg.dur = avg.min.ann.duration, ann.min.max.dur = max.min.ann.duration)) 178 | } 179 | 180 | 181 | } 182 | -------------------------------------------------------------------------------- /R/high.spells.R: -------------------------------------------------------------------------------- 1 | high.spells <- function(flow.ts, quant = 0.9, threshold = NULL, ind.days = 5, duration = TRUE, volume = TRUE, plot = TRUE, ignore.zeros = FALSE, ctf.threshold = 0.1, ann.stats = TRUE, ann.stats.only = FALSE, 2 | inter.flood = FALSE, hydro.year = FALSE) { 3 | 4 | gauge <- deparse(substitute(flow.ts)) 5 | 6 | 7 | if (hydro.year == TRUE) { 8 | print("Returning results based on hydrologic year") 9 | flow.ts <- hydro.year(flow.ts, hydro.year = "hydro") 10 | record.year <- flow.ts[["hydro.year"]] 11 | } else { 12 | record.year <- strftime(flow.ts[["Date"]], format = "%Y") 13 | flow.ts <- data.frame(flow.ts, hydro.year = record.year) 14 | } 15 | n.years <- nlevels(as.factor(record.year)) 16 | 17 | if (ann.stats == T) { 18 | 19 | flow.ts.comp <- na.omit(flow.ts) 20 | 21 | n.days <- tapply(flow.ts.comp[["Q"]], flow.ts.comp[["hydro.year"]], length) 22 | n.most.days <- which(n.days >= 350) 23 | 24 | if (length(n.most.days) == 0) { 25 | ann.maxs.mean <- NA 26 | ann.maxs.sd <- NA 27 | avg.max.day <- data.frame(mean.doy = NA, sd.doy = NA) 28 | min.max.ann.duration <- NA 29 | avg.max.ann.duration <- NA 30 | max.max.ann.duration <- NA 31 | flood.skewness <- NA 32 | cv.ann.duration <- NA 33 | cv.max.ann <- NA 34 | } else { 35 | flow.ts.comp <- flow.ts.comp[which(flow.ts.comp[["hydro.year"]] %in% names(n.most.days)), ] 36 | flow.ts.comp[["hydro.year"]] <- factor(flow.ts.comp[["hydro.year"]]) 37 | record.year <- flow.ts.comp[["hydro.year"]] 38 | n.years <- nlevels(as.factor(record.year)) 39 | 40 | 41 | ann.maxs <- aggregate(flow.ts.comp["Q"], flow.ts.comp["hydro.year"], max, na.rm = T) 42 | 43 | mean.ann <- data.frame(mean = mean(flow.ts.comp[["Q"]], na.rm = T)) 44 | 45 | 46 | ann.max.days <- merge(ann.maxs, flow.ts.comp) 47 | 48 | 49 | ann.maxs.mean <- mean(ann.maxs[["Q"]], na.rm = T) 50 | 51 | ann.maxs.sd <- sd(ann.maxs[["Q"]], na.rm = T) 52 | 53 | avg.ann.max.days <- aggregate(ann.max.days["Date"], ann.max.days["hydro.year"], function(x) t(day.dist(x)[["mean.doy"]])) 54 | 55 | avg.max.day <- day.dist(days = avg.ann.max.days[["Date"]], years = avg.ann.max.days[["hydro.year"]]) 56 | 57 | 58 | 59 | max.ann.flow.threshold <- min(ann.maxs$Q, na.rm = T) 60 | 61 | ann.max.spells <- ifelse(flow.ts.comp[["Q"]] >= max.ann.flow.threshold, 1, 0) 62 | # ann.max.spell.runs <- rle(ann.max.spells) now done seperately for each year below. 63 | 64 | ann.runs <- tapply(ann.max.spells, flow.ts.comp[["hydro.year"]], rle) 65 | ann.runs.cum <- lapply(ann.runs, function(x) cumsum(x$lengths)) 66 | 67 | ann.max.index.all.events <- tapply(flow.ts.comp[["Q"]], flow.ts.comp[["hydro.year"]], function(x) which(x == max(x))) 68 | 69 | if (max(sapply(ann.max.index.all.events, length)) > 1) { 70 | ann.max.event.rles <- lapply(ann.max.index.all.events, function(x) rle(diff(x))) 71 | idx <- !(sapply(ann.max.event.rles, function(x) length(x$lengths))) 72 | ann.max.event.rles[idx] <- lapply(ann.max.event.rles[idx], function(x) { 73 | x$lengths <- 0 74 | x$values <- 1 75 | x 76 | }) 77 | 78 | ann.max.events.max.dur <- unlist(lapply(ann.max.event.rles, function(x) max(x$lengths[which(x$values == 1)], na.rm=T) + 1)) #add 1 because diff function returns index-1 79 | ann.max.events.max.dur <- ann.max.events.max.dur[is.finite(ann.max.events.max.dur)] 80 | 81 | } else { 82 | 83 | ann.max.rle.event.indices <- sapply(ann.runs, function(x) which(x$values == 1)) #list of vectors 84 | 85 | ann.max.rle.event.indices <- mapply(function(x, y) x[y], ann.runs.cum, ann.max.rle.event.indices) #list 86 | 87 | ann.max.rle.run.indices <- lapply(ann.runs, function(x) which(x$values == 1)) #list 88 | 89 | largest.event.index <- mapply(function(x, y) which.min(abs(x - y)), ann.max.index.all.events, ann.max.rle.event.indices) #vector 90 | 91 | final.event.index <- mapply(function(x, y) x[y], ann.max.rle.run.indices, largest.event.index) #vector 92 | 93 | ann.max.events.max.dur <- unlist(mapply(function(x, y) x$lengths[y], ann.runs, final.event.index, SIMPLIFY = TRUE)) #vector 94 | 95 | } 96 | 97 | 98 | 99 | min.max.ann.duration <- min(ann.max.events.max.dur, na.rm = T) 100 | avg.max.ann.duration <- mean(ann.max.events.max.dur, na.rm = T) 101 | max.max.ann.duration <- max(ann.max.events.max.dur, na.rm = T) 102 | cv.max.ann <- (ann.maxs.sd/ann.maxs.mean) * 100 103 | flood.skewness <- ann.maxs.mean/mean.ann[["mean"]] 104 | cv.ann.duration <- (sd(ann.max.events.max.dur))/mean(ann.max.events.max.dur) * 100 105 | 106 | } 107 | } 108 | if (ann.stats.only == T) { 109 | 110 | return(data.frame(avg.max.ann = ann.maxs.mean, cv.max.ann = cv.max.ann, ann.max.timing = avg.max.day[["mean.doy"]], ann.max.timing.sd = avg.max.day[["sd.doy"]], flood.skewness = flood.skewness, 111 | ann.max.min.dur = min.max.ann.duration, ann.max.avg.dur = avg.max.ann.duration, ann.max.max.dur = max.max.ann.duration, ann.max.cv.dur = cv.ann.duration)) 112 | 113 | } else { 114 | 115 | if (!is.null(threshold)) { 116 | 117 | 118 | flow.threshold <- threshold 119 | 120 | } else { 121 | 122 | 123 | if (ignore.zeros == T) { 124 | 125 | flow.threshold <- quantile(flow.ts[which(flow.ts[, "Q"] > ctf.threshold), "Q"], quant, na.rm = T) 126 | names(flow.threshold) <- NULL 127 | } else { 128 | flow.threshold <- quantile(flow.ts[, "Q"], quant, na.rm = T) 129 | names(flow.threshold) <- NULL 130 | } 131 | 132 | } 133 | 134 | rise.fall <- c(NA, flow.ts[2:nrow(flow.ts), "Q"] - flow.ts[1:nrow(flow.ts) - 1, "Q"]) 135 | 136 | 137 | 138 | high.flows <- ifelse(flow.ts[, "Q"] >= flow.threshold, 1, 0) 139 | 140 | if (ind.days > 0) { 141 | 142 | high.flow.runs <- rle(high.flows) 143 | too.short <- which(high.flow.runs$lengths < ind.days & high.flow.runs$values == 0) 144 | spell.factor <- rep(seq_along(high.flow.runs$lengths), times = high.flow.runs$lengths) 145 | add.to.spell <- which(spell.factor %in% too.short) 146 | high.flows[add.to.spell] <- 1 147 | } 148 | 149 | 150 | high.flow.runs <- rle(high.flows) 151 | 152 | #find peaks 153 | x<-flow.ts[["Q"]] 154 | r<-rle(x) 155 | peaks<- which(rep(x = diff(sign(diff(c(-Inf,r$values, -Inf)))) == -2, 156 | times = r$lengths)) 157 | ## 158 | 159 | high.flow.pk.av <- mean(flow.ts[peaks, "Q"], na.rm = T) 160 | high.flow.pk.sd <- sd(flow.ts[peaks, "Q"], na.rm = T) 161 | 162 | high.flow.rf <- rise.fall[which(high.flows == 1)] 163 | mean.rise <- abs(mean(high.flow.rf[high.flow.rf > 0], na.rm = T)) 164 | mean.fall <- abs(mean(high.flow.rf[high.flow.rf < 0], na.rm = T)) 165 | 166 | good.high.flow.runs <- which(!is.na(high.flow.runs$values)) 167 | flow.runs.values <- high.flow.runs$values[good.high.flow.runs] 168 | flow.runs.lengths <- high.flow.runs$lengths[good.high.flow.runs] 169 | 170 | n.events <- length(flow.runs.values[flow.runs.values == 1]) 171 | flood.frequency <- n.events/n.years 172 | 173 | 174 | 175 | 176 | if (duration == TRUE) { 177 | 178 | min.duration <- min(high.flow.runs$lengths[which(high.flow.runs$values == 1)], na.rm = T) 179 | avg.duration <- mean(high.flow.runs$lengths[which(high.flow.runs$values == 1)], na.rm = T) 180 | med.duration <- quantile(high.flow.runs$lengths[which(high.flow.runs$values == 1)], 0.5, na.rm = T, names = F) 181 | max.duration <- max(high.flow.runs$lengths[which(high.flow.runs$values == 1)], na.rm = T) 182 | sd.duration <- sd(high.flow.runs$lengths[which(high.flow.runs$values == 1)], na.rm = T) 183 | cv.duration <- sd.duration/avg.duration * 100 184 | } 185 | 186 | if (inter.flood == TRUE) { 187 | 188 | avg.interval <- mean(high.flow.runs$lengths[which(high.flow.runs$values == 0)], na.rm = T) 189 | min.interval <- min(high.flow.runs$lengths[which(high.flow.runs$values == 0)], na.rm = T) 190 | max.interval <- max(high.flow.runs$lengths[which(high.flow.runs$values == 0)], na.rm = T) 191 | 192 | sd.interval <- sd(high.flow.runs$lengths[which(high.flow.runs$values == 0)], na.rm = T) 193 | cv.interval <- sd.interval/avg.interval * 100 194 | return(data.frame(high.spell.threshold = flow.threshold, average.interval = avg.interval, min.interval = min.interval, max.interval = max.interval)) 195 | } 196 | 197 | if (volume == TRUE) { 198 | spell.factor <- rep(seq_along(high.flow.runs$lengths), times = high.flow.runs$lengths) 199 | spells <- split(flow.ts[, "Q"], spell.factor) 200 | spell.volumes <- flow.ts[, "Q"] 201 | spell.volumes <- sapply(spells, sum) 202 | spell.volumes.below.threshold <- sapply(spells, length) * flow.threshold 203 | spell.volumes <- spell.volumes[which(high.flow.runs$values == 1)] - spell.volumes.below.threshold[which(high.flow.runs$values == 1)] 204 | 205 | } 206 | 207 | 208 | if (plot == TRUE) { 209 | plot(flow.ts[["Date"]], flow.ts[["Q"]], type = "l", main = gauge, xlab = "Date", ylab = "Q") 210 | 211 | points(flow.ts[which(high.flows == 1), "Date"], flow.ts[which(high.flows == 1), "Q"], col = "red", cex = 0.25) 212 | 213 | abline(h = flow.threshold) 214 | } 215 | 216 | } 217 | 218 | if (ann.stats == F) { 219 | return(data.frame(high.spell.threshold = flow.threshold, n.events = n.events, spell.frequency = flood.frequency, ari = 1/flood.frequency, min.high.spell.duration = min.duration, avg.high.spell.duration = avg.duration, 220 | med.high.spell.duration = med.duration, max.high.spell.duration = max.duration, avg.spell.volume = mean(spell.volumes, na.rm = T), avg.spell.peak = high.flow.pk.av, sd.spell.peak = high.flow.pk.sd, 221 | avg.rise = mean.rise, avg.fall = mean.fall)) 222 | } else { 223 | return(data.frame(high.spell.threshold = flow.threshold, n.events = n.events, spell.freq = flood.frequency, ari = 1/flood.frequency, min.high.spell.duration = min.duration, avg.high.spell.duration = avg.duration, 224 | med.high.spell.duration = med.duration, max.high.spell.duration = max.duration, avg.spell.volume = mean(spell.volumes, na.rm = T), avg.spell.peak = high.flow.pk.av, sd.spell.peak = high.flow.pk.sd, 225 | avg.rise = mean.rise, avg.fall = mean.fall, avg.max.ann = ann.maxs.mean, cv.max.ann = cv.max.ann, flood.skewness = flood.skewness, ann.max.timing = avg.max.day[["mean.doy"]], ann.max.timing.sd = avg.max.day[["sd.doy"]], 226 | ann.max.min.dur = min.max.ann.duration, ann.max.avg.dur = avg.max.ann.duration, ann.max.max.dur = max.max.ann.duration, ann.max.cv.dur = cv.ann.duration)) 227 | } 228 | 229 | 230 | } 231 | -------------------------------------------------------------------------------- /data/cooper.txt: -------------------------------------------------------------------------------- 1 | Date Q 1/01/1967 0 2/01/1967 0 3/01/1967 0 4/01/1967 0 5/01/1967 0 6/01/1967 0 7/01/1967 0 8/01/1967 0 9/01/1967 0 10/01/1967 0 11/01/1967 0 12/01/1967 0 13/01/1967 0 14/01/1967 0 15/01/1967 0 16/01/1967 0 17/01/1967 0 18/01/1967 0 19/01/1967 0 20/01/1967 0 21/01/1967 0 22/01/1967 0 23/01/1967 0 24/01/1967 0 25/01/1967 0 26/01/1967 0 27/01/1967 0 28/01/1967 0 29/01/1967 0 30/01/1967 0 31/01/1967 0 1/02/1967 0 2/02/1967 0 3/02/1967 0 4/02/1967 0 5/02/1967 0 6/02/1967 0 7/02/1967 0 8/02/1967 0 9/02/1967 0 10/02/1967 0 11/02/1967 0 12/02/1967 0 13/02/1967 0 14/02/1967 0 15/02/1967 536.65 16/02/1967 2334.911 17/02/1967 3095.16 18/02/1967 3538.955 19/02/1967 3847.047 20/02/1967 4004.694 21/02/1967 3892.126 22/02/1967 2950.723 23/02/1967 1854.813 24/02/1967 1185.701 25/02/1967 861.817 26/02/1967 688.648 27/02/1967 1182.975 28/02/1967 3224.719 1/03/1967 2731.604 2/03/1967 2030.822 3/03/1967 1447.279 4/03/1967 1516.269 5/03/1967 2751.115 6/03/1967 2850.232 7/03/1967 2468.302 8/03/1967 5170.812 9/03/1967 19364.76 10/03/1967 38458.14 11/03/1967 47164.46 12/03/1967 32490.4 13/03/1967 27126.65 14/03/1967 24725.16 15/03/1967 22831.52 16/03/1967 19637.31 17/03/1967 14955.51 18/03/1967 10505.59 19/03/1967 7933.302 20/03/1967 6933.384 21/03/1967 6584.469 22/03/1967 6544.282 23/03/1967 6544.282 24/03/1967 6520.511 25/03/1967 6362.183 26/03/1967 6220.854 27/03/1967 5996.7 28/03/1967 5352.761 29/03/1967 3816.633 30/03/1967 2241.441 31/03/1967 1248.328 1/04/1967 784.875 2/04/1967 572.751 3/04/1967 430.686 4/04/1967 323.272 5/04/1967 280.99 6/04/1967 221.117 7/04/1967 180.436 8/04/1967 157.833 9/04/1967 131.015 10/04/1967 125.085 11/04/1967 110.798 12/04/1967 89.327 13/04/1967 84.607 14/04/1967 73.408 15/04/1967 56.862 16/04/1967 53.255 17/04/1967 44.875 18/04/1967 32.001 19/04/1967 23.868 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23/10/1987 0 24/10/1987 0 25/10/1987 0 26/10/1987 0 27/10/1987 0 28/10/1987 0 29/10/1987 0 30/10/1987 0 31/10/1987 0 1/11/1987 0 2/11/1987 0 3/11/1987 0 4/11/1987 0 5/11/1987 0 6/11/1987 0 7/11/1987 0 8/11/1987 0 9/11/1987 0 10/11/1987 0 11/11/1987 162.624 12/11/1987 636.024 13/11/1987 1143.559 14/11/1987 1216.388 15/11/1987 899.065 16/11/1987 579.993 17/11/1987 398.266 18/11/1987 878.722 19/11/1987 1393.119 20/11/1987 1349.892 21/11/1987 1084.143 22/11/1987 1121.908 23/11/1987 1710.552 24/11/1987 1919.338 25/11/1987 1827.754 26/11/1987 1664.093 27/11/1987 1652.644 28/11/1987 1920.096 29/11/1987 2717.506 30/11/1987 4142.73 1/12/1987 5830.392 2/12/1987 7193.302 3/12/1987 7704.622 4/12/1987 6821.463 5/12/1987 5207.905 6/12/1987 3735.291 7/12/1987 2651.569 8/12/1987 1911.284 9/12/1987 1420.528 10/12/1987 1067.312 11/12/1987 830.184 12/12/1987 657.756 13/12/1987 509.772 14/12/1987 470.068 15/12/1987 601.928 16/12/1987 714.592 17/12/1987 755.508 18/12/1987 709.859 19/12/1987 575.29 20/12/1987 394.575 21/12/1987 262.226 22/12/1987 173.779 23/12/1987 108.32 24/12/1987 66.121 25/12/1987 39.105 26/12/1987 20.232 27/12/1987 10.934 28/12/1987 17.541 29/12/1987 23.525 30/12/1987 752.547 31/12/1987 1710.658 --------------------------------------------------------------------------------