My first steps with R & R low-flow statistics cheat sheet

BaseflowSeparation(streamflow, filter_parameter , passes)  # BFS method [1], “streamflow” is a vector
get_usgs_gage(flowgage_id,begin_date, end_date)  # imports streamflow data from the USGS database in m3/day, decimal Lat&Lon, catchment area above gage
ConvertFlowUnits(cfs, cmdL, cms, WA, mmd, AREAunits)  # converts flow units, e.g. from cfs to mm/day etc.

rpackage

vector2zoo(x, dates, date.fmt)  # converts character, factor, POSIXct into zoo object (the package uses zoo)
smry(x, na.rm, digits,…)  # computes statistics: min, 1^stquartile, mean, median, 3^rd quartile, interquartile range, standard deviation, coefficient of variation, skewness, kurtosis, total number of elements, amount of missing values)
monthlyfunction(x, FUN, na.rm, output,…)  # Generic function for obtaining 12 monthly values by applying any R function to ALL the values in the object belonging to each one of the 12 calendar months (Jan…Dec)
dwi(x, out.unit, from, to, date.fmt, tstep, …)  # generates a table with the number of days with information different from NA within a zoo object
matrixplot(x, ColorRamp, ncolors, main, …)  # plots a color matrix representing the values stored in x
extract(x, trgt, …)  # extracts from zoo object all the values belonging to a given month, year, or weather season
daily2monthly(x, FUN, na.rm = TRUE, …)  # transforming daily (sub-dayly or weekly) regular timeseries into a monthly one
daily2annual(x, FUN, na.rm = TRUE, out.fmt = “%Y-%m-%d”, …)  # transforming daily to annual timeseries
hydroplot(x, FUN, na.rm, ptype, pfreq, var.type, var.unit, main, xlab, ylab, win.len1, win.len2, tick.tstep, lab.tstep, lab.fmt, cex, cex.main, cex.lab, cex.axis=1.3, col, from, to, date.fmt, stype, season.names, h=NULL, …)  # plots (a maximum of) 9 graphs (timeseries plots, boxplots and/or histograms) of the daily, monthly, annual and/or seasonal time series, can’t handle multivariate zoo

rpackage

baseflows(flow.ts, a, ts)  # BFS method: [1] can’t chose number of filter passes, ignores NA values, date must be in POSIX format
low.spells(flow.ts, quant, threshold, duration, volume, plot, ann.stats, ann.stats.only, hydro.year)  # calculates a suite of statistics describing low-flow spell characteristics, such as the timing, frequency and duration of events below a threshold

cran, rpackage

baseflow_sep(runoff, method, parms)  # option for multiple BFS methods: single pass of [1], constant slope,  re-scaled LOWESS-smoothed window minima; “runoff” is a vector

bfi(runoff, …)  # baseflow index

rpackage

IHA stands for Indicators for Hydrologic Alteration and it is a R package implementing the Nature Conservancy’s Indicators of Hydrologic Alteration software.

water.year(x)  # returns a string with water year, x is a zoo object
water.month(x, label, abbr)  # returns month of a water year (Oct-Sept)
group1(x, year, FUN)  # magnitude of monthly water conditions (mean or median for each month)
group2(x, year, mimic.tnc,…)  # magnitude and duration of annual extreme water conditions (annual minima and maxima, 1,3,7,30,90 day means; number of 0 flow days, BFI=7-day min flow/mean flow for the year)
group3(x, year, mimic.tnc)  # timing of annual extreme water conditions (Julian date of each annual 1-day maximum, Julian date of each annual 1-day minimum)
group4(x, year, thresholds)  # frequency and duration of high and low pulses (Number of low and high pulses within each water year, mean or median duration of low and high pulses (days))
group5(x, year)  # rate and frequency of water condition changes (rise and fall rates: Mean or median of all positive and negative differences between consecutive daily values; number of hydrologic reversals)

rpackage, indicators of hydrologic alteration

lfstat stands for Low Flow Statistics and follows the World Meteorological Organisation’s “Manual on Low-flow Estimation and Prediction” (link at the bottom).

BFI(lfobj, year, breakdays, yearly)  # BFS method [2], hydrological year possible, lfbobj is a special object type created for this package
bfplot(lfobj, year, col, bfcol, ylog)  # plots streamflow and baseflow
MAM(lfobj, n, year, breakdays, yearly)  # computes Mean Annual Minimum for a given n (MAM-n); if n=1 and yearly=TRUE – computes annual minima
Qxx(lfobj, Qxx, year, monthly, yearly, breakdays, na.rm)
Q95 or Q90 or Q70(lfobj, year, monthly, yearly, breakdays, na.rm)
fdc(lfobj, year, breakdays, colors, xnorm, ylog, legend, separate, …)  # flow duration curve
lfnacheck(lfobj)  # check for missing (NA) values
lfnainterpolate(lfobj)  # replacing missing values with linear interpolation
meanflow(lfobj, year, monthly, yearly, breakdays, na.rm)  # calculates meanflow
tyears(lfobj, event, n, dist, legend, zetawei)  # where dist is the distribution, dist=c(“exp”, “gam”, “gev”, “glo”, “gpa”, “gno”, “gum”, “kap”, “lognormal”, “normal”, “pe3”, “wak”, “wei”): minimum D-day-mean streamflow (n=D) expected to occur on average once during an interval of T-years (event=T). Can be used for 7Q10 or similar indices.

rpackage, cran, EGU abstract, WMO

cleanUp(dataset, task, replace)  # identifies and fixes common problems with hydrologic data (negative and 0 values)
fillMiss(dataset, block, pmiss, model,  smooth, …)  # fills missing values
importDVs(staid, code, stat, sdate,  edate)  # imports daily hydrologic time series data given a USGS streamgage identification number
siteInfo(staid)  # retrieves information about a USGS streamgage site – name, decimal latitude and longitude
summaryStats(dataset, staid = 1)  # returns matrix with summary statistics (beginning and end of timeseries, number of rows, number NA and negative, min, max, median, mean, Q1, Q3, StdDev, IQR)

USGS, cran