metrics.all(TS, Qmax = 0.95, Dur = 5, Qdr = 0.2, WinSize = 30,
Season = c(4:9), NAthresh = 0.5, language = "English")create.ts containing a data.frame of flow
time seriesmqt. Default is 30.screen.summary
to label individual plots. Options are "English" or "French". Defapk.maxpk.max.doypkspks.durQnQnpk.covpk.covpk.covcovQnQndr.seasdr.seasdr.seasdr.seasdr.seasMAMnMAMnMAMnbf.statsbf.statsbf.statsbf.statsbf.statsbf.statspk.bf.statspk.bf.statspk.bf.statspk.bf.statszyp.trend.vectorzyp.trend.vector and looks
for changpoints in mean and variance using cpt.meanvar
This function is intended for use as a data quality screening tool aimed
at identifying streamflow records with anthropogenic impacts and should not be used
to complete a temporal trend analysis, as the calculated metrics may not be
appropriate for all catchments. See the functions linked in the following section
for details on how each metric is calculated.screen.metric to create individual plots for each metric.# load subset of daily streamflow time series for the Caniapiscau River
data(cania.sub.ts)
# further subset to meet maximum example run-time requirements
cania.sub.ts <- subset(cania.sub.ts, cania.sub.ts$hyear %in% c(1973:1987))
# calculate low flow, high flow, and baseflow metrics
res <- metrics.all(cania.sub.ts)Run the code above in your browser using DataLab