FlowScreen: Screen Daily Discharge Time Series for Temporal Trends and...
In FlowScreen: Daily Streamflow Trend and Change Point Screening

Description Details Author(s) References See Also Examples

This package can be used to calculate more than 30 different streamflow metrics and identify temporal trends and changepoints. It is intended for use as a data quality screening tool aimed at identifying streamflow records that may have anthropogenic impacts or data inhomogeneity.

Package:	FlowScreen
Type:	Package
Version:	1.2.6
Date:	2019-04-05
License:	GPL (>= 2)

Daily streamflow time series downloaded with the Environment Canada Data Explorer can be loaded with read.flows. The read.flows function can also be used to load daily streamflow time series from the USGS. The streamflow regime can be visualized with regime. A list of 30 streamflow metrics that describe high flows, low flows, and baseflows can be calculated using metrics.all. The temporal occurrence of changepoints for all metrics or for only the high flow, baseflow, or low flow metrics can be analyzed using screen.cpts. If the streamflow time series has multiple metrics exhibiting changepoints within the same year (or few years), the time series can be further analyzed using screen.summary which creates a summary plot showing the significant temporal trends and changepoints for the high flow, low flow, or baseflow metrics. The screen.metric can be used to create a time series plot for one metric at a time. The screen.metric function works with individual metrics output from the following functions: pk.max, pk.max.doy, Qn, pk.bf.stats, dr.seas, MAMn, bf.stats, pk.cov, and bf.seas.The screen.frames function creates individual plots from the screen.summary function. The screen.frames function can also be used to create custom summary plots, see the example code in the function documentation.

Jennifer Dierauer, Paul H. Whitfield

Maintainer: Jennifer Dierauer <jen.r.brand@gmail.com>

Bard, A., Renard, B., Lang, M. 2011. The AdaptAlp Dataset: Description, guidance, and analyses. In AdaptAlp WP 4 Report, 15. Lyon, France: Cemagraf.

Bard, A., Renard, B., Lang, M., Giuntoli, I., Korck, J., Koboltschnig, G., Janza, M., d'Amico, M., Volken, D. 2015. Trends in the hydrologic regime of Alpine rivers. Journal of Hydrology online.

Svensson, C., Kundzewicz, Z.W., Maurer, T. 2005. Trend detection in river flow series: 2. Flood and low-flow index series. Hydrological Sciences Journal 50:811-824.

Whitfield, P.H. 2012. Why the provenance of data matters: Assessing "Fitness for Purpose" for environmental data. Canadian Water Resources Journal 37:23-36.

Whitfield, P.H. 2013. Is 'Center of Volume' a robust indicator of changes in snowmelt timing? Hydrological Processes 27:2691-2698.

pot, decluster, cpt.meanvar, zyp.trend.vector, Kendall

## Not run: 
# load daily streamflow time series for the Caniapiscau River
data(caniapiscau)

# summary plot of the annual flow regime
caniapiscau.ts <- create.ts(caniapiscau)
regime(caniapiscau.ts)

# calculate high flow, low flow, and baseflow metrics
res <- metrics.all(caniapiscau.ts)

# plot histogram of changepoints for high flow, low flow, and baseflow metrics
screen.cpts(res, type="h")
screen.cpts(res, type="l")
screen.cpts(res, type="b")

# or plot all changepoints together
cpts <- screen.cpts(res)

# create screening plots for high, low, and baseflow metrics
screen.summary(res, type="h")
screen.summary(res, type="l")
screen.summary(res, type="b")

## End(Not run)