Flow Duration Curve

Description

Produces a flow duration curve plot with optional Gustard type-curves that can be used to estimate catchment permeability.

Usage

FDC(
  TS,
  normalize.flow = TRUE,
  ylog = TRUE,
  title = FALSE,
  normal = FALSE,
  gust = TRUE,
  ylimits = NULL
)

Arguments

Author(s)

Paul Whitfield

References

Gustard, A., Bullock, A., and Dixon, J.M. (1992). Report No. 108: Low flow estimation in the United Kingdom. Oxfordshire, United Kingdom: Institute of Hydrology.

Examples

data(caniapiscau)
FDC(caniapiscau, title="Caniapiscau River")

Screen Daily Discharge Time Series for Temporal Trends and Change Points

Description

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.

Details

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.

Author(s)

Jennifer Dierauer, Paul H. Whitfield

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

References

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.

See Also

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

Examples

## 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)

Calculate mean annual minimum n-day flows

Description

This function calculates the calculates the mean annual minimum n-day flow by calendar year or by hydrologic year. This function can also be used to find the annual minimum series by setting n=1.

Usage

MAMn(TS, n = 7, by = "hyear", threshold.missing = 0.5)

Arguments

Value

Returns a numeric vector containing the calculated MAM n-day flow for each year in the input time series. The "times" attribute provides the corresponding year for each calculated value. Note: a partial start year or end year in the time series that exceeds the threshold set by 'threshold.missing' will be automatically truncated from the output.

Author(s)

Jennifer Dierauer

See Also

screen.metric

Examples

data(caniapiscau)
cania.ts <- create.ts(caniapiscau, hyrstart = 4)
cania.ts <- drop.years(cania.ts)
cania.ts <- set.plot.titles(cania.ts, 
title.elements = c("StationID", "StnName", "StateProv"))

# find the annual minimum series and plot 
res <- MAMn(cania.ts, n=1)
res2 <- screen.metric(res, ylabel = "Q (m3/s)", title = TRUE)

# do the same with MAM 7-day flow instead of annual minimum
res <- MAMn(cania.ts, n=7)
res2 <- screen.metric(res, ylabel = "Q (m3/s)", title = TRUE)

Sum missing data points from a daily time series

Description

Counts the number of missing data points by calendar year, hydrologic year, or month

Usage

NA.count.runs(input, by = "hyear", hyrstart = 1)

Arguments

Value

Returns a numeric vector of the number of missing observations per summary period. The "times" attribute of the returned vector provides the corresponding year, hyear, or month.

Author(s)

Jennifer Dierauer

See Also

NA.runs

Examples

data(caniapiscau)
cania.ts <- create.ts(caniapiscau)
res <- NA.runs(cania.ts)
print(res)
res2 <- NA.count.runs(res)
print(res2)

Missing data runs for daily time series.

Description

This function takes a data.frame from create.ts and returns a data.frame of missing data runs.

Usage

NA.runs(TS, quiet = FALSE)

Arguments

Value

Returns a data.frame with the following columns:

• Start - Date of the start of the missing data period

• End - Date of the end of the missing data period

• Duration - number of days in the missing data period

Author(s)

Jennifer Dierauer

See Also

create.ts to create input, NA.count.runs to sum the the missing data occurrences by year or month.

Examples

data(caniapiscau)
cania.sub <- caniapiscau[300:1800,]
cania.ts <- create.ts(cania.sub)
res <- NA.runs(cania.ts)
print(res)

Calculate flow quantiles

Description

This function calculates flow quantiles by hydrologic year, calendar year, month, or doy.

Usage

Qn(TS, n = 0.1, by = "hyear")

Arguments

Value

Returns a numeric vector of the calculated flow quantile for the time periods indicated with the "by" argument. The "times" attribute contains the hydrologic year, calendar year, month, or day of year for each data point.

Author(s)

Jennifer Dierauer

Examples

data(caniapiscau)
cania.ts <- create.ts(caniapiscau, hyrstart = 4)
cania.ts <- drop.years(cania.ts)
cania.ts <- set.plot.titles(cania.ts, 
title.elements = c("StationID", "StnName", "StateProv"))

# 50% quantile, i.e. mean, by calendar year
res <- Qn(cania.ts, n=0.5, by="year")
res2 <- screen.metric(res, ylabel = "Q (m3/s)")

# Default 10% quantile, by hydrologic year
res <- Qn(cania.ts)
res2 <- screen.metric(res, ylabel = "Q (m3/s)")

Add calendar year, month, and day of year columns

Description

Add calendar year, month, and day of year columns

Usage

YMD.internal(TS)

Arguments

Value

Returns a data.frame with year, month, and doy columns appended.

Author(s)

Jennifer Dierauer

Add MetaData to Database

Description

Adds user-supplied station metadata to package database. Can also be used to update metadata for a station that is already present in the metadata.

Usage

add.station.metadata(
  Agency,
  StationID,
  StnName,
  StateProv = NA,
  Country,
  Lat,
  Lon,
  CatchmentArea_km2 = NA,
  RHN = FALSE,
  StationID_Alternate = NA,
  Overwrite = FALSE
)

Arguments

Author(s)

Jennifer Dierauer

Examples

## Not run: 
met_added <- add.station.metadata(
  Agency = "Foo Bar",
  StationID = "01234",
  StnName = "Example Station",
  Country = NA,
  Lat = 40.0,
  Lon = -89.0
)

## End(Not run)

Create custom axis starting on hydrologic year start month

Description

Create custom axis starting on hydrologic year start month

Usage

axis_doy.internal(hyrstart = 10)

Arguments

Author(s)

Paul Whitfield

Seasonal baseflow percentage

Description

This function estimates the percentage of baseflow in a given period relative to the total annual baseflow.

Usage

bf.seas(TS, seas = c(6:8))

Arguments

Details

This function calls bf_eckhardt to complete the baseflow separation.

Value

Returns a vector containing the calculated percentage for each year in the input time series. The "times" attribute provides the corresponding year for each calculated value.

Author(s)

Jennifer Dierauer

See Also

See bf.stats to calculate additional baseflow metrics.

Examples

data(cania.sub.ts)
cania.sub.ts <- set.plot.titles(cania.sub.ts, title.elements = c("StationID", "Country"))
res <- bf.seas(cania.sub.ts)
res2 <- screen.metric(res, "Percent Annual Baseflow in Jun-Aug", title = TRUE)

Baseflow statistics

Description

This function estimates the baseflow and calculates the mean, max, and min baseflow and baseflow index for a user defined time period.

Usage

bf.stats(TS, by = "hyear")

Arguments

Details

This function calls bf_eckhardt to complete the baseflow separation.

Value

Returns a data.frame with the following columns:

• By - Unique values representing the summary periods, e.g. a list of unique years, months, or days of year

• MeanQ - Mean daily streamflow for the summary period, in m3/s

• MeanBF - Mean daily baseflow for the summary period, in m3/s

• MaxBF - Maximum daily baseflow for the summary period, in m3/s

• MinBF - Minimum daily baseflow for the summary period, in m3/s

• BFVol - Baseflow volume for the summary period, in km3

• MeanBFI - Mean daily baseflow index for the summary period, dimensionless

• MaxBFI - Maximum daily baseflow index for the summary period, dimensionless

• MinBFI - Minimum daily baseflow index for the summary period, dimensionless

Author(s)

Jennifer Dierauer

Examples

data(cania.sub.ts)

cania.sub.ts <- set.plot.titles(cania.sub.ts, title.elements = c("StationID", "Country"))
res <- bf.stats(cania.sub.ts)
res2 <- screen.metric(res[,2], "m3/s", title = TRUE)

Boughton recursive digital filter

Description

This function estimates baseflow

Usage

bf_boughton(discharge, k, C)

Arguments

Value

Returns a numeric vector of the estimated baseflow.

Author(s)

Paul H. Whitfield

References

Boughton, WC. 1993. A hydrograph-based model for estimating the water yield of ungauged catchments.In Hydrology and Water Resources Symposium, Institution of Engineers Australia, Newcastle, NSW; 317-324.

Examples

data(cania.sub.ts)
res <- bf_boughton(cania.sub.ts$Flow, k=0.9, C=0.1)
plot(cania.sub.ts$Date, cania.sub.ts$Flow, xlab="", ylab="Q (m3/s)", type="l")
points(cania.sub.ts$Date, res, type="l", col="blue")

Eckhardt two parameter recursive digital filter

Description

This function takes vector of discharge data and estimates the baseflow

Usage

bf_eckhardt(discharge, a, BFI)

Arguments

Value

Returns

Author(s)

Paul Whitfield

References

Eckhardt, K. 2012. Technical note: Analytical sensitivity analysis of two parameter recursive digital baseflow separation filter. Hydrology and Earth System Sciences 16: 451-455.

Examples

data(cania.sub.ts)
bf <- bf_eckhardt(cania.sub.ts$Flow, 0.97, 0.8)
plot(cania.sub.ts$Date, cania.sub.ts$Flow, type="l")
points(cania.sub.ts$Date, bf, type="l", col="blue")

One parameter recursive digital filter

Description

This function estimates baseflow.

Usage

bf_oneparam(discharge, k)

Arguments

Value

Returns a numeric vector of the estimated baseflow.

Author(s)

Paul H. Whitfield

References

Eckhardt, K. 2005. How to construct recursive digital filters for baseflow separation methods. Journal of Hydrology 352: 168-173.

Examples

data(cania.sub.ts)
res <- bf_oneparam(cania.sub.ts$Flow, k=0.9)
plot(cania.sub.ts$Date, cania.sub.ts$Flow, xlab="", ylab="Q (m3/s)", type="l")
points(cania.sub.ts$Date, res, type="l", col="blue")

Subset of the Caniapiscau River Daily Flows

Description

This data set includes a subset of the mean daily streamflow for the Caniapiscau Rivers. It includes observations from 1970-1995 (hydrologic years). The code used to subset and modify the original data is shown below.

Usage

data(caniapiscau)

Format

Formatted as a data.frame with the following columns:

• ID - Water Survey Canada Station ID

• Date - Date of observation, formatted as YYYY-mm-dd

• Flow - Mean daily streamflow, measured in m3/s

• Code - Data Quality Code

• Agency - Source Agency (Water Survey Canada)

• FlowUnits - Unit of streamflow

• year - Calendar year

• month - Calendar month

• doy - Calendar day of year

• hyear - Hydrologic year

• hmonth - Hydrologic month

• hdoy - Hydrologic day of year

Source

Environment Canada. 2010. EC Data Explorer V1.2.30.
Water Survey of Canada V1.2.30 https://www.ec.gc.ca/rhc-wsc/

Examples

# Code used to subset and modify original Caniapiscau series:
## Not run: 
data(caniapiscau)
cania.ts <- create.ts(caniapiscau, hyrstart=3)
cania.sub.ts <- subset(cania.ts, cania.ts$hyear %in% c(1963:1976))

## End(Not run)
# example use of example subset flow series
data(cania.sub.ts)
head(cania.sub.ts)
str(cania.sub.ts)

Caniapiscau River Daily Flows

Description

This data set includes the mean daily streamflow for the Caniapiscau River. The file has been read from the original .csv format using read.flows. The Caniapiscau River is located in Nunavik, Quebec, Canada, and flows northward. The headwaters (representing 45 percent of the total flow) were dammed to create the Caniapiscau Reservoir, which started filling in 1981. In 1985, the reservoir was diverted to the west into the La Grande hydroelectric complex. This flow time series is used as an example of a river with a known change point to demonstrate the package's screening capabilities.

Usage

data(caniapiscau)

Format

Formatted as a data.frame with the following columns:

• ID - Water Survey Canada Station ID

• PARAM - Parameter ID (1 indicates flow)

• Date - Date of observation, formatted as YYYY-mm-dd

• Flow - Mean daily streamflow, measured in m3/s

• Agency - Source Agency (Water Survey Canada)

Source

Environment Canada. 2010. EC Data Explorer V1.2.30.
Water Survey of Canada V1.2.30 https://www.ec.gc.ca/rhc-wsc/

Examples

data(caniapiscau)
head(caniapiscau)
str(caniapiscau)

Screening results for the Caniapiscau River

Description

Contains the results from metrics.all for the full Caniapiscau River daily flow series. Data set created as indicated below. This data set is used in the example documentation for the screen.frames, screen.summary, and screen.cpts functions in order to reduce example run times.

Usage

data(caniapiscau)

Format

Formatted as indicated in the documentation for metrics.all

Source

Original flow series from Environment Canada. 2010. EC Data Explorer V1.2.30.
Water Survey of Canada V1.2.30 https://www.ec.gc.ca/rhc-wsc/

Examples

# Code used produce this data set:
## Not run: 
data(caniapiscau)
caniapiscau.ts <- create.ts(caniapiscau, hyrstart=3)
caniapiscau.ts <- subset(caniapiscau.ts, caniapiscau.ts$hyear > 1962)
caniapiscau.res <- metrics.all(caniapiscau.ts)

## End(Not run)
# example use of example subset flow series
data(caniapiscau.res)

Check Completeness

Description

Determine if the desired completeness criteria are being met. This considers if the date range of interest is complete, and whether internal gaps are longer than the criteria. There is an option for allowing the data set to be padded with an optional number of years and then tested using the same criteria. Function returns TRUE if the criteria are met and FALSE if not, and a numeric code that indicates the mode of failure or success.

Usage

check_completeness(
  data,
  st_yr,
  nd_yr,
  allowed = 3,
  period = 10,
  pad = NULL,
  my = NULL
)

Arguments

Value

A list containing:

• TRUE or FALSE indicating conditions were met or not

• code: 0 "met", 1 "not long enough period", 2 "gaps longer than allowed", and 3 "more gaps in period than allowed", 10 "met with pad", 11 "not long enough period with pad", 12 "gaps longer than allowed with pad", and 13 "more gaps in period than allowed with pad"

Messages indicating the reason for failure

Author(s)

Paul Whitfield

Examples

robin_path <- system.file("extdata", "ROBIN_example.csv", package = "FlowScreen")

TS <- read.flows(robin_path)
res <- missingness(TS, title = TRUE)
check_completeness(res, st_yr = 1985, nd_yr = 2014, allowed = 3, period = 10)

Create a Time Series of daily streamflow observations

Description

This function creates a daily time series formatted for use with the functions in this package. This function is executed within the read.flows function. To use separately, the 'Flows' input data.frame must have include the columns: ID, PARAM, date, Flow, SYM, Agency, and FlowUnits. This function would be used in the case the user has data files containing dates and flows and this function would convert the original data into the the form used by the FlowScreen functions.

Usage

create.ts(Flows, hyrstart = 10)

Arguments

Value

Returns a data.frame with year, month, doy, and hyear columns appended to the original input data.frame.

Author(s)

Jennifer Dierauer

Examples

data(caniapiscau)
# subset flow series for shorter example run time
# first, drop the rows with missing streamflow
caniapiscau <- caniapiscau[!is.na(caniapiscau$Flow),]
caniapiscau.sub <- caniapiscau[300:1800,]
caniapiscau.sub.ts <- create.ts(caniapiscau.sub, hyrstart = 3)

Partial Duration Series and Event Statistics for streamflow droughts

Description

This function extracts the partial duration series for all streamflow droughts based on a moving window quantile threshold. Also returns summary information (start date, end date, duration, deficit volume) for each drought event.

Usage

dr.events(TS, Qdr = 0.2, WinSize = 30, IntEventDur = 10, EventDur = 15)

Arguments

Value

Returns a list with the following elements:

DroughtEvents: A data.frame with the following columns:

• Event - Integer indicating the original event number assigned before minor drought events were removed.

• Start - Date of the start of the drought event.

• End - Date of the end of the drought event

• maxDef - Numeric value of the maximum streamflow deficit.

• Severity - Numeric value indicating the drought severity, calculated as the cumulative daily streamflow deficit in m3/s.

• Duration - Numeric value of the drought duration in days.

• Magnitude - Numeric value indicating the drought magnitude, which is calculated as the mean daily streamflow deficit in m3/s.

• stdtotDef - Numeric value indicating the standardized cumulative streamflow deficit, calculated as the drought severity divided by the mean annual daily streamflow.

DroughtPDS: A data.frame of the original input TS that has been subset to include only the days on which the streamflow was below the drought threshold. The data.frame also has the following columns appended:

• Thresh - Numeric value indicating the streamflow drought threshold, as calculated by mqt

• BelowThresh - Logical indicating whether the observed streamflow was below the streamflow drought threshold.

• Def - Numeric value of the streamflow defict, calculated as the streamflow drought threshold (m3/s) minus the observed streamflow (m3/s).

Author(s)

Jennifer Dierauer

See Also

See dr.seas to calculate metrics for droughts occurring in a user-defined season.

This function calls dr.pds which calls mqt.

Examples

data(cania.sub.ts)
res1 <- dr.events(cania.sub.ts)
events <- res1$DroughtEvents

opar <- graphics::par(no.readonly = TRUE)
par(mar=c(5,6,2,2))
plot(events$Start, events$Duration, pch=19, ylab="Drought Duration (days)", xlab="")
graphics::par(opar)

Get the partial duration series for streamflow droughts

Description

This function returns the partial duration series for streamflow droughts based on a moving window quantile threshold.

Usage

dr.pds(TS, Qdr = 0.2, WinSize = 30)

Arguments

Details

This function defines a daily streamflow threshold and finds the partial duration series of streamflow droughts. Drought events are identified in the daily streamflow time series with the threshold level approach. In this function, the threshold is defined by a moving quantile, where daily threshold values are based on the 80th percentile of the flow duration curve from a 30-day moving window (Beyene et al. 2014). With this method, every day of the year has a different threshold based on the streamflow measured on the day, the 15 days before the day, and the 15 days after the day. The size of the moving window can be modified with the WinSize argument, and the percentile can be modified with the Qdr argument.

Value

Returns the input TS data.frame with "Thresh" and "BelowThresh" columns appended. The Thresh column contains the daily flow threshold, and the BelowThresh column is a binary indicating whether the flow on each day was below the drought threshold.

Author(s)

Jennifer Dierauer

References

Beyene, B.S., Van Loon, A.F., Van Lanen, H.A.J., Torfs, P.J.J.F., 2014. Investigation of variable threshold level approaches for hydrological drought identification. Hydrol. Earth Syst. Sci. Discuss. 11, 12765-12797. http://dx.doi.org/10.5194/hessd-11-12765-2014.

See Also

See create.ts to format the input flow series.

See mqt to return only the daily moving quantile threshold.

See dr.events to pool drought events, remove minor events, and calculate metrics.

See dr.seas to calculate metrics for streamflow droughts that start in a specific month or months.

Examples

data(cania.sub.ts)
pds <- dr.pds(cania.sub.ts)
pds <- subset(pds, pds$BelowThresh==TRUE)

# plot the flow time series with black and the drought events in red
plot(cania.sub.ts$Date, cania.sub.ts$Flow, ylab="m3/s", xlab="", type="l")
points(pds$Date, pds$Flow, pch=19, cex=0.7, col="red")

Find the start, middle, end, and duration of seasonal droughts

Description

This function returns the day of year for the start, middle, and end of seasonal droughts. It also returns the duration and severity of each drought event. The function allows for seasonal analysis by defining a season argument which lists months during which droughts of interest may start.

Usage

dr.seas(
  TS,
  Qdr = 0.2,
  WinSize = 30,
  IntEventDur = 10,
  EventDur = 15,
  Season = c(4:9)
)

Arguments

Details

This function calls dr.events which calls dr.pds and mqt

Value

Returns a data.frame of drought event metrics; the columns are:

• StartDay - day of year that the drought event started on

• MidDay - day of year for the middle of the drought event, which is defined as the day when the cumulative drought deficit reached 50 total cumulative daily streamflow deficit. Total cumulative streamflow deficit is also referred to as drought severity in this package.

• EndDay - day of year that the drought ended on

• Duration - length of the drought event, in days

• Severity - severity of the drought event, calculated as the total cumulative daily streamflow deficit

The "times" attribute provides the start date to preserve year information and aid in plotting the time series.

Author(s)

Jennifer Dierauer

See Also

See create.ts to format the input flow series.
See dr.events and mqt for details on how drought events are defined.

Examples

data(cania.sub.ts)
res <- dr.seas(cania.sub.ts)
res2 <- screen.metric(res[,1], "Day of Year")

Drop hydrologic years

Description

Removesthose hydrologic years where the fraction of missing data is above the defined threshold.

Usage

drop.years(TS, NAthresh = 0.8)

Arguments

Value

Returns TS data.frame with hydrologic years with above the user-defined threshold dropped.

Author(s)

Jennifer Dierauer

Examples

data(caniapiscau)
cania.ts <- create.ts(caniapiscau, hyrstart = 4)
cania.ts <- drop.years(cania.ts, NAthresh = 0.75)

Get station information for hydrometric stations

Description

Get station information for ROBIN, USGS, or WSC hydrometric stations.

Usage

get.station.internal(Agency, StationID)

Arguments

Value

Returns a data.frame of station information

Author(s)

Jennifer Dierauer

Returns plot titles and labels based on plot type and language preference

Description

Returns plot titles and labels based on plot type and language preference

Usage

get.titles.internal(type, flow.units, language = "English", Qmax)

Arguments

Author(s)

Jennifer Dierauer

Add hydrologic Year, month, and doy columns to a daily time series

Description

Add hydrologic Year, month, and doy columns to a daily time series

Usage

hyear.internal(TS, hyrstart = 10)

Arguments

Value

Returns a data.frame with hyear, hmonth, and hdoy columns appended to the original input data.frame.

Author(s)

Jennifer Dierauer

Streamflow metrics

Description

Calculates 30 different flow metrics, 10 each for high flows, low flows, and baseflow.

Usage

metrics.all(
  TS,
  Qmax = 0.95,
  Dur = 5,
  Qdr = 0.2,
  WinSize = 30,
  Season = c(4:9),
  NAthresh = 0.5,
  language = "English"
)

Arguments

Details

This function calculates streamflow metrics and calculates the prewhitened trend using zyp.trend.vector and looks for changepoints 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.

Value

Returns a list with the following elements:

metricTS: a list containing a vector of each metric calculated. Each vector has a times attribute providing either the year for metrics with one observation per year or a date for metrics that may have more than one observation per year (e.g., Peaks Over Threshold). This list has the following elements:

• Annual Maximum Series - calculated with pk.max

• Day of Annual Maximum - calculated with pk.max.doy

• Peaks Over Threshold (Qmax) - calculated with pks

• Inter-Event Duration - calculated with pks.dur

• Q80 - calculated with Qn

• Q90 - calculated with Qn

• Day of Year 25 percent Annual Volume - calculated with pk.cov

• Center of Volume - calculated with pk.cov

• Day of Year 75 percent Annual Volume - calculated with pk.cov

• Duration between 25 percent and 75 percent Annual Volume - calculated with pk.cov

• Q10 - calculated with Qn

• Q25 - calculated with Qn

• Drought Start - calculated with dr.seas

• Drought Center - calculated with dr.seas

• Drought End - calculated with dr.seas

• Drought Duration - calculated with dr.seas

• Drought Severity - calculated with dr.seas

• Annual Minimum Flow - calculated with MAMn

• Mean Annual Minimum 7-day Flow - calculated with MAMn

• Mean Annual Minimum 10-day Flow - calculated with MAMn

• Mean Daily Discharge - calculated with bf.stats

• Annual Baseflow Volume - calculated with bf.stats

• Annual Mean Baseflow - calculated with bf.stats

• Annual Maximum Baseflow - calculated with bf.stats

• Annual Minimum Baseflow - calculated with bf.stats

• Mean Annual Baseflow Index - calculated with bf.stats

• Day of Year 25 percent Baseflow Volume - calculated with pk.bf.stats

• Center of Volume Baseflow - calculated with pk.bf.stats

• Day of Year 75 percent Baseflow Volume - calculated with pk.bf.stats

• Duration between 25 percent and 75 percent Baseflow Volume - calculated with pk.bf.stats

tcpRes: this list contains the results of the trend and changepoint analysis for each of the metrics in the metricTS list described above. Each list element is a list containing the following elements:

• MetricID - integer used to identify the metric

• MetricName - Name of the metric.

• Slope - numeric vector containing the intercept and slope of the prewhitened linear trend calculated using the Yue Pilon method. See zyp.trend.vector

• ci1 - upper bound of the trend's 95 percent confidence interval

• ci2 - lower bound of the trend's 95 percent conficence interval

• pval - Kendall's P-value computed for the detrended time series

• cpts - Most probable location of a changepoint, if one is detected.

• means - Mean before and after the changepoint

• NumObs - The number of data points for the metric

inData: A data.frame of the original input daily streamflow time series.

OmitYrs: A data.frame containing the years and the number of observations for any years omitted from the analysis due to insufficient data. If no years were omitted, NA is returned.

Author(s)

Jennifer Dierauer

See Also

See the documentation for individual functions linked in the output description for details on methods.

See screen.metric to create individual plots for each metric.

Examples

# load subset of daily streamflow time series for the Caniapiscau River
data(cania.sub.ts)

## Not run: 
# calculate low flow, high flow, and baseflow metrics
res <- metrics.all(cania.sub.ts)

## End(Not run)

missingness test

Description

Determine the annual amount of missing data and generate an optional missingness plot.

Usage

missingness(
  TS,
  title = FALSE,
  plot = TRUE,
  increasing = TRUE,
  cols = c("white", "blue"),
  omar = c(2, 2, 2, 2),
  mar = c(3, 5, 3, 2)
)

Arguments

Details

Determine the Annual Amount of Missing Data and Generate a Missingness Plot

Value

A list containing:

• years_total: Number of years from start to ending year.

• years_with_obs: Number of years with observations.

• years_no_missing_obs: Number of years with no missing days.

• complete_years: Individual years with no missing data.

• partial_years: Individual years with some observations.

• longest_common_period_years: Number of sequential years with complete data.

• lcperiod_st: Starting year of the sequence of years with complete data.

• lcperiod_nd: Ending year of the sequence of years with complete data.

• table: A dataframe with years, and counts and fractions of missing data.

• missingness plot: A plot showing the missingness data (if plot = TRUE).

Author(s)

Paul Whitfield

Examples

robin_path <- system.file("extdata", "ROBIN_example.csv", package = "FlowScreen")

TS <- read.flows(robin_path)
res <- missingness(TS, cols = c("white", "red"), increasing = FALSE)

Moving quantile threshold

Description

This function calculates the daily moving window quantile threshold for use in identifying the partial duration series of streamflow droughts.

Usage

mqt(TS, Qdr = 0.2, WinSize = 30)

Arguments

Details

The threshold is defined by a moving quantile, where daily threshold values are based on the 80th percentile of the flow duration curve (i.e. 0.2 quantile) from a 30-day moving window (Beyene et al. 2014). With this method, every day of the year has a different threshold based on the streamflow measured on the day, the 15 days before the day, and the 15 days after the day.The size of the moving window can be modified with the WinSize argument, and the percentile can be modified with the Qdr argument.

Value

Returns a numeric vector containing the streamflow drought threshold in m3/s for each day of the year.

Author(s)

Jennifer Dierauer

References

Beyene, B.S., Van Loon, A.F., Van Lanen, H.A.J., Torfs, P.J.J.F., 2014. Investigation of variable threshold level approaches for hydrological drought identification. Hydrol. Earth Syst. Sci. Discuss. 11, 12765-12797. http://dx.doi.org/10.5194/hessd-11-12765-2014.

See Also

See create.ts to format the input flow series.

The following functions use this function: dr.pds, dr.events, dr.seas

Examples

data(cania.sub.ts)
res <- mqt(cania.sub.ts)

# subset one year of the flow series
flow.sub <- cania.sub.ts[cania.sub.ts$year == 1972,]

# plot the 1972 observed flows in dark blue and the daily drought threshold in red
plot(flow.sub$doy, flow.sub$Flow, ylab="Q (m3/s)", xlab="Day of Year",
 pch=19, col="darkblue", type="b")
points(res, pch=19, cex=0.7, col="red")

Calculate baseflow peak statistics

Description

This function finds the start, middle, end, and duration of the baseflow peak based on percent of the total annual baseflow volume. A value of 0 is returned for years with no flow. Hydrologic years with fewer than normal observations (outliers) are excluded from the analysis, and for stations with seasonal flow records, additional seasonal subsetting is done to include only days with observations in all years.

Usage

pk.bf.stats(TS, bfpct = c(25, 50, 75))

Arguments

Details

This function calculates metrics intended to focus on snowmelt-related streamflow occuring in spring and summer. For catchments in cold climates, the baseflow peak can be interpreted as snowmelt-induced. Baseflow is estimated with bf_eckhardt. If total annual flow is equal to 0, returns NA for that year.

Value

Returns a data.frame with the following columns:

• Start - day of year defining the start of the baseflow peak

• Mid - day of year defining the middle of the baseflow peak

• End - day of year defining the end of the baseflow peak

• Dur - duration of the baseflow peak, in days

Author(s)

Jennifer Dierauer

Examples

data(caniapiscau)
cania.ts <- create.ts(caniapiscau, hyrstart = 4)
cania.ts <- drop.years(cania.ts)
cania.ts <- set.plot.titles(cania.ts, 
title.elements = c("StationID", "StnName", "StateProv"))
res1 <- pk.bf.stats(cania.ts)

# trend and changepoint plot for baseflow peak start doy
res2 <- screen.metric(res1[,1], ylabel = "Day of Year")

Center of Volume

Description

This function calculates center of volume metrics, including the day of the hydrologic year that 25 percent, 50 percent, and 75 percent of the total annual streamflow is reached. A value of 0 is returned for years with no flow. Hydrologic years with fewer than normal observations (outliers) are excluded from the analysis, and for stations with seasonal flow records, additional seasonal subsetting is done to include only days with observations in all years.

Usage

pk.cov(TS, threshold.missing = 0.5)

Arguments

Value

Returns a data.frame with the following columns:

• hYear - Hydrologic Years

• Q25 - day of hydrologic year for 25 percent of the total annual streamflow

• Q50 - day of hydrologic year for 50 percent of the total annual streamflow, i.e. Center of Volume

• Q75 - day of hydrologic year for 75 percent of the total annual streamflow

• Dur - duration of between the 25 percent and 75 percent day of year, in days

Author(s)

Jennifer Dierauer

Examples

data(cania.sub.ts)
cania.sub.ts <- set.plot.titles(cania.sub.ts, 
title.elements = c("StationID", "StnName", "StateProv"))
res1 <- pk.cov(cania.sub.ts)

# trend and changepoint plot for baseflow peak start doy
res2 <- screen.metric(res1[,2], "Day of Year")
res2 <- screen.metric(res1[,2], "Day of Year", title = TRUE)

Annual maximum series

Description

This function returns the annual maximum series from a daily streamflow time series.

Usage

pk.max(TS)

Arguments

Value

Returns a numeric vector containing the annual maximum flow (m3/s) series, by hydrologic year. The "times" attribute contains the hydrologic year for each element in the vector.

Author(s)

Jennifer Dierauer

See Also

See create.ts to format the input flow series.

See pk.max.doy to find the day of year for each annual maximum flow event.

Examples

data(caniapiscau)
cania.ts <- create.ts(caniapiscau, hyrstart = 4)
cania.ts <- drop.years(cania.ts)
cania.ts <- set.plot.titles(cania.ts, 
title.elements = c("StationID", "StnName", "StateProv"))

res <- pk.max(cania.ts)
res2 <- screen.metric(res, ylabel = "Q (m3/s)", title = TRUE)

Day of year for annual maximum series

Description

This function returns the day of the hydrologic year for each annual maximum flow.

Usage

pk.max.doy(TS)

Arguments

Value

Returns a numeric vector containing the day of the (hydrologic) year for each annual maximum flow. The "times" attribute contains the hydrologic year for each element in the vector.

Author(s)

Jennifer Dierauer

See Also

See create.ts to format the input flow series.

See pk.max for the annual maximum flow series.

Examples

data(caniapiscau)
cania.ts <- create.ts(caniapiscau, hyrstart = 4)
cania.ts <- drop.years(cania.ts)
cania.ts <- set.plot.titles(cania.ts, 
title.elements = c("StationID", "StnName", "StateProv"))

res <- pk.max.doy(cania.ts)
res2 <- screen.metric(res, ylabel = "Day of Year", title = TRUE)

Get the flow peaks over a threshold

Description

This function finds the flow peaks over a user defined threshold and declusters to remove dependent peaks.

Usage

pks(TS, Dur = 5, Qmax = 0.95)

Arguments

Details

Peaks Over Threshold values are calcuated as mean daily streamflow (m3/s) minus the threshold streamflow value (m3/s) defined by the input quantile (Qmax). Peaks are identified with pot and the minimum inter-event duration (Dur) is applied by decluster.

Value

Returns a numeric vector of peaks of threshold values in m3/s. The "times" attribute contains the date by calendar year, and the "names" attribute contains the hydrologic year and hydrologic day of year, e.g., 2012 55.

Author(s)

Jennifer Dierauer

Examples

data(caniapiscau)
cania.ts <- create.ts(caniapiscau, hyrstart = 4)
cania.ts <- drop.years(cania.ts)
cania.ts <- set.plot.titles(cania.ts, 
title.elements = c("StationID", "StnName", "StateProv"))

res <- pks(cania.ts)
res2 <- screen.metric(res, ylabel = "Peak Over Threshold (m3/s)", title = TRUE)

Calculate the inter-event duration

Description

This function calculates duration (in days) between flow peaks.

Usage

pks.dur(Peaks)

Arguments

Value

Returns a numeric vector containing the duration (in days) between peaks over threshold from pks. The "times" attribute contains the calendar year date of the earlier peak. The "names" attribute contains the hydrologic year and the day (1-366) of the hydrologic year.

Author(s)

Jennifer Dierauer

Examples

data(caniapiscau)
cania.ts <- create.ts(caniapiscau, hyrstart = 4)
cania.ts <- drop.years(cania.ts)
cania.ts <- set.plot.titles(cania.ts, 
title.elements = c("StationID", "StnName", "StateProv"))

res1 <- pks(cania.ts)
res2 <- pks.dur(res1)
res3 <- screen.metric(res2, ylabel = "Inter-Event Duration (days)")

Read file of streamflows

Description

Reads .csv, .Rdata, or .rds files of daily streamflow time series. Recognizes several formats, including those used by Water Survey Canada (WSC), United States Geological Survey (USGS), and ROBIN. Reads fixed width .txt files in GRDC format only. Uses read.csv(), load(), readRDS(), read.fwf() functions from base package and returns data frame with ID, Date, Flow, Agency, and, if available, associated quality codes and source agency. Replaces negative values that are sometimes used to denote missing data with NAs.

Usage

read.flows(filename, flow.units = "m3/s", convert.to = NULL, hyrstart = 10)

Arguments

Details

Streamflow records in .csv, .Rdata, or .rds format that are not from the USGS, WSC, ROBIN, or GRDC can be read by read.flows() if they contain the following required columns. Date format is auto-detected as long as it is some version of YYYY/mm/dd, mm/dd/YYYY, mm-dd-yy, etc. The file-to-be-read must contain, at a minimum, columns containing a partial matches to the following (not case sensitive):

• flow | val | value for the daily streamflow discharge (Flow column)

• id | site for the ID column

• date for the Date column

Optional columns names for partial matching include:

• sym | code | flag for the SYM column (quality codes)

• agency for the Agency column

Author(s)

Jennifer Dierauer

Examples

# example code to read a file, not run
# my_file_path <- "/Project/file1.csv"
# dat1 <- read.flows(my_file_path)

# Example code using external files included with the package
wsc_path <- system.file("extdata", "WSC_example.csv", package = "FlowScreen")
wsc_dat <- read.flows(wsc_path)

usgs_path <- system.file("extdata", "USGS_example.csv", package = "FlowScreen")
usgs_dat <- read.flows(usgs_path, flow.units = 'ft3/s', convert.to = 'm3/s')

robin_path <- system.file("extdata", "ROBIN_example.csv", package = "FlowScreen")
robin_dat <- read.flows(robin_path)

## Not run: 
grdc_path <- system.file("extdata", "GRDC_example.txt", package = "FlowScreen")
grdc_dat <- read.flows(grdc_path)

## End(Not run)

Plot flow regime

Description

This function plots the min, max, mean, and two user-defined quantiles of daily streamflow to provide visual summary of the flow regime. Flow record must have at least 10 years of data to produce a plot. A visual summary is not shown for any days of the year that are missing >80 Area between the upper and lower quantile is shaded grey, the dark blue line represents the mean daily discharge, gray line represents the median daily discharge, and the period of record daily maximum and minimum are shown with the blue points.

Usage

regime(
  TS,
  q = c(0.9, 0.1),
  title = FALSE,
  hyear.start = 10,
  y.lims = NA,
  legend = TRUE,
  change.margins = TRUE
)

Arguments

Author(s)

Jennifer Dierauer

Examples

# Load example ROBIN streamflow data
robin_path <- system.file("extdata", "ROBIN_example.csv", package = "FlowScreen")

TS <- read.flows(robin_path)
TS <- set.plot.titles(TS)
regime(TS, title = TRUE)

Remove MetaData for one station from database

Description

Removes a station's metadata from the package database based on the Agency and StationID. If the agency is "USGS" and the station is not found, it will also check by adding a "0" to the beginning of the StationID. Used to remove a case added in error.

Usage

remove.station.metadata(Agency, StationID)

Arguments

Value

The metadata of the removed station if found and removed, or NULL if not found.

Examples

## Not run: 
# Add station metadata
met_added <- add.station.metadata(
  Agency = "Foo Bar",
  StationID = "01234",
  StnName = "Example Station",
  StateProv = "Example State",
  Country = "Example Country",
  Lat = 40.0,
  Lon = -89.0,
  CatchmentArea_km2 = 500,
  RHN = TRUE,
  StationID_Alternate = "01234A",
  Overwrite = FALSE
)


# Remove the added station metadata
met_removed <- remove.station.metadata(
  Agency = "Foo Bar",
  StationID = "01234"
)

## End(Not run)

Change point time series plot

Description

Compiles change point information for all metrics and outputs a daily flow time series plot overlain with a bar plot of changepoint counts by year.

Usage

screen.cpts(metrics, type = "a", title = FALSE, change.margins = TRUE)

Arguments

Value

When type="a", returns a data.frame of changepoint counts by metric type and year.

Author(s)

Jennifer Dierauer

See Also

metrics.all

Examples

# load results from metrics.all function for the Caniapiscau River
data(caniapiscau.res)

# plot changepoints for all groups of metrics
screen.cpts(caniapiscau.res, type="l")
screen.cpts(caniapiscau.res, type="h")
screen.cpts(caniapiscau.res, type="b")

Plot one or more frames from the summary screening plot

Description

This function plots one or more frames (i.e. time series plot) from any of the three plot.screening summary plots at a time. It can be used to create custom summary plots - see the example code.

Usage

screen.frames(
  metrics,
  type = "h",
  element = NULL,
  language = "English",
  mmar = c(3, 4, 0.5, 0.5),
  title = FALSE,
  multi = F,
  xaxis = T
)

Arguments

Details

High flow metrics include:

1. Annual Maximum Series

2. Annual Maximum Day of Year

3. Peaks Over Threshold (Qmax)

4. Inter-Event Duration

5. Q80

6. Q90

7. Day of Year 25 percent Annual Flow

8. Center of Volume

9. Day of Year 75 percent Annual Flow

10. Duration between 25 percent and 75 percent Annual Flow

Low flow metrics include:

1. Q10

2. Q25

3. Drought Start

4. Drought Center

5. Drought End

6. Drought Duration

7. Drought Severity

8. Annual Minimum Flow

9. Mean Annual Minimum 7-day Flow

10. Mean Annual Minimum 10-day Flow

Baseflow metrics include:

1. Mean Daily Discharge

2. Annual Baseflow Volume

3. Annual Mean Baseflow

4. Annual Maximum Baseflow

5. Annual Minimum Baseflow

6. Mean Annual Baseflow Index

7. Day of Year 25 percent Baseflow Volume

8. Center of Volume Baseflow

9. Day of Year 75 percent Baseflow Volume

10. Duration between 25 percent and 75 percent Baseflow Volume

Author(s)

Jennifer Dierauer and Paul Whitfield

Examples

# load results from metrics.all function for the Caniapiscau River
data(caniapiscau.res)
caniapiscau.ts <- caniapiscau.res$indata

# plot one frame from the baseflow screening plot
screen.frames(caniapiscau.res, type="b", element=1)

# plot three frames from the low flow screening plot
screen.frames(caniapiscau.res, type="l", element=c(1:3))

# create a custom summary plot
opar <- par(no.readonly = TRUE)
layout(matrix(c(1,2,3,4), 2, 2, byrow=TRUE))
par(oma=c(0,0,3,0))
stninfo <- station.info(caniapiscau.ts$Agency[1], caniapiscau.ts$ID[1], Plot=TRUE)
screen.frames(caniapiscau.res, type="h", element=1, multi=TRUE)
screen.frames(caniapiscau.res, type="l", element=1, multi=TRUE)
screen.frames(caniapiscau.res, type="b", element=1, multi=TRUE)

par <- opar
layout(1,1,1)

# or plot everything!
opar <- par(no.readonly = TRUE)
layout(matrix(c(1:30), 5, 6, byrow=TRUE))
screen.frames(caniapiscau.res, type="h", multi=TRUE)
screen.frames(caniapiscau.res, type="l", multi=TRUE)
screen.frames(caniapiscau.res, type="b", multi=TRUE)
par <- opar
layout(1,1,1)

Internal wrapper for creating trend and change-point plots

Description

Internal wrapper for creating trend and change-point plots

Usage

screen.frames.internal(
  input,
  mparam,
  mylab,
  DataType,
  maf,
  mmar,
  text,
  xaxis,
  Year1,
  YearEnd,
  hyrstart
)

Arguments

Author(s)

Jennifer Dierauer

Plot a metric with trend and change points

Description

This function plots a time series of a streamflow metric with the prewhitened linear trend and any detected changepoints in mean and variance.

Usage

screen.metric(y, ylabel = "", title = FALSE, change.margins = TRUE)

Arguments

Details

This function plots detected changepoints as a vertical dashed line. The means on either side of a changepoint are plotted as solid black lines. If the temporal trend is significant (p-value < 0.1), the trend is plotted as a blue or red line for an increasing or decreasing trend, respectively. The upper and lower 95 dotted red or blue lines. If a trend is not significant, it is not plotted.

Value

Returns a list containing results from the trend and changepoint analysis. This list has the following elements:

• slope - Numeric vector containing the intercept and slope of the prewhitened linear trend computed with zyp.trend.vector using Yue Pilon's method

• ci1 - numeric vector containing the intercept and slope of the upper confidence bound. See confint.zyp

• ci2 - numeric vector of length 2 containing the intercept and slope of the lower confidence bound. See confint.zyp

• pval - numeric value indicatng the significance value of the detected trend, Kendall test computed within zyp.trend.vector

• cpts - numeric vector of changepoints if any are found, computed with cpt.meanvar. Will be NULL if changepoint analysis was not run due to insufficient data.

• means - numeric vector of means computed with cpt.meanvar. Will be NULL if changepoint analysis was not run due to insufficient data.

Author(s)

Jennifer Dierauer

See Also

See screen.summary to create a summary screening plot of high flow, low flow, or baseflow metrics.

See metrics.all to calculate 30 different streamflow metrics at once. The screen.metric function could then be used to loop through the metrics and create an individual plot for each.

Examples

data(cania.sub.ts)

# calculate and plot the annual maximum series
cania.sub.ts <- set.plot.titles(cania.sub.ts)
res <- pk.max(cania.sub.ts)
res1 <- screen.metric(res, ylabel=cania.sub.ts$FlowUnit[1], 
title = TRUE)

# calculate and plot the annual minimum series
res <- MAMn(cania.sub.ts, n=1)
res1 <- screen.metric(res, ylabel="Discharge (m3/s)", 
title = TRUE)

Create a plot of the daily streamflow time series

Description

Plots the daily streamflow time series and color codes points by data quality codes if the data are from Water Survey Canada. Also highlights date ranges with missing observations.

Usage

screen.series(TS, title = FALSE, change.margins = TRUE)

Arguments

Author(s)

Jennifer Dierauer and Paul Whitfield

Examples

# load flow time series for the Caniapiscau River
data(cania.sub.ts)

# plot daily time series with default margin text
screen.series(cania.sub.ts)

Create a summary screening plot

Description

Produces summary screening plots of high flow, low flow, or baseflow metrics. Each plot shows significant temporal trends and step changes. Intended for use as a data quality screening tool aimed at identifying streamflow records with anthropogenic impacts or data inhomogeneities.

Usage

screen.summary(metrics, type = "h", language = "English")

Arguments

Details

For the center of volume (COV) plots on the high flow and baseflow screening plots, the correlation coefficients for COV and years and for mean annual flow (MAF) and years are added to the plot. The ratio of the correlation coefficients (r COV-years / r COV-MAF) is included as a rudimentary indication of whether or not the temporal trend in COV is meaningful. See Whitfield (2013) for a discussion of COV.

Drought metrics for the low flow plot may not be applicable to intermittent streams, and plots will be empty in this case.

Important note: If "French" is the language wanted for the plot labels, the language option must also be specified in metrics.all, as this plotting function pulls the metric names from the output metrics.all output.

Author(s)

Jennifer Dierauer

References

Whitfield, P.H. 2013. Is 'Center of Volume' a robust indicator of changes in snowmelt timing? Hydrological Processes 27:2691-8.

Examples

# load results from metrics.all function for the Caniapiscau River
data(caniapiscau.res)

# create a summary flow screening plot of the high flow metrics
screen.summary(caniapiscau.res, type="h")
# screen.summary(caniapiscau.res, type = "l")
# screen.summary(caniapiscau.res, type = "b)

Internal wrapper for creating trend and change-point summary plots

Description

Internal wrapper for creating trend and change-point summary plots

Usage

screen.summary.internal(
  input,
  mparam,
  type,
  ylabs,
  i,
  DataType,
  maf,
  Year1,
  YearEnd,
  hyrstart
)

Arguments

Author(s)

Jennifer Dierauer

Set plot titles

Description

Sets the title to be used for all plots.

Usage

set.plot.titles(
  TS,
  title.elements = c("StationID", "StnName", "Country"),
  delimeter = " - ",
  custom.title = NULL,
  title.size = 1
)

Arguments

Value

Returns the input TS data.frame with a 'plot title' attribute added. This attribute will be the default option used for all plot titles unless an alternative title is passed to the plotting function, e.g. with regime.

Author(s)

Jennifer Dierauer

Examples

# Load example ROBIN streamflow data
robin_path <- system.file("extdata", "ROBIN_example.csv", package = "FlowScreen")
TS <- read.flows(robin_path)
TS <- set.plot.titles(TS, title.elements = c("StationID", "StnName"))
regime(TS, title = TRUE)

TS <- set.plot.titles(TS, custom.title = "My Custom Plot Title")
regime(TS, title = TRUE)

Retrieve Station Info

Description

Returns station information from metadata included in the package data files. If there is no metadata match for the StationID AND Agency, returns NA values for all other columns.

Usage

station.info(Agency, StationID, Plot = FALSE, Language = "English")

Arguments

Value

Returns a data.frame of the following station information:

• $Agency - Name of Agency from which the record came

• $StationID

• $StnName

• $StateProv - State, Province, or Territory where the station is located

• $Country - Country in which the station is located

• $Lat - Latitude of the station, numeric

• $Lon - Longitude of the station, numeric

• $CatchmentArea_km2 - total drainage area in square kilometers

• $RHN - Indication whether the station is part of a reference hydrologic network, TRUE/FALSE

• $MetadataSource - Indication of where the metadata came from, e.g. WSC Hydat, USGS, user-supplied.

• $StationID_Alternate - Alternate station ID, e.g. original station ID versus ROBIN database

Author(s)

Jennifer Dierauer

Examples

data(cania.sub.ts)
stn_metdat <- station.info(cania.sub.ts$Agency[1], cania.sub.ts$ID[1])
print(stn_metdat)