The Flow Analysis Summary Statistics Tool for R (‘fasstr’) is a set of R functions to clean, summarize, analyze, trend, and visualize streamflow data. This package summarizes continuous daily mean streamflow data into various daily, monthly, annual, and long-term statistics, completes annual trends and frequency analyses, in both table and plot formats.
This package provides functions with solutions for streamflow data:
- cleaning (to prepare data for analyses;
- screening (to look for outliers and missing data;
- analyzing (basic summary statistics, frequency analyses, trending
- visualizing (to plot statistics;
plot_*functions), amongst others.
Useful features of functions include:
- the integration of the ‘tidyhydat’ package to pull streamflow data from a Water Survey of Canada HYDAT database for analyses;
- arguments for filtering of years and months in analyses and plotting (internally tidys your data);
- choosing the start month of your water year;
- selecting for rolling day averages (e.g. 7-day rolling average);
- plotting options;and,
- choosing how missing dates are handled, amongst others.
You can install ‘fasstr’ using the following code. It may take a few moments as there are several dependency packages will also be installed, including ‘tidyhydat’ for downloading Water Survey of Canada hydrometric data, ‘zyp’ for trending, ‘ggplot2’ for creating plots, and ‘dplyr’ and ‘tidyr’ for various data wrangling and summarizing functions, amongst others.
To install the development version of the ‘fasstr’ package, you need to install the remotes package then the ‘fasstr’ package.
if(!requireNamespace("remotes")) install.packages("remotes") remotes::install_github("bcgov/fasstr")
To call the ‘fasstr’ functions you can either load the package using the
library(fasstr) function or access a specific function using a
To utilize the ‘tidyhydat’ features (using the station_number
argument), you will need to download a HYDAT database using the
All functions in ‘fasstr’ require a daily mean streamflow dataset from
one or more hydrometric stations. Long-term and continuous datasets are
preferred for most analyses, but seasonal and partial data can be used.
Other daily time series data, like temperature, precipitation or water
levels, may also be used, but with certain caution as some
calculations/conversions are based on units of streamflow (cubic metres
per second). Data is provided to each function using the either the
data argument, as a data frame, or the
station_number argument, as a
list of Water Survey of Canada HYDAT station numbers.
data option, a data frame of daily data containing columns
of dates (YYYY-MM-DD in date format), values (mean daily discharge in
cubic metres per second in numeric format), and, optionally, grouping
identifiers (character string of station names or numbers) is called. By
default the functions will look for columns identified as ‘Date’,
‘Value’, and ‘STATION_NUMBER’, respectively, to be compatible with
the ‘tidyhydat’ defaults, but columns of different names can be
identified using the
groups column arguments (ex.
values = Yield_mm). The following is an example of an appropriate
dataframe (STATION_NUMBER not required):
#> STATION_NUMBER Date Value #> 1 08NM116 1949-04-01 1.13 #> 2 08NM116 1949-04-02 1.53 #> 3 08NM116 1949-04-03 2.07 #> 4 08NM116 1949-04-04 2.07 #> 5 08NM116 1949-04-05 2.21 #> 6 08NM116 1949-04-06 2.21
Alternatively, you can directly extract a flow data set directly from a
HYDAT database by listing station numbers in the
station_number = "08NM116" or
station_number = c("08NM116", "08NM242")) while leaving the data arguments blank. A data
frame of daily streamflow data for all stations listed will be extracted
using ‘tidyhydat’. Use the following function to download a HYDAT
This package allows for multiple stations (or other groupings) to be
analyzed in many of the functions provided identifiers are provided
groups column argument (defaults to STATION_NUMBER). If
grouping column doesn’t exist or is improperly named, then all values
listed in the
values column will be summarized.
These functions, that start with
fill_*, add columns and
rows, respectively, to your streamflow data frame to help set up your
data for further analysis. Examples include adding rolling means, adding
date variables (Year, Month, DayofYear, etc.), adding basin areas,
adding columns of volumetric and yield discharge, and filling dates with
missing flow values with
The analysis functions summarize your discharge values into various
screen_* functions summarize annual data for outliers and
calc_* functions calculate daily, monthly, annual, and
long-term statistics (e.g. mean, median, maximum, minimum, percentiles,
amongst others) of daily, rolling days, and cumulative flow data.
compute_* functions also analyze data but produce more in-depth
analyses, like frequency and trending analysis, and may produce multiple
plots and tables as a result. All tables are in tibble data frame
formats. Can use
write_results() to customize
saving tibbles to a local drive.
The visualization functions, which begin with
plot_* plot the various
summary statistics and analyses as a way to visualize the data. While
most plotting functions are as customizable as the analysis functions,
some come pre-set with statistics that cannot be changed for
consistency. Plots can be modified by the user using the
package and its functions. All plots functions produce lists of plots
(even if just one produced). Can use
write_plots() to customize saving
the lists of plots to a local drive (within folders or PDF documents).
Daily Rolling Means
If certain n-day rolling mean statistics are desired to be analyzed
(e.g. 3- or 7-day rolling means) some functions provide the ability to
select for that as function arguments (e.g.
rolling_days = 7 and
rolling_align = "right"). The rolling day align is the placement of
the date amongst the n-day means, where “right” averages the day-of and
previous n-1 days, “centre” date is in the middle of the averages, and
“left” averages the day-of and the following n-1 days. For your own
analyses you can add rolling means to your dataset using the
Year and Month Filtering
To customize your analyses for specific time periods, you can designate
the start and end years of your analysis using the
end_year arguments and remove any unwanted years (for partial datasets
for example) by listing them in the
excluded_years = c(1990, 1992:1994)). Alternatively, some
functions have an argument called
complete_years that summarizes data
from just those years which have a complete flow record. Some functions
will also allow you to select the months of a year to analyze, using the
months argument, as opposed to all months (if you want just summer
low-flows, for example). Leaving these arguments blank will result in
the summary/analysis of all years and months of the provided dataset.
To group analyses by water, or hydrologic, years instead of calendar
years, if desired, you can set
water_year_start within most functions
to another month than 1 (for January). A water year can be defined as a
12-month period that comprises a complete hydrologic cycle (wet seasons
can typically cross calendar year), typically starting with the month
with minimum flows (the start of a new water recharge cycle). As water
years commonly start in October, the default water year is October for
‘fasstr’. If another start month is desired, you can choose is using
water_year_start argument (numeric month) to designate the water
year time period. The water year label is designated by the year it ends
in (e.g. water year 2000 goes from Oct 1, 1999 to Sep 30, 2000). Start,
end and excluded years will be based on the specified water year.
For your own analyses, you can add date variables to your dataset using
Drainage Basin Area
Yield runoff statistics (in millimetres) calculated in the some of the
functions require an upstream drainage basin area (in sq. km) using the
basin_area argument, where required. If no basin areas are supplied,
all yield results will be
NA. To apply a basin area (10 sqkm for
example) to all daily observations, set the argument as
basin_area = 10. If there are multiple stations or groups to apply multiple basin
areas (using the
groups argument), set them individually using this
basin_area = c("08NM116" = 795, "08NM242" = 22). If a
STATION_NUMBER column exists with HYDAT station numbers, the function
will automatically use the basin areas provided in HYDAT, if available,
basin_area is not required. For your own analyses, you can add
basin areas to your dataset using the
Handling Missing Dates
With the use of the
ignore_missing argument in most function, you can
decide how to handle dates with missing flow values in calculations.
When you set
ignore_missing = TRUE a statistic will be calculated for
a given year, all years, or month regardless of if there are missing
flow values. When
ignore_missing = FALSE the returned value for the
period will be
NA if there are missing values.
Some functions have an argument called
complete_years which can be
used, when set to
TRUE, to filter out years that have partial datasets
(for seasonal or other reasons) and only years with full data are used
to calculate statistics.
Summary statistics example: long-term statistics
To determine the summary statistics of daily data by month (mean,
median, maximum, minimum, and some percentiles) you can use the
calc_longterm_daily_stats() function. If the ‘Mission Creek near East
Kelowna’ hydrometric station is of interest you can list the station
number in the
station_number argument to obtain the data (if
‘tidyhydat’ and HYDAT are installed).
calc_longterm_daily_stats(station_number = "08NM116", start_year = 1981, end_year = 2010, custom_months = 7:9, custom_months_label = "Summer") #> # A tibble: 14 x 8 #> STATION_NUMBER Month Mean Median Maximum Minimum P10 P90 #> <chr> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 08NM116 Jan 1.22 1 9.5 0.160 0.540 1.85 #> 2 08NM116 Feb 1.16 0.970 4.41 0.140 0.474 1.99 #> 3 08NM116 Mar 1.85 1.40 9.86 0.380 0.705 3.80 #> 4 08NM116 Apr 8.32 6.26 37.9 0.505 1.63 17.5 #> 5 08NM116 May 23.6 20.8 74.4 3.83 9.33 41.2 #> 6 08NM116 Jun 21.5 19.5 84.5 0.450 6.10 38.9 #> 7 08NM116 Jul 6.48 3.90 54.5 0.332 1.02 15 #> 8 08NM116 Aug 2.13 1.57 13.3 0.427 0.775 4.29 #> 9 08NM116 Sep 2.19 1.58 14.6 0.364 0.735 4.35 #> 10 08NM116 Oct 2.10 1.60 15.2 0.267 0.794 3.98 #> 11 08NM116 Nov 2.04 1.73 11.7 0.260 0.560 3.90 #> 12 08NM116 Dec 1.30 1.05 7.30 0.342 0.5 2.33 #> 13 08NM116 Long-term 6.17 1.89 84.5 0.140 0.680 19.3 #> 14 08NM116 Summer 3.61 1.98 54.5 0.332 0.799 7.64
Plotting example: daily summary statistics
To visualize the daily streamflow patterns on an annual basis, the
plot_daily_stats() function will plot out various summary statistics
for each day of the year. Data can also be filtered for certain years of
interest (a 1981-2010 normals period for this example) using the
end_year arguments. We can also compare individual
years against the statistics using
add_year argument like below.
plot_daily_stats(station_number = "08NM116", start_year = 1981, end_year = 2010, log_discharge = TRUE, add_year = 1991, ignore_missing = TRUE) #> $Daily_Statistics
Plotting example: flow duration curves
Flow duration curves can be produced using the
plot_flow_duration(station_number = "08NM116", start_year = 1981, end_year = 2010) #> $Flow_Duration
Analysis example: low-flow frequency analysis
This package also provides a function,
to complete frequency analyses (using the same methods as
HEC-SSP). The default
fitting distribution is ‘log-Pearson Type III’, but the ‘Weibull’
distribution can also be used. Other default plotting and fitting
methods are described in the function documentation. For this example,
the 7-day low-flow (low-flow is default) quantiles are calculated for
the Mission Creek hydrometric station using the ‘log-Pearson Type III’
distribution. With this, several low-flow indicators can be determined
(i.e. 7Q5, 7Q10).
freq_results <- compute_annual_frequencies(station_number = "08NM116", start_year = 1981, end_year = 2010, roll_days = 7) freq_results$Freq_Fitted_Quantiles #> # A tibble: 11 x 4 #> Distribution Probability `Return Period` `7-Day` #> <chr> <dbl> <dbl> <dbl> #> 1 PIII 0.01 100 0.193 #> 2 PIII 0.05 20 0.277 #> 3 PIII 0.1 10 0.332 #> 4 PIII 0.2 5 0.408 #> 5 PIII 0.5 2 0.588 #> 6 PIII 0.8 1.25 0.812 #> 7 PIII 0.9 1.11 0.946 #> 8 PIII 0.95 1.05 1.07 #> 9 PIII 0.975 1.03 1.17 #> 10 PIII 0.98 1.02 1.21 #> 11 PIII 0.99 1.01 1.31
The probability of observed extreme events can also be plotted (using selected plotting position) along with the computed quantiles curve for comparison.
freq_results <- compute_annual_frequencies(station_number = "08NM116", start_year = 1981, end_year = 2010, roll_days = c(1,3,7,30)) freq_results$Freq_Plot
This package is set for delivery. This package is maintained by the Water Protection and Sustainability Branch of the British Columbia Ministry of Environment and Climate Change Strategy.
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Copyright 2019 Province of British Columbia Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.