fasstr: compute_frequency_analysis – R documentation

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compute_frequency_analysis

Perform a custom volume frequency analysis

Description

Performs a volume frequency analysis on custom data. Defaults to ranking by minimums; use use_max for to rank by maximum flows. Calculates the statistics from events and flow values provided. Columns of events (e.g. years), their values (minimums or maximums), and identifiers (low-flows, high-flows, etc.). Function will calculate using all values in the provided data (no grouped analysis). Analysis methodology replicates that from HEC-SSP. Returns a list of tibbles and plots.

Usage

compute_frequency_analysis(
  data,
  events = Year,
  values = Value,
  measures = Measure,
  use_max = FALSE,
  use_log = FALSE,
  prob_plot_position = c("weibull", "median", "hazen"),
  prob_scale_points = c(0.9999, 0.999, 0.99, 0.9, 0.5, 0.2, 0.1, 0.02, 0.01, 0.001,
    1e-04),
  fit_distr = c("PIII", "weibull"),
  fit_distr_method = ifelse(fit_distr == "PIII", "MOM", "MLE"),
  fit_quantiles = c(0.975, 0.99, 0.98, 0.95, 0.9, 0.8, 0.5, 0.2, 0.1, 0.05, 0.01),
  plot_curve = TRUE
)

Arguments

`data`	A data frame of data that contains columns of events, flow values, and measures (data type).
`events`	Column in `data` that contains event identifiers, typically year values. Default `'Year'`.
`values`	Column in `data` that contains numeric flow values, in units of cubic metres per second. Default `'Value'`.
`measures`	Column in `data` that contains measure identifiers (example data: '7-day low' or 'Annual Max'). Can have multiple measures (ex. '7-day low' and '30-day low') in column if multiple statistics are desired. Default `'Measure'`.
`use_max`	Logical value to indicate using maximums rather than the minimums for analysis. Default `FALSE`.
`use_log`	Logical value to indicate log-scale transforming of flow data before analysis. Default `FALSE`.
`prob_plot_position`	Character string indicating the plotting positions used in the frequency plots, one of `'weibull'`, `'median'`, or `'hazen'`. Points are plotted against (i-a)/(n+1-a-b) where `i` is the rank of the value; `n` is the sample size and `a` and `b` are defined as: (a=0, b=0) for Weibull plotting positions; (a=.2; b=.3) for Median plotting positions; and (a=.5; b=.5) for Hazen plotting positions. Default `'weibull'`.
`prob_scale_points`	Numeric vector of probabilities to be plotted along the X axis in the frequency plot. Inverse of return period. Default `c(.9999, .999, .99, .9, .5, .2, .1, .02, .01, .001, .0001)`.
`fit_distr`	Character string identifying the distribution to fit annual data, one of `'PIII'` (Log Pearson Type III) or `'weibull'` (Weibull) distributions. Default `'PIII'`.
`fit_distr_method`	Character string identifying the method used to fit the distribution, one of `'MOM'` (method of moments) or `'MLE'` (maximum likelihood estimation). Selected as `'MOM'` if `fit_distr ='PIII'` (default) or `'MLE'` if `fit_distr = 'weibull'`.
`fit_quantiles`	Numeric vector of quantiles to be estimated from the fitted distribution. Default `c(.975, .99, .98, .95, .90, .80, .50, .20, .10, .05, .01)`.
`plot_curve`	Logical value to indicate plotting the computed curve on the probability plot. Default `TRUE`.

Value

A list with the following elements:

`Freq_Analysis_Data`	Data frame with computed annual summary statistics used in analysis.
`Freq_Plot_Data`	Data frame with co-ordinates used in frequency plot.
`Freq_Plot`	ggplot2 object with frequency plot
`Freq_Fitting`	List of fitted objects from fitdistrplus.
`Freq_Fitted_Quantiles`	Data frame with fitted quantiles.

Examples

## Not run: 

 # Working example:

# Calculate some values to use for a frequency analysis 
# (requires years, values for those years, and the name of the measure/metric)
low_flows <- calc_annual_lowflows(station_number = "08NM116", 
                                  start_year = 1980, 
                                  end_year = 2000,
                                  roll_days = 7)
low_flows <- dplyr::select(low_flows, Year, Value = Min_7_Day)
low_flows <- dplyr::mutate(low_flows, Measure = "7-Day")

# Compute the frequency analysis using the default parameters
results <- compute_frequency_analysis(data = low_flows,
                                      events = Year,
                                      values = Value,
                                      measure = Measure)
                            

## End(Not run)

fasstr

Analyze, Summarize, and Visualize Daily Streamflow Data

v0.3.2

Apache License 2.0

Authors

Jon Goetz [aut, cre] (<https://orcid.org/0000-0002-4993-1119>), Carl James Schwarz [aut], Robin Pike [ctb], Province of British Columbia [cph]

Initial release