fasstr (version 0.5.3)

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),
  compute_fitting = TRUE,
  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,
  plot_axis_title = "Discharge (cms)"
)

Value

A list with the following elements:

Freq_Analysis_Data

Data frame with provided data for analysis.

Freq_Plot_Data

Data frame with plotting positions used in frequency plot.

Freq_Plot

ggplot2 object with plotting positions and (optional) fitted curve.

Freq_Fitting

List of fitted objects from fitdistrplus.

Freq_Fitted_Quantiles

Data frame with fitted quantiles.

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).

compute_fitting

Logical value to indicate whether to fit plotting positions to a distribution. If 'FALSE' the output will return only the data, plotting positions, and plot. Default TRUE.

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.

plot_axis_title

Character string of the plot y-axis title. Default 'Discharge (cms)'.

Examples

if (FALSE) {

 # 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)
                            
}