fasstr (version 0.3.1)

compute_frequency_analysis: Perform a custom annual frequency analysis

Description

Performs a flow volume frequency analysis from a streamflow dataset. 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 (years), their values (mins or maxs), and identifiers (lowflows, highflows, etc), Function will calculate using all values in the provided data (no grouped analysis). Analysis methodology replicates that from HEC-SSP.

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 flow 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 annual 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" (Pearson Log III distribution) or "weibull" (Weibull distribution). 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)
                            
# }