Stationary FFA

Case Study

This vignette will explore data from the Athabasca River at Athabasca (07BE001), an unregulated hydrological monitoring station. A statistical analysis of the data from this station does not reveal any evidence of trends in the mean or variability of the data. Data for this station is provided as CAN-07BE001.csv in the ffaframework package.

library(ffaframework)

df <- data_local("CAN-07BE001.csv")
head(df)
#>   year  max
#> 1 1913 1670
#> 2 1914 3090
#> 3 1915 2760
#> 4 1916 2080
#> 5 1917 2490
#> 6 1918 1470

plot_ams_data(df$max, df$year, title = "Athabasca River at Athabasca (07BE001)")

Distribution Selection

First, a suitable probability distribution for the data is selected using the L-moments.

1. select_ldistance chooses the distribution with theoretical L-skewness (\(\tau_3\)) and L-kurtosis (\(\tau_4\)) closest to the sample, based on Euclidean distance.
2. select_lkurtosis selects the distribution with the smallest difference between sample and theoretical L-kurtosis (for three-parameter distributions only).
3. select_zstatistic uses a fitted 4-parameter Kappa distribution to estimate the sampling distribution of the L-kurtosis and selects the distribution with the smallest z-statistic.

selection <- select_ldistance(df$max)

print(selection$recommendation)
#> [1] "GEV"

plot_lmom_diagram(selection)

Recommendation: Use the generalized extreme value (GEV) distribution.

Note: For information about the other distributions, see the selection$metrics item.

You can find more information about the probability distributions supported by the framework here.

Parameter Estimation

The ffaframework package provides three methods for parameter estimation. See here for more information.

• fit_lmoments: L-moments parameter estimation.
• fit_mle: Maximum likelihood parameter estimation.
• fit_gmle: Generalized maximum likelihood parameter estimation.

fit <- fit_lmoments(df$max, "GEV")

print(fit$params)
#> [1] 1600.219872  616.666030    0.120747

plot_sffa_fit(fit)

Conclusion: The GEV distribution with location = 1600, scale = 616, and shape = 0.12 will be used.

Uncertainty Quantification

Once a probability distribution is fitted, return levels (design events) can be readily estimated. However, point estimates alone can be misleading –it is more informative to report confidence intervals to reflect estimation uncertainty. The uncertainty_bootstrap function performs uncertainty quantification using the parametric bootstrap method. It requires three arguments:

• data: A vector of annual maximum series data.
• model: A three-letter code for a probability distribution (ex. "GEV").
• method: A parameter estimation method. Must be "L-moments", "MLE", or "GMLE".

By default, return levels are computed 2-, 5-, 10-, 20-, 50-, and 100- year return periods.

uncertainty <- uncertainty_bootstrap(df$max, "GEV", "L-moments")

plot_sffa_estimates(fit, ci = uncertainty$ci)

Example Conclusion: Every 10 years, we can expect a flood of roughly \(3{\small,}200\text{m}^3/\text{s}\) or greater.

Model Assessment

Model performance is assessed using model_assessment, which reports a collection of assessment statistics about the flood frequency analysis. plot_sffa_assessment compares the data (the “empirical quantiles”) and the predictions of the parametric model at the plotting positions (the “theoretical quantiles”). The black line represents a perfect 1:1 correspondence between the model and the data.

assessment <- model_assessment(df$max, "GEV", "L-moments")

plot_sffa_assessment(assessment)

Conclusion: The parametric model generally matches the plotting positions. The model slightly underestimates the data from \(3{\small,}500\text{m}^3/\text{s}\) to \(5{\small,}000\text{m}^3/\text{s}\).