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Decompose Annual Maximum Series

Source: R/data-decomposition.R
data_decomposition.Rd

Decomposes a nonstationary annual maxima series to derive its stationary stochastic component, which can be used to identify a best-fit distribution using conventional stationary methods, like those based on L-moments. The decomposition procedure follows that proposed by Vidrio-Sahagún and He (2022), which relies on the statistical representation of nonstationary stochastic processes.

Usage

data_decomposition(data, ns_years, ns_structure)

Arguments

data

Numeric vector of observed annual maximum series values. Must be strictly positive, finite, and not missing.

ns_years

For NS-FFA only: Numeric vector of observation years corresponding to data. Must be the same length as data and strictly increasing.

ns_structure

For NS-FFA only: Named list indicating which distribution parameters are modeled as nonstationary. Must contain two logical scalars:

  • location: If TRUE, the location parameter has a linear temporal trend.

  • scale: If TRUE, the scale parameter has a linear temporal trend.

Value

Numeric vector of decomposed data.

Details

Internally, the function does the following:

  1. If there is a trend in the location, fit Sen's trend estimator and subtract away the fitted trend.

  2. If there is a trend in the scale, estimate the variability of the data with data_mw_variability(), fit Sen's trend estimator to the variability vector, and rescale the data to remove the trend.

  3. If necessary, shift the data so that its minimum is at least 1.

References

Vidrio-Sahagún, C. T., and He, J. (2022). The decomposition-based nonstationary flood frequency analysis. Journal of Hydrology, 612 (September 2022), 128186. doi:10.1016/j.jhydrol.2022.128186

See also

data_mw_variability(), eda_sens_trend()

Examples

data <- rnorm(n = 100, mean = 100, sd = 10)
ns_years <- seq(from = 1901, to = 2000)
ns_structure <- list(location = TRUE, scale = FALSE)
data_decomposition(data, ns_years, ns_structure)
#>   [1] 102.48618  75.49338  99.74332 105.94756 111.14916  81.37988  97.05781
#>   [8]  97.02208  96.57003  93.79310 105.55292 119.84636  82.81923 104.18640
#>  [15]  80.36503  93.70802  98.33514 104.22413  89.58643 103.34173 102.22271
#>  [22]  85.48077 105.83698 117.27727  97.35078  88.89977  98.03175  89.85637
#>  [29]  82.93328 107.34391  99.68818 100.29315 114.02480  98.84270  96.31536
#>  [36]  78.48746  94.72897  94.31990 108.06044  98.02072  90.86215  96.68674
#>  [43]  94.60457 101.50038 124.53960  97.38374 102.62853  97.96640  77.60025
#>  [50] 105.27133  94.15111  94.45562  96.64127  96.67811 101.81379  73.50738
#>  [57] 123.00711  92.50233  98.18111 106.72402  89.26269 106.98611  93.32063
#>  [64]  83.93697  85.88710 106.22919  96.82833 100.33093  78.38314  80.60329
#>  [71]  98.08517 108.54988  97.47665 108.22563 100.21480 100.97619  93.74237
#>  [78]  96.49655  93.80447 113.88418 107.55769 101.99468 100.00202  88.89021
#>  [85] 105.41115  68.11546  92.61488  98.44372  90.21832  98.21272 104.53487
#>  [92] 104.32399  93.38884  98.56437 110.36472  90.02529 102.96539 106.60318
#>  [99]  90.40153  89.42880