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Estimate saturated hydraulic conductivity from ponded ring infiltration

Source: R/ring_conductivity.R
ring_conductivity.Rd

A convenience wrapper that combines infiltration_cumulative(), infiltration_rate(), and fit_infiltration_horton() into a single pipeline step. Takes raw time–volume readings from a ponded ring infiltrometer and returns Horton (1940) model parameters, where .fc (the asymptotic final rate) is an estimate of field-saturated hydraulic conductivity Kfs.

Usage

ring_conductivity(data, time, volume, radius, workers = 1L)

Arguments

data

A data frame or tibble, optionally grouped with dplyr::group_by(). If grouped, the model is fitted independently per group.

time

Bare column name; elapsed time in seconds.

volume

Bare column name; water volume in the reservoir (mL), decreasing as water infiltrates.

radius

Ring radius in cm. No default — ring sizes vary and a silent wrong value would produce incorrect Kfs estimates.

workers

Number of parallel workers passed to fit_infiltration_horton(). Defaults to 1. Values > 1 use parallel::mclapply() (Unix only).

Value

A tibble with one row per group containing:

  • Group keys (if grouped)

  • .fc — final/steady-state infiltration rate ≈ Kfs (cm/s)

  • .f0 — initial infiltration rate (cm/s)

  • .k — exponential decay constant (1/s)

  • .fc_std_error, .f0_std_error, .k_std_error

  • .convergence — logical; TRUE if NLS converged

Details

The Horton model is: $$f(t) = f_c + (f_0 - f_c) \cdot e^{-k t}$$

where \(f_c\) ≈ Kfs (cm/s), \(f_0\) is the initial infiltration rate, and \(k\) (1/s) is the exponential decay constant.

When to use the step-by-step approach instead: If you want to visualise the raw cumulative infiltration curve or the rate decay before fitting, call infiltration_cumulative(), infiltration_rate(), and fit_infiltration_horton() individually.

References

Horton, R. E. (1940). An approach toward a physical interpretation of infiltration capacity. Soil Science Society of America Proceedings, 5, 399–417.

See also

fit_infiltration_horton(), infiltration_cumulative(), infiltration_rate(), minidisk_conductivity()

Examples

library(tibble)
library(dplyr)

# Single ring — 100 cm ring radius, 1-hour run
ring <- tibble(
  time   = seq(0, 3600, 360),
  volume = c(500, 440, 390, 355, 328, 308, 292, 280, 271, 265, 260)
)

ring_conductivity(ring, time = time, volume = volume, radius = 10)
#> # A tibble: 1 × 7
#>        .fc     .f0      .k .fc_std_error .f0_std_error .k_std_error .convergence
#>      <dbl>   <dbl>   <dbl>         <dbl>         <dbl>        <dbl> <lgl>       
#> 1 -1.46e-5 6.18e-4 7.14e-4     0.0000187     0.0000156    0.0000611 TRUE        

# Multi-site: one group_by feeds the full pipeline
multi <- tibble(
  site   = rep(c("A", "B"), each = 11),
  time   = rep(seq(0, 3600, 360), 2),
  volume = c(
    500, 440, 390, 355, 328, 308, 292, 280, 271, 265, 260,
    500, 450, 410, 375, 348, 328, 313, 302, 294, 288, 284
  )
)

multi |>
  group_by(site) |>
  ring_conductivity(time = time, volume = volume, radius = 10)
#> # A tibble: 2 × 8
#>   site         .fc      .f0       .k .fc_std_error .f0_std_error .k_std_error
#>   <chr>      <dbl>    <dbl>    <dbl>         <dbl>         <dbl>        <dbl>
#> 1 A     -0.0000146 0.000618 0.000714     0.0000187     0.0000156    0.0000611
#> 2 B     -0.0000846 0.000492 0.000476     0.0000309     0.0000114    0.0000547
#> # ℹ 1 more variable: .convergence <lgl>