The goal of infiltrodiscR is to provide functions for the modeling of data derived from the Minidisk Infiltrometer device. If you use this package please cite using this DOI code
You can install the infiltrodiscR package from GitHub with:
# install.packages("devtools")
devtools::install_github("biofisicasuelos/infiltrodiscR")
or install it from CRAN
install.packages("infiltrodiscR")
A dataframe or tibble with the following columns:
- texture: soil texture according to USDA: as.character() and lowercase, for example: "clay loam".
- suction: as.character() and lowercase, in this format: "2cm".
- volume: volume recorded in the infiltration measurements in mL, as.numeric().
- time: time recorded in the infiltration measurements in seconds, as.numeric().
- In the lab's YT channel, we uploaded short sessions [ES] about data wrangling and the package.
- Slides [EN] explaining the package are available here
infiltration()
This function calculates cumulative infiltration and the square root of time, using time and volume recorded.
vg_par
This function returns the parameter A, no_h and alpha related to the van Genuchten parameters (van Genuchten, 1980), from tabulated data calculated for a radius of 2.25 cm, including 12 soil texture classes and suctions from -0.5 cm to -7 cm. Data from (Decagon Devices, Inc., 2005)
parameter_A
This function returns the parameter A calculated from the equation proposed by Zhang (1997).
Minidisk infiltrometers have been used to understand infiltration in soils, hydrophobicity, irrigation system design, erosion and burn severity.
While it is simple and easy to use, the data from Minidisk Infiltrometer must be processed in a reproducible manner. A common way is to use an Excel macro provided by the supplier on their website, but for handling a large number of samples this can be complex.
To determine the unsaturated hydraulic conductivity at specific suctions, the Minidisk Infiltrometer data can be fitted to the method proposed by Zhang (1997). The first step is to calculate the cumulative infiltration vs. the square root of time.
library(tidyverse)
library(infiltrodiscR)
infiltration_data <- tibble(
soil = c(rep("soil_a",11), rep("soil_b",11)),
time = c(0, 30, 60, 90, 120, 150, 180, 210, 240, 270, 300,
0, 35, 65, 95, 125, 155, 185, 215, 245, 275, 305),
volume = c(95, 89, 86, 83, 80, 77, 74, 73, 71, 69, 67,
83, 77, 64, 61, 58, 45, 42, 35, 29, 17, 15)
)
infilt_cum_sqrt <-
infiltration_data %>%
group_by(soil) %>%
nest() %>%
mutate(data = map(data, ~ infiltration(.), data = .x)) %>%
unnest(cols = data)
infilt_cum_sqrt
Now three Van Genuchten parameters: n, alpha and A are extracted from the Minidisk Infiltrometer Table (Decagon Devices, Inc., 2005). The radius (2.25 cm) corresponds to the Minidisk Infiltrometer specs.
soil_data <- tibble(soil = c("soil_a", "soil_b"),
texture = c("sandy loam", "clay loam"), #USDA
suction = c("4cm","2cm"),
om_content = c(1,10))
infilt_cum_sqrt_par <-
infiltration_data %>%
group_by(soil) %>%
nest() %>%
left_join(soil_data) %>%
infiltrodiscR::vg_par() %>%
mutate(data = map(data, ~ infiltration(.), data = .x))
infilt_cum_sqrt_par
The hydraulic conductivity of the soil K at a specific suctions is calculated as: K(h) = C1 / A. Parameter C1 is calculated fitting a polynomial function of the second degree (y = ax2+b), where a is parameter C1, x is the square root of time and y is the cumulative infiltration. For this step, we use the package broom and base R.
library(broom)
infilt_cum_sqrt_par_fit <-
infilt_cum_sqrt_par %>%
mutate(
fit = map(data, ~ lm(infiltration ~ poly(sqrt_time, 2, raw = TRUE), data = .x)),
tidied = map(fit, tidy)
) %>%
unnest(tidied) %>%
filter(term == "poly(sqrt_time, 2, raw = TRUE)2")
infilt_cum_sqrt_par_fit
The column estimate
corresponds to the parameter C1
infilt_cum_sqrt_par_fit <-
infilt_cum_sqrt_par_fit %>%
rename(C1 = estimate)
The hydraulic conductivity of the soil K at a specific suctions is calculated as: K(h) = C1 / A. Parameter C1 is already calculated, and parameter A is calculated using the function.
parameter_A(infilt_cum_sqrt_par_fit) %>%
mutate(K_h = C1 / parameter_A) %>%
select(K_h)
You can check that the values of A from the equation and the table are equivalent.