A synthetic but physically realistic soil characterisation dataset generated to illustrate the functions in soilFlux. The data mimics the structure of the Florida Soil Characterization Database (FSCD) used in Rodrigues et al. / Norouzi et al. (2025), with van Genuchten curves used to produce internally consistent water-content observations.
Format
A data frame with 4 800 rows and 14 columns:
- PEDON_ID
Character. Unique soil profile identifier (e.g.
"P0001").- sand_total
Numeric. Total sand content (%).
- silt
Numeric. Silt content (%).
- clay
Numeric. Clay content (%).
- soc
Numeric. Soil organic carbon (%).
- bd
Numeric. Bulk density (g/cm3).
- sand_vf
Numeric. Very fine sand fraction (%).
- sand_f
Numeric. Fine sand fraction (%).
- sand_m
Numeric. Medium sand fraction (%).
- sand_c
Numeric. Coarse sand fraction (%).
- matric_head
Numeric. Matric head (cm H2O, positive).
- water_content
Numeric. Volumetric water content (m3/m3). Approximately 1% of values are
NAto simulate missing measurements.- depth
Character. Depth interval (e.g.
"0-5","5-15", etc.).- Texture
Character. USDA texture class (one of: Sand, Loamy Sand, Sandy Loam, Loam, Silt Loam, Clay Loam, Clay).
Details
The dataset contains 120 unique profiles (PEDON_ID) across five
depth intervals (0–5, 5–15, 15–30, 30–60, 60–100 cm) and eight
matric-head points per depth (pF approximately 0, 1, 1.5, 2, 2.5, 3, 4.2, 7).
Profiles are evenly distributed across seven USDA texture classes.
Water-content observations were generated with the van Genuchten (1980) equation using parameters that vary realistically with texture and depth, then small Gaussian noise was added.
References
van Genuchten, M. T. (1980). A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Science Society of America Journal, 44(5), 892–898.
Examples
data("swrc_example")
head(swrc_example)
#> PEDON_ID sand_total silt clay soc bd sand_vf sand_f sand_m sand_c
#> sand P0001 88.79 5.71 5.5 1.51 1.248 11.29 28.84 28.15 20.52
#> sand1 P0001 88.79 5.71 5.5 1.51 1.248 11.29 28.84 28.15 20.52
#> sand2 P0001 88.79 5.71 5.5 1.51 1.248 11.29 28.84 28.15 20.52
#> sand3 P0001 88.79 5.71 5.5 1.51 1.248 11.29 28.84 28.15 20.52
#> sand4 P0001 88.79 5.71 5.5 1.51 1.248 11.29 28.84 28.15 20.52
#> sand5 P0001 88.79 5.71 5.5 1.51 1.248 11.29 28.84 28.15 20.52
#> matric_head water_content depth Texture
#> sand 1 0.5598 0-5 Sand
#> sand1 10 0.5448 0-5 Sand
#> sand2 32 0.4301 0-5 Sand
#> sand3 100 0.1512 0-5 Sand
#> sand4 316 0.0595 0-5 Sand
#> sand5 1000 0.0306 0-5 Sand
table(swrc_example$Texture[!duplicated(swrc_example$PEDON_ID)])
#>
#> Clay Clay Loam Loam Loamy Sand Sand Sandy Loam Silt Loam
#> 17 17 17 17 18 17 17
# Prepare for modelling
df <- prepare_swrc_data(swrc_example, depth_col = "depth")