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The rainerosr package provides tools for calculating
rainfall intensity and erosivity indices from breakpoint rainfall data.
This vignette demonstrates the main functions and typical workflows.
I30 is the maximum 30-minute rainfall intensity, expressed in mm/hr. It is calculated by finding the 30-minute period within a storm that has the highest average rainfall intensity. This value is a key component in erosion prediction models like the Universal Soil Loss Equation (USLE) and its revisions.
EI30 is the rainfall erosivity index, calculated as the product of: - E: Total storm kinetic energy (MJ ha⁻¹) - I30: Maximum 30-minute intensity (mm hr⁻¹)
The result (MJ mm ha⁻¹ hr⁻¹) quantifies the erosive potential of a rainfall event.
library(rainerosr)Let’s create a sample storm event:
storm_data <- data.frame(
datetime = as.POSIXct(c(
"2024-01-15 14:00:00",
"2024-01-15 14:10:00",
"2024-01-15 14:20:00",
"2024-01-15 14:30:00",
"2024-01-15 14:40:00",
"2024-01-15 14:50:00",
"2024-01-15 15:00:00"
)),
rainfall_mm = c(2.5, 5.8, 8.2, 6.1, 3.4, 2.0, 1.5)
)
print(storm_data)
#> datetime rainfall_mm
#> 1 2024-01-15 14:00:00 2.5
#> 2 2024-01-15 14:10:00 5.8
#> 3 2024-01-15 14:20:00 8.2
#> 4 2024-01-15 14:30:00 6.1
#> 5 2024-01-15 14:40:00 3.4
#> 6 2024-01-15 14:50:00 2.0
#> 7 2024-01-15 15:00:00 1.5i30_result <- calculate_i30(storm_data, "datetime", "rainfall_mm")
print(i30_result)
#> $i30
#> [1] 49.2
#>
#> $total_rainfall
#> [1] 29.5
#>
#> $duration
#> [1] 70
#>
#> $start_time
#> [1] "2024-01-15 14:10:00 IST"
#>
#> $end_time
#> [1] "2024-01-15 14:20:00 IST"
#>
#> $interval_type
#> [1] "equal"The result shows: - i30: Maximum 30-minute intensity (mm/hr) - total_rainfall: Total storm depth (mm) - duration: Storm duration (minutes) - start_time and end_time: Period of maximum intensity
ei30_result <- calculate_ei30(storm_data, "datetime", "rainfall_mm")
print(ei30_result)
#> $ei30
#> [1] 347.37
#>
#> $total_energy
#> [1] 7.06
#>
#> $i30
#> [1] 49.2
#>
#> $total_rainfall
#> [1] 29.5
#>
#> $duration
#> [1] 70
#>
#> $ke_equation
#> [1] "brown_foster"The EI30 result includes: - ei30: Erosivity index - total_energy: Total kinetic energy - i30: Maximum 30-minute intensity - Plus other storm characteristics
The package supports three kinetic energy equations:
# Brown & Foster (default)
ei30_bf <- calculate_ei30(storm_data, "datetime", "rainfall_mm",
ke_equation = "brown_foster")
# Wischmeier & Smith
ei30_ws <- calculate_ei30(storm_data, "datetime", "rainfall_mm",
ke_equation = "wischmeier")
# McGregor & Mutchler
ei30_mm <- calculate_ei30(storm_data, "datetime", "rainfall_mm",
ke_equation = "mcgregor_mutchler")
# Compare results
comparison <- data.frame(
Equation = c("Brown & Foster", "Wischmeier", "McGregor & Mutchler"),
EI30 = c(ei30_bf$ei30, ei30_ws$ei30, ei30_mm$ei30),
Energy = c(ei30_bf$total_energy, ei30_ws$total_energy, ei30_mm$total_energy)
)
print(comparison)
#> Equation EI30 Energy
#> 1 Brown & Foster 347.37 7.06
#> 2 Wischmeier 358.85 7.29
#> 3 McGregor & Mutchler -4511.82 -91.70When you have data containing multiple storms, use
process_storm_events():
# Create data with two storms
multi_storm <- data.frame(
datetime = as.POSIXct(c(
# Storm 1
"2024-03-10 09:00:00", "2024-03-10 09:15:00",
"2024-03-10 09:30:00", "2024-03-10 09:45:00",
# Storm 2 (10 hours later)
"2024-03-10 19:00:00", "2024-03-10 19:20:00",
"2024-03-10 19:40:00", "2024-03-10 20:00:00"
)),
rainfall_mm = c(
4.5, 7.2, 9.1, 5.3, # Storm 1
6.8, 10.2, 8.5, 4.9 # Storm 2
)
)
# Process all events
events_summary <- process_storm_events(
multi_storm,
"datetime",
"rainfall_mm",
min_gap_hours = 6,
min_rainfall_mm = 10,
calculate_ei30 = TRUE
)
print(events_summary)
#> event_id start_time end_time duration_min
#> 1 1 2024-03-10 09:00:00 2024-03-10 09:45:00 60
#> 2 2 2024-03-10 19:00:00 2024-03-10 20:00:00 80
#> total_rainfall_mm i30 n_breakpoints ei30 total_energy
#> 1 26.1 36.4 4 223.48 6.14
#> 2 30.4 30.6 4 209.80 6.86Always validate your data before analysis:
validation <- validate_rainfall_data(storm_data, "datetime", "rainfall_mm")
if (validation$valid) {
cat("✓ Data validation passed\n")
} else {
cat("✗ Data validation failed:\n")
print(validation$issues)
}
#> ✓ Data validation passed
if (length(validation$warnings) > 0) {
cat("\nWarnings:\n")
print(validation$warnings)
}plot_rainfall_pattern(storm_data, "datetime", "rainfall_mm", plot_type = "both",
title = "Storm Event - January 15, 2024")
plot_intensity_profile(storm_data, "datetime", "rainfall_mm",
highlight_i30 = TRUE,
title = "Rainfall Intensity Profile")
If you have irregular time intervals or want to specify them explicitly:
data_with_intervals <- data.frame(
time = as.POSIXct(c(
"2024-01-01 10:00:00",
"2024-01-01 10:05:00",
"2024-01-01 10:15:00",
"2024-01-01 10:30:00"
)),
depth = c(3.2, 5.1, 4.8, 2.9),
interval = c(5, 5, 10, 15) # minutes
)
result <- calculate_i30(data_with_intervals, "time", "depth", "interval")
print(result)
#> $i30
#> [1] 61.2
#>
#> $total_rainfall
#> [1] 16
#>
#> $duration
#> [1] 35
#>
#> $start_time
#> [1] "2024-01-01 10:00:00 IST"
#>
#> $end_time
#> [1] "2024-01-01 10:05:00 IST"
#>
#> $interval_type
#> [1] "explicit"If you have your own event IDs:
data_with_event_id <- data.frame(
datetime = as.POSIXct(c(
"2024-01-01 10:00", "2024-01-01 10:15",
"2024-01-01 11:00", "2024-01-01 11:15"
)),
rainfall_mm = c(6, 8, 7, 9),
event_id = c(1, 1, 2, 2)
)
events <- process_storm_events(
data_with_event_id,
"datetime",
"rainfall_mm",
event_col = "event_id"
)
#> Warning in calculate_i30(event_data, time_col, depth_col, interval_col, : Data validation warnings:
#> Very few data points (< 3) may produce unreliable results
#> Warning in calculate_i30(event_data, time_col, depth_col, interval_col, : Data validation warnings:
#> Very few data points (< 3) may produce unreliable results
print(events)
#> event_id start_time end_time duration_min
#> 1 1 2024-01-01 10:00:00 2024-01-01 10:15:00 30
#> 2 2 2024-01-01 11:00:00 2024-01-01 11:15:00 30
#> total_rainfall_mm i30 n_breakpoints ei30 total_energy
#> 1 14 32 2 107.05 3.35
#> 2 16 36 2 142.88 3.97Here’s a complete workflow for analyzing rainfall data:
# 1. Load your data
my_data <- read.csv("rainfall_data.csv")
my_data$datetime <- as.POSIXct(my_data$datetime)
# 2. Validate the data
validation <- validate_rainfall_data(my_data, "datetime", "rainfall_mm")
if (!validation$valid) {
stop("Data validation failed")
}
# 3. Process multiple events
events <- process_storm_events(
my_data,
"datetime",
"rainfall_mm",
min_gap_hours = 6,
min_rainfall_mm = 12.7,
calculate_ei30 = TRUE,
ke_equation = "brown_foster"
)
# 4. Analyze results
summary(events$ei30)
mean_erosivity <- mean(events$ei30)
# 5. Visualize individual storms
for (evt_id in unique(events$event_id)) {
evt_data <- my_data[my_data$event_id == evt_id, ]
p <- plot_intensity_profile(evt_data, "datetime", "rainfall_mm")
print(p)
}
# 6. Export results
write.csv(events, "erosivity_results.csv", row.names = FALSE)Brown, L.C., & Foster, G.R. (1987). Storm erosivity using idealized intensity distributions. Transactions of the ASAE, 30(2), 379-386.
Wischmeier, W.H., & Smith, D.D. (1978). Predicting rainfall erosion losses: A guide to conservation planning. USDA Agriculture Handbook No. 537.
Renard, K.G., et al. (1997). Predicting soil erosion by water: A guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). USDA Agriculture Handbook No. 703.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.