Type:	Package
Title:	Calculate Rainfall Intensity and Erosivity Indices
Version:	0.1.1
Description:	Calculates I30 (maximum 30-minute rainfall intensity) and EI30 (erosivity index) from rainfall breakpoint data. Supports multiple storm events, rainfall validation, and visualization for soil erosion modeling and hydrological analysis. Methods are based on Brown and Foster (1987) <doi:10.13031/2013.30422>, Wischmeier and Smith (1978) "Predicting Rainfall Erosion Losses: A Guide to Conservation Planning" <doi:10.22004/ag.econ.171903>, and Renard 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).
License:	MIT + file LICENSE
Encoding:	UTF-8
Language:	en-US
LazyData:	true
LazyDataCompression:	bzip2
Depends:	R (≥ 3.5.0)
Imports:	dplyr (≥ 1.1.0), ggplot2 (≥ 3.4.0), lubridate (≥ 1.9.0)
Suggests:	testthat (≥ 3.0.0), knitr, rmarkdown
VignetteBuilder:	knitr
Config/testthat/edition:	3
RoxygenNote:	7.3.3
NeedsCompilation:	no
Packaged:	2026-03-16 09:21:55 UTC; acer
Author:	Sadikul Islam [aut, cre]
Maintainer:	Sadikul Islam <sadikul.islamiasri@gmail.com>
Repository:	CRAN
Date/Publication:	2026-03-19 14:50:10 UTC

Calculate EI30 (Rainfall Erosivity Index)

Description

Calculates the EI30 erosivity index, which is the product of total storm kinetic energy (E) and the maximum 30-minute intensity (I30). This is a key parameter in the USLE/RUSLE erosion prediction equations.

Usage

calculate_ei30(
  data,
  time_col,
  depth_col,
  interval_col = NULL,
  ke_equation = "brown_foster",
  validate = TRUE
)

Arguments

Details

The function calculates kinetic energy for each rainfall increment using one of three equations:

Brown & Foster (1987): e = 0.29 * (1 - 0.72 * exp(-0.05 * i)) where e is unit energy (MJ ha^-1 mm^-1) and i is intensity (mm hr^-1)

Wischmeier & Smith (1978): e = 0.119 + 0.0873log10(i)

McGregor & Mutchler (1976): e = 0.273 + 0.2168i - 0.0083i^2 (for i < 76 mm/hr)

Value

A list with components:

Examples

data <- data.frame(
  time = as.POSIXct(c("2024-01-01 10:00", "2024-01-01 10:15",
                      "2024-01-01 10:30", "2024-01-01 10:45")),
  depth_mm = c(5.2, 8.3, 4.1, 2.5)
)
result <- calculate_ei30(data, "time", "depth_mm")
print(paste("EI30 =", result$ei30, "MJ mm ha^-1 hr^-1"))

Calculate I30 (Maximum 30-minute Rainfall Intensity)

Description

Calculates the maximum 30-minute rainfall intensity from breakpoint data. This is commonly used in erosion prediction equations like USLE and RUSLE.

Usage

calculate_i30(data, time_col, depth_col, interval_col = NULL, validate = TRUE)

Arguments

Details

The function uses a sliding 30-minute window to find the period with maximum rainfall intensity. The intensity is expressed in mm/hr as:

I30 = (max_rainfall_in_30min / 30) * 60

For equal-interval data (e.g., every 5, 10, 15, or 30 min), the function automatically detects the fixed interval from timestamps and applies it consistently to all records including the first.

For irregular-interval data, actual elapsed time between timestamps is used directly. When a rainfall interval spans more than 30 minutes, linear interpolation is applied to estimate rainfall at the exact 30-minute boundary.

Value

A list with components:

Examples

# Equal interval example (15-min)
data <- data.frame(
  time = as.POSIXct(c("2024-01-01 10:00", "2024-01-01 10:15",
                      "2024-01-01 10:30", "2024-01-01 10:45")),
  depth_mm = c(5.2, 8.3, 4.1, 2.5)
)
result <- calculate_i30(data, "time", "depth_mm")
print(paste("I30 =", result$i30, "mm/hr"))

# Irregular interval example
data2 <- data.frame(
  time = as.POSIXct(c("2024-01-01 10:00", "2024-01-01 10:10",
                      "2024-01-01 10:35", "2024-01-01 11:00")),
  depth_mm = c(3, 12, 8, 2)
)
result2 <- calculate_i30(data2, "time", "depth_mm")
print(paste("I30 =", result2$i30, "mm/hr"))

Plot Rainfall Intensity Profile

Description

Creates a bar plot showing rainfall intensity over time, highlighting the maximum 30-minute intensity period.

Usage

plot_intensity_profile(
  data,
  time_col,
  depth_col,
  interval_col = NULL,
  highlight_i30 = TRUE,
  title = NULL
)

Arguments

Value

A ggplot2 object

Examples

data <- data.frame(
  time = as.POSIXct(c("2024-01-01 10:00", "2024-01-01 10:15",
                      "2024-01-01 10:30")),
  depth_mm = c(5.2, 8.3, 4.1)
)

plot_intensity_profile(data, "time", "depth_mm")

Plot Rainfall Pattern

Description

Creates a visualization of the rainfall pattern over time, showing cumulative rainfall and rainfall intensity.

Usage

plot_rainfall_pattern(
  data,
  time_col,
  depth_col,
  interval_col = NULL,
  plot_type = "both",
  title = NULL
)

Arguments

Value

A ggplot2 object

Examples

data <- data.frame(
  time = as.POSIXct(c("2024-01-01 10:00", "2024-01-01 10:15",
                      "2024-01-01 10:30")),
  depth_mm = c(5.2, 8.3, 4.1)
)

plot_rainfall_pattern(data, "time", "depth_mm")

Process Multiple Storm Events

Description

Processes rainfall data containing multiple storm events, calculating I30 and EI30 for each event separately.

Usage

process_storm_events(
  data,
  time_col,
  depth_col,
  interval_col = NULL,
  event_col = NULL,
  min_gap_hours = 6,
  min_rainfall_mm = 12.7,
  calculate_ei30 = TRUE,
  ke_equation = "brown_foster"
)

Arguments

Value

A data frame with one row per storm event containing:

Examples

data <- data.frame(
  datetime = as.POSIXct(c("2024-01-01 10:00", "2024-01-01 10:30",
                          "2024-01-01 11:00", "2024-01-01 20:00",
                          "2024-01-01 20:30")),
  rainfall_mm = c(5, 8, 3, 6, 4)
)
results <- process_storm_events(data, "datetime", "rainfall_mm",
                                min_gap_hours = 6, min_rainfall_mm = 1)
print(results)

Multiple Storm Events Rainfall Data

Description

Hypothetical rainfall breakpoint data containing three separate storm events recorded during August 2023. Events are separated by gaps greater than six hours and differ in intensity, duration, and recording interval, making this dataset suitable for demonstrating multi-event processing.

Usage

rainfall_multi

Format

A data frame with 22 rows and 2 columns:

datetime

POSIXct. Date and time of each breakpoint observation (UTC).

rainfall_mm

numeric. Incremental rainfall depth (mm) recorded in the interval ending at the corresponding datetime.

Details

The three storms differ in character:

• Storm 1 (2023-08-03): Moderate convective storm, 15-minute intervals, ~34 mm total over 2 hours.

• Storm 2 (2023-08-11): Short intense burst, 10-minute intervals, ~34 mm total over 50 minutes.

• Storm 3 (2023-08-22): Gentle frontal rainfall, 30-minute intervals, ~19 mm total over 3 hours.

This dataset is intended for demonstrating process_storm_events() and comparing erosivity across events of different types.

Source

Hypothetical data generated for package illustration purposes.

Examples

data(rainfall_multi)

# Process all storm events
events <- process_storm_events(rainfall_multi, "datetime", "rainfall_mm",
                               min_gap_hours = 6, min_rainfall_mm = 1)
print(events)

# Compare I30 across events
print(events[, c("event_id", "total_rainfall_mm", "i30", "ei30")])

Single Storm Event Rainfall Data

Description

Hypothetical rainfall breakpoint data for a single convective storm event recorded on 2023-07-15, with observations at 15-minute intervals. The storm represents a moderate-intensity summer convective event with a peak near the middle of the storm and a total depth of approximately 38.5 mm.

Usage

rainfall_single

Format

A data frame with 12 rows and 2 columns:

datetime

POSIXct. Date and time of each breakpoint observation (UTC).

rainfall_mm

numeric. Incremental rainfall depth (mm) recorded in the 15-minute interval ending at the corresponding datetime.

Details

This dataset is intended for demonstrating and testing the calculate_i30(), calculate_ei30(), and validate_rainfall_data() functions. The rainfall pattern follows a typical bell-shaped hyetograph with intensity peaking in the 14:45–15:00 UTC window.

Source

Hypothetical data generated for package illustration purposes.

Examples

data(rainfall_single)

# Calculate I30
result <- calculate_i30(rainfall_single, "datetime", "rainfall_mm")
print(paste("I30 =", result$i30, "mm/hr"))

# Calculate EI30
ei <- calculate_ei30(rainfall_single, "datetime", "rainfall_mm")
print(paste("EI30 =", ei$ei30, "MJ mm ha^-1 hr^-1"))

Validate Rainfall Breakpoint Data

Description

Checks rainfall data for common issues including missing values, negative values, non-monotonic time, and duplicate timestamps.

Usage

validate_rainfall_data(data, time_col, depth_col, interval_col = NULL)

Arguments

Value

A list with components:

Examples

data <- data.frame(
  time = as.POSIXct(c("2024-01-01 10:00", "2024-01-01 10:15",
                      "2024-01-01 10:30")),
  depth_mm = c(5.2, 3.1, 2.8)
)
result <- validate_rainfall_data(data, "time", "depth_mm")
print(result$valid)