WEMo (Wave Exposure Model) is an R package that implements a simplified hydrodynamic model to quantify wind-wave exposure in coastal and inland waters. It uses linear wave theory to estimate wave height and energy, accounting for local water depth effects like shoaling and wave breaking. It combines wind data, shoreline geometry, and bathymetry to estimate wave conditions through a reproducible and scriptable workflow in R.
This package provides a modern, open-source, and accessible implementation of the original WEMo, which was developed by NOAA’s Center for Coastal Fisheries and Habitat Research and previously required a proprietary plug-in for ESRI’s ArcMap software. As the original tool is no longer compatible with modern GIS software, this R package provides a sustainable and license-free path forward for researchers and resource managers.
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Quantitative Wave Exposure: Calculates wave height and Representative Wave Energy (RWE) based on wind, fetch, and bathymetry.
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Data Preparation Tools: Includes convenience functions to download and prepare input data, such as historical wind observations from NOAA’s Integrated Surface Database (
get_wind_data()) and bathymetry from NOAA’s Continuously Updated Digital Elevation Model dataset (get_noaa_cudem()). -
Flexible Inputs: Works with standard R spatial objects (
sfandterra), allowing users to easily integrate their own data. -
Scenario Modeling: Designed for efficient re-analysis, making it easy to model the effects of changing conditions like sea-level rise or different wind regimes.
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Visualization: Integrates with
ggplot2to help visualize inputs and results.
You can install the development version of WEMo from
GitHub with:
# If you don't have the 'remotes' package, install it first
# install.packages("remotes")
remotes::install_github("QAWalker/WEMo")This is a basic workflow demonstrating how to run the model using the package’s built-in example data. The example data are located in the immediate area around the NOAA Beaufort Lab on Pivers Island in Beaufort, North Carolina.
# 1. Load required libraries
library(WEMo)
library(sf)
#> Linking to GEOS 3.13.1, GDAL 3.11.0, PROJ 9.6.0; sf_use_s2() is TRUE
library(terra)
#> terra 1.8.54
library(ggplot2)
#> Warning: package 'ggplot2' was built under R version 4.5.1
library(tidyterra)
#>
#> Attaching package: 'tidyterra'
#> The following object is masked from 'package:stats':
#>
#> filter
# 2. Load the package's example data
# These are pre-loaded with the package
# PI_points: sf object with 3 site points
# PI_shoreline: sf polygon of the Mean Higher High Water shoreline (0.522 m NAVD88)
# PI_wind_data: data.frame of raw hourly wind history
# PI_bathy: terra SpatRaster of bathymetry
PI_bathy <- terra::unwrap(PI_bathy)
# Plot input spatial data
ggplot() +
geom_spatraster(data = PI_bathy) +
geom_sf(data = PI_shoreline, fill = "honeydew3") +
geom_sf(data = PI_points, color = 'red', size = 3) +
labs(title = "WEMo Inputs",
fill = "Bathymetry\n(m NAVD88)") +
theme_minimal()
# 3. Summarize wind data for the model
# WEMo needs a summary of wind speed and proportion by direction
wind_summary <- summarize_wind_data(
wind_data = PI_wind_data,
wind_percentile = 0.95, # Use 95th percentile wind speed
directions = seq(0, 350, by = 10) # Bin directions into 10-degree increments
)
# create and print wind rose
plot_wind_rose(wind_data = wind_summary)
# 4. Run the full WEMo model
wemo_results <- wemo_full(
site_points = PI_points,
shoreline = PI_shoreline,
bathy = PI_bathy,
wind_data = wind_summary,
max_fetch = 1000, # Max distance (m) to calculate fetch
water_level = 0.552 # Water level (m) relative to bathy vertical datum
)
# 5. Explore and plot the final results
print(wemo_results$wemo_final)
#> Simple feature collection with 3 features and 9 fields
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: 346536.4 ymin: 3842620 xmax: 346986.4 ymax: 3843270
#> Projected CRS: NAD83 / UTM zone 18N
#> # A tibble: 3 × 10
#> site RWE avg_wave_height max_wave_height direction_of_max_wave avg_fetch
#> <int> <dbl> <dbl> <dbl> <chr> <dbl>
#> 1 1 31.8 0.102 0.141 220 444.
#> 2 2 23.3 0.0895 0.123 50 345.
#> 3 3 10.0 0.0755 0.112 170 256.
#> # ℹ 4 more variables: max_fetch <dbl>, avg_efetch <dbl>, max_efetch <dbl>,
#> # geometry <POINT [m]>
# Plot the average wave height at each point
ggplot() +
geom_sf(data = PI_shoreline, fill = "honeydew3") +
geom_sf(data = wemo_results$wemo_final, aes(color = avg_wave_height), size = 5) +
scale_color_viridis_c() +
labs(title = "WEMo Results: Average Wave Height",
color = "Avg. Wave\nHeight (m)") +
theme_minimal()
# Plot the wave height of the wave arriving at the site from each fetch ray
ggplot() +
geom_sf(data = PI_shoreline, fill = "honeydew3") +
geom_sf(data = wemo_results$wemo_details, aes(color = wave_height_final), linewidth = 1) +
scale_color_viridis_c(option = "H") +
labs(title = "WEMo Results: Final Wave Height",
color = "Final Wave\nHeight (m)") +
theme_minimal()
WEMo provides several functions to help you acquire and prepare the necessary input for your area of interest
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Wind Data: Download historical wind data from NOAA’s Integrated Surface Database using
get_wind_data(). -
Bathymetry Data: Fetch bathymetric rasters from NOAA’s CUDEM dataset using
get_noaa_cudem(). -
Shoreline Polygon: Generate a shoreline from a bathymetry raster using
generate_shoreline_from_bathy(). -
Site Points: Create a regular grid of points for analysis using
generate_grid_points().
For a detailed, step-by-step tutorial on using these functions, please
see the “Gathering WEMo Data” vignette by running
vignette("Gathering_WEMo_Data", package = "WEMo").
If you use WEMo in your research, please cite both the R package and the original publications that describe the model’s methodology.
Walker, Q.A. (2024). WEMo: An R Implementation of the Wave Exposure Model. R package version X.X.X. https://github.com/QAWalker/WEMo
Malhotra, A., & Fonseca, M. S. (2007). WEMo (Wave Exposure Model): Formulation, Procedures and Validation. NOAA Technical Memorandum NOS NCCOS 65. 28 pp.
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