The goal of swmm is to provide a cross-platform interface to the United States Environmental Protection Agency’s Stormwater Management Model (SWMM) (the source for SWMM is also available on GitHub).
Installation
The swmm package is not on CRAN, but you can install a recent development binary with:
# Mac
remotes::install_url(
"https://github.com/paleolimbot/swmm/releases/download/v0.0.9000/swmm_0.0.0.9000.tgz",
build = FALSE
)
# Windows
remotes::install_url(
"https://github.com/paleolimbot/swmm/releases/download/v0.0.9000/swmm_0.0.0.9000.zip",
build = FALSE
)You can install and build the development version from GitHub with:
# install.packages("remotes")
remotes::install_github("paleolimbot/swmm")If you can load the package, you’re all set! There is no need to install SWMM (the package contains a copy).
library(swmm)
#> Warning: package 'swmm' was built under R version 3.6.2Running SWMM
Run SWMM using the swmm_run() function and an “.inp” input file.
result <- swmm_run(swmm_example_file("Example8.inp"))
result
#> $inp
#> [1] "/Library/Frameworks/R.framework/Versions/3.6/Resources/library/swmm/swmm_examples/Example8.inp"
#>
#> $rpt
#> [1] "/private/var/folders/bq/2rcjstv90nx1_wrt8d3gqw6m0000gn/T/RtmpQPw99t/file1304b2e8530de.rpt"
#>
#> $out
#> [1] "/private/var/folders/bq/2rcjstv90nx1_wrt8d3gqw6m0000gn/T/RtmpQPw99t/file1304b2a4b56ae.out"
#>
#> $last_error
#> [1] 0
#>
#> $last_warning
#> [1] 0You can find out which version of SWMM is included in the package with swmm_version():
swmm_version()
#> [1] "5.1.13"Reading swmm output
Use the swmm_read_*() functions (highly experimental):
out_file <- result$out
swmm_read_subcatchments(out_file)
#> # A tibble: 5,040 x 10
#> object_name step evaporation_loss groundwater_ele… groundwater_flow
#> <chr> <int> <dbl> <dbl> <dbl>
#> 1 S1 1 0 0 0
#> 2 S1 2 0 0 0
#> 3 S1 3 0 0 0
#> 4 S1 4 0 0 0
#> 5 S1 5 0 0 0
#> 6 S1 6 0 0 0
#> 7 S1 7 0 0 0
#> 8 S1 8 0 0 0
#> 9 S1 9 0 0 0
#> 10 S1 10 0 0 0
#> # … with 5,030 more rows, and 5 more variables: infiltration_loss <dbl>,
#> # rainfall_rate <dbl>, runoff_flow <dbl>, snow_depth <dbl>,
#> # soil_moisture <dbl>
swmm_read_links(out_file)
#> # A tibble: 25,200 x 7
#> object_name step average_water capacity flow_rate flow_velocity
#> <chr> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 C_Aux3 1 0.000100 8.33e-6 0 0
#> 2 C_Aux3 2 0.000100 8.33e-6 0 0
#> 3 C_Aux3 3 0.000100 8.33e-6 0 0
#> 4 C_Aux3 4 0.000100 8.33e-6 0 0
#> 5 C_Aux3 5 0.000100 8.33e-6 0 0
#> 6 C_Aux3 6 0.000100 8.33e-6 0 0
#> 7 C_Aux3 7 0.000100 8.33e-6 0 0
#> 8 C_Aux3 8 0.000100 8.33e-6 0 0
#> 9 C_Aux3 9 0.000100 8.33e-6 0 0
#> 10 C_Aux3 10 0.000100 8.33e-6 0 0
#> # … with 25,190 more rows, and 1 more variable: volume_of <dbl>
swmm_read_nodes(out_file)
#> # A tibble: 22,320 x 8
#> object_name step hydraulic_head inflow lateral_inflow stored_water
#> <chr> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 Aux3 1 4969. 0.00512 0.00512 0
#> 2 Aux3 2 4969. 0.00752 0.00752 0
#> 3 Aux3 3 4969. 0.0105 0.0105 0
#> 4 Aux3 4 4969. 0.0134 0.0134 0
#> 5 Aux3 5 4969. 0.0162 0.0162 0
#> 6 Aux3 6 4969. 0.0195 0.0195 0
#> 7 Aux3 7 4969. 0.0223 0.0223 0
#> 8 Aux3 8 4969. 0.0247 0.0247 0
#> 9 Aux3 9 4969. 0.0266 0.0266 0
#> 10 Aux3 10 4969. 0.0281 0.0281 0
#> # … with 22,310 more rows, and 2 more variables: surface_flooding <dbl>,
#> # water_depth <dbl>
swmm_read_system(out_file)
#> # A tibble: 720 x 17
#> object_name step actual_evaporat… air_temperature average_losses
#> <chr> <int> <dbl> <dbl> <dbl>
#> 1 system 1 0 70 0.0292
#> 2 system 2 0 70 0.0292
#> 3 system 3 0 70 0.0292
#> 4 system 4 0 70 0.0292
#> 5 system 5 0 70 0.0334
#> 6 system 6 0 70 0.0334
#> 7 system 7 0 70 0.0334
#> 8 system 8 0 70 0.0334
#> 9 system 9 0 70 0.0334
#> 10 system 10 0 70 0.0382
#> # … with 710 more rows, and 12 more variables: direct_inflow <dbl>,
#> # dry_weather <dbl>, external_flooding <dbl>, external_inflow <dbl>,
#> # groundwater_inflow <dbl>, nodal_storage <dbl>, outflow_from <dbl>,
#> # potential_evaporation <dbl>, rainfall <dbl>, RDII_inflow <dbl>,
#> # runoff <dbl>, snow_depth <dbl>To find out which variables there are to choose from (for the purposes of only reading part of the output, which you probably want to do), use swmm_read_variables():
swmm_read_variables(out_file)
#> # A tibble: 432 x 8
#> element_type type_index variable_index variable variable_name unit
#> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 links 2 0 flow_ra… flow rate flow…
#> 2 links 2 0 flow_ra… flow rate flow…
#> 3 links 2 0 flow_ra… flow rate flow…
#> 4 links 2 0 flow_ra… flow rate flow…
#> 5 links 2 0 flow_ra… flow rate flow…
#> 6 links 2 0 flow_ra… flow rate flow…
#> 7 links 2 0 flow_ra… flow rate flow…
#> 8 links 2 0 flow_ra… flow rate flow…
#> 9 links 2 0 flow_ra… flow rate flow…
#> 10 links 2 0 flow_ra… flow rate flow…
#> # … with 422 more rows, and 2 more variables: object_name <chr>,
#> # object_index <dbl>
