Modeling code in a targets pipeline for understanding characteristics of rivers that experience episodic salinization from winter road salting events.
This repository contains reproducible code for downloading, processing, and modeling data related to river salinization dynamics. Using The Turing Ways's definitions, this code and analysis are intended to be fully reproducible and could be somewhat replicable with different states and/or dates.
The code is adapted from the analysis for Lindsay Platt's (@lindsayplatt) Master's Thesis:
Platt, L. (2024). Basins modulate signatures of river salinization (Master's thesis). University of Wisconsin-Madison, Freshwater and Marine Sciences. Platt, L. (2024). Source code: Basins modulate signatures of river salinization (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.11130548
This code is associated with Platt, L and Dugan, HA. (2025) Episodic salinization of Midwestern and Northeastern US rivers by road salt. Water Resources Research.
This repository is setup as an automated pipeline using the targets R package in order to orchestrate a complex, modular workflow where dependency tracking determines which components need to be built. As written, this pipeline will need about 2.5 hours to build and will need to have an internet connection.
The pipeline is broken into 7 different phases:
1_Downloadcontains all the code that pulls from NWIS, ScienceBase, NHD+, etc. It will require an internet connection. It also sets the spatial and temporal limits for the analysis.2_Preparethis phase is doing all of the heavy lifting to process data into a useable state for both our time series data (prefixed withp2_ts) and our static attributes data (prefixed withp2_attr).3_Filterapplies all of our criteria to remove sites that are not up to our minimum standards and/or are sites with unexpected features (tidal, in a really high agricultural area, don't have an associated NHD+ catchment area, etc).4_EpisodicSalinizationapplies an algorithm that has been used to identify storms by finding steep peaks in a hydrograph to the specific conductance time series in order to identify winter storms where road salts are washed into streams and cause sharp peaks (similar to storm hydrographs). In the end, this phase identifies sites with specific conductance data that exhibit this episodic behavior.5_DefineCharacteristicsuses the information gathered in4_EpisodicSalinizationand applies random forest models to these categorizations with the collection of static attributes prepared and filtered in2_Prepareand3_Filterto define the attributes and values that are important for determining a site's category.6_PredictClassuses the optimized model and defined outlet locations to download/prepare attributes for ALL COMIDs within the upstream watershed of these outlet locations to predict whether they are likely to be episodic or not. TODO: APPLY TO MORE THAN JUST YAHARA, ROCK, AND MAUMEE.7_Disseminatetakes all of the model input output to generate figures and explain the results. The figures generated in this phase were all used in the manuscript. Three datasets are also saved in this step and represent the final salinization signature classifications for each site, values for all 16 static attributes, and metadata for all 16 attributes. The first two datasets were used by the random forest models to create final results explaining which characteristics were important for each of the salinization signatures.
Run the following command to make sure you have all the necessary packages before trying to build the pipeline.
install.packages(c(
'targets',
'tarchetypes',
'accelerometry',
'arrow',
'cowplot',
'dataRetrieval',
'exactextractr',
'GGally',
'httr',
'MESS',
'nhdplusTools',
'pdp',
'qs',
'randomForest',
'raster',
'sbtools',
'scico',
'sf',
'tidytext',
'tidyverse',
'units',
'usmap',
'yaml',
'zip'
))The following package versions were used during the original pipeline build. You shouldn't need to install these versions specifically, but if there are errors cropping up, you could try installing these specific versions and see if you can get past the issue.
Known issues:
- We have had problems with the arrow package on MacOS. The version used to build this pipeline was Arrow 19.0.1
- DOI-USGS/dataRetrieval#759 dataRetrieval has a limit of 400 navigation requests per hour per user. Since this pipeline pulls > 400 sites, this presents a problem. The dataRetrieval may have to be run in chunks or slowed down.
- We used dataRetrieval 2.7.17
─ Session info ──────────────────────────────────────────────────────────────────────────────────────
setting value
version R version 4.4.2 (2024-10-31)
os macOS Sequoia 15.3.1
system aarch64, darwin20
ui RStudio
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz America/Chicago
date 2025-09-09
rstudio 2024.12.0+467 Kousa Dogwood (desktop)
pandoc 3.2 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/aarch64/ (via rmarkdown)
quarto 1.5.57 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/quarto
─ Packages ──────────────────────────────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
abind * 1.4-8 2024-09-12 [1] CRAN (R 4.4.1)
accelerometry * 3.1.2 2018-08-24 [1] CRAN (R 4.4.0)
arrow * 19.0.1 2025-02-26 [1] CRAN (R 4.4.1)
assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.4.1)
backports 1.5.0 2024-05-23 [1] CRAN (R 4.4.1)
base64url 1.4 2018-05-14 [1] CRAN (R 4.4.1)
BiocGenerics * 0.52.0 2024-11-08 [1] Bioconductor 3.20 (R 4.4.1)
bit 4.6.0 2025-03-06 [1] CRAN (R 4.4.1)
bit64 4.6.0-1 2025-01-16 [1] CRAN (R 4.4.1)
broom 1.0.9 2025-07-28 [1] CRAN (R 4.4.1)
cachem 1.1.0 2024-05-16 [1] CRAN (R 4.4.1)
callr 3.7.6 2024-03-25 [1] CRAN (R 4.4.0)
car 3.1-3 2024-09-27 [1] CRAN (R 4.4.1)
carData 3.0-5 2022-01-06 [1] CRAN (R 4.4.1)
class 7.3-23 2025-01-01 [1] CRAN (R 4.4.1)
classInt 0.4-11 2025-01-08 [1] CRAN (R 4.4.1)
cli 3.6.5 2025-04-23 [1] CRAN (R 4.4.1)
clipr 0.8.0 2022-02-22 [1] CRAN (R 4.4.1)
codetools 0.2-20 2024-03-31 [1] CRAN (R 4.4.2)
cowplot * 1.2.0 2025-07-07 [1] CRAN (R 4.4.1)
crayon 1.5.3 2024-06-20 [1] CRAN (R 4.4.1)
curl 6.4.0 2025-06-22 [1] CRAN (R 4.4.1)
data.table 1.17.8 2025-07-10 [1] CRAN (R 4.4.1)
dataRetrieval * 2.7.20 2025-07-15 [1] CRAN (R 4.4.1)
DBI 1.2.3 2024-06-02 [1] CRAN (R 4.4.1)
DelayedArray * 0.32.0 2024-11-08 [1] Bioconductor 3.20 (R 4.4.1)
dichromat 2.0-0.1 2022-05-02 [1] CRAN (R 4.4.1)
digest 0.6.37 2024-08-19 [1] CRAN (R 4.4.1)
doParallel * 1.0.17 2022-02-07 [1] CRAN (R 4.4.0)
dplyr * 1.1.4 2023-11-17 [1] CRAN (R 4.4.0)
dvmisc 1.1.4 2019-12-16 [1] CRAN (R 4.4.0)
e1071 1.7-16 2024-09-16 [1] CRAN (R 4.4.1)
evaluate 1.0.4 2025-06-18 [1] CRAN (R 4.4.1)
exactextractr * 0.10.0 2023-09-20 [1] CRAN (R 4.4.0)
farver 2.1.2 2024-05-13 [1] CRAN (R 4.4.1)
fastmap 1.2.0 2024-05-15 [1] CRAN (R 4.4.1)
forcats * 1.0.0 2023-01-29 [1] CRAN (R 4.4.0)
foreach * 1.5.2 2022-02-02 [1] CRAN (R 4.4.0)
Formula 1.2-5 2023-02-24 [1] CRAN (R 4.4.1)
fs 1.6.6 2025-04-12 [1] CRAN (R 4.4.1)
fst 0.9.8 2022-02-08 [1] CRAN (R 4.4.0)
fstcore 0.10.0 2025-02-10 [1] CRAN (R 4.4.1)
geeM 0.10.1 2018-06-18 [1] CRAN (R 4.4.0)
geepack 1.3.12 2024-09-23 [1] CRAN (R 4.4.1)
generics 0.1.4 2025-05-09 [1] CRAN (R 4.4.1)
GGally * 2.3.0 2025-07-18 [1] CRAN (R 4.4.1)
ggformula 0.12.2 2025-07-31 [1] CRAN (R 4.4.1)
ggplot2 * 3.5.2 2025-04-09 [1] CRAN (R 4.4.1)
ggridges 0.5.6 2024-01-23 [1] CRAN (R 4.4.0)
ggspatial * 1.1.9 2023-08-17 [1] CRAN (R 4.4.0)
ggstats 0.10.0 2025-07-02 [1] CRAN (R 4.4.1)
glue 1.8.0 2024-09-30 [1] CRAN (R 4.4.1)
gtable 0.3.6 2024-10-25 [1] CRAN (R 4.4.1)
haven 2.5.5 2025-05-30 [1] CRAN (R 4.4.1)
hms 1.1.3 2023-03-21 [1] CRAN (R 4.4.0)
htmltools 0.5.8.1 2024-04-04 [1] CRAN (R 4.4.1)
httr * 1.4.7 2023-08-15 [1] CRAN (R 4.4.0)
httr2 1.2.1 2025-07-22 [1] CRAN (R 4.4.1)
hydroloom * 1.1.0 2024-08-26 [1] CRAN (R 4.4.1)
igraph 2.1.4 2025-01-23 [1] CRAN (R 4.4.1)
IRanges * 2.40.1 2024-12-05 [1] Bioconductor 3.20 (R 4.4.2)
iterators * 1.0.14 2022-02-05 [1] CRAN (R 4.4.1)
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jsonlite 2.0.0 2025-03-27 [1] CRAN (R 4.4.1)
kableExtra 1.4.0 2024-01-24 [1] CRAN (R 4.4.0)
KernSmooth 2.23-26 2025-01-01 [1] CRAN (R 4.4.1)
knitr 1.50 2025-03-16 [1] CRAN (R 4.4.1)
labeling 0.4.3 2023-08-29 [1] CRAN (R 4.4.1)
labelled 2.14.1 2025-05-06 [1] CRAN (R 4.4.1)
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lwgeom * 0.2-14 2024-02-21 [1] CRAN (R 4.4.0)
magick * 2.8.7 2025-06-06 [1] CRAN (R 4.4.1)
magrittr * 2.0.3 2022-03-30 [1] CRAN (R 4.4.1)
MASS 7.3-65 2025-02-28 [1] CRAN (R 4.4.1)
Matrix * 1.7-3 2025-03-11 [1] CRAN (R 4.4.1)
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memoise 2.0.1 2021-11-26 [1] CRAN (R 4.4.0)
MESS * 0.5.12 2023-08-20 [1] CRAN (R 4.4.0)
mime 0.13 2025-03-17 [1] CRAN (R 4.4.1)
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paws.common 0.8.5 2025-07-25 [1] CRAN (R 4.4.1)
paws.storage * 0.9.0 2025-03-14 [1] CRAN (R 4.4.1)
pbapply 1.7-4 2025-07-20 [1] CRAN (R 4.4.1)
pdp * 0.8.2 2024-10-28 [1] CRAN (R 4.4.1)
pillar 1.11.0 2025-07-04 [1] CRAN (R 4.4.1)
pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.4.1)
prettymapr * 0.2.5 2024-02-23 [1] CRAN (R 4.4.0)
processx 3.8.6 2025-02-21 [1] CRAN (R 4.4.1)
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ps 1.9.1 2025-04-12 [1] CRAN (R 4.4.1)
purrr * 1.1.0 2025-07-10 [1] CRAN (R 4.4.1)
qs * 0.27.3 2025-03-11 [1] CRAN (R 4.4.1)
R.methodsS3 1.8.2 2022-06-13 [1] CRAN (R 4.4.1)
R.oo 1.27.1 2025-05-02 [1] CRAN (R 4.4.1)
R.utils 2.13.0 2025-02-24 [1] CRAN (R 4.4.1)
R6 2.6.1 2025-02-15 [1] CRAN (R 4.4.1)
randomForest * 4.7-1.2 2024-09-22 [1] CRAN (R 4.4.1)
RANN 2.6.2 2024-08-25 [1] CRAN (R 4.4.1)
RApiSerialize 0.1.4 2024-09-28 [1] CRAN (R 4.4.1)
rappdirs 0.3.3 2021-01-31 [1] CRAN (R 4.4.1)
Rarr * 1.6.0 2024-11-08 [1] Bioconductor 3.20 (R 4.4.1)
raster * 3.6-32 2025-03-28 [1] CRAN (R 4.4.1)
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RColorBrewer 1.1-3 2022-04-03 [1] CRAN (R 4.4.1)
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scico * 1.5.0 2023-08-14 [1] CRAN (R 4.4.0)
secretbase 1.0.5 2025-03-04 [1] CRAN (R 4.4.1)
sessioninfo * 1.2.3 2025-02-05 [1] CRAN (R 4.4.1)
sf * 1.0-21 2025-05-15 [1] CRAN (R 4.4.1)
SnowballC 0.7.1 2023-04-25 [1] CRAN (R 4.4.1)
sp * 2.2-0 2025-02-01 [1] CRAN (R 4.4.1)
SparseArray * 1.6.2 2025-02-20 [1] Bioconductor 3.20 (R 4.4.2)
spData * 2.3.4 2025-01-08 [1] CRAN (R 4.4.1)
stringfish 0.17.0 2025-07-13 [1] CRAN (R 4.4.1)
stringi 1.8.7 2025-03-27 [1] CRAN (R 4.4.1)
stringr * 1.5.1 2023-11-14 [1] CRAN (R 4.4.0)
survey 4.4-2 2024-03-20 [1] CRAN (R 4.4.0)
survival 3.8-3 2024-12-17 [1] CRAN (R 4.4.1)
svglite 2.2.1 2025-05-12 [1] CRAN (R 4.4.1)
systemfonts 1.2.3 2025-04-30 [1] CRAN (R 4.4.1)
tab 5.1.1 2021-08-02 [1] CRAN (R 4.4.0)
tarchetypes * 0.13.0 2025-04-10 [1] CRAN (R 4.4.1)
targets * 1.8.0 2024-10-02 [1] CRAN (R 4.4.2)
terra 1.8-60 2025-07-21 [1] CRAN (R 4.4.1)
textshaping 1.0.1 2025-05-01 [1] CRAN (R 4.4.1)
tibble * 3.3.0 2025-06-08 [1] CRAN (R 4.4.1)
tidyr * 1.3.1 2024-01-24 [1] CRAN (R 4.4.1)
tidyselect 1.2.1 2024-03-11 [1] CRAN (R 4.4.0)
tidytext * 0.4.3 2025-07-25 [1] CRAN (R 4.4.1)
tidyverse * 2.0.0 2023-02-22 [1] CRAN (R 4.4.0)
timechange 0.3.0 2024-01-18 [1] CRAN (R 4.4.1)
tokenizers 0.3.0 2022-12-22 [1] CRAN (R 4.4.0)
tzdb 0.5.0 2025-03-15 [1] CRAN (R 4.4.1)
units * 0.8-7 2025-03-11 [1] CRAN (R 4.4.1)
usmap * 0.8.0 2025-05-28 [1] CRAN (R 4.4.1)
utf8 1.2.6 2025-06-08 [1] CRAN (R 4.4.1)
vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.4.0)
viridisLite 0.4.2 2023-05-02 [1] CRAN (R 4.4.1)
vroom 1.6.5 2023-12-05 [1] CRAN (R 4.4.0)
withr 3.0.2 2024-10-28 [1] CRAN (R 4.4.1)
xfun 0.52 2025-04-02 [1] CRAN (R 4.4.1)
xml2 1.3.8 2025-03-14 [1] CRAN (R 4.4.1)
XVector 0.46.0 2024-11-08 [1] Bioconductor 3.20 (R 4.4.1)
yaml * 2.3.10 2024-07-26 [1] CRAN (R 4.4.1)
zip * 2.3.3 2025-05-13 [1] CRAN (R 4.4.1)
zlibbioc 1.52.0 2024-11-08 [1] Bioconductor 3.20 (R 4.4.1)
[1] /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/library
* ── Packages attached to the search path.
─────────────────────────────────────────────────────────────────────────────────────────────────────
To build this pipeline (after running the setup section), you should
- Open the
run_pipeline.Rscript. - Click on the
Background Jobstab in RStudio (next toConsoleandTerminal). - Choose
Start Background Joband make sure therun_pipeline.Rscript is selected. - Accept the defaults and click
Startto kick off the pipeline build.
This will build the pipeline in the background, so that your RStudio session can still be used as the job is running.
Many of the pipeline's artifacts are "object targets" but there are some files created. As of 09/09/2025, the best way to see how the pipeline and analysis ran is to open the figures and data stored in 7_Disseminate/out/. This will only have built if all the other pipeline steps were successfully run.
Final manuscript figures and manuscript stats are available in 7_Disseminate/out/.