https://github.com/jhollist/nsink
Flow path based nitrogen removal estimation
https://github.com/jhollist/nsink
huc nitrogen r raster rspatial rstats spatial vector
Last synced: about 1 year ago
JSON representation
Flow path based nitrogen removal estimation
- Host: GitHub
- URL: https://github.com/jhollist/nsink
- Owner: jhollist
- License: mit
- Created: 2019-06-06T15:40:51.000Z (about 7 years ago)
- Default Branch: main
- Last Pushed: 2023-04-27T19:32:48.000Z (about 3 years ago)
- Last Synced: 2025-04-13T04:52:25.798Z (about 1 year ago)
- Topics: huc, nitrogen, r, raster, rspatial, rstats, spatial, vector
- Language: R
- Homepage: https://jhollist.github.io/nsink/
- Size: 75.4 MB
- Stars: 6
- Watchers: 5
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
---
title: "nsink"
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
[](https://github.com/jhollist/nsink/actions)
[](https://codecov.io/gh/jhollist/nsink?branch=main)
[](https://lifecycle.r-lib.org/articles/stages.html#stable)
[](https://doi.org/10.5281/zenodo.6341565)
[](https://doi.org/10.21105/joss.04039)
# Statement of need
The `nsink` package is an R implementation of the methods described in [Kellogg et. al (2010)](https://doi.org/10.1016/j.ecoleng.2010.02.006). Previous implementation of this approach relied on a manual, vector based approach that was time consuming to prepare. This approach uses a hybrid raster-vector approach that takes relatively little time to set up for each new watershed and relies on readily available data. Total run times vary, but range from minutes up to 5 hours depending on options selected. Previous versions took weeks of manual data manipulation. Thus, `nsink` was developed to satisfy the need for quicker implementation of the NSink method as described in [Kellogg et. al (2010)](https://doi.org/10.1016/j.ecoleng.2010.02.006).
# `nsink` functionality
As of `r lubridate::today()` user functions for the `nsink` package are:
- `nsink_get_huc_id()`: A function for searching the name of a USGS Watershed Boundary Dataset Hydrologic Unit () and retrieving its 12-digit Hydrologic Unit Code (HUC).
- `nsink_get_data()`: Using any acceptable HUC ID (e.g. 2-digit to 12-digit), this function downloads the NHDPlus, SSURGO, NLCD Land Cover, and the NLCD Impervious for that HUC.
- `nsink_prep_data()`: `nsink` needs data in a common coordinate reference system, from mutliple NHDPlus tables, and from different portions of SSURGO. This function completes these data preparation steps and outputs all data, clipped to the HUC boundary.
- `nsink_calc_removal()`: Quantifying relative N removal across a landscape is a key aspects of an `nsink` analysis. The `nsink_calc_removal()` function takes the object returned from `nsink_prep_data()` and calculates relative N removal for each landscape sink. See Kellogg et al [-@kellogg2010geospatial] for details on relative N removal estimation for each sink.
- `nsink_generate_flowpath()`: This function uses a combination of flow determined by topography, via a flow-direction raster, for the land-based portions of a flow path and of downstream flow along the NHDPlus stream network.
- `nsink_summarize_flowpath()`: Summarizing removal along a specified flow path requires relative N removal and a generated flow path. This function uses these and returns a summary of relative N removal along a flow path for each sink.
- `nsink_generate_static_maps()`: This function analyzes N removal at the watershed scale by summarizing the results of multiple flow paths. Four static maps are returned: 1)removal efficiency; 2)loading index; 3)transport index; 4)delivery index. Removal efficiency is a rasterized version of the `nsink_calc_removal()` output. Loading index is N sources based on NLCD categories. Transport index is a heat map with the cumulative relative N removal along flow paths originating from a grid of points, density set by the user, across a watershed, highlighting the gradient of downstream N retention. Delivery index is the result of multiplying the loading index and the transport index, and shows potential N delivery from different sources, taking into account the relative N removal as water moves downstream.
- `nsink_plot()`: A function that plots each raster in the list returned from `nsink_generate_static_maps()`.
- `nsink_build()`: One of the drivers behind the development of the `nsink` package was to provide `n-sink` analysis output that could be used more broadly (e.g. within a GIS). The `nsink_build()` runs a complete `nsink` analysis and outputs R objects, shapefiles and/or TIFFs.
- `nsink_load()`: Essentially the inverse of the `nsink_build()` function, this function takes a folder of files, likely created by `nsink_build()`, and reads them into R.
# Installation instructions
At this time we plan on maintaining the `nsink` package as a GitHub only package and thus it won't be available directly from CRAN. You may use the `install_github()` function from the `remotes` package to install it. The code below will take care of installing `remotes` and installing `nsink` from the GitHub repository.
```{r, eval=FALSE}
install.packages("remotes")
remotes::install_github("usepa/nsink", dependencies = TRUE, build_vignettes = TRUE)
```
And then to load up the package:
```{r eval=FALSE}
library(nsink)
```
# Documentation and examples
All functions are documented, with examples, and that documentation may be accessed, in R, via the usual help functions. Additionally, an introduction to the `nsink` package with a more detailed workflow is documented in a vignette.
```{r eval=FALSE}
# Load up package
library(nsink)
# Access package level help
help(package = "nsink")
# Access the Introduction to nsink vignette
vignette("intro", package = "nsink")
```
# Contributing
If you would like to contribute to the `nsink` package, please first read the [CONTRIBUTING](.github/CONTRIBUTING.md). In short, contributions are happily
accepted either via suggestions in the
[Issues](https://github.com/USEPA/nsink/issues) or via pull request.