Last updated: 2021-06-24

Checks: 2 0

Knit directory: globalIRmap/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Repository version: 0e72638

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 0e72638. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    R/.Rhistory
    Ignored:    analysis/.Rhistory
    Ignored:    renv/library/
    Ignored:    renv/staging/

Untracked files:
    Untracked:  .drake/
    Untracked:  .gitignore
    Untracked:  figtabres.docx
    Untracked:  schema.ini

Unstaged changes:
    Modified:   log/drake.log
    Deleted:    output/README.md

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/about.Rmd) and HTML (docs/about.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
html 6e12f71 messamat 2021-06-14 Build site.
Rmd 5e433c3 messamat 2021-06-14 Publish new pages
html af5fe44 messamat 2021-02-23 Build site.
Rmd eb09752 messamat 2021-02-23 wflow_publish(“analysis/About.Rmd”)
Rmd 772154a messamat 2021-02-23 Update gauging stations page and app
html 772154a messamat 2021-02-23 Update gauging stations page and app
html 9b48bde messamat 2021-01-06 Build site.
Rmd f1d9dcf messamat 2021-01-06 Start building up workflowr website, start incorporating mandrake (but wait as very unstable still), plan gauge selection documentation
html f1d9dcf messamat 2021-01-06 Start building up workflowr website, start incorporating mandrake (but wait as very unstable still), plan gauge selection documentation
html a77a5ae Mathis Loïc Messager 2020-04-16 Build site.
html 4797458 Mathis Loïc Messager 2020-04-16 Build site.
html 28d8bfd Mathis Loïc Messager 2020-04-16 Build site.
html 80008f7 Mathis Loïc Messager 2020-04-14 Build site.
Rmd ce60469 Mathis Loïc Messager 2020-04-14 First publication
Rmd b8ce7d1 unknown 2020-04-14 change to workflowr project structure
Rmd b206686 Mathis Loïc Messager 2020-04-14 Start workflowr project.

Global prevalence of non-perennial rivers and streams

Authors

Mathis Loïc Messager ()
Bernhard Lehner ()
Charlotte Cockburn
Nicolas Lamouroux
Hervé Pella
Ton Snelder
Klement Tockner
Tim Trautmann
Caitlin Watt
Thibault Datry ()

Contents

This repository contains the research compendium for the article: Messager, M. L., Lehner, B., Cockburn, C., Lamouroux, N., Pella, H., Snelder, T., Tockner, K., Trautmann, T., Watt, C. & Datry, T. (2021). Global prevalence of non-perennial rivers and streams. Nature. https://doi.org/10.1038/s41586-021-03565-5

Abstract

In this study, we developed a statistical Random Forest model to produce the first reach-scale estimate of the global distribution of non-perennial rivers and streams. For this purpose, we linked quality-checked observed streamflow data from 5,615 gauging stations (on 4,428 perennial and 1,187 non-perennial reaches) with 113 candidate environmental predictors available globally. Predictors included variables describing climate, physiography, land cover, soil, geology, and groundwater as well as estimates of long-term naturalised (i.e., without anthropogenic water use in the form of abstractions or impoundments) mean monthly and mean annual flow (MAF), derived from a global hydrological model (WaterGAP 2.2; Müller Schmied et al. 2014). Following model training and validation, we predicted the probability of flow intermittence for all river reaches in the RiverATLAS database (Linke et al. 2019), a digital representation of the global river network at high spatial resolution.

Accessing study data

The data repository for this study includes two datasets:

How to use this compendium

The main purpose of this compendium is to provide guidance for reproducing the analysis in the manuscript.

The License tab details the terms of use of the code associated with this study.

The Data tab focuses on our treatment of the two main sources of data for this project:

The Workflow tab explains the requirements and analytical steps involved in the analysis for this study.

All of the source code that generated the datasets, statistical results and figures contained in the manuscript is on two GitHub repositories: one for Python code used in spatial analysis, and one for R code used for statistical analysis and figure productions.

Acknowledgements

Comments and issues

To report any problem or ask questions on this website or the code, please use the Github issues tracker.
For other inquiries on the article or the data, please email