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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

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

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The Workflow tab explains the requirements and analytical steps and provides guidelines to reproduce/understand the analysis for this study.

All of the source code that generated the datasets, statistical results and figures contained in the manuscript is on three GitHub repositories: 1. globalIRmap_HydroATLAS_py: Python code used in computing new global river network hydro-environmental attributes.
2. globalIRmap_py: Python code used in processing all spatial data aside from global river network hydro-environmental attributes.
3. globalIRmap: R code used in statistical analysis, reporting, and figure production.

Acknowledgements

Comments and issues

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