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RETRACTED ARTICLE: Global analysis of streamflow response to forest management

This article was retracted on 12 February 2020

Matters Arising to this article was published on 12 February 2020

Matters Arising to this article was published on 12 February 2020

An Addendum to this article was published on 30 September 2019

An Author Correction to this article was published on 30 September 2019

This article has been updated

Abstract

Predicting the responses of streamflow to changes in forest management is fundamental to the sustainable regulation of water resources. However, studies of changes in forest cover have yielded unclear and largely unpredictable results. Here we compile a comprehensive and spatially distributed database of forest-management studies worldwide, to assess the factors that control streamflow response to forest planting and removal. We introduce a vegetation-to-bedrock model that includes seven key landscape factors in order to explain the impacts of forest removal and planting on water yield. We show that the amount of water stored in a landscape is the most important factor in predicting streamflow response to forest removal, whereas the loss of water through evaporation and transpiration is the most important factor in predicting streamflow response to forest planting. Our findings affect model parameterizations in climate change mitigation schemes (involving, for example, afforestation or deforestation) in different geologic and climate regions around the world, and inform practices for the sustainable management of water resources.

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Fig. 1: Global paired watershed studies.
Fig. 2: Controls on water-yield response.
Fig. 3: A geoclimate conceptual model for streamflow response to forest management.
Fig. 4: Framework for energy–water balance in the response of streamflow to forest removal.
Fig. 5: Biome-level responses of water yield to forestation and deforestation.
Fig. 6: Changes in tree canopy cover and effects on runoff.

Data availability

The datasets generated during and/or analysed here are available in the Figshare repository44 (https://doi.org/10.6084/m9.figshare.7770035).

Code availability

Codes used (available in C) for statistical modelling are available from https://github.com/jevaristo/pws.

Change history

  • 12 February 2020

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

  • 30 September 2019

    An Amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Acknowledgements

We thank P. Brooks and N. (C.) Tague for their useful feedback, K. Janzen for helpful edits and M. Logies for Fig. 3. Support for this study was provided by a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant to J.J.M.

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Nature thanks Paul Brooks and Naomi (Christina) Tague for their contribution to the peer review of this work.

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J.J.M. conceived the idea of a global synthesis of paired watershed studies. J.E. and J.J.M. brainstormed on how to do this. J.E. designed the synthesis framework, compiled the dataset and conducted the statistical modelling. J.E. wrote the first draft of the paper. J.J.M. edited and commented on the manuscript and contributed to the text and figure presentations in later iterations.

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Correspondence to Jaivime Evaristo.

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J.J.M. provided consulting advice to Arauco Chile on three occasions, most recently in 2015.

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Extended data figures and tables

Extended Data Fig. 1 Global database of paired watershed studies.

Numbers of published manuscripts from 1933 to 2017 that match our Scopus database queries in the title, abstract or keyword. We identified a manuscript as a ‘hydrology’ paper (filled area) if it carried the tag ‘hydrolog*’ as a catch-all keyword for terms such as ‘hydrology’, ‘hydrological’, and so on. We identified a manuscript as a ‘paired watershed’ or ‘paired catchment’ study (dark green line) if it carried either of these phrases. The inset shows numbers of manuscripts (sizes of bubbles) according to a country’s United Nations World Economic Situation and Prospects (WESP) classification189. Source Data

Extended Data Fig. 2 Budyko plot of catchments in the PWS database.

A Budyko plot of study sites within each intervention scheme (planting or removal), with dryness index (PET/P; x axis) plotted against evaporative index (AET/P; y axis). Also shown are kernel density plots of intervention schemes (top and right). The solid curve is the Budyko prediction; dashed lines represent upper (forests) and lower (grasslands) limits according to equation (10) in previously published study200. Source Data

Extended Data Fig. 3 Model comparison.

a, b, Comparison of modelling for planting (a) and removal (b) intervention schemes. Also shown are the model-fit statistics, R2, RASE and AAE. Source Data

Extended Data Fig. 4 Catchments.

Locations of catchments (n = 442,319) for which data for all seven factors are available and in which the gradient-boosted-tree predictions are implemented. Histograms show distributions of catchments along latitude and longitude.

Extended Data Fig. 5 Modelling catchments with complete and incomplete data.

a, b, Histograms showing model output for removal (a) and planting (b) schemes. Complete (blue) and incomplete (red) refer to catchments in which all seven vegetation-to-bedrock factors are available (n = 442,319; complete) or for which one or more factors are not available (n = 1,777,463; incomplete). Values are median and interquartile range (in brackets).

Extended Data Table 1 Uncertainty estimates for water-yield responses

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Evaristo, J., McDonnell, J.J. RETRACTED ARTICLE: Global analysis of streamflow response to forest management. Nature 570, 455–461 (2019). https://doi.org/10.1038/s41586-019-1306-0

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