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Climate-induced changes in continental-scale soil macroporosity may intensify water cycle

Naturevolume 561pages100103 (2018) | Download Citation


Soil macroporosity affects field-scale water-cycle processes, such as infiltration, nutrient transport and runoff1,2, that are important for the development of successful global strategies that address challenges of food security, water scarcity, human health and loss of biodiversity3. Macropores—large pores that freely drain water under the influence of gravity—often represent less than 1 per cent of the soil volume, but can contribute more than 70 per cent of the total soil water infiltration4, which greatly magnifies their influence on the regional and global water cycle. Although climate influences the development of macropores through soil-forming processes, the extent and rate of such development and its effect on the water cycle are currently unknown. Here we show that drier climates induce the formation of greater soil macroporosity than do more humid ones, and that such climate-induced changes occur over shorter timescales than have previously been considered—probably years to decades. Furthermore, we find that changes in the effective porosity, a proxy for macroporosity, predicted from mean annual precipitation at the end of the century would result in changes in saturated soil hydraulic conductivity ranging from −55 to 34 per cent for five physiographic regions in the USA. Our results indicate that soil macroporosity may be altered rapidly in response to climate change and that associated continental-scale changes in soil hydraulic properties may set up unexplored feedbacks between climate and the land surface and thus intensify the water cycle.

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Data availability

The soil and climatological datasets generated and analysed during this study are publicly available in the GitHub repository, https://github.com/danielhirmas/nature2017-07-09186B. The soil datasets used in this study are also publicly available through the USDA-NRCS NCSS data repository, http://ncsslabdatamart.sc.egov.usda.gov/.

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D.R.H. and D.G. thank R. Miskewitz for assistance in assigning Köppen–Geiger classes to the samples in the dataset. A.N., D.G. and D.R.H. thank the Norwegian Institute of Bioeconomy Research (NIBIO) for financial support. N.A.B. acknowledges funding support through USDA-AFRI 2014-67003-22070.

Reviewer information

Nature thanks P. Hallett, D. Robinson and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Author notes

    • Cassandra J. Wilson

    Present address: KRNV News 4, Reno, NV, USA


  1. Department of Environmental Sciences, University of California, Riverside, CA, USA

    • Daniel R. Hirmas
  2. Department of Environmental Sciences, Rutgers University, New Brunswick, NJ, USA

    • Daniel Giménez
  3. Division of Environment and Natural Resources, Norwegian Institute of Bioeconomy Research, Ås, Norway

    • Attila Nemes
  4. Department of Geography, Brigham Young University, Provo, UT, USA

    • Ruth Kerry
  5. Department of Geography and Atmospheric Science, University of Kansas, Lawrence, KS, USA

    • Nathaniel A. Brunsell
    •  & Cassandra J. Wilson


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D.R.H., D.G., A.N. and N.A.B. designed the study examining effective porosity with climate. A.N. and D.R.H. compiled the USDA-NRCS NCSS data. N.A.B. and C.J.W. compiled and analysed the atmospheric data. D.R.H. wrote the first draft of the paper and, with R.K., conducted the statistical analyses. All authors edited and commented on the manuscript and contributed to later iterations.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Daniel R. Hirmas.

Extended data figures and tables

  1. Extended Data Fig. 1 Distribution of selected soil samples and USHCN weather stations used in this study.

    ad, Locations of A horizons (a), B horizons (b), Ap horizons (c) and USHCN weather stations (d). Weather stations that recorded continuous data since at least 1951 were selected. Soil sample data were obtained from the NCSS Characterization Database on 10 July 2013. Depth and soil morphological criteria for selection of the A, Ap and B horizon samples are given in Extended Data Table 1.

  2. Extended Data Fig. 2 Interpolated maps of mean precipitation magnitude per event and mean precipitation event timing from USHCN weather station data.

    a, Interpolated map of the mean precipitation magnitude per event (PM; in millimetres), calculated assuming that the magnitude of precipitation events followed an exponential distribution. b, Interpolated map of the mean precipitation event timing (PT; in events per day), calculated from a Poisson distribution for days with a precipitation event. Weather stations that recorded continuous data since at least 1951 were selected.

  3. Extended Data Table 1 Data selection criteria for samples used in this study

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