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Global prevalence of non-perennial rivers and streams

Nature volume 594, pages 391–397 (2021)Cite this article

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Abstract

Flowing waters have a unique role in supporting global biodiversity, biogeochemical cycles and human societies1,2,3,4,5. Although the importance of permanent watercourses is well recognized, the prevalence, value and fate of non-perennial rivers and streams that periodically cease to flow tend to be overlooked, if not ignored6,7,8. This oversight contributes to the degradation of the main source of water and livelihood for millions of people5. Here we predict that water ceases to flow for at least one day per year along 51–60 per cent of the world’s rivers by length, demonstrating that non-perennial rivers and streams are the rule rather than the exception on Earth. Leveraging global information on the hydrology, climate, geology and surrounding land cover of the Earth’s river network, we show that non-perennial rivers occur within all climates and biomes, and on every continent. Our findings challenge the assumptions underpinning foundational river concepts across scientific disciplines9. To understand and adequately manage the world’s flowing waters, their biodiversity and functional integrity, a paradigm shift is needed towards a new conceptual model of rivers that includes flow intermittence. By mapping the distribution of non-perennial rivers and streams, we provide a stepping-stone towards addressing this grand challenge in freshwater science.

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Fig. 1: Global distribution of non-perennial rivers and streams.
Fig. 2: Climate-induced aridity and hydrologic variables are the main predictors of global flow intermittence.
Fig. 3: Flow intermittence classification accuracy decreases and prediction bias increases in river basins with fewer streamflow gauging stations.

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Data availability

The global river network dataset and the associated attribute information for every river reach—that is, the hydro-environmental attributes, predicted probability of intermittence and associated binary class—as well as the main results of the study are available at https://doi.org/10.6084/m9.figshare.14633022. The dataset can be used together with the published source code (see ‘Code availability’) to recalculate the main study results with updated data and parameters. The streamflow time series from the Global Runoff Data Centre are available in summarized format. The daily records are not available in the data repository owing to licensing issues but are freely available upon written request through https://www.bafg.de/GRDC/EN/Home/homepage_node.html. Original data that supported the study are freely available and their sources are summarized in Extended Data Fig. 7b. Source data are provided with this paper.

Code availability

The source code and results of this research are available under the GNU General Public License v3.0 at https://messamat.github.io/globalIRmap/.

References

  1. Larned, S. T., Datry, T., Arscott, D. B. & Tockner, K. Emerging concepts in temporary-river ecology. Freshw. Biol. 55, 717–738 (2010).

    Article  Google Scholar 

  2. Leigh, C. & Datry, T. Drying as a primary hydrological determinant of biodiversity in river systems: a broad-scale analysis. Ecography 40, 487–499 (2017).

    Article  Google Scholar 

  3. Datry, T. et al. A global analysis of terrestrial plant litter dynamics in non-perennial waterways. Nat. Geosci. 11, 497–503 (2018).

    Article  ADS  CAS  Google Scholar 

  4. Marcé, R. et al. Emissions from dry inland waters are a blind spot in the global carbon cycle. Earth Sci. Rev. 188, 240–248 (2019).

    Article  ADS  Google Scholar 

  5. Steward, A. L., von Schiller, D., Tockner, K., Marshall, J. C. & Bunn, S. E. When the river runs dry: human and ecological values of dry riverbeds. Front. Ecol. Environ. 10, 202–209 (2012).

    Article  Google Scholar 

  6. Acuña, V. et al. Why should we care about temporary waterways? Science 343, 1080–1081 (2014).

    Article  ADS  PubMed  Google Scholar 

  7. Fritz, K., Cid, N. & Autrey, B. Governance, legislation, and protection of intermittent rivers and ephemeral streams. In Intermittent Rivers and Ephemeral Streams: Ecology and Management 477–507 (Academic Press, 2017); https://doi.org/10.1016/B978-0-12-803835-2.00019-X.

  8. Sullivan, S. M. P., Rains, M. C., Rodewald, A. D., Buzbee, W. W. & Rosemond, A. D. Distorting science, putting water at risk. Science 369, 766–768 (2020).

    Article  ADS  CAS  PubMed  Google Scholar 

  9. Allen, D. C. et al. River ecosystem conceptual models and non‐perennial rivers: a critical review. Wiley Interdiscip. Rev. Water 7, e1473 (2020).

    Article  Google Scholar 

  10. Datry, T., Larned, S. T. & Tockner, K. Intermittent rivers: a challenge for freshwater ecology. Bioscience 64, 229–235 (2014).

    Article  Google Scholar 

  11. Ficklin, D. L., Abatzoglou, J. T., Robeson, S. M., Null, S. E. & Knouft, J. H. Natural and managed watersheds show similar responses to recent climate change. Proc. Natl Acad. Sci. USA 115, 8553–8557 (2018).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  12. Jaeger, K. L., Olden, J. D. & Pelland, N. A. Climate change poised to threaten hydrologic connectivity and endemic fishes in dryland streams. Proc. Natl Acad. Sci. USA 111, 13894–13899 (2014).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  13. Pumo, D., Caracciolo, D., Viola, F. & Noto, L. V. Climate change effects on the hydrological regime of small non-perennial river basins. Sci. Total Environ. 542, 76–92 (2016).

    Article  ADS  CAS  PubMed  Google Scholar 

  14. Stubbington, R. et al. Biomonitoring of intermittent rivers and ephemeral streams in Europe: current practice and priorities to enhance ecological status assessments. Sci. Total Environ. 618, 1096–1113 (2018).

    Article  ADS  CAS  PubMed  Google Scholar 

  15. Acuña, V. et al. Accounting for flow intermittency in environmental flows design. J. Appl. Ecol. 57, 742–753 (2020).

    Article  Google Scholar 

  16. Arthington, A. H. et al. The Brisbane Declaration and Global Action Agenda on Environmental Flows (2018). Front. Environ. Sci. 6, 45 (2018).

    Article  Google Scholar 

  17. Zimmer, M. A. et al. Zero or not? Causes and consequences of zero-flow stream gage readings. Wiley Interdiscip. Rev. Water 7, e1436 (2020).

    Article  Google Scholar 

  18. Beaufort, A., Lamouroux, N., Pella, H., Datry, T. & Sauquet, E. Extrapolating regional probability of drying of headwater streams using discrete observations and gauging networks. Hydrol. Earth Syst. Sci. 22, 3033–3051 (2018).

    Article  ADS  Google Scholar 

  19. Jaeger, K. L. & Olden, J. D. Electrical resistance sensor arrays as a means to quantify longitudinal connectivity of rivers. River Res. Appl. 28, 1843–1852 (2012).

    Article  Google Scholar 

  20. Yu, S. et al. Evaluating a landscape-scale daily water balance model to support spatially continuous representation of flow intermittency throughout stream networks. Hydrol. Earth Syst. Sci. 24, 5279–5295 (2020).

    Article  ADS  CAS  Google Scholar 

  21. Snelder, T. H. et al. Regionalization of patterns of flow intermittence from gauging station records. Hydrol. Earth Syst. Sci. 17, 2685–2699 (2013).

    Article  ADS  Google Scholar 

  22. Jaeger, K. L. et al. Probability of Streamflow Permanence Model (PROSPER): a spatially continuous model of annual streamflow permanence throughout the Pacific Northwest. J. Hydrol. X 2, 100005 (2019).

    Article  Google Scholar 

  23. Yu, S., Bond, N. R., Bunn, S. E. & Kennard, M. J. Development and application of predictive models of surface water extent to identify aquatic refuges in eastern Australian temporary stream networks. Water Resour. Res. 55, 9639–9655 (2019).

    Article  ADS  Google Scholar 

  24. Kennard, M. J. et al. Classification of natural flow regimes in Australia to support environmental flow management. Freshw. Biol. 55, 171–193 (2010).

    Article  Google Scholar 

  25. Lane, B. A., Dahlke, H. E., Pasternack, G. B. & Sandoval‐Solis, S. Revealing the diversity of natural hydrologic regimes in California with relevance for environmental flows applications. J. Am. Water Resour. Assoc. 53, 411–430 (2017).

    Article  ADS  Google Scholar 

  26. Müller Schmied, H. et al. Sensitivity of simulated global-scale freshwater fluxes and storages to input data, hydrological model structure, human water use and calibration. Hydrol. Earth Syst. Sci. 18, 3511–3538 (2014).

    Article  ADS  Google Scholar 

  27. Linke, S. et al. Global hydro-environmental sub-basin and river reach characteristics at high spatial resolution. Sci. Data 6, 283 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Tooth, S. Process, form and change in dryland rivers: a review of recent research. Earth Sci. Rev. 51, 67–107 (2000).

    Article  ADS  Google Scholar 

  29. Costigan, K. H., Jaeger, K. L., Goss, C. W., Fritz, K. M. & Goebel, P. C. Understanding controls on flow permanence in intermittent rivers to aid ecological research: integrating meteorology, geology and land cover. Ecohydrology 9, 1141–1153 (2016).

    Article  Google Scholar 

  30. Benstead, J. P. & Leigh, D. S. An expanded role for river networks. Nat. Geosci. 5, 678–679 (2012).

    Article  ADS  CAS  Google Scholar 

  31. Godsey, S. E. & Kirchner, J. W. Dynamic, discontinuous stream networks: hydrologically driven variations in active drainage density, flowing channels and stream order. Hydrol. Processes 28, 5791–5803 (2014).

    Article  ADS  Google Scholar 

  32. Metzger, M. J. et al. A high-resolution bioclimate map of the world: a unifying framework for global biodiversity research and monitoring. Glob. Ecol. Biogeogr. 22, 630–638 (2013).

    Article  Google Scholar 

  33. Tolonen, K. E. et al. Parallels and contrasts between intermittently freezing and drying streams: From individual adaptations to biodiversity variation. Freshw. Biol. 64, 1679–1691 (2019).

    Article  Google Scholar 

  34. Prancevic, J. P. & Kirchner, J. W. Topographic controls on the extension and retraction of flowing streams. Geophys. Res. Lett. 46, 2084–2092 (2019).

    Article  ADS  Google Scholar 

  35. FAO. AQUAMAPS: Global Spatial Database on Water and Agriculture (Food and Agriculture Organization of the United Nations, accessed 15 October 2020); https://data.apps.fao.org/aquamaps/

  36. Schneider, A. et al. Global-scale river network extraction based on high-resolution topography and constrained by lithology, climate, slope, and observed drainage density. Geophys. Res. Lett. 44, 2773–2781 (2017).

    Article  ADS  Google Scholar 

  37. Raymond, P. A. et al. Global carbon dioxide emissions from inland waters. Nature 503, 355–359 (2013); erratum 507, 387 (2014).

    Article  ADS  CAS  PubMed  Google Scholar 

  38. Tramblay, Y. et al. Trends in flow intermittence for European rivers. Hydrol. Sci. J. 66, 37–49 (2021).

    Article  Google Scholar 

  39. Döll, P., Douville, H., Güntner, A., Müller Schmied, H. & Wada, Y. Modelling freshwater resources at the global scale: challenges and prospects. Surv. Geophys. 37, 195–221 (2016).

    Article  ADS  Google Scholar 

  40. Hammond, J. C. et al. Spatial patterns and drivers of nonperennial flow regimes in the contiguous United States. Geophys. Res. Lett. 48, e2020GL090794 (2021).

    Article  ADS  Google Scholar 

  41. Döll, P. & Schmied, H. M. How is the impact of climate change on river flow regimes related to the impact on mean annual runoff? A global-scale analysis. Environ. Res. Lett. 7, 014037 (2012).

    Article  ADS  Google Scholar 

  42. Gleeson, T. et al. The water planetary boundary: interrogation and revision. One Earth 2, 223–234 (2020).

    Article  Google Scholar 

  43. Dickens, C. et al. Incorporating Environmental Flows into “Water Stress” Indicator 6.4.2: Guidelines for a Minimum Standard Method for Global Reporting (FAO, 2019); http://www.fao.org/documents/card/en/c/ca3097en/

  44. Sood, A. et al. Global Environmental Flow Information for the Sustainable Development Goals. IWMI Research Report 168 (International Water Management Institute, 2017); https://doi.org/10.5337/2017.201

  45. Vannote, R. L., Minshall, G. W., Cummins, K. W., Sedell, J. R. & Cushing, C. E. The River Continuum Concept. Can. J. Fish. Aquat. Sci. 37, 130–137 (1980).

    Article  Google Scholar 

  46. Grill, G. et al. Mapping the world’s free-flowing rivers. Nature 569, 215–221 (2019); correction 572, E9 (2019).

    Article  ADS  CAS  PubMed  Google Scholar 

  47. Stanley, E. H., Fisher, S. G. & Grimm, N. B. Ecosystem expansion and contraction in streams: desert streams vary in both space and time and fluctuate dramatically in size. Bioscience 47, 427–435 (1997).

    Article  Google Scholar 

  48. Datry, T. et al. Flow intermittence and ecosystem services in rivers of the Anthropocene. J. Appl. Ecol. 55, 353–364 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Nembrini, S., König, I. R. & Wright, M. N. The revival of the Gini importance? Bioinformatics 34, 3711–3718 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Lehner, B. & Grill, G. Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrol. Processes 27, 2171–2186 (2013).

    Article  ADS  Google Scholar 

  51. Lehner, B., Verdin, K. & Jarvis, A. New global hydrography derived from spaceborne elevation data. Eos 89, 93–94 (2008).

    Article  ADS  Google Scholar 

  52. Messager, M. L., Lehner, B., Grill, G., Nedeva, I. & Schmitt, O. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nat. Commun. 7, 13603 (2016).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  53. Global Runoff Data Centre. In-situ river discharge data (World Meteorological Organization, accessed 15 May 2015); https://portal.grdc.bafg.de/applications/public.html?publicuser=PublicUser#dataDownload/Home

  54. Do, H. X., Gudmundsson, L., Leonard, M. & Westra, S. The Global Streamflow Indices and Metadata Archive (GSIM) – Part 1: The production of a daily streamflow archive and metadata. Earth Syst. Sci. Data 10, 765–785 (2018).

    Article  ADS  Google Scholar 

  55. Gudmundsson, L., Do, H. X., Leonard, M. & Westra, S. The Global Streamflow Indices and Metadata Archive (GSIM) – Part 2: Quality control, time-series indices and homogeneity assessment. Earth Syst. Sci. Data 10, 787–804 (2018).

    Article  ADS  Google Scholar 

  56. Lehner, B. et al. High‐resolution mapping of the world’s reservoirs and dams for sustainable river‐flow management. Front. Ecol. Environ. 9, 494–502 (2011).

    Article  Google Scholar 

  57. Mackay, S. J., Arthington, A. H. & James, C. S. Classification and comparison of natural and altered flow regimes to support an Australian trial of the Ecological Limits of Hydrologic Alteration framework. Ecohydrology 7, 1485–1507 (2014).

    Article  Google Scholar 

  58. Zhang, Y., Zhai, X., Shao, Q. & Yan, Z. Assessing temporal and spatial alterations of flow regimes in the regulated Huai River Basin, China. J. Hydrol. 529, 384–397 (2015).

    Article  ADS  Google Scholar 

  59. Reynolds, L. V., Shafroth, P. B. & LeRoy Poff, N. Modeled intermittency risk for small streams in the Upper Colorado River Basin under climate change. J. Hydrol. 523, 768–780 (2015).

    Article  ADS  Google Scholar 

  60. Costigan, K. H. et al. Flow regimes in intermittent rivers and ephemeral streams. In Intermittent Rivers and Ephemeral Streams: Ecology and Management 51–78 (Academic Press, 2017); https://doi.org/10.1016/B978-0-12-803835-2.00003-6

  61. Pickens, A. H. et al. Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series. Remote Sens. Environ. 243, 111792 (2020).

    Article  ADS  Google Scholar 

  62. Hengl, T. et al. SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).

    Article  Google Scholar 

  64. Trabucco, A. & Zomer, R. Global Aridity Index and Potential Evapotranspiration (ET0) Climate Database v2. figshare https://doi.org/10.6084/m9.figshare.7504448.v3 (2018).

  65. Bond, N. R. & Kennard, M. J. Prediction of hydrologic characteristics for ungauged catchments to support hydroecological modeling. Water Resour. Res. 53, 8781–8794 (2017).

    Article  ADS  Google Scholar 

  66. Kotsiantis, S. B., Zaharakis, I. D. & Pintelas, P. E. Machine learning: a review of classification and combining techniques. Artif. Intell. Rev. 26, 159–190 (2006).

    Article  Google Scholar 

  67. Wainer, J. Comparison of 14 different families of classification algorithms on 115 binary datasets. Preprint at https://arxiv.org/abs/1606.00930 (2016).

  68. Malley, J. D., Kruppa, J., Dasgupta, A., Malley, K. G. & Ziegler, A. Probability machines. Methods Inf. Med. 51, 74–81 (2012).

    Article  CAS  PubMed  Google Scholar 

  69. Wright, M. N. & Ziegler, A. ranger: a fast implementation of random forests for high dimensional data in C++ and R. J. Stat. Softw. 77, https://doi.org/10.18637/jss.v077.i01 (2017).

  70. Lang, M. et al. mlr3: a modern object-oriented machine learning framework in R. J. Open Source Softw. 4, 1903 (2019).

    Article  ADS  Google Scholar 

  71. Landau, W. M. The drake R package: a pipeline toolkit for reproducibility and high-performance computing. J. Open Source Softw. 3, 550 (2018).

    Article  ADS  Google Scholar 

  72. Hothorn, T., Hornik, K. & Zeileis, A. Unbiased recursive partitioning: a conditional inference framework. J. Comput. Graph. Stat. 15, 651–674 (2006).

    Article  MathSciNet  Google Scholar 

  73. Hothorn, T. & Zeileis, A. Partykit: a modular toolkit for recursive partytioning in R. J. Mach. Learn. Res. 16, 3905–3909 (2015).

    MathSciNet  MATH  Google Scholar 

  74. Wright, M. N., Dankowski, T. & Ziegler, A. Unbiased split variable selection for random survival forests using maximally selected rank statistics. Stat. Med. 36, 1272–1284 (2017).

    Article  MathSciNet  PubMed  Google Scholar 

  75. Zhang, G. & Lu, Y. Bias-corrected random forests in regression. J. Appl. Stat. 39, 151–160 (2012).

    Article  MathSciNet  MATH  Google Scholar 

  76. Japkowicz, N. & Stephen, S. The class imbalance problem: a systematic study. Intell. Data Anal. 6, 429–449 (2002).

    Article  MATH  Google Scholar 

  77. Bischl, B., Mersmann, O., Trautmann, H. & Weihs, C. Resampling methods for meta-model validation with recommendations for evolutionary computation. Evol. Comput. 20, 249–275 (2012).

    Article  CAS  PubMed  Google Scholar 

  78. Probst, P., Wright, M. N. & Boulesteix, A. L. Hyperparameters and tuning strategies for random forest. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 9, e1301 (2019).

    Article  Google Scholar 

  79. Probst, P. & Boulesteix, A. L. To tune or not to tune the number of trees in random forest. J. Mach. Learn. Res. 18, 1–8 (2018).

    MathSciNet  MATH  Google Scholar 

  80. Schratz, P., Muenchow, J., Iturritxa, E., Richter, J. & Brenning, A. Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data. Ecol. Modell. 406, 109–120 (2019).

    Article  Google Scholar 

  81. Brenning, A. Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: the R package sperrorest. In 2012 IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS) 5372–5375 (2012); https://doi.org/10.1109/IGARSS.2012.6352393

  82. Meyer, H., Reudenbach, C., Hengl, T., Katurji, M. & Nauss, T. Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation. Environ. Model. Softw. 101, 1–9 (2018).

    Article  Google Scholar 

  83. Meyer, H., Reudenbach, C., Wöllauer, S. & Nauss, T. Importance of spatial predictor variable selection in machine learning applications – moving from data reproduction to spatial prediction. Ecol. Modell. 411, 108815 (2019).

    Article  Google Scholar 

  84. Brodersen, K. H., Ong, C. S., Stephan, K. E. & Buhmann, J. M. The balanced accuracy and its posterior distribution. In Proc. Int. Conf. Pattern Recognition 3121–3124 (2010); https://doi.org/10.1109/ICPR.2010.764

  85. Altmann, A., Toloşi, L., Sander, O. & Lengauer, T. Permutation importance: a corrected feature importance measure. Bioinformatics 26, 1340–1347 (2010).

    Article  CAS  PubMed  Google Scholar 

  86. Amaratunga, D., Cabrera, J. & Lee, Y.-S. Enriched random forests. Bioinformatics 24, 2010–2014 (2008).

    Article  CAS  PubMed  Google Scholar 

  87. Evans, J. S., Murphy, M. A., Holden, Z. A. & Cushman, S. A. Modeling species distribution and change using random forest. In Predictive Species and Habitat Modeling in Landscape Ecology: Concepts and Applications 139–159 (Springer, 2011); https://doi.org/10.1007/978-1-4419-7390-0_8

  88. Jones, Z. M. & Linder, F. J. edarf: Exploratory Data Analysis using Random Forests. J. Open Source Softw. 1, 92 (2016).

    Article  ADS  Google Scholar 

  89. Friedman, J. H. Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001).

    Article  MathSciNet  MATH  Google Scholar 

  90. Bondarenko, M., Kerr, D., Sorichetta, A. & Tatem, A. J. Census/projection-disaggregated gridded population datasets for 189 countries in 2020 using Built-Settlement Growth Model (BSGM) outputs (WorldPop, University of Southampton, accessed 26 November 2020); https://doi.org/10.5258/SOTON/WP00684

  91. Colvin, S. A. R. et al. Headwater streams and wetlands are critical for sustaining fish, fisheries, and ecosystem services. Fisheries 44, 73–91 (2019).

    Article  Google Scholar 

  92. Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer Science & Business Media, 2009).

  93. Clauset, A., Shalizi, C. R. & Newman, M. E. J. Power-law distributions in empirical data. SIAM Rev. 51, 661–703 (2009).

    Article  ADS  MathSciNet  MATH  Google Scholar 

  94. Fritz, K. M. et al. Comparing the extent and permanence of headwater streams from two field surveys to values from hydrographic databases and maps. J. Am. Water Resour. Assoc. 49, 867–882 (2013).

    Article  ADS  Google Scholar 

  95. Stoddard, J. L. et al. Environmental Monitoring and Assessment Program (EMAP): Western Streams and Rivers Statistical Summary. Report no. EPA/620/R-05/006 (NTIS PB2007-102088) (US Environmental Protection Agency, 2005).

  96. Hafen, K. C., Blasch, K. W., Rea, A., Sando, R. & Gessler, P. E. The influence of climate variability on the accuracy of NHD perennial and nonperennial stream classifications. J. Am. Water Resour. Assoc. 56, 903–916 (2020).

    Article  ADS  Google Scholar 

  97. Colson, T., Gregory, J., Dorney, J. & Russell, P. Topographic and soil maps do not accurately depict headwater stream networks. Natl Wetlands Newsl. 30, 25–28 (2008).

    Google Scholar 

  98. Allen, D. C. et al. Citizen scientists document long-term streamflow declines in intermittent rivers of the desert southwest, USA. Freshw. Sci. 38, 244–256 (2019).

    Article  Google Scholar 

  99. Datry, T., Pella, H., Leigh, C., Bonada, N. & Hugueny, B. A landscape approach to advance intermittent river ecology. Freshw. Biol. 61, 1200–1213 (2016).

    Article  Google Scholar 

  100. McShane, R. R., Sando, R. & Hockman-Wert, D. P. Streamflow observation points in the Pacific Northwest, 1977–2016. U.S. Geological Survey data release https://doi.org/10.5066/F7BV7FSP (2017).

  101. Observatoire National des étiages (ONDE) (French Office for Biodiversity (OFC), accessed 21 June 2020); https://onde.eaufrance.fr/content/t%C3%A9l%C3%A9charger-les-donn%C3%A9es-des-campagnes-par-ann%C3%A9e

  102. Aguas Continentales de Argentina (Argentinian National Geographic Institute (IGN), accessed 11 June 2020); https://www.ign.gob.ar/NuestrasActividades/InformacionGeoespacial/CapasSIG

  103. Australian Hydrological Geospatial Fabric (Geofabric, v. 3.2) (Australian Bureau of Meteorology (BOM), accessed 11 June 2020); ftp://ftp.bom.gov.au/anon/home/geofabric/Geofabric_Metadata_GDB_V3_2.zip

  104. Base Cartográfica Continua do Brasil (BC250, 2019 version) (Brazilian Institute of Geography and Statistics (IBGE); accessed 11 June 2020); https://geoftp.ibge.gov.br/cartas_e_mapas/bases_cartograficas_continuas/bc250/versao2019/

  105. National Hydrography Dataset Plus (NHDPlus, medium resolution, v.2) (US Geological Survey, accessed 11 June 2020); https://www.epa.gov/waterdata/get-nhdplus-national-hydrography-dataset-plus-data

  106. Busch, M. H. et al. What’s in a name? Patterns, trends, and suggestions for defining non-perennial rivers and streams. Water 12, 1980 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  107. Datry, T. et al. Science and management of intermittent rivers and ephemeral streams (SMIRES). Res. Ideas Outcomes 3, e21774 (2017).

    Article  Google Scholar 

  108. Trabucco, A. & Zomer, R. J. Global high-resolution soil–water balance. https://doi.org/10.6084/m9.figshare.7707605.v3 (2010).

  109. Hall, D. K. & Riggs, G. A. MODIS/Aqua Snow Cover Daily L3 Global 500m SIN Grid, Version 6. [2002–2015] (NASA National Snow and Ice Data Center Distributed Active Archive Center, accessed 15 February 2017); https://doi.org/10.5067/MODIS/MYD10A1.006

  110. Fan, Y., Li, H. & Miguez-Macho, G. Global patterns of groundwater table depth. Science 339, 940–943 (2013).

    Article  ADS  CAS  PubMed  Google Scholar 

  111. Fluet-Chouinard, E., Lehner, B., Rebelo, L.-M., Papa, F. & Hamilton, S. K. Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data. Remote Sens. Environ. 158, 348–361 (2015).

    Article  ADS  Google Scholar 

  112. Döll, P., Kaspar, F. & Lehner, B. A global hydrological model for deriving water availability indicators: model tuning and validation. J. Hydrol. 270, 105–134 (2003).

    Article  ADS  Google Scholar 

  113. Bartholomé, E. & Belward, A. S. GLC2000: a new approach to global land cover mapping from Earth observation data. Int. J. Remote Sens. 26, 1959–1977 (2005).

    Article  ADS  Google Scholar 

  114. GLIMS and National Snow and Ice Data Center. GLIMS Glacier Database V1 (2012); https://doi.org/10.7265/N5V98602

  115. Gruber, S. Derivation and analysis of a high-resolution estimate of global permafrost zonation. Cryosphere 6, 221–233 (2012).

    Article  ADS  Google Scholar 

  116. Ramankutty, N. & Foley, J. A. Estimating historical changes in global land cover: croplands from 1700 to 1992. Glob. Biogeochem. Cycles 13, 997–1027 (1999).

    Article  ADS  CAS  Google Scholar 

  117. Lehner, B. & Döll, P. Development and validation of a global database of lakes, reservoirs and wetlands. J. Hydrol. 296, 1–22 (2004).

    Article  ADS  Google Scholar 

  118. Robinson, N., Regetz, J. & Guralnick, R. P. EarthEnv-DEM90: a nearly-global, void-free, multi-scale smoothed, 90m digital elevation model from fused ASTER and SRTM data. ISPRS J. Photogramm. Remote Sens. 87, 57–67 (2014).

    Article  ADS  Google Scholar 

  119. Williams, P. W. & Ford, D. C. Global distribution of carbonate rocks. Z. Geomorphol. Suppl. 147, 1–2 (2006).

    Google Scholar 

  120. Hartmann, J. & Moosdorf, N. The new global lithological map database GLiM: a representation of rock properties at the Earth surface. Geochem. Geophys. Geosyst. 13, Q12004 (2012).

    Article  ADS  Google Scholar 

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Acknowledgements

We thank T. Elrick and the Geographic Information Centre at McGill University for providing us with high-performance computing resources, and the Global Runoff Data Centre (GRDC) for providing us with global streamflow gauging data. Funding for this study was provided in part by the Natural Sciences and Engineering Research Council of Canada (B.L., C.C., C.W., M.L.M., NSERC Discovery grants RGPIN/341992-2013 and RGPIN/04541-2019); McGill University (M.L.M., Tomlinson Fellowship), Montreal, Quebec, Canada; H2O’Lyon Doctoral School (M.L.M., Doctoral Fellowship, ANR-17-EURE-0018), Lyon, France; T.D., N.L., H.P. and T.T. were supported by the DRYvER project (http://www.dryver.eu/), which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 869226.

Author information

Author notes

  1. Charlotte Cockburn

    Present address: Department of Earth Science, Dartmouth College, Hanover, NH, USA

  2. Caitlin Watt

    Present address: Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, Lethbridge, Alberta, Canada

Authors and Affiliations

  1. Department of Geography, McGill University, Montreal, Quebec, Canada

    Mathis Loïc Messager, Bernhard Lehner, Charlotte Cockburn & Caitlin Watt

  2. RiverLY Research Unit, National Research Institute for Agriculture, Food and Environment (INRAE), Villeurbanne, France

    Mathis Loïc Messager, Nicolas Lamouroux, Hervé Pella & Thibault Datry

  3. LWP Ltd, Christchurch, New Zealand

    Ton Snelder

  4. Senckenberg Society for Nature Research, Frankfurt am Main, Germany

    Klement Tockner

  5. Faculty of Biological Sciences, Goethe University Frankfurt, Frankfurt am Main, Germany

    Klement Tockner

  6. Institute of Physical Geography, Goethe University Frankfurt, Frankfurt am Main, Germany

    Tim Trautmann

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  1. Mathis Loïc Messager
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Contributions

CRediT (Contributor Roles Taxonomy): conceptualization—T.D., B.L., K.T., M.L.M.; methodology—M.L.M., B.L., T.S., C.C., N.L.; data curation—M.L.M., B.L., C.C., C.W., T.S., H.P.; software, validation, visualization—M.L.M.; formal analysis—M.L.M., C.C.; writing original draft—M.L.M., T.D., B.L.; writing, review and editing—all authors; project administration and supervision—M.L.M., B.L., T.D.; funding acquisition—B.L., T.D., M.L.M.

Corresponding authors

Correspondence to Mathis Loïc Messager, Bernhard Lehner or Thibault Datry.

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The authors declare no competing interests.

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Peer review information Nature thanks Kristin Jaeger, Georgia Papacharalampous and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Global prevalence of IRES with at least one zero-flow month per year on average.

a, Distribution of global climate zones used in this study. Data provided by Global Environmental Stratification (GEnS)32. b, Predicted probability of river flow intermittence, defined as at least one zero-flow month (30 days) per year on average, across the global river and stream network27. The median probability threshold of 0.5 was used to determine the binary flow intermittence class for each reach. c, Global prevalence of IRES with at least one zero-flow month (30 days) per year on average, across climate zones and streamflow size classes (based on long-term average naturalized discharge). Note that in regions with sparse training data, the model results can differ substantially from the results shown in Table 1, as the underlying random forest and extrapolation models were developed independently. No stations were available in climate zones Arctic 1 and Arctic 2, and few stations were available in ‘Extremely cold and wet’ (1 and 2) and in ‘Extremely hot and arid’ climates (together representing 3% of global river and stream length). Rows are sorted in the same order as in Table 1, and the same footnotes as in Table 1 apply. Mapping software: ArcMap (ESRI).

Extended Data Fig. 2 Distribution of cross-validation results.

a, Maps of spatially cross-validated predictive accuracy of flow intermittence for streamflow gauging stations. See Supplementary Fig. 3 for the distribution of spatial cross-validation folds and details on the cross-validation procedure. The classification errors shown here are not necessarily present in the final predictions but illustrate the ability of the model to predict the flow intermittence class for each region if that region was excluded from the training set. For instance, it shows that the model would be unable to predict the presence of IRES in western France and northern Spain (inset ii, dark red dots), or in western India (inset iii) without training stations in these regions. b–e, Intermittence prediction residuals versus gauging station characteristics and environmental variables. The mean intermittence prediction residual (IPR) is the difference between the average predicted probability of flow intermittence (across three cross-validation folds and two repetitions) and the observed flow intermittence of the gauging station (1 = non-perennial, 0 = perennial). Overall, prediction errors and uncertainties decrease with an increase in the number of recorded years by gauging stations as well as the drainage area and the degree of flow intermittence (average annual number of zero-flow days and flow cessation events) of the corresponding reaches. Mapping software: ArcMap (ESRI).

Extended Data Fig. 3 Comparing global predictions to national maps of IRES in the USA and Australia.

Comparison of a, the US National Hydrography Dataset (NHDPlus, medium resolution) and d, the Australian hydrological geospatial fabric, with our model predictions based on two thresholds of flow intermittence, either ≥1 zero-flow day per year (b, e), or ≥1 zero-flow month (30 days) per year (c, f), on average. Only rivers and streams with MAF ≥ 0.1 m3 s−1 are shown for the USA (a–c) and with drainage area ≥10 km2 for Australia (d–f). The US reference dataset portrays 19–22% of the length of rivers and streams as non-perennial, depending on whether reaches without flow intermittence status are assumed to be perennial or removed; our estimates range from 51% (≥1 zero-flow day per year) to 36% (≥1 zero-flow month per year). We hypothesize that the remaining gap in IRES prevalence is attributable to a tendency of our model to overpredict intermittence across the eastern USA and an under-accounting of intermittence in medium to large rivers by the national dataset. The Australian reference dataset portrays 91% of the length of rivers and streams as non-perennial; our estimates range from 95% (≥1 zero-flow day per year) to 92% (≥1 zero-flow month per year). See Extended Data Fig. 7b for data sources. Mapping software: ArcMap (ESRI).

Extended Data Fig. 4 Comparing global predictions to national maps of IRES in Brazil, Argentina, and France.

Comparison of a, the continuous cartographic base of Brazil (BC250), d, the Argentinian hydrographic network, and g, model predictions for France from Snelder et al.21, with our model predictions based on two thresholds of flow intermittence, either ≥1 zero-flow day per year (b, e, h) or ≥1 zero-flow month (30 days) per year (c, f), on average. In a and d, only first-order streams (determined through network analysis) are visually differentiated (finer, semi-transparent lines), owing to the lack of a watercourse-size attribute in the Brazilian and Argentinian datasets. In b, c, e–h, only rivers and streams with MAF ≥ 0.1 m3 s−1 are shown. Snelder et al.21 predict that 17% of the length of rivers and streams in France are non-perennial. We predict that 14% are non-perennial. This slight divergence may be partly driven by the difference in definition of flow intermittence: Snelder et al.21 classified stations with ≥1 zero-flow day in the streamflow record as IRES whereas we used a threshold of 1 zero-flow day per year across the streamflow record. See Extended Data Fig. 7b for data sources. Mapping software: ArcMap (ESRI).

Extended Data Fig. 5 Quantitative comparison between the predicted prevalence of flow intermittence and national estimates.

a–f, Comparisons were conducted for France (a, b), the USA (c, d), and Australia (e, f), on the basis of two thresholds of flow intermittence, either ≥1 zero-flow day per year (a, c, e) or ≥1 zero-flow month (30 days) per year (b, d, f), on average. Bars for mapped rivers and streams with MAF < 0.1 m3 s−1 (for France and the USA) are greyed out as they were not included in the calculation of summary statistics. Inset graphs in b, d, f show comparisons of total river network length (log-transformed y axis), which in case of discrepancies can explain some of the differences in the predicted prevalence of intermittence.

Extended Data Fig. 6 Comparing global predictions to on-the-ground observations of flow cessation.

a, b, Maps show individual RiverATLAS reaches and their predictive accuracy for France (a), and the US Pacific Northwest (b). Maps are drawn at identical cartographic scales. France (n = 2,297): balanced accuracy = 0.59, classification accuracy = 51%, sensitivity = 24%, specificity = 94%. US Pacific Northwest (n = 3,725): balanced accuracy = 0.47, classification accuracy = 80%, sensitivity = 10%, specificity = 83%. See Extended Data Fig. 7b for data sources. Mapping software: ArcMap (ESRI).

Extended Data Fig. 7 Overview of study design and main data sources.

a, Diagram of modelling workflow. b, Main data sources used in model development, predictions, diagnostics and comparisons. Data sources: Global Runoff Data Centre53, Do et al.54, Gudmundsson et al.55, Linke et al.27, Snelder et al.21, McShane et al.100, ONDE eau 2012–2019101, National Hydrographic Data102,103,104,105, WorldPop90.

Extended Data Fig. 8 Spatial and environmental distribution of streamflow gauging stations used in model training and cross-validation.

a, b, Gauging stations (n = 5,615) were deemed perennial (a) if their streamflow record included less than one zero-flow day per year, on average, across their record, or non-perennial (b) if they included at least one zero-flow day per year, on average, and at least one zero-flow day in every 20-year moving window across their record. Stations fulfilling neither condition a nor b were excluded. Darker points symbolize longer streamflow records. Only gauging stations with streamflow time series spanning at least 10 years were included in this analysis, excluding years with more than 20 missing days. c–p, Distribution of values for 14 hydro-environmental variables across the streamflow gauging stations used for model training/testing (purple, n = 5,615) and across all reaches of the global river network (blue, n = 6.2 × 106). The distribution plots show empirical probability density functions (that is, the area under each density function is equal to one) for all variables, aside from climate zones (g) for which the relative frequency distribution is shown. All variables were averaged across the total drainage area upstream of the reach pour point associated with each gauging station or river reach, respectively. See Extended Data Table 2 for a description of the variables and Extended Data Fig. 1a for a description of the climate zones. No stations were available for climate zones Arctic 1 and Arctic 2. Mapping software: R statistical software (R Core Team).

Extended Data Table 1 Definitions of commonly used terms for non-perennial rivers and streams
Full size table
Extended Data Table 2 Hydro-environmental characteristics used as candidate predictor variables in the split random forest model
Full size table
Extended Data Table 3 Performance summary of binary flow intermittence class predictions
Full size table

Supplementary information

Supplementary Information

This file contains Supplementary Information Sections I-VI, Supplementary Tables S1-S3, Supplementary Figures S1-S8, and Supplementary References. Sections include: a comparison between model predictions and previous estimates of the global prevalence of intermittent rivers and ephemeral streams (Section I), additional information on the pre-processing and validation of input data (gauging stations and discharge data in Section II, national hydrographic datasets and local on-the-ground visual observations of flow intermittence in Section VI), and technical documentation on model development and diagnostics (Sections III-V).

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Supplementary Table 1

Predicted global prevalence of intermittent rivers and ephemeral streams across streamflow size classes by climate zone, terrestrial biome, freshwater major habitat type, and country.

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Messager, M.L., Lehner, B., Cockburn, C. et al. Global prevalence of non-perennial rivers and streams. Nature 594, 391–397 (2021). https://doi.org/10.1038/s41586-021-03565-5

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