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Global prevalence of non-perennial rivers and streams


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.

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 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 Original data that supported the study are freely available and their sources are summarized in Extended Data Fig. 7bSource 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


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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 (, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 869226.

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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.

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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. be, 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 (ac) and with drainage area ≥10 km2 for Australia (df). 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, eh, 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.

af, Comparisons were conducted for France (a, b), the USA (cd), and Australia (ef), 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. cp, 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
Extended Data Table 2 Hydro-environmental characteristics used as candidate predictor variables in the split random forest model
Extended Data Table 3 Performance summary of binary flow intermittence class predictions

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).

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