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Global prevalence of non-perennial rivers and streams

Messager, Mathis Loïc, Lehner, Bernhard, Cockburn, Charlotte, Lamouroux, Nicolas, Pella, Hervé, Snelder, Ton, Tockner, Klement, Trautmann, Tim, Watt, Caitlin, Datry, Thibault·2021·Nature
DOI 10.1038/s41586-021-03565-5
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  1. Larned, S. T., Datry, T., Arscott, D. B. & Tockner, K. Emerging concepts in temporary-river ecology. Freshw. Biol. 55, 717–738 (2010). · DOI · Paper page
  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). · DOI
  3. Datry, T. et al. A global analysis of terrestrial plant litter dynamics in non-perennial waterways. Nat. Geosci. 11, 497–503 (2018). · DOI
  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). · DOI
  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). · DOI · Paper page
  6. Acuña, V. et al. Why should we care about temporary waterways? Science 343, 1080–1081 (2014). · DOI · Paper page
  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. · DOI
  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). · DOI
  9. Allen, D. C. et al. River ecosystem conceptual models and non‐perennial rivers: a critical review. Wiley Interdiscip. Rev. Water 7, e1473 (2020). · DOI
  10. 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). · DOI
  11. 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). · DOI · Paper page
  12. 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). · DOI
  13. 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). · DOI
  14. Acuña, V. et al. Accounting for flow intermittency in environmental flows design. J. Appl. Ecol. 57, 742–753 (2020). · DOI
  15. Zimmer, M. A. et al. Zero or not? Causes and consequences of zero-flow stream gage readings. Wiley Interdiscip. Rev. Water 7, e1436 (2020). · DOI
  16. 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). · DOI
  17. 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). · DOI
  18. 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). · DOI
  19. Snelder, T. H. et al. Regionalization of patterns of flow intermittence from gauging station records. Hydrol. Earth Syst. Sci. 17, 2685–2699 (2013). · DOI
  20. 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). · DOI
  21. 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). · DOI
  22. Kennard, M. J. et al. Classification of natural flow regimes in Australia to support environmental flow management. Freshw. Biol. 55, 171–193 (2010). · DOI · Paper page
  23. 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). · DOI
  24. Linke, S. et al. Global hydro-environmental sub-basin and river reach characteristics at high spatial resolution. Sci. Data 6, 283 (2019). · DOI · Paper page
  25. Tooth, S. Process, form and change in dryland rivers: a review of recent research. Earth Sci. Rev. 51, 67–107 (2000). · DOI · Paper page
  26. 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). · DOI
  27. Benstead, J. P. & Leigh, D. S. An expanded role for river networks. Nat. Geosci. 5, 678–679 (2012). · DOI
  28. 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). · DOI · Paper page
  29. 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). · DOI · Paper page
  30. 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). · DOI
  31. Prancevic, J. P. & Kirchner, J. W. Topographic controls on the extension and retraction of flowing streams. Geophys. Res. Lett. 46, 2084–2092 (2019). · DOI
  32. 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). · DOI
  33. Tramblay, Y. et al. Trends in flow intermittence for European rivers. Hydrol. Sci. J. 66, 37–49 (2021). · DOI
  34. 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). · DOI
  35. Hammond, J. C. et al. Spatial patterns and drivers of nonperennial flow regimes in the contiguous United States. Geophys. Res. Lett. 48, e2020GL090794 (2021). · DOI
  36. 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). · DOI · Paper page
  37. 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 · DOI
  38. 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). · DOI · Paper page
  39. 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). · DOI · Paper page
  40. Datry, T. et al. Flow intermittence and ecosystem services in rivers of the Anthropocene. J. Appl. Ecol. 55, 353–364 (2018). · DOI
  41. Nembrini, S., König, I. R. & Wright, M. N. The revival of the Gini importance? Bioinformatics 34, 3711–3718 (2018). · DOI · Paper page
  42. 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). · DOI · Paper page
  43. 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). · DOI · Paper page
  44. 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). · DOI · Paper page
  45. 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). · DOI
  46. 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). · DOI · Paper page
  47. 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). · DOI
  48. 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). · DOI
  49. 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). · DOI
  50. 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 · DOI
  51. 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). · DOI · Paper page
  52. 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). · DOI · Paper page
  53. Bond, N. R. & Kennard, M. J. Prediction of hydrologic characteristics for ungauged catchments to support hydroecological modeling. Water Resour. Res. 53, 8781–8794 (2017). · DOI
  54. Wainer, J. Comparison of 14 different families of classification algorithms on 115 binary datasets. Preprint at https://arxiv.org/abs/1606.00930 (2016). · arXiv
  55. Malley, J. D., Kruppa, J., Dasgupta, A., Malley, K. G. & Ziegler, A. Probability machines. Methods Inf. Med. 51, 74–81 (2012). · DOI
  56. 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). · DOI · Paper page
  57. Lang, M. et al. mlr3: a modern object-oriented machine learning framework in R. J. Open Source Softw. 4, 1903 (2019). · DOI · Paper page
  58. Landau, W. M. The drake R package: a pipeline toolkit for reproducibility and high-performance computing. J. Open Source Softw. 3, 550 (2018). · DOI
  59. Hothorn, T. & Zeileis, A. Partykit: a modular toolkit for recursive partytioning in R. J. Mach. Learn. Res. 16, 3905–3909 (2015).
  60. 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). · DOI
  61. Zhang, G. & Lu, Y. Bias-corrected random forests in regression. J. Appl. Stat. 39, 151–160 (2012). · DOI · Paper page
  62. 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). · DOI
  63. 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). · DOI · Paper page
  64. 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).
  65. 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). · DOI · Paper page
  66. 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 · DOI · Paper page
  67. 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). · DOI · Paper page
  68. 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). · DOI · Paper page
  69. Altmann, A., Toloşi, L., Sander, O. & Lengauer, T. Permutation importance: a corrected feature importance measure. Bioinformatics 26, 1340–1347 (2010). · DOI · Paper page
  70. Amaratunga, D., Cabrera, J. & Lee, Y.-S. Enriched random forests. Bioinformatics 24, 2010–2014 (2008). · DOI
  71. 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 · DOI · Paper page
  72. Jones, Z. M. & Linder, F. J. edarf: Exploratory Data Analysis using Random Forests. J. Open Source Softw. 1, 92 (2016). · DOI
  73. 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 · DOI
  74. Colvin, S. A. R. et al. Headwater streams and wetlands are critical for sustaining fish, fisheries, and ecosystem services. Fisheries 44, 73–91 (2019). · DOI
  75. Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer Science & Business Media, 2009). · DOI · Paper page
  76. Clauset, A., Shalizi, C. R. & Newman, M. E. J. Power-law distributions in empirical data. SIAM Rev. 51, 661–703 (2009). · DOI · Paper page
  77. 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). · DOI
  78. 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). · DOI
  79. 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).
  80. 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). · DOI
  81. Datry, T., Pella, H., Leigh, C., Bonada, N. & Hugueny, B. A landscape approach to advance intermittent river ecology. Freshw. Biol. 61, 1200–1213 (2016). · DOI
  82. 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). · DOI
  83. 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). · DOI
  84. Datry, T. et al. Science and management of intermittent rivers and ephemeral streams (SMIRES). Res. Ideas Outcomes 3, e21774 (2017). · DOI
  85. 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 · DOI
  86. Fan, Y., Li, H. & Miguez-Macho, G. Global patterns of groundwater table depth. Science 339, 940–943 (2013). · DOI · Paper page
  87. 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). · DOI · Paper page
  88. 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). · DOI · Paper page
  89. 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). · DOI · Paper page
  90. GLIMS and National Snow and Ice Data Center. GLIMS Glacier Database V1 (2012); https://doi.org/10.7265/N5V98602 · DOI
  91. Gruber, S. Derivation and analysis of a high-resolution estimate of global permafrost zonation. Cryosphere 6, 221–233 (2012). · DOI · Paper page
  92. Lehner, B. & Döll, P. Development and validation of a global database of lakes, reservoirs and wetlands. J. Hydrol. 296, 1–22 (2004). · DOI · Paper page
  93. 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). · DOI · Paper page
  94. Williams, P. W. & Ford, D. C. Global distribution of carbonate rocks. Z. Geomorphol. Suppl. 147, 1–2 (2006).
  95. 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). · DOI · Paper page

Cited by

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Metadata

Authors
Messager, M. L., Lehner, B., Cockburn, C., Lamouroux, N., Pella, H., Snelder, T., Tockner, K., Trautmann, T., Watt, C., & Datry, T.
Venue
Nature
Publisher
Springer Science and Business Media LLC
Published
2021-06-16
Volume
594
Issue
7863
Pages
391-397
ISSN
  • 0028-0836 (ISSN-L)
  • 1476-4687
DOI
10.1038/s41586-021-03565-5
OpenAlex
W3172697043
Cited by
581
References
14
FWCI
27.30
Type
journal article
Language
en
Open access
closed
Licence

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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

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