1. Common Implementation Strategy for the Water Framework Directive—Guidance Document No. 2: Identification of Water Bodies ISBN 92-894-5122-X (European Commission, 2003).

2. Sackett versus Environmental Protection Agency, 25 May 2023 (Supreme Court of the United States, 2023); https://www.supremecourt.gov/opinions/22pdf/21-454_4g15.pdf

3. Sitati, A., Yegon, M. J., Masese, F. O. & Graf, W. Ecological importance of low-order streams to macroinvertebrate community composition in Afromontane headwater streams. Environ. Sustain. Indic. 21, 100330 (2024).

   Google Scholar 

4. Creed, I. F. et al. Enhancing protection for vulnerable waters. Nat. Geosci. 10, 809–815 (2017).

   Article 
   CAS 
   PubMed 
   PubMed Central 

   Google Scholar 

5. Messager, M. L., Pella, H. & Datry, T. Inconsistent regulatory mapping quietly threatens rivers and streams. Environ. Sci. Technol. https://doi.org/10.1021/acs.est.4c01859 (2024).

6. Downing, J. A. et al. Global abundance and size distribution of streams and rivers. Inland Waters 2, 229–236 (2012).

   Article 

   Google Scholar 

7. Lane, C. R. et al. Vulnerable waters are essential to watershed resilience. Ecosystems 26, 1–28 (2023).

   Article 
   CAS 

   Google Scholar 

8. Li, L. et al. Toward catchment hydro-biogeochemical theories. WIREs Water 8, e1495 (2021).

   Article 

   Google Scholar 

9. Alexander, R. B. et al. Differences in sources and recent trends in phosphorus and nitrogen delivery to the Gulf of Mexico from the Mississippi River and Atchafalaya River Basins. Environ. Sci. Technol. 42, 822–830 (2007).

   Article 

   Google Scholar 

10. Rupp, D. E., Chegwidden, O. S., Nijssen, B. & Clark, M. P. Changing river network synchrony modulates projected increases in high flows. Water Resour. Res. 57, e2020WR028713 (2021).

    Article 

    Google Scholar 

11. Finn, D. S., Bonada, N., Múrria, C. & Hughes, J. M. Small but mighty: headwaters are vital to stream network biodiversity at two levels of organization. J. N. Am. Benthol. Soc. 30, 963–980 (2011).

    Article 

    Google Scholar 

12. Lowe, W. H. & Likens, G. E. Moving headwater streams to the head of the class. BioScience 55, 196–197 (2005).

    Article 

    Google Scholar 

13. 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 

14. Revised Definition of “Waters of the United States”; Conforming, A Rule by The Engineers Corps and the Environmental Protection Agency on 09/08/2023 (Federal Register, 2023); https://www.federalregister.gov/documents/2023/09/08/2023-18929/revised-definition-of-waters-of-the-united-states-conforming

15. Arce, M. I. et al. A conceptual framework for understanding the biogeochemistry of dry riverbeds through the lens of soil science. Earth Sci. Rev. 188, 441–453 (2019).

    Article 
    CAS 

    Google Scholar 

16. Brinkerhoff, C. B., Gleason, C. J., Kotchen, M. J., Kysar, D. A. & Raymond, P. A. Ephemeral stream water contributions to United States drainage networks. Science 384, 1476–1482 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar 

17. Harvey, J. W. & Kampf, S. K. The transitory origins of rivers. Science 384, 1402–1403 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar 

18. Zipper, S. C. et al. Pervasive changes in stream intermittency across the United States. Environ. Res. Lett. 16, 084033 (2021).

    Article 

    Google Scholar 

19. Freeman, M. C., Pringle, C. M. & Jackson, C. R. Hydrologic connectivity and the contribution of stream headwaters to ecological integrity at regional scales. J. Am. Water Resour. Assoc. 43, 5–14 (2007).

    Article 

    Google Scholar 

20. Liu, S. et al. The importance of hydrology in routing terrestrial carbon to the atmosphere via global streams and rivers. Proc. Natl Acad. Sci. USA 119, e2106322119 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

21. McDonnell, J. J. et al. Moving beyond heterogeneity and process complexity: a new vision for watershed hydrology. Water Resour. Res. https://doi.org/10.1029/2006WR005467 (2007).

22. Richardson, J. S. & Danehy, R. J. A synthesis of the ecology of headwater streams and their riparian zones in temperate forests. For. Sci. 53, 131–147 (2007).

    Google Scholar 

23. Allen, G. H. et al. Similarity of stream width distributions across headwater systems. Nat. Commun. 9, 610 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

24. Clark, M. P. et al. Framework for Understanding Structural Errors (FUSE): a modular framework to diagnose differences between hydrological models. Water Resour. Res. https://doi.org/10.1029/2007WR006735 (2008).

25. Marx, A. et al. A review of CO₂ and associated carbon dynamics in headwater streams: a global perspective. Rev. Geophys. 55, 560–585 (2017).

    Article 

    Google Scholar 

26. Gomi, T., Sidle, R. C. & Richardson, J. S. Understanding processes and downstream linkages of headwater systems: headwaters differ from downstream reaches by their close coupling to hillslope processes, more temporal and spatial variation, and their need for different means of protection from land use. BioScience 52, 905–916 (2002).

    Article 

    Google Scholar 

27. Imberger, M. et al. Headwater streams in an urbanizing world. Freshw. Sci. 42, 323–336 (2023).

    Article 

    Google Scholar 

28. Poff, N. L. et al. The natural flow regime. BioScience 47, 769–784 (1997).

    Article 

    Google Scholar 

29. Wohl, E. The Upstream Extent of a River Network: A Review of Scientific Knowledge of Channel Heads (US Army Corps of Engineers, Engineer Research and Development Center, CR-18-1, 2018); https://erdc-library.erdc.dren.mil/jspui/bitstream/11681/27410/1/ERDC-CRREL%20CR-18-1.pdf

30. Montgomery, D. R. & Dietrich, W. E. Where do channels begin? Nature 336, 232–234 (1988).

    Article 

    Google Scholar 

31. Montgomery, D. R. & Dietrich, W. E. Source areas, drainage density, and channel initiation. Water Resour. Res. 25, 1907–1918 (1989).

    Article 

    Google Scholar 

32. Covino, T. Hydrologic connectivity as a framework for understanding biogeochemical flux through watersheds and along fluvial networks. Geomorphology 277, 133–144 (2017).

    Article 

    Google Scholar 

33. The NHDPlus High Resolution (NHDPlus HR) Dataset (US Geological Survey, 2023); https://www.usgs.gov/national-hydrography/access-national-hydrography-products

34. Nadeau, T.-L. & Rains, M. C. Hydrological connectivity between headwater streams and downstream waters: how science can inform policy. J. Am. Water Resour. Assoc. 43, 118–133 (2007).

    Article 

    Google Scholar 

35. National Hydro Network – NHN – GeoBase Series (Natural Resources Canada, 2024); https://open.canada.ca/data/en/dataset/a4b190fe-e090-4e6d-881e-b87956c07977

36. UKCEH Digital River Network of Great Britain (1:50,000) (UK Centre for Ecology and Hydrology, Environmental Information Data Centre, 2024); https://catalogue.ceh.ac.uk/documents/7d5e42b6-7729-46c8-99e9-f9e4efddde1d

37. EU-Hydro River Network Database 2006–2012 (Vector), Europe (EU-Hydro, 2024); https://doi.org/10.2909/393359a7-7ebd-4a52-80ac-1a18d5f3db9c

38. Christensen, J. R. et al. Headwater streams and inland wetlands: status and advancements of geospatial datasets and maps across the United States. Earth Sci. Rev. 235, 104230 (2022).

    Article 

    Google Scholar 

39. 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 

    Google Scholar 

40. Lin, P., Pan, M., Wood, E. F., Yamazaki, D. & Allen, G. H. A new vector-based global river network dataset accounting for variable drainage density. Sci. Data 8, 28 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

41. Observatoire national des étiages (L’Office Français de la Biodiversité, 2023); https://onde.eaufrance.fr/

42. Final Rule: Revised Definition of “Waters of the United States” Conforming, 88 FR 61964, Docket ID No. EPA–HQ–OW–2023–0346 (US Department of Defense Corps of Engineers and the Environmental Protection Agency, 2023); https://www.govinfo.gov/content/pkg/FR-2023-01-18/pdf/2022-28595.pdf

43. Hewlett, J. D. & Hibbert, A. R. in International Symposium on Forest Hydrology (eds Sopper W. E. & Lull H. W.) 275–290 (Pergamon, 1967).

44. Zhang, L. et al. CHOSEN: a synthesis of hydrometeorological data from intensively monitored catchments and comparative analysis of hydrologic extremes. Hydrol. Processes 35, e14429 (2021).

    Article 

    Google Scholar 

45. Horton, R. E. Hydrologic interrelations of water and soils. Soil Sci. Soc. Am. J. 1, 401–429 (1937).

    Article 

    Google Scholar 

46. Jones, J. A. et al. Ecosystem processes and human influences regulate streamflow response to climate change at long-term ecological research sites. BioScience 62, 390–404 (2012).

    Article 

    Google Scholar 

47. Wlostowski, A. N. et al. Signatures of hydrologic function across the critical zone observatory network. Water Resour. Res. 57, e2019WR026635 (2021).

    Article 

    Google Scholar 

48. Datry, T. et al. Non-perennial segments in river networks. Nat. Rev. Earth Environ. 4, 815–830 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

49. Fritz, K., Cid, N. & Autrey, B. in Intermittent Rivers and Ephemeral Streams (eds Datry, T. et al.) 477–507 (Academic Press, 2017).

50. McMillan, H., Araki, R., Gnann, S., Woods, R. & Wagener, T. How do hydrologists perceive watersheds? A survey and analysis of perceptual model figures for experimental watersheds. Hydrol. Processes 37, e14845 (2023).

    Article 

    Google Scholar 

51. Burt, T. P. & McDonnell, J. J. Whither field hydrology? The need for discovery science and outrageous hydrological hypotheses. Water Resour. Res. 51, 5919–5928 (2015).

    Article 

    Google Scholar 

52. Van Stan, J. T. et al. Shower thoughts: why scientists should spend more time in the rain. BioScience 73, 441–452 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

53. Likens, G. E. The Runoff of Water and Nutrients from Watersheds Tributary to Cayuga Lake, New York Report No. 81 (Cornell University Water Resources and Marine Center, 1974).

54. van Meerveld, H. J., Seibert, J. & Peters, N. E. Hillslope–riparian-stream connectivity and flow directions at the Panola Mountain Research Watershed. Hydrol. Processes 29, 3556–3574 (2015).

    Article 

    Google Scholar 

55. Allen, S. T., Keim, R. F., Barnard, H. R., McDonnell, J. J. & Renée Brooks, J. The role of stable isotopes in understanding rainfall interception processes: a review. WIREs Water 4, e1187 (2017).

    Article 

    Google Scholar 

56. Boano, F. et al. Hyporheic flow and transport processes: mechanisms, models, and biogeochemical implications. Rev. Geophys. 52, 603–679 (2014).

    Article 

    Google Scholar 

57. Younger, S. E., Cannon, J. B. & Brantley, S. T. Impacts of longleaf pine (Pinus palustris Mill.) on long-term hydrology at the watershed scale. Sci. Total Environ. 902, 165999 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar 

58. Hrachowitz, M. et al. A decade of Predictions in Ungauged Basins (PUB)—a review. Hydrol. Sci. J. 58, 1198–1255 (2013).

    Article 

    Google Scholar 

59. Chapin, T. P., Todd, A. S. & Zeigler, M. P. Robust, low-cost data loggers for stream temperature, flow intermittency, and relative conductivity monitoring. Water Resour. Res. 50, 6542–6548 (2014).

    Article 

    Google Scholar 

60. 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 

61. Jensen, C. K., McGuire, K. J., McLaughlin, D. L. & Scott, D. T. Quantifying spatiotemporal variation in headwater stream length using flow intermittency sensors. Environ. Monit. Assess. 191, 226 (2019).

    Article 
    PubMed 

    Google Scholar 

62. Keys, T. A., Jones, C. N., Scott, D. T. & Chuquin, D. A cost-effective image processing approach for analyzing the ecohydrology of river corridors. Limnol. Oceanogr. Methods 14, 359–369 (2016).

    Article 

    Google Scholar 

63. Noto, S. et al. Technical note: low cost stage-camera system for continuous water level monitoring in ephemeral streams. Hydrol. Earth Syst. Sci. Discuss. 2021, 1–17 (2021).

    Google Scholar 

64. Gilmore, T. E., Birgand, F. & Chapman, K. W. Source and magnitude of error in an inexpensive image-based water level measurement system. J. Hydrol. 496, 178–186 (2013).

    Article 

    Google Scholar 

65. Epting, S. M. et al. Landscape metrics as predictors of hydrologic connectivity between Coastal Plain forested wetlands and streams. Hydrol. Processes 32, 516–532 (2018).

    Article 

    Google Scholar 

66. Assendelft, R. S. & van Meerveld, H. J. I. A low-cost, multi-sensor system to monitor temporary stream dynamics in mountainous headwater catchments. Sensors 19, 4645 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

67. Allen, G. H. & Pavelsky, T. M. Global extent of rivers and streams. Science 361, 585–588 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar 

68. Helm, C., Hassan, M. A. & Reid, D. Characterization of morphological units in a small, forested stream using close-range remotely piloted aircraft imagery. Earth Surf. Dynam. 8, 913–929 (2020).

    Article 

    Google Scholar 

69. Dralle, D. N., Lapides, D. A., Rempe, D. M. & Hahm, W. J. Mapping surface water presence and hyporheic flow properties of headwater stream networks with multispectral satellite imagery. Water Resour. Res. 59, e2022WR034169 (2023).

    Article 

    Google Scholar 

70. Fernandez, N., Camacho, L. A. & Nejadhashemi, A. P. Modeling streamflow in headwater catchments: a data-based mechanistic grounded framework. J. Hydrol. Reg. Stud. 44, 101243 (2022).

    Article 

    Google Scholar 

71. National Water Information System Data Available on the World Wide Web (USGS Water Data for the Nation) (US Geological Survey, 2023); http://waterdata.usgs.gov/nwis/

72. Hammond, J. C. et al. Spatial patterns and drivers of non-perennial flow regimes in the contiguous U.S. Geophys. Res. Lett. 48, 2020GL090794 (2021).

    Article 

    Google Scholar 

73. Price, A. N., Jones, C. N., Hammond, J. C., Zimmer, M. A. & Zipper, S. C. The drying regimes of non-perennial rivers and streams. Geophys. Res. Lett. 48, e2021GL093298 (2021).

    Article 

    Google Scholar 

74. 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 

75. Kratzert, F. et al. Caravan—a global community dataset for large-sample hydrology. Sci. Data 10, 61 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

76. Seibert, J. & van Meerveld, H. J. Bridge over changing waters—citizen science for detecting the impacts of climate change on water. PLoS Clim. 1, e0000088 (2022).

    Article 

    Google Scholar 

77. 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).

    Google Scholar 

78. Shi, Y., Davis, K. J., Duffy, C. J. & Yu, X. Development of a coupled land surface hydrologic model and evaluation at a critical zone observatory. J. Hydrometeorol. 14, 1401–1420 (2013).

    Article 

    Google Scholar 

79. Wigmosta, M. S., Vail, L. W. & Lettenmaier, D. P. A distributed hydrology–vegetation model for complex terrain. Water Resour. Res. 30, 1665–1679 (1994).

    Article 

    Google Scholar 

80. Durighetto, N. et al. Probabilistic description of streamflow and active length regimes in rivers. Water Resour. Res. 58, e2021WR031344 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

81. Paniconi, C. & Putti, M. Physically based modeling in catchment hydrology at 50: survey and outlook. Water Resour. Res. 51, 7090–7129 (2015).

    Article 

    Google Scholar 

82. Tague, C. L. & Band, L. E. RHESSys: Regional Hydro-Ecologic Simulation System—an object-oriented approach to spatially distributed modeling of carbon, water, and nutrient cycling. Earth Interact. 8, 1–42 (2004).

    <a data-track="click_references" rel="nofollow noopener" data-track-label="10.1175/1087-3562(2004)82.0.CO;2″ data-track-item_id=”10.1175/1087-3562(2004)82.0.CO;2″ data-track-value=”article reference” data-track-action=”article reference” href=”https://doi.org/10.1175%2F1087-3562%282004%298%3C1%3ARRHSSO%3E2.0.CO%3B2″ aria-label=”Article reference 82″ data-doi=”10.1175/1087-3562(2004)82.0.CO;2″>Article 

    Google Scholar 

83. Beck, H. E. et al. Global fully distributed parameter regionalization based on observed streamflow from 4,229 headwater catchments. J. Geophys. Res. Atmos. 125, e2019JD031485 (2020).

    Article 

    Google Scholar 

84. Razavi, T. & Coulibaly, P. Streamflow prediction in ungauged basins: review of regionalization methods. J. Hydrol. Eng. 18, 958–975 (2013).

    Article 

    Google Scholar 

85. Clark, M. P. et al. Improving the representation of hydrologic processes in Earth System Models. Water Resour. Res. 51, 5929–5956 (2015).

    Article 

    Google Scholar 

86. Maxwell, R. M. et al. Surface-subsurface model intercomparison: a first set of benchmark results to diagnose integrated hydrology and feedbacks. Water Resour. Res. 50, 1531–1549 (2014).

    Article 

    Google Scholar 

87. Gupta, H. V., Kling, H., Yilmaz, K. K. & Martinez, G. F. Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. J. Hydrol. 377, 80–91 (2009).

    Article 

    Google Scholar 

88. Pushpalatha, R., Perrin, C., Moine, N. L. & Andréassian, V. A review of efficiency criteria suitable for evaluating low-flow simulations. J. Hydrol. 420-421, 171–182 (2012).

    Article 

    Google Scholar 

89. Miller, M. P., Carlisle, D. M., Wolock, D. M. & Wieczorek, M. A database of natural monthly streamflow estimates from 1950 to 2015 for the conterminous United States. J. Am. Water Resour. Assoc. 54, 1258–1269 (2018).

    Article 

    Google Scholar 

90. PRISM Gridded Climate Data, Oregon State University (PRISM Climate Group, 2023); https://prism.oregonstate.edu/

91. McMillan, H. et al. When good signatures go bad: applying hydrologic signatures in large sample studies. Hydrol. Processes 37, e14987 (2023).

    Article 

    Google Scholar 

92. The National Water Model (NOAA, 2023); https://water.noaa.gov/about/nwm

93. Regan, R. S. et al. in Description of the National Hydrologic Model for Use with the Precipitation-Runoff Modeling System (PRMS) Book 6, Ch. B9, 38 (US Geological Survey, 2018); https://doi.org/10.3133/tm6B9

94. McMillan, H. K., Booker, D. J. & Cattoën, C. Validation of a national hydrological model. J. Hydrol. 541, 800–815 (2016).

    Article 

    Google Scholar 

95. Wagener, T., Sivapalan, M., Troch, P. & Woods, R. A. Catchment classification and hydrologic similarity. Geogr. Compass 1/4, 901–931 (2007).

    Article 

    Google Scholar 

96. Bergström, S. The HBV-Model—Its Structure and Applications (SMHI, 1992).

97. Nearing, G. S. et al. What role does hydrological science play in the age of machine learning? Water Resour. Res. 57, e2020WR028091 (2021).

    Article 

    Google Scholar 

98. Atkinson, S. E., Sivapalan, M., Woods, R. A. & Viney, N. R. Dominant physical controls on hourly flow predictions and the role of spatial variability: Mahurangi catchment, New Zealand. Adv. Water Res. 26, 219–235 (2003).

    Article 

    Google Scholar 

99. Fenicia, F., McDonnell, J. J. & Savenije, H. H. G. Learning from model improvement: on the contribution of complementary data to process understanding. Water Resour. Res. https://doi.org/10.1029/2007WR006386 (2008).

100. Wrede, S. et al. Towards more systematic perceptual model development: a case study using 3 Luxembourgish catchments. Hydrol. Processes 29, 2731–2750 (2015).

     Article 

     Google Scholar 

101. Peters, N. E., Freer, J. & Beven, K. Modelling hydrologic responses in a small forested catchment (Panola Mountain, Georgia, USA): a comparison of the original and a new dynamic TOPMODEL. Hydrol. Processes 17, 345–362 (2003).

     Article 

     Google Scholar 

102. Schofield, K. A. et al. Biota connect aquatic habitats throughout freshwater ecosystem mosaics. J. Am. Water Resour. Assoc. 54, 372–399 (2018).

     Article 
     PubMed 
     PubMed Central 

     Google Scholar 

103. Amatulli, G. et al. Hydrography90m: a new high-resolution global hydrographic dataset. Earth Syst. Sci. Data 14, 4525–4550 (2022).

     Article 

     Google Scholar 

104. National Science Foundation NEON data. National Ecological Observatory Network https://www.neonscience.org/ (2024).

105. Shen, Q., Cong, Z. & Lei, H. Evaluating the impact of climate and underlying surface change on runoff within the Budyko framework: a study across 224 catchments in China. J. Hydrol. 554, 251–262 (2017).

     Article 

     Google Scholar 

106. Sit, M. et al. A comprehensive review of deep learning applications in hydrology and water resources. Water Sci. Technol. 82, 2635–2670 (2020).

     Article 
     PubMed 

     Google Scholar 

107. Shen, C., Chen, X. & Laloy, E. Editorial: broadening the use of machine learning in hydrology. Front. Water https://doi.org/10.3389/frwa.2021.681023 (2021).

108. Liu, Y., Duffy, K., Dy, J. G. & Ganguly, A. R. Explainable deep learning for insights in El Niño and river flows. Nat. Commun. 14, 339 (2023).

     Article 
     CAS 
     PubMed 
     PubMed Central 

     Google Scholar 

109. Shen, C. et al. Differentiable modelling to unify machine learning and physical models for geosciences. Nat. Rev. Earth Environ. 4, 552–567 (2023).

     Article 

     Google Scholar 

110. Arora, B. et al. Building cross-site and cross-network collaborations in critical zone science. J. Hydrol. 618, 129248 (2023).

     Article 

     Google Scholar 

111. Consortion of Universities for the Advancement of Hydrologic Science, HydroShare online data, model, and code sharing environment. CUASHI https://www.hydroshare.org/ (2023).

112. Jaeger, K. L. et al. Beyond streamflow: call for a national data repository of streamflow presence for streams and rivers in the United States. Water 13, 1627 (2021).

     Article 

     Google Scholar 

113. Addor, N., Newman, A. J., Mizukami, N. & Clark, M. P. The CAMELS data set: catchment attributes and meteorology for large-sample studies. Hydrol. Earth Syst. Sci. 21, 5293–5313 (2017).

     Article 

     Google Scholar 

114. Newman, A. et al. CAMELS: Catchment Attributes and MEeorology for Large-sample Studies. Version 1.2 (UCAR, NCAR, GDEX, 2022); https://doi.org/10.5065/D6MW2F4D

115. Doyle, M. W. & Bernhardt, E. S. What is a stream? Environ. Sci. Technol. 45, 354–359 (2011).

     Article 
     CAS 
     PubMed 

     Google Scholar 

116. 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 

     Google Scholar 

117. Kratzert, F. et al. Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets. Hydrol. Earth Syst. Sci. 23, 5089–5110 (2019).

     Article 

     Google Scholar 

118. Ouyang, W. et al. Continental-scale streamflow modeling of basins with reservoirs: towards a coherent deep-learning-based strategy. J. Hydrol. 599, 126455 (2021).

     Article 

     Google Scholar 

119. Falcone, J. A. GAGES-II: Geospatial Attributes of Gages for Evaluating Streamflow (US Geological Survey, 2011); https://doi.org/10.3133/70046617

120. Cartographic boundary files. US Census Bureau https://www.census.gov/geographies/mapping-files/time-series/geo/cartographic-boundary.html (2023).

121. National Water Information System, USGS Water Data for the Nation. US Geological Survey http://nwis.waterdata.usgs.gov/nwis (2023).

Download references