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Streamflow observations can be used to understand, predict, and contextualize hydrologic, ecological, and biogeochemical processes and conditions in streams. Stream gages are point measurements along rivers where streamflow is measured, and are often used to infer upstream watershed-scale processes. When stream gages read zero, this may indicate that the stream has dried at this location; however, zero-flow readings can also be caused by a wide range of other factors. Our ability to identify whether or not a zero-flow gage reading indicates a dry fluvial system has far reaching environmental implications. Incorrect identification and interpretation by the data user can lead to inaccurate hydrologic, ecological, and/or biogeochemical predictions from models and analyses. Here, we describe several causes of zero-flow gage readings: frozen surface water, flow reversals, instrument error, and natural or human-driven upstream source losses or bypass flow. For these examples, we discuss the implications of zero-flow interpretations. We also highlight additional methods for determining flow presence, including direct observations, statistical methods, and hydrologic models, which can be applied to interpret causes of zero-flow gage readings and implications for reach- and watershed-scale dynamics. Such efforts are necessary to improve our ability to understand and predict surface flow activation, cessation, and connectivity across river networks. Developing this integrated understanding of the wide range of possible meanings of zero-flows will only attain greater importance in a more variable and changing hydrologic climate. This article is categorized under: Science of Water > Methods Science of Water > Hydrological Processes Water and Life > Conservation, Management, and Awareness ; National Science FoundationNational Science Foundation (NSF) [DEB-1754389]; NSFNational Science Foundation (NSF) [DEB-1830178, EAR-1653998, EAR-1652293]; NSF Konza Long Term Ecological Research grant [1440484]; Department of Energy Office of Science Multisector Dynamics ProgramUnited States Department of Energy (DOE); Australian Research CouncilAustralian Research Council [DE150100302]; Department of EnergyUnited States Department of Energy (DOE) [DE-SC0019377] ; This manuscript is a product of the Dry Rivers Research Coordination Network, which was supported by funding from the National Science Foundation (DEB-1754389). DelVecchia was supported in part by funding from NSF DEB-1830178. Dodds was supported in part by NSF Konza Long Term Ecological Research grant number 1440484. Godsey was supported in part by NSF award EAR-1653998. Kaiser was supported in part by the Department of Energy Office of Science Multisector Dynamics Program. Shanafield was supported in part by funding from the Australian Research Council under grant DE150100302. Ward was supported in part by Department of Energy award DE-SC0019377 and NSF award EAR-1652293. The opinions expressed are those of the researchers, and not necessarily the funding agencies. Although this work was reviewed by the USGS and USEPA, and approved for publication, it might not necessarily reflect official USEPA policy. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. The authors thank Heather Golden, Brent Johnson, Rosemary Fanelli, Albert Ruhi, as well as two anonymous reviewers for helpful comments on earlier versions of the manuscript. USGS data used to support this study are available from the U.S. Geological Survey National Water Information System database (U.S. Geological Survey, 2019). For the exact dataset used in this study, see: Hammond (2020). ; Public domain authored by a U.S. government employee