Suchergebnisse

Non-perennial streams are widespread, critical to ecosystems and society, and the subject of ongoing policy debate. Prior large-scale research on stream intermittency has been based on long-term averages, generally using annually aggregated data to characterize a highly variable process. As a result, it is not well understood if, how, or why the hydrology of non-perennial streams is changing. Here, we investigate trends and drivers of three intermittency signatures that describe the duration, timing, and dry-down period of stream intermittency across the continental United States (CONUS). Half of gages exhibited a significant trend through time in at least one of the three intermittency signatures, and changes in no-flow duration were most pervasive (41% of gages). Changes in intermittency were substantial for many streams, and 7% of gages exhibited changes in annual no-flow duration exceeding 100 days during the study period. Distinct regional patterns of change were evident, with widespread drying in southern CONUS and wetting in northern CONUS. These patterns are correlated with changes in aridity, though drivers of spatiotemporal variability were diverse across the three intermittency signatures. While the no-flow timing and duration were strongly related to climate, dry-down period was most strongly related to watershed land use and physiography. Our results indicate that non-perennial conditions are increasing in prevalence over much of CONUS and binary classifications of 'perennial' and 'non-perennial' are not an accurate reflection of this change. Water management and policy should reflect the changing nature and diverse drivers of changing intermittency both today and in the future. ; US National Science FoundationNational Science Foundation (NSF) [DEB-1754389] ; Published version ; This manuscript is a product of the Dry Rivers Research Coordination Network, which was supported by funding from the US National Science Foundation (DEB-1754389). Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government. This manuscript was improved by constructive feedback from Kristin Jaeger and three anonymous reviews.

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