Suchergebnisse

Over half of global rivers and streams lack perennial flow, and understanding the distribution and drivers of their flow regimes is critical for understanding their hydrologic, biogeochemical, and ecological functions. We analyzed nonperennial flow regimes using 540 U.S. Geological Survey watersheds across the contiguous United States from 1979 to 2018. Multivariate analyses revealed regional differences in no-flow fraction, date of first no flow, and duration of the dry-down period, with further divergence between natural and human-altered watersheds. Aridity was a primary driver of no-flow metrics at the continental scale, while unique combinations of climatic, physiographic and anthropogenic drivers emerged at regional scales. Dry-down duration showed stronger associations with nonclimate drivers compared to no-flow fraction and timing. Although the sparse distribution of nonperennial gages limits our understanding of such streams, the watersheds examined here suggest the important role of aridity and land cover change in modulating future stream drying. Plain Language Summary A majority of global streams are nonperennial, flowing only part of the year, and are critical for sustaining flow downstream, providing habitat for many organisms, and regulating chemical and biological processes. Using long-term U.S. Geological Survey measurements for 540 watersheds across the contiguous United States, we mapped patterns and examined the causes of no-flow fraction, the fraction of each climate year with no flow, no-flow timing, the date of the climate year on which the first recorded no flow takes place, and length of the dry-down period, the average number of days from a local peak in daily flow to the first occurrence of no flow. We found differences in patterns of no-flow characteristics between regions, with higher no-flow fraction, earlier timing, and shorter dry-down duration in the western United States. No-flow fractions were greater and less variable in natural watersheds, while no-flow timing was earlier and dry-down duration was shorter in human-modified watersheds. Aridity had the greatest effect on intermittence across the United States, but unique combinations of climate, biophysical, and human impacts were important in different regions. The number of gages measuring streamflow in nonperennial streams is small compared to perennial streams, and increased monitoring is needed to better understand drying behavior. Key Points . Three metrics reveal regional and human-driven patterns of nonperennial flow: no-flow fraction, day of first no flow, and dry-down duration Streams with human modifications generally dry more quickly than unmodified streams, especially in California and the Southern Great Plains Climate strongly influences no-flow fraction and timing, but physiographic variables are more important for the duration of dry down ; National Science FoundationNational Science Foundation (NSF) [DEB-1754389]; Kansas Water Resources Institute ; Published version ; This manuscript is a product of the Dry Rivers Research Coordination Network, supported by funding from the National Science Foundation (DEB-1754389). Although this work was approved for publication by the EPA, it may not reflect official Agency policy. S.C.Z. was supported by the Kansas Water Resources Institute. We thank David Wolock and two anonymous reviewers for their suggestions and edits. ; Public domain authored by a U.S. government employee

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