Premium
Time‐Variable Transit Time Distributions in the Hyporheic Zone of a Headwater Mountain Stream
Author(s) -
Ward Adam S.,
Schmadel Noah M.,
Wondzell Steven M.
Publication year - 2018
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/2017wr021502
Subject(s) - environmental science , forcing (mathematics) , streams , hyporheic zone , variable (mathematics) , hydrology (agriculture) , biogeochemical cycle , spatial variability , atmospheric sciences , geology , surface water , computer science , ecology , computer network , mathematical analysis , mathematics , geotechnical engineering , environmental engineering , biology , statistics
Exchange of water between streams and their hyporheic zones is known to be dynamic in response to hydrologic forcing, variable in space, and to exist in a framework with nested flow cells. The expected result of heterogeneous geomorphic setting, hydrologic forcing, and between‐feature interaction is hyporheic transit times that are highly variable in both space and time. Transit time distributions (TTDs) are important as they reflect the potential for hyporheic processes to impact biogeochemical transformations and ecosystems. In this study we simulate time‐variable transit time distributions based on dynamic vertical exchange in a headwater mountain stream with observed, heterogeneous step‐pool morphology. Our simulations include hyporheic exchange over a 600 m river corridor reach driven by continuously observed, time‐variable hydrologic conditions for more than 1 year. We found that spatial variability at an instance in time is typically larger than temporal variation for the reach. Furthermore, we found reach‐scale TTDs were marginally variable under all but the most extreme hydrologic conditions, indicating that TTDs are highly transferable in time. Finally, we found that aggregation of annual variation in space and time into a “master TTD” reasonably represents most of the hydrologic dynamics simulated, suggesting that this aggregation approach may provide a relevant basis for scaling from features or short reaches to entire networks.