Transpiration and subsurface controls of streamflow recession characteristics

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Abstract
In headwater catchments, streamflow recedes between periods of rainfall at a predictable rate generally defined by a power–law relationship relating streamflow decay to streamflow. Research over the last four decades has applied this relationship to predictions of water resource availability as well as estimations of basin‐wide physiographic characteristics and ecohydrologic conditions. However, the interaction of biophysical processes giving rise to the form of these power–law relationships remains poorly understood, and recent investigations into the variability of streamflow recession characteristics between discrete events have alternatively suggested evapotranspiration, water table elevation, and stream network contraction as dominant factors, without consensus. To assess potential temporal variability and interactions in the mechanism(s) driving streamflow recession, we combine long‐term observational data from a headwater stream in the southern Appalachian Mountains with state and flux conditions from a process‐based ecohydrologic model. Streamflow recession characteristics are nonunique and vary systematically with seasonal fluctuations in both rates of transpiration and watershed wetness conditions, such that transpiration dominates recession signals in the early growing season and diminishes in effect as the water table elevation progressively drops below and decouples with the root zone with topographic position. As a result of this decoupling, there exists a seasonal hysteretic relationship between streamflow decay and both evapotranspiration and watershed wetness conditions. Results indicate that for portions of the year, forest transpiration may actively compete with subsurface drainage for the same water resource that supplies streamflow, though for extended time periods, these processes exploit distinct water stores. Our analysis raises concerns about the efficacy of assessing humid headwater systems using traditional recession analysis, with recession curve parameters treated as static features of the watershed, and we provide novel alternatives for evaluating interacting biological and geophysical drivers of streamflow recession.
Funding Information
  • National Science Foundation Long-term Ecological Research (DEB-0823293)

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