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Effect of bedrock permeability on stream base flow mean transit time scaling relationships: 2. Process study of storage and release

V. Cody HaleJeffrey J. McDonnellGlobal Institute for Water Security, National Hydrology Research Centre, University of Saskatchewan Saskatoon Saskatchewan CanadaM. K. StewartAquifer Dynamics and GNS Science Lower Hutt New ZealandD. Kip SolomonDepartment of Geology and Geophysics University of Utah Salt Lake City Utah USAJim DoolitteUSDA Natural Resources Conservation Service Harrisburg Pennsylvania USAGeorge G. IceNational Council for Air and Stream Improvement, Inc. Corvallis Oregon USARobert T. PackCivil and Environmental Engineering, Utah State University Logan Utah USA
2016en
ABI

Аннотация

Abstract In Part 1 of this two‐part series, Hale and McDonnell (2016) showed that bedrock permeability controlled base flow mean transit times (MTTs) and MTT scaling relations across two different catchment geologies in western Oregon. This paper presents a process‐based investigation of storage and release in the more permeable catchments to explain the longer MTTs and (catchment) area‐dependent scaling. Our field‐based study includes hydrometric, MTT, and groundwater dating to better understand the role of subsurface catchment storage in setting base flow MTTs. We show that base flow MTTs were controlled by a mixture of water from discrete storage zones: (1) soil, (2) shallow hillslope bedrock, (3) deep hillslope bedrock, (4) surficial alluvial plain, and (5) suballuvial bedrock. We hypothesize that the relative contributions from each component change with catchment area. Our results indicate that the positive MTT‐area scaling relationship observed in Part 1 is a result of older, longer flow path water from the suballuvial zone becoming a larger proportion of streamflow in a downstream direction (i.e., with increasing catchment area). Our work suggests that the subsurface permeability structure represents the most basic control on how subsurface water is stored and therefore is perhaps the best direct predictor of base flow MTT (i.e., better than previously derived morphometric‐based predictors). Our discrete storage zone concept is a process explanation for the observed scaling behavior of Hale and McDonnell (2016), thereby linking patterns and processes at scales from 0.1 to 100 km 2 .

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