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Automated Algorithms for Heuristic Base‐Flow Separation 1
Author(s) -
Schwartz Stuart S.
Publication year - 2007
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/j.1752-1688.2007.00130.x
Subject(s) - base flow , streamflow , flow (mathematics) , computer science , heuristic , algorithm , hydrogeology , baseflow , hydrology (agriculture) , geology , mathematics , drainage basin , artificial intelligence , geotechnical engineering , geography , geometry , cartography
The subjective nature of graphical base‐flow separation combined with the many applications of base‐flow time series derived from continuous streamflow data, motivates the development and application of automated algorithms for heuristic base‐flow separation. Base‐flow time series derived from gauged streamflow support diverse applications in engineering hydrology, catchment analysis, hydrogeologic investigations, regional low‐flow analysis, and recharge estimation. Whether based on graphical procedures for recession analysis or analytical expressions derived from fundamental equations of ground‐water flow, the variety of base‐flow separation algorithms belies the array of base‐flow definitions and interpretations that variously refer to dominant process, source, flow path, and characteristic response time. Algorithms that are invariant in their consistent – though heuristic – characterization of base‐flow response are particularly useful for interbasin comparisons of low‐flow characteristics and hydrologic regionalization. More adaptable algorithms provide application‐specific flexibility in allocating flow components like interflow to either quickflow or slowflow. Four widely used algorithms that produce consistent base‐flow time series using only gauged streamflow records are compared and contrasted with a complementary heuristic algorithm that incorporates hydrologic judgment explicitly, through manual parameterization. The utility of these inherently subjective algorithms is illustrated through a simple example of flow phase separation in a two‐component end‐member mixing model of dissolved chlorides in the Cuyahoga River.