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Baseflow Separation Methods: A Unified Filter Framework, Multi-Catchment Evaluation, and Open-Source Computational Tools
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DOI：10.3390/w18172135.png)
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Abstract
En 中文
Baseflow cannot be measured directly, so many separation methods exist, and they disagree. We review 16 methods spanning digital filters, graphical partitioning, recession analysis, and conductivity mass balance (CMB). We show that most recursive filters are special cases of a generalized three-parameter equation, which separates linear reservoir from signal processing families and identifies the Boughton and Eckhardt filters as algebraically equivalent. All 16 are implemented in baseflowx, an open-source Python (version 3.9 or later) package, with eight of them validated against independently published results. The methods rank the 398 reference catchments similarly but differ in level: pairs correlating above 0.93 diverge by up to 0.22 in mean absolute baseflow index (BFI). We compared 15 streamflow-only methods with a CMB reference at 31 screened catchments, with 24 more as a robustness check. All estimated larger baseflows than CMB on average, reflecting the quantities separated: hydrograph-based methods count bank storage return, slow interflow, and other delayed water as baseflow, whereas CMB partitions by conductance. CMB-derived BFImax values were below the conventional 0.80 Eckhardt default at all 55 catchments. Parameter choice also produced most of the variation attributed to method choice. Streamflow-only methods can support relative comparisons and trends but cannot constrain an absolute baseflow fraction without an external reference.
Keywords:
baseflow separation
baseflow index
digital filter
conductivity mass balance
method comparison
multi-catchment evaluation
Python
open-source software
recession analysis
streamflow
Journal
W
IF:
3
Papers:
3.2W
Citations:
7.4W
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