Calibrated Eckhardt’s filter versus alternative baseflow separation methods: A silica-based approach in a Brazilian catchment

• Eckhardt’s filter was calibrated for a Brazilian rural catchment using dissolved silica data. • An optimum BFI max value of 0.653 was derived, yielding long-term BFI = 54 %. • The calibrated Eckhardt’s filter outperformed three other separation models. • The ‘backward filter’ produced unrealistic baseflows, almost identical to stream flows. • Graphical method overestimated baseflow (+19 %); CM filter underestimated baseflow (−19 %). The Eckhardt’s filter, a recursive digital filter widely used in hydrology to separate baseflow, relies on two parameters: the recession constant ( a ) and the maximum allowable long-term ratio of baseflow to streamflow ( BFI max ). While a can be determined through regression analyses, BFI max is often assigned an arbitrary value because of limited catchment information for a more objective determination. This study aimed to assess and calibrate BFI max for a 1.2 km 2 rural catchment in southern Brazil using dissolved silica data. Calibration involved minimizing differences between filter-generated baseflow hydrographs and those derived from dissolved silica measurements and mass balance across 13 monitored rainfall-runoff events. The calibrated model was compared to three established models: a modified graphical method based on local minima, a backward filter model, and a one-parameter filter. The results yielded an optimal BFI max of 0.653 with high model performance ( PBias ≈ 0, NSE = 0.85, KGE = 0.74, and NRMSD = 13 %) against silica-derived baseflow. Applying the calibrated filter to a six-year streamflow dataset yielded a long-term baseflow index ( BFI ) value of 54 %, aligned with regional and global averages. Comparisons with other models showed that the backward filter model produced unrealistic baseflow values nearly identical to streamflow values; the local minima method overestimated baseflow by +19 % and failed to accurately represent the smoothness and peaks of the baseflow hydrographs; and the one-parameter filter represented the shape and timing of the baseflow hydrographs well, but underestimated magnitude by −19 %. These findings validate the effectiveness of the Eckhardt’s filter in generating accurate baseflow hydrographs and confirm the importance of calibrating BFI max for optimal performance. The research also endorses the use of dissolved silica measurements and the mass balance method as a reliable approach for calibrating BFI ma x for long-term assessment of baseflows.