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 (BFImax). While a can be determined through regression analyses, BFImax is often assigned an arbitrary value because of limited catchment information for a more objective determination. This study aimed to assess and calibrate BFImax for a 1.2 km2 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 BFImax 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 BFImax for optimal performance. The research also endorses the use of dissolved silica measurements and the mass balance method as a reliable approach for calibrating BFImax for long-term assessment of baseflows.