Enhanced baseflow separation in rural catchments: Event-specific calibration of recursive digital filters with tracer-derived data

Abstract

This study investigates the performance of baseflow separation methods in a small rural catchment, emphasizing the calibration of three Recursive Digital Filters (RDFs): Eckhardt, Lyne and Hollick (LH), and Chapman and Maxwell (CM). We introduced silica concentration as a reference tracer, and refined the parameterization of BFImax in the Eckhardt's filter and Beta in the LH filter. An innovative event-specific calibration methodology was applied, where rainfall events were categorized by intensity to tailor filter parameters accordingly. Results indicate that the Eckhardt's filter, when calibrated dynamically per event magnitude, yields the most accurate baseflow estimates, closely aligning with observed data. The event-based calibration significantly enhanced accuracy, particularly for the Eckhardt's and LH filters, compared to a general calibration method. The CM filter, despite generating reasonable hydrograph shapes, consistently underestimated baseflow due to its fixed parameters. These findings highlight the necessity of customized calibration strategies for improved baseflow separation and underscore the superior performance of the Eckhardt's filter when integrated with event-specific calibrations.