Enhanced Baseflow Separation in Rural Catchments: Event-Specific Calibration of Recursive Digital Filters with Tracer-Derived Data

This study presents a comprehensive analysis of baseflow separation techniques within a small rural catchment, focusing on the calibration and application of three Recursive Digital Filters (RDFs): the Eckhardt, Lyne and Hollick (LH), and Chapman and Maxwell (CM) filters. The research aimed to refine baseflow estimation methods by calibrating the BFI max and Beta parameters of the Eckhardt’s and LH filters, respectively, using dissolved silica concentration data to derive reference baseflow. A novel event-based calibration approach was introduced, categorizing rainfall-runoff events by their magnitudes to optimize filter parameters accordingly. The findings reveal that the calibrated Eckhardt’s filter, incorporating event-specific parameter values, provides the most accurate baseflow estimations, closely aligning with observed data across various performance metrics. The event-based calibration demonstrated significant improvements in baseflow prediction accuracy, particularly for the Eckhardt’s and LH filters, compared to the general calibration approach. The study confirms the dynamic nature of the BFI max and Beta parameters, which vary with event magnitude, underscoring the importance of tailored calibration strategies for precise baseflow separation. The CM filter, while offering plausible baseflow hydrograph shapes and peak timings, was limited by its lack of adjustable parameters, leading to consistent underestimation of baseflow volumes. In contrast, the adjustable parameters of the Eckhardt’s and LH models enabled a more accurate representation of baseflow dynamics, particularly when calibrated for specific event magnitudes. This research confirms the superior efficacy of the Eckhardt’s filter in baseflow separation for small rural catchments, advocating for its use with event-based calibration when tracer data is available. Overall, the findings contribute valuable insights into baseflow modeling, offering improved methodologies for hydrologists and environmental scientists to enhance water resource management and conservation strategies.