Performance assessment and improvement of recursive digital baseflow filters for catchments with different physical characteristics and hydrological inputs

  • Authors:

  • Li Li;Holger R. Maier;Daniel Partington;Martin F. Lambert;Craig T. Simmons

  • Affiliations:

  • School of Civil, Environmental and Mining Engineering, The University of Adelaide, Adelaide 5005, South Australia, Australia;School of Civil, Environmental and Mining Engineering, The University of Adelaide, Adelaide 5005, South Australia, Australia;School of Civil, Environmental and Mining Engineering, The University of Adelaide, Adelaide 5005, South Australia, Australia;School of Civil, Environmental and Mining Engineering, The University of Adelaide, Adelaide 5005, South Australia, Australia;National Centre for Groundwater Research and Training and School of the Environment, Flinders University, GPO Box 2100, Adelaide 5001, South Australia, Australia

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2014

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Abstract

Recursive digital filters (RDFs) are one of the most commonly used methods of baseflow separation. However, how accurately they estimate baseflow and how to select appropriate values of filter parameters is generally unknown. In this paper, the output of fully integrated surface water/groundwater (SW/GW) models is used to obtain optimal parameters for, and assess the accuracy of, three commonly used RDFs under a range of physical catchment characteristics and hydrological inputs. The results indicate that the Lyne and Hollick (LH) filter performs better than the Boughton and Eckhardt filters, over a larger range of conditions. In addition, the optimal values of the filter parameters vary considerably for all three filters, depending on catchment characteristics and hydrological inputs. The dataset of the 66 catchment characteristics and hydrological inputs, as well as the corresponding simulated total streamflow and baseflow hydrographs obtained using the SW/GW model, can be downloaded as Supplementary material.