Journal
ENVIRONMENTAL MODELLING & SOFTWARE
Volume 54, Issue -, Pages 39-52Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.envsoft.2013.12.011
Keywords
Baseflow; Recursive digital filters; Framework; Prediction; Regression models
Categories
Funding
- Chinese Scholarship Council (CSC)
- University of Adelaide
Ask authors/readers for more resources
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. (c) 2013 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available