Abstract

Goodness-of-fit measures are important for an objective evaluation of runoff model performance. The Kling-Gupta efficiency (R-KG), which has been introduced as an improvement of the widely used Nash-Sutcliffe efficiency, considers different types of model errors, namely the error in the mean, the variability, and the dynamics. The calculation of R-KG is implicitly based on the assumptions of data linearity, data normality, and the absence of outliers. In this study, we propose a modification of R-KG as an efficiency measure comprising non-parametric components, i.e. the Spearman rank correlation and the normalized flow-duration curve. The performances of model simulations for 100 catchments using the new measure were compared to those obtained using R-KG based on a number of statistical metrics and hydrological signatures. The new measure resulted overall in better or comparable model performances, and thus it was concluded that efficiency measures with non-parametric components provide a suitable alternative to commonly used measures.

Keywords

runoff modelling; calibration; non-parametric; multi-objective; Kling-Gupta efficiency

Published in

Hydrological Sciences Journal
2018, Volume: 63, number: 13-14, pages: 1941-1953
Publisher: TAYLOR & FRANCIS LTD

SLU Authors

• Seibert, Jan

  • Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences
    
  • University of Zürich

UKÄ Subject classification

Oceanography, Hydrology, Water Resources


Publication identifier

DOI: https://doi.org/10.1080/02626667.2018.1552002

Permanent link to this page (URI)

https://res.slu.se/id/publ/97660