Evaluating model performance: towards a non-parametric variant of the Kling-Gupta efficiency. Issue 13 (26th October 2018)

• Record Type: Journal Article
• Authors: Pool, Sandra; Vis, Marc; Seibert, Jan
• Is Part Of: Hydrological sciences journal. Volume 63:Issue 13/14(2018)
• Page Start: 1941
• Page End: 1953
• Publication Date: 2018-10-26
• Subjects: runoff modelling -- calibration -- non-parametric -- multi-objective -- Kling-Gupta efficiency; Hydrology -- Periodicals; 551.4805
• DOI: 10.1080/02626667.2018.1552002
• Languages: English
• ISSNs: 0262-6667

Abstract

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.