2013
DOI: 10.30955/gnj.000879
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Using an informational entropy-based metric as a diagnostic of flow duration to drive model parameter identification

Abstract: Calibration of rainfall-runoff models is made complicated by uncertainties in data, and by the arbitrary emphasis placed on various magnitudes of the model residuals by most traditional measures of fit. Current research highlights the importance of driving model identification by assimilating information from the data. In this paper, we evaluate the potential use of an entropybased measure as an objective function or as a model diagnostic in hydrological modelling, with particular interest in providing an appr… Show more

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Cited by 7 publications
(1 citation statement)
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“…-The KGE (Gupta et al, 2009) consists of a diagonal decomposition of the N SE (Nash and Sutcliffe, 1970) to separate Pearson's correlation coefficient r p , representation of bias β KGE , and variability α KGE . Thus, the KGE is comparable to multi-objective criteria for calibration purpose (Pechlivanidis et al, 2013). The sub-panel offers (i) a bi-plot of the et al, 2011) and could impact the hydrological model performance (Ficchì et al, 2016).…”
Section: Model Evaluationmentioning
confidence: 98%
“…-The KGE (Gupta et al, 2009) consists of a diagonal decomposition of the N SE (Nash and Sutcliffe, 1970) to separate Pearson's correlation coefficient r p , representation of bias β KGE , and variability α KGE . Thus, the KGE is comparable to multi-objective criteria for calibration purpose (Pechlivanidis et al, 2013). The sub-panel offers (i) a bi-plot of the et al, 2011) and could impact the hydrological model performance (Ficchì et al, 2016).…”
Section: Model Evaluationmentioning
confidence: 98%