On typical range, sensitivity, and normalization of Mean Squared Error and Nash-Sutcliffe Efficiency type metrics

Hoshin Vijai Gupta, Harald Kling

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Abstract

We show that Mean Squared Error (MSE) and Nash-Sutcliffe Efficiency (NSE) type metrics typically vary on bounded ranges under optimization and that negative values of NSE imply severe mass balance errors in the data. Further, by constraining simulated mean and variability to match those of the observations (diagnostic approach), the sensitivity of both metrics is improved, and NSE becomes linearly related to the cross-correlation coefficient. Our results have important implications for analysis of the information content of data and hence about inferences regarding achievable parameter precision.

Original languageEnglish (US)
Article numberW10601
JournalWater Resources Research
Volume47
Issue number10
DOIs
StatePublished - Nov 1 2011

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ASJC Scopus subject areas

  • Water Science and Technology

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