On typical range, sensitivity, and normalization of Mean Squared Error and Nash-Sutcliffe Efficiency type metrics
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.
- Publication:
-
Water Resources Research
- Pub Date:
- October 2011
- DOI:
- 10.1029/2011WR010962
- Bibcode:
- 2011WRR....4710601G
- Keywords:
-
- calibration;
- information;
- Mean Squared Error;
- metrics;
- Nash Sutcliffe Efficiency;
- normalization;
- Hydrology: Model calibration (3333);
- Hydrology: Instruments and techniques: modeling;
- Informatics: Data assimilation;
- integration and fusion;
- General or Miscellaneous: Techniques applicable in three or more fields