Kling-Gupta efficiency
Kling Gupta efficiency, introduced by Gupta et al. (2009), offers a comprehensive approach to assessing model performance in hydrological modeling. It serves as a diagnostic tool by breaking down the Nash-Sutcliffe efficiency (NSE) into its constituent components, namely correlation, bias, and variability. This decomposition enables a deeper understanding of the relative contributions of these components to the overall model performance and aids in pinpointing areas for improvement.
In essence, Kling Gupta's efficiency allows hydrologists to disentangle the various factors influencing model accuracy, providing insights into both the strengths and weaknesses of the model. By examining how well the model captures the correlation between observed and simulated values, the degree of bias in the predictions, and the variability of the model outputs, researchers can refine their models and enhance their predictive capabilities.
In a later development, Kling et al. (2012) proposed a refined version of this index to address potential issues of cross-correlation between bias and variability ratios. This revision ensures a more robust and accurate assessment of model performance, thereby enhancing the utility of Kling Gupta's efficiency in hydrological modeling studies.
Where CC is the Pearson coefficient value and rm is the average of observed values and cm is average of forecast values and rd is standard deviation of observation values and cd is standard deviation of forecast values.
How To Cite
Please provide the data in a two-column format (observed vs. simulated). You can copy from Excel, text, or any other format, separated by space.