 The Nash-Sucliffe Efficiency, NSE, and Klingupta Efficiency, KGE, are two commonly used metrics in hydrological modeling. While both metrics measure the accuracy of a model's predictions compared to observed data, they differ in how they calculate this accuracy. NSE measures the average error between predicted and observed data over all time steps, while KGE measures the average error between predicted and observed data over all time steps weighted by the standard deviation of the errors. This means that KGE takes into account the variability of the errors, which can lead to different results when comparing models with similar NSE values. For example, a model with a high NSE value may have a low KGE value due to its large variability in prediction errors. This suggests that KGE is a better indicator of model performance than NSE alone. This article was authored by W. J. M. Nobin, W. J. M. Nobin, J. E. Freer, and others. We are article.tv. Links in the description below.