In hydrology, the Nash–Sutcliffe efficiency (NSE) coefficient is used to determine model efficiency. Similar to the Coefficient of Determination (better known as R^2), where - as a rule of thumb - everything above a value of around 0.7 is considered to be a decent fit (or better), which value of the NSE is considered acceptable when you model e.g. a discharge time series?
According to Moriasi et al. (2015) for a daily, monthly or annual hydrological analysis (discharge or flow) the table bellow can be applied as an evaluation criteria:
| Temporal scale | Unsatisfactory | Satisfactory | Good | Very good |
|---|---|---|---|---|
| Annual | NSE =< 0.60 | 0.60 < NSE< 0.70 | 0.70 <= NSE =< 0.75 | NSE > 0.75 |
| Monthly | NSE =< 0.70 | 0.70 < NSE < 0.80 | 0.80 <= NSE =< 0.85 | NSE > 0.85 |
| Daily | NSE =< 0.50 | 0.50 < NSE < 0.70 | 0.70 <= NSE =< 0.85 | NSE > 0.85 |
Source 1: Moriasi, D., N., Gitau, M., W., Pai, N., Daggupati, P. (2015). Hydrologic and water quality models: performance measures and evaluation criteria. Transactions of the ASABE. 58(6): 1763-1785. https://doi.org/10.13031/trans.58.10715
Moreover, I would like to add that I always look at the King-Gupta (KGE) metric as well, as this metric seems to take in consideration possible bias between modelled and measured discharge, or in another words, it seems to be a mixed of the NSE and the Percent Bias (PBIAS) metrics.
I will leave two interesting articles about this matter below:
Source 2: Clark, M., P., Vogel, R., M., Lamontagne, J., R. et al. (2021). The abuse of popular performance metrics in hydrologic modelling. Water Resource Research. 57, e2020WR029001. https://doi.org/10.1029/2020WR029001
Source 3: Knoben, W., J., M., Freer, J., E., Woods, R., A. (2019). Technical note: Inherent benchmark or not? Comparing Nash-Sutcliffe and King-Gupta efficiency scores. Hydrology & Eath System Sciences. Discussions. https://doi.org/10.5194/hess-2019-327