Table 1 The evaluation metrics used to quantify the potential model performance improvements for different characteristics of the streamflow time series
From: Hybrid approaches enhance hydrological model usability for local streamflow prediction
Characteristic of the streamflow signal | Evaluation metric | Abbreviation | Equation |
|---|---|---|---|
Total volume | Scaled mean absolute error | SMAE | \({MAE}=\frac{{\sum }_{t=1}^{T}\left|{y}_{o}^{t}-{y}_{m}^{t}\right|}{T}\) \({SMAE}=\frac{{MAE}}{\bar{{y}_{o}}}\) |
High streamflow extreme | Nash-sutcliffe efficiency | NSE | \({NSE}=1-\frac{{\sum }_{t=1}^{T}{({y}_{o}^{t}-{y}_{m}^{t})}^{2}}{{\sum }_{t=1}^{T}{({y}_{o}^{t}-\bar{{y}_{0}})}^{2}}\) |
Low streamflow extreme | Logarithmic nash-sutcliffe efficiency | logNSE | \(\log {NSE}=1-\frac{{\sum }_{t=1}^{T}(\log ({y}_{o}^{t})-\log ({y}_{m}^{t}))^{2}}{{\sum }_{t=1}^{T}(\log ({y}_{o}^{t})-\log (\bar{{y}_{0}}))^{2}}\) |
