# Evaluating CHIRPS and ERA5 for Long-Term Runoff Modelling with SWAT in Alpine Headwaters
## 3 Results
### Performance Evaluation Using Hydrological Model
 } & \multirow{3}{*}{_mean m m_} & **Jan** & **Feb** & **Mar** & **Apr** & **May** & **Jun** & **Jul** & **Aug** & **Sep** & **Oct** & **Nov** & **Dec** \\ \hline \multirow{3}{*}{\(Q\)} & OBS & 80 & 29 & 26 & 30 & 50 & 128 & 165 & 142 & 112 & 83 & 69 & 55 & 69 \\  & CHIRPS & 72 & 16 & 11 & 14 & 46 & 127 & 147 & 133 & 104 & 76 & 63 & 54 & 69 \\  & ERA5 & 100 & 40 & 32 & 45 & 89 & 142 & 169 & 164 & 143 & 115 & 97 & 86 & 83 \\  & MEAS & 70 & 17 & 11 & 14 & 45 & 118 & 141 & 125 & 98 & 82 & 71 & 56 & 66 \\ \hline \multirow{3}{*}{\(R^{2}\)} & CHIRPS & 0.93 & 0.91 & 0.86 & 0.80 & 0.94 & 0.96 & **0.99** & 0.98 & 0.97 & 0.93 & 0.96 & 0.92 \\  & ERA5 & **0.94** & **0.92** & **0.88** & **0.90** & **0.95** & **0.95** & 0.98 & **0.98** & **0.98** & **0.98** & **0.97** & **0.97** \\  & MEAS & 0.95 & 0.89 & 0.86 & 0.85 & 0.94 & 0.98 & 0.98 & 0.98 & 0.99 & 0.98 & 0.99 & 0.99 & 0.94 \\ \hline \multirow{3}{*}{\(PB\)} & CHIRPS & \(-17\%\) & \(-45\%\) & \(-55\%\) & \(-52\%\) & \(-8\%\) & \(0\%\) & \(-11\%\) & \(-6\%\) & \(-7\%\) & \(-\%\) & \(-\%\) & \(-\%\) & \(-\%\) & \(6\%\) & \(-\%\) \\  & ERA5 & 34\% & **38\%** & **24\%** & **51\%** & **7\%** & **11\%** & **3\%** & **16\%** & **28\%** & **40\%** & **40\%** & 58\%** & \(-19\%\) \\  & MEAS & \(-17\%\) & \(-42\%\) & \(-57\%\) & \(-53\%\) & \(-11\%\) & \(-8\%\) & \(-15\%\) & \(-12\%\) & \(-13\%\) & \(-1\%\) & \(4\%\) & \(3\%\) & \(-5\%\) \\ \hline \multirow{3}{*}{\(MAE\)} & CHIRPS & **15** & **13** & **14** & **16** & **10** & **18** & **23** & **17** & **generally overestimate precipitation amounts, their model outputs may show lower uncertainty when compared directly to observed discharge (OBS) but higher uncertainty relative to the model forced with measured precipitation (MEAS). This discrepancy is accounted for in the evaluation of GPP performance using the hydrological model. The SWAT model results for both products follow the observed runoff regime, with higher runoff in the warm than in the cold season (Table 12). The performance of the annual model runoff is similar for both products (_RMAE_Q_ = 12% CHIRPS, 19% ERA5); however, the CHIRPS generates more consistent annual and seasonal runoff bias than ERA5 when compared to the model forced with measured precipitation (MEAS). The annual runoff uncertainty in pilot catchments follows the uncertainty of the annual precipitation amounts. The CHIRPS shows higher performance in the annual runoff in catchment b1 (_PB_Q_ = -11% CHIRPS, 28% ERA5, \(-\)13% MEAS), while the ERA5 performance is higher in catchment b3 (_PB_Q_ = 11% CHIRPS, \(-\)9% ERA5, \(-\)15% MEAS). Both products generate a higher model runoff uncertainty in the cold season (_RMAE_Q_ = 30% CHIRPS, 44% ERA5) than in the warm season (_RMAE_Q_ = 10% CHIRPS, 14% ERA5), which is attributed to the poorer estimates of precipitation amounts in the cold season (see previous section). The CHIRPS generates consistently negative runoff bias in the catchments (_PB_Q_ = -11%), while the ERA5 generates alternating signs in the runoff bias, positive in catchment b1 Sill (_PB_Q_ = 28%) and negative (_PB_Q_ = -9%) in catchment b3 Isel. The unexpected positive runoff bias in catchment b1 for ERA5 is mainly contributed by the high runoff bias in the cold season (_PB_Q_cold_ = 56%). The lower ERA5 model performance in catchment b1 can also be attributed to a lower number of grid points (a single grid point at the upper boundary), while the two grid points in the center of catchment b3 improve the ERA5 model performance (see Figure 9a). The model _NSE_ performance at all hydrological stations in catchments b1 and b3 is presented for the GPPs and the measured precipitation forcing (Figure 9b). A correlation between model performance (_NSE_) and upstream catchment size shows that CHIRPS generates increasing model performance with increasing catchment size and follows the performance when observed precipitation is used, while the ERA5 generates an unexpected decrease in model performance with increasing area.