# Evaluating CHIRPS and ERA5 for Long-Term Runoff Modelling with SWAT in Alpine Headwaters
## 3 Results
### Performance Evaluation Using Hydrological Model
The unexpected ERA5 correlation is mainly influenced by the performance in catchment b1 Sill. A comparison of the model results forced with GPPs and measured precipitation against the observed runoff shows that CHIRPS generates a lower uncertainty for the annual and seasonal runoff (_PB_Q_, _RMAE_) in catchment b1 and that ERA5 generates a lower uncertainty for the annual and warm season runoff (_PB_Q_, _RMAE_) in catchment b3 than the model with measured precipitation (MEAS). For the monthly time step (Figure 10), the runoffs generated by CHIRPS show a higher performance (lower _PB_Q_, higher _NSE_) than ERA5 for all months except May and October. The model was able to replicate the lower ERA5 May precipitation error to the lower ERA5 May runoff uncertainty. The ERA5 mean monthly runoff bias is positive, which is inconsistent with the negative runoff bias generated with measured precipitation. For the monthly time step, the runoff generation with measured precipitation (MEAS) generally outperforms the best GPPs by a relatively small margin (_NSE_ = 0.871 CHIRPS, 0.877 MEAS in catchment b1; _NSE_ = 0.871 ERA5, 0.885 MEAS in catchment b3). The GPP-generated runoff slightly outperforms the MEAS-generated runoff in April (CHIRPS) and June (ERA5). The runoff results of the SWAT model indicate that both products provide generally comparable performance, with a lower overall runoff _RMAE_ and a higher \(R^{2}\) for CHIRPS. Both products show higher runoff uncertainty in the cold season, likely due to increased precipitation uncertainty in that season. CHIRPS delivers consistent \(Q\) performance across pilot catchments, consistently underestimating runoff and increasing skill with increasing catchment size, similar to the model with observed precipitation. In contrast, ERA5 shows spatially inconsistent model performance. At annual and seasonal scales, the model forced with the best-performing GPP outperforms the model using measured precipitation. At the monthly scale, the model with measured precipitation (MEAS) performs slightly better overall, although the GPP-forced simulations outperform measured precipitation in two individual months. The observed trend in annual runoff (\(TQ_{annual}\)) is generally positive in all catchments (Table 13). Although not statistically significant, the positive annual runoff trend in catchment b3 Isel (\(TQ_{annual}\) = 50.0 mm 10\(y^{-1}\)) is much stronger than in catchment b1 Sill (\(TQ_{annual}\) = 30.4 mm 10\(y^{-1}\)). The observed cold seasonal trend is uniformly positive and strong in the pilot areas (statistically significant at \(p<0.05\) level), dominated mainly by the strong April and November trends (positive April trend is statistically significant at \(p<0.01\) level at 66% of stations). However, the strong April runoff trend is primarily related to the increased snow and ice melt and not to the increase in precipitation (observed April precipitation trend is negative). The observed warm seasonal trend is also positive but weaker than in the cold season and with alternating strength between catchments and months. Positive \(TQ_{monthly}\) is observed in May and September (positive September trend is statistically significant at \(p<0.05\) level at 33% of the stations) and negative in July and October. The negative precipitation trends in July and October are also reflected in negative runoff trends. Figure 10: Uncertainty in monthly model runoff estimates (\(Q\)) at all stations using GPPs and measured precipitation for all catchments (**left**) and boxplot per catchment (**right**). The boxplot depicts the 25 th, 50 th and 75 th percentile of the monthly runoff bias, while the whiskers represent the extreme values. Figure 9: Locations of ERA5 grid points in pilot catchment (**a**), and model runoff performance (_NSE_) in relation to catchment size using GPPs and measured precipitation (**b**). The model runoff trends (\(TQ\)) forced with GPPs were compared with the observed trends and with the model runoff trends forced with measured precipitation (MEAS).