# Evaluating CHIRPS and ERA5 for Long-Term Runoff Modelling with SWAT in Alpine Headwaters
## 4 Discussion
### Evaluation Using Hydrological Modelling
These consistent performance cascades from precipitation and precipitation trends to runoff and runoff trends confirm the substantial impact of precipitation performance on hydrological model outputs in the pilot area. Despite spatially homogeneous ERA5 performance for annual \(P\) (_PB_P_ = 40% in b1, 43% in b3) and annual _TP_ (_PB_TP_ = 10% in b1, 14% in b3), this consistency is not reflected in homogeneous \(Q\) performance (_PB_Q_ = 28% in b1, \(-\)9% in b3). In contrast, \(Q\) performance for CHIRPS (_PB_Q_ = \(-\)11% in b1, \(-\)11% in b3) and measured precipitation (_PB_Q_ = \(-\)13% in b1, \(-\)15% in b3) are spatially homogeneous, even though CHIRPS shows notable spatial variability in annual \(P\) (_PB_Q_ = \(-\)3% in b1, 45% in b3) and annual _TP_ (_PB_TQ_ = \(-\)6% in b1, 31% in b3). Furthermore, ERA5 shows the highest performance among all three precipitation inputs in catchment b3 for \(Q\) (_PB_Q_ = \(-\)11% CHIRPS, \(-\)9% ERA5, \(-\)15% MEAS) and _TQ_ (_PB_TQ_ = 35% CHIRPS, 27% ERA5, 44% MEAS) but the lowest in catchment b1 (_PB_Q_ = \(-\)11% CHIRPS, 28% ERA5, \(-\)13% MEAS) and _TQ_ (_PB_TQ_ = \(-\)13% CHIRPS, \(-\)25% ERA5, \(-\)41% MEAS). This spatial inconsistency is largely attributed to ERA5's coarser resolution and fewer grid points. Catchment b3 benefits from two ERA5 grid points near its centroid, while catchment b1 has only one point, located at its upper boundary. Additionally, ERA5 performance in b1 does not improve with increasing catchment size, whereas such a correlation is evident in b3 (Figure 9b). These findings suggest that the spatial resolution and placement of GPP grid points are critical for reliable hydrological modelling in mountainous terrain. Coarser-resolution products with few or poorly placed grid points can yield spatially inconsistent runoff estimates, regardless of their apparent precipitation accuracy. In mountainous catchments smaller than 1000 km\({}^{2}\), CHIRPS consistently generates lower runoff and trend uncertainties. In catchments larger than 1000 km\({}^{2}\), runoff uncertainty becomes comparable between CHIRPS and ERA5. This aligns with findings from North America [78], where ERA5-generated performance was largely independent of catchment size in catchments larger than 1000 km\({}^{2}\). Although ERA5 exhibits a larger bias monthly precipitation compared to CHIRPS, its consistently higher correlation with observed precipitation indicates that ERA5 effectively captures the temporal precipitation dynamics across the study area. This bias in ERA5 could be mitigated by applying one of the bias-correction methods prior to its use in hydrological models. Recent studies applying univariate/multivariate statistical methods and supervised machine learning techniques to the ERA5-Land product in Alto Adige in Italy have demonstrated the effectiveness of multivariate and machine learning approaches for ERA5 bias correction in complex terrains [130]. Despite the observed biases in both GPPs, this study implemented only indirect correction through the calibration of snowmelt-related parameters in SWAT model development (Table 8), a recommended approach for mountainous catchments of complex topography [89] and when snowmelt parameters influence model performance [68; 131]. Nevertheless, previous studies have shown that directly bias-corrected precipitation inputs significantly improve SWAT model performance [132]. Therefore, even with its initial precipitation bias, ERA5 could potentially outperform CHIRPS in hydrological modelling applications if appropriate bias correction techniques are applied. Some uncertainties in hydrological modelling remain. Although CHIRPS shows lower uncertainty in monthly _TQ_, this is inconsistent with the lower monthly _TP_ error observed for ERA5. Several modelling-related factors may explain this discrepancy.