# Evaluating CHIRPS and ERA5 for Long-Term Runoff Modelling with SWAT in Alpine Headwaters
## 4 Discussion  
### Evaluation Using Precipitation Observations  
In this study, the performance of two gridded precipitation products (GPPs), namely the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS-V2.0) and the fifth generation of the global weather and climate reanalysis ERA5, was assessed for long-term runoff simulations and runoff trend evaluations in mountainous regions. The pilot area comprised three catchments (b1 Sill with 853 km\({}^{2}\), b2 Drava with 669 km\({}^{2}\), and b3 Isel catchment with 1197 km\({}^{2}\)) in the inner Alps in Austria, with similar geophysical characteristics and with a good coverage of ground meteorological and hydrological stations. The catchments share a substantial elevation difference, very steep slopes and similar soil mix. The b1 Sill River flows mainly northwards, and the catchment is covered by 3.6% of glacial ice. The b2 Drava River originates from a spring and flows mainly easterly, and the b3 Isel River rises at the glacier gate and flows mainly southerly. The  
Figure 11: Uncertainty in monthly runoff trend estimates (_TQ_) from model simulations for all stations when using GPPs and measured precipitation as inputs 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 trend bias, while the whiskers represent the extreme values. evergreen forests dominate in catchments b1 and b2, while rock cover predominates in catchment b3. Given that runoff regimes and trends in mountainous areas are driven by a combination of surface (precipitation, evapotranspiration, infiltration, snow/ice melt) and subsurface processes, a spatiotemporal assessment framework was adopted to credibly evaluate GPP performance. The first step involved assessing climate and runoff variations over a longer timeframe to select a relatively uniform trend period. Analyses using 11-year moving averages, linear trends, RAPS, Mann-Kendall statistics, and trend significances for _TG_, \(P\), _SD_, and \(Q\) between 1951 and 2018 revealed a break around the 1980s. The identified break and all of the resulting long-term trends in the indices (Table 7), along with an increasing _TG_ trend of approximately +0.3 \({}^{\circ}\)C 10y\({}^{-1}\), are consistent with other studies [20; 26; 29; 30; 32; 124; 125; 126; 127]. The post-1991 period was adopted for GPP assessment due to more consistent trends with fewer perturbations. Secondly, the number of GPP grid points (4/6/9 ERA5 and 39/33/52 CHIRPS grid points in or around catchments b1/b2/b3) differs from the available precipitation gauges (15/12/13 gauges, respectively). To address this mismatch, interpolation methods such as simple averaging, inverse distance weighting, and Thiessen polygons were tested, though no universally optimal method could be identified [51]. To overcome the scale discrepancy, two approaches were evaluated using double mass curves: spatially averaged precipitation from nearby gauges and single gauge data. As the averaged data showed significantly higher bias, selected gauge-grid point pairs were used in the subsequent analysis. The results show that both CHIRPS and ERA5 overestimate annual and seasonal precipitation, particularly at lower elevations and during the cold season. CHIRPS shows lower annual _RMAE_P_ than ERA5 (25% CHIRPS, 37% ERA5). The CHIRPS annual \(P\) performance aligns with findings from the Alto Adige region in Italy, which reported the same absolute \(P\) error (25%) [51]. Although ERA5 shows higher annual _RMAE_P_, it exhibits a lower and more consistent correlation (\(R^{2}\_P=0.97\)) across the pilot area. This pattern of ERA5 overestimation with higher correlation is also observed in the Alps and elsewhere in Europe [58; 94].