# Evaluating CHIRPS and ERA5 for Long-Term Runoff Modelling with SWAT in Alpine Headwaters
## 4 Discussion
### Evaluation Using Precipitation Observations
The lower performance in winter was previously reported [128] and can be linked to non-convective precipitation systems, which are difficult for satellite algorithms to detect, and to snow and ice cover that interfere with microwave-based retrievals by producing signals similar to atmospheric ice particles [51]. This leads to mismatches in vertical precipitation lapse rates, which are present in both GPPs but are not accurately captured. A study in Turkey similarly found that GPPs tend to overestimate precipitation in leeward regions and underestimate it in windward areas during winter [57]. On a monthly scale, CHIRPS again shows a lower _RMAE_ (40%) compared to ERA5 (44%), though ERA5 has a higher correlation (\(R^{2}\_P=0.83\) CHIRPS, 0.91 ERA5). A comparable monthly absolute error for CHIRPS (\(MAE\_P\_monthly=34\) mm) was reported in other studies (\(MAE\_P\_monthly=30.6\) mm) [80]. The pattern of precipitation trend performance of products aligns with their \(P\) performance. Both products overestimate \(P\) trends and more evidently during the warm season and in valleys. CHIRPS shows lower \(P\) trend bias, but ERA5 shows a higher \(P\) trend correlation with fewer misses of statistically significant monthly trends. CHIRPS shows higher annual \(P\) and _TP_ performance in catchments b1 and b2, while ERA5 shows higher \(P\) and _TP_ performance in catchment b3. The cascaded performance of GPPs from \(P\) to _TP_ has been reported, along with similar _TP_ performance between CHIRPS and ERA-Interim (\(Error\_TP\) = 1.87% y\({}^{-1}\) CHIRPS, 1.97% y\({}^{-1}\) ERA-Interim) [80]. Regarding monthly precipitation trends, ERA5 exhibits a closer alignment with observed trends (\(MAE\_TP\_monthly=9.5\) mm 10y\({}^{-1}\) CHIRPS, 7.5 mm 10y\({}^{-1}\) ERA5) and fewer missed trend significances compared to CHIRPS. Both products successfully captured the observed significant positive _TP_ trendsin January, February, and August. The August trend, observed at 78% of gauges, is likely linked to increased topographically induced summer convection [16]. The point-to-point assessment of GPPs indicates that both CHIRPS and ERA5 tend to overestimate annual and seasonal precipitation amounts as well as precipitation trends, particularly at lower elevations. The observed vertical precipitation lapse rate is inconsistent across the pilot area, resonating with findings from other mountainous regions [57] and reinforcing the idea that a simple lapse rate cannot be reliably applied in the Alps and should be used with caution [23; 57]. While observed annual _TP_ during the 1991-2018 period is consistently positive across all catchments and elevation bands, seasonal _TP_ trends show varying strengths across elevation bands and between catchments. This strong spatial and vertical variability in seasonal _TP_ is consistent with findings from other studies in the Alps [129]. These results highlight a pronounced topographic influence on precipitation, with slope and terrain shielding playing a significant role in modulating precipitation patterns in the Alps. These complexities limit the effectiveness of point-to-point GPP evaluation and underscore the importance of integrating hydrological modelling to support a more comprehensive assessment of GPP performance in mountainous regions.