# Evaluating CHIRPS and ERA5 for Long-Term Runoff Modelling with SWAT in Alpine Headwaters
## 4 Discussion
### Evaluation Using Hydrological Modelling  
Several processes, in addition to precipitation, were found to significantly influence runoff regimes and trends. The observed runoff regime shows a strong seasonal pattern, with higher flows in late spring and early summer driven by snowmelt and early summer storms. In catchment b2, runoff is primarily driven by spring discharge, which is reflected in the low performance of the SWAT model in this catchment. Long-term annual runoff (1951-2018) in catchment b2 shows a decreasing trend, in contrast to the increasing trends observed in the other two catchments (see Figure 2). During the 1991-2018 period, runoff trends show notable spatial and temporal variability, consistent with findings from other mountainous regions [19; 25; 26; 27; 28]. Secondly, annual \(Q\) trends are positive across all elevation bands, with stronger increases at lower elevations. In contrast, reduced runoff at higher elevations (stations b1_KRO and b3_BRU) is likely linked to increased evapotranspiration and snow depth decreases [25; 26]. Thirdly, significantly increasing April flows, statistically significant at four hydrological stations, could not be explained by the April precipitation trend but rather by enhanced and earlier snowmelt [19] and a shift from snow to rain due to rising air temperatures [16; 17; 25; 31]. Similarly, the strong May runoff trend significantly contributes to the overall warm season runoff trend (\(TQ_{warm}=19.8\) mm \(10y^{-1}\)) in catchments b1 and b3. Both catchments, located above 3000 m a.s.l., are glacier-covered, and the pronounced April-May runoff trends (Table 13) are likely driven by intensified glacier melt in recent decades [21; 22]. Land cover change in the pilot area between 1990 and 2018 confirms a substantial reduction in glacier-covered areas in catchments b1 and b3 (Table 14), consistent with EEA findings [29]. Snow depth records also show a marked decline across the pilot area (Table 7, Figure 2), aligning with broader observations in the Alps [124]. Alongside these cryospheric changes, a pronounced increase in May precipitation (_TP_ = 10.1 mm \(10y^{-1}\)) was observed, suggesting that increasing May runoff results from both enhanced snow and ice melt in spring and increased precipitation. Lastly, the warm season runoff trend in catchment b3 (\(TQ_{warm}=29.6\) mm \(10y^{-1}\)) is significantly stronger than in catchment b1 (\(TQ_{warm}=9.9\) mm \(10y^{-1}\)), a difference not explained by precipitation trends alone, as catchment b3 has a lower warm season precipitation increase (\(TP_{warm}=31\) mm \(10y^{-1}\)) than catchment b1 (\(TP_{warm}=38\) mm \(10y^{-1}\)). This spatial discrepancy likely stems from differences in ice and permafrost melt, driven by differing physical characteristics: b3 is generally south-facing and at a lower elevation, while b1 is north-facing and at a higher elevation. While dominant processes such as spring dis charge in catchment b2 or enhanced melt influencing April/May runoff trend are relatively easy identifiable, quantifying the relative contributions of climatic, environmental, and geophysical drivers in a transient environment is more complex and beyond the scope of this study. Nevertheless, key hydrometeorological processes and factors were considered while assessing GPP performance. The SWAT model performs well in catchments b1 and b3 (\(NSE=0.900\) and \(0.930\) at the outlets), accurately reproducing the observed runoff regime. In contrast, model performance is lower in catchment b2 (\(NSE=0.670\)), likely due to its spring-dominated runoff regime. SWAT simulations confirm that both CHIRPS and ERA5 reproduce the seasonal runoff pattern, with higher runoff in the warm season, and provide comparable model performance. Overall model performance improves from ERA5 (\(NSE=0.758\)) to CHIRPS (0.865) and observed precipitation (0.881), consistent with previous SWAT applications in Alpine catchments using CHIRPS and measured precipitation as inputs (\(NSE=0.810\) and 0.830, respectively) [64].