# Evaluating CHIRPS and ERA5 for Long-Term Runoff Modelling with SWAT in Alpine Headwaters
## 5 Conclusions
However, its strong performance in catchment b3, where grid alignment is more centrally located, emphasizes the importance of spatial alignment between GPP resolution and catchment morphology. Given its lower error and finer spatial resolution, CHIRPS can be recommended for hydrological modelling in catchments smaller than 1000 km\({}^{2}\), whereas ERA5 can be comparably suitable for applications in larger catchments, though this requires further confirmation. * ERA5 demonstrates a higher correlation with observed precipitation despite exhibiting a larger precipitation bias than CHIRPS. This suggests that ERA5 more accurately captures precipitation temporal variability, indicating the potential for improved runoff generation if bias correction techniques are applied. While CHIRPS shows better raw precipitation and runoff estimates due to its finer spatial resolution and lower initial biases, ERA5 could ultimately generate improved model performance after appropriate debiasing. Future work should explicitly investigate the impacts of bias correction on the ERA5 generated model performance in catchments with complex topography. * Model runoffs generated with measured precipitation outperform those generated using the best GPPs by a relatively small margin. This indicates that GPPs are not only viable substitutes for ground-based data but may, under certain conditions, outperform sparse or biased observational datasets in estimating both runoff and runoff trends. However, in this study, the GPP runoff was generated by a hydrologic model developed based on observed precipitation, so the GPP-generated runoff would perform differently if the model had been developed based on precipitation products. * Scale discrepancies between precipitation gauges and GPPs are a common challenge. Although interpolation techniques (simple averaging, inverse distance weighting, Thiessen polygons) are often used to mitigate these mismatches, no single method proves universally optimal across all areas [51]. This study observed better performance with selected point-to-point comparisons rather than with spatially averagedprecipitation. This highlights the importance of the careful selection of gauge-grid pairs in mountainous regions, where orographic effects are significant. Further research on preprocessing techniques for GPP assessments in complex terrains is recommended to enhance accuracy and applicability. * The performance of GPPs in representing precipitation amounts and trends cascades directly into the accuracy of simulated runoff and runoff trends. CHIRPS exhibits lower uncertainties in runoff estimates for catchments b1 and b2, while ERA5 performs better in catchment b3. Beyond precipitation data quality, the performance of runoff simulations is also influenced by local geomorphological and hydroclimatic conditions. Therefore, careful consideration of these site-specific factors is essential when evaluating the long-term suitability of GPPs for hydrological applications in mountainous regions. The study results demonstrate that CHIRPS is a reliable gridded precipitation product (GPP) suitable for long-term hydrological modelling in small- to medium-sized Alpine catchments, particularly in areas with limited or unreliable gauged precipitation data. However, several uncertainties remain. The absence of a glacier melt module in SWAT may reduce model accuracy in glacierized regions. Additionally, the use of time-invariant model parameters limits the model's ability in capturing climate-sensitive processes, such as snow dynamics and evapotranspiration. Future research should consider the application of distributed hydrological models with glacier components and explore time-varying calibration strategies to better represent changing snow, ice, and soil moisture conditions. Moreover, further studies should investigate the impacts of ERA5 bias correction to comprehensively assess its contribution to runoff generation in catchments with complex topography.