# Evaluating CHIRPS and ERA5 for Long-Term Runoff Modelling with SWAT in Alpine Headwaters
## 4 Discussion
### Evaluation Using Hydrological Modelling
Previous studies [75; 76] similarly report acceptable SWAT performance using CHIRPS and other satellite-based products (such as CFSR, structurally comparable to ERA5), with CHIRPS often performing higher. Findings using other hydrological models support the SWAT model results. A study in a mountainous catchment in Turkey [128] showed that ERA5 underperformed when the model was calibrated with observed precipitation, while CHIRPS2.0 performed higher when GPP was used for model calibration (\(NSE=0.750\) CHIRPS, 0.630 ERA5). Similarly, a study over African catchments [8] reported that CHIRPS slightly outperformed ERA5 in streamflow modelling with emphasis on climate change scenarios. Both GPPs generate lower runoff performance during the cold season, cascading lower GPP precipitation performance in that season. CHIRPS generates consistent runoff performance across the pilot areas, generally underestimating runoff but increasing skill with increasing catchment size, mirroring correlation when using observed precipitation. ERA5, in contrast, produces inconsistent model performance across pilot catchments. At the monthly time step, CHIRPS generates lower runoff uncertainty than ERA5 (\(RMAE\_Qmonth=20\%\) CHIRPS, 26% ERA5). At the annual and seasonal time steps, the model forced with the best-performing GPP outperforms the model using measured precipitation, while at the monthly time steps, the model using measured precipitation performs marginally higher than the model using the best-performing GPP. Simulated runoff trends (\(TQ\)) show that annual runoff trend performance closely follows precipitation trend performance (\(TP\)). Both products generate similar annual \(TQ\) uncertainty (\(MAE\_TQannual=10.8\) mm \(10\)y\({}^{-1}\) CHIRPS, 10.6 mm \(10\)y\({}^{-1}\) ERA5) and lower \(TQ\) performance in the warm season, cascading the similar annual and lower warm season \(TP\) performances. Similarly, CHIRPS generates lower \(TP\) and \(TQ\) uncertainties in catchment b1 and ERA5 in catchment b3. CHIRPS generates consistent \(TQ\) and \(TP\) biases for the annual and seasonal time steps, while ERA5 generates opposite precipitation and runoff biases in catchment b1 (\(PB\_TPannual=25\%\); \(PB\_TQ=-25\%\)). At the monthly time step, CHIRPS outperforms ERA for \(TQ\), generating higher correlation (\(R^{2}\_TQmonth=0.55\) CHIRPS, 0.43 ERA5), lower mean error (\(MAE\_TQmonth=4.0\) mm \(10\)y\({}^{-1}\) CHIRPS, 5.0 mm \(10\)y\({}^{-1}\) ERA5) and a lower amount of missed trend significance. Both products generate a relatively \begin{table}
\begin{tabular}{c|c c c|c c c} \hline \hline
**Catchment** & **Area 1990** & **Area 2006** & **Area 2012** & **Area 2018** & **1990–2018** & **1990–2018** \\  & **(km\({}^{2}\))** & **(km\({}^{2}\))** & **(km\({}^{2}\))** & **(km\({}^{2}\))** & **Difference** (km\({}^{2}\))** & **(\%)** \\ \hline b1 Sill & 29.47 & 19.48 & 20.33 & 19.35 & 10.12 & 34\% \\ b3 Isel & 86.44 & 51.00 & 49.32 & 48.40 & 38.04 & 44\% \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Glacierized area in pilot catchments from the Corine Land Cover (km\({}^{2}\)). strong negative _TQ_ in July, matching the observed runoff trend. Annual and seasonal _TQ_ uncertainty is lower for the best-performing GPP than for the model using measured precipitation, indicating their strong potential for long-term hydrological simulations. The model forced with the best-performing GPP can outperform simulations using measured precipitation for some months. CHIRPS's lower _RMAE_ in annual precipitation (25% CHIRPS, 37% ERA5) is cascaded to a lower _RMAE_ in simulated annual runoff (12% CHIRPS, 19% ERA5). Similarly, higher cold season precipitation _RMAE_ (39% CHIRPS, 51% ERA5) corresponds to elevated runoff _RMAE_ during the same period (30% CHIRPS, 44% ERA5). A higher annual _TP_ performance, CHIRPS in catchment b1 and ERA5 in catchment b3, also translates into higher _TQ_ performance in the respective catchments. Conversely, lower warm season _TP_ estimates from both GPPs are mirrored by lower _TQ_ performance during the same season.