# Evaluating CHIRPS and ERA5 for Long-Term Runoff Modelling with SWAT in Alpine Headwaters
## 2 Materials and Methods
### Input Datasets  
The observations of daily precipitation \(P\), air temperature \(TG\) and snow depth \(SD\) were available from the European Climate Assessment & Dataset ([PERSON], 2011) and from the national meteorological databases, including the Bundesministerium fur Nachhaltigkeit und Tourismus (eHYD) in Austria and the Autonome Provinz Bozen in Italy. The daily discharge data \(Q\) were available from the Austrian database (Bundesministerium fur  
Figure 1: Pilot areas b1 Sill, b2 Drava and b3 Isel with stream network and locations of meteorological and hydrological stations.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Catchment** & **Area** & \begin{tabular}{c} **Mean** \\ **Discharge** \\ \end{tabular} & \begin{tabular}{c} **Orthic** \\ **Podzols** \\ \end{tabular} & \begin{tabular}{c} **Dystric** \\ **Cambisols** \\ \end{tabular} & \begin{tabular}{c} **Lithosols** \\ \end{tabular} & \begin{tabular}{c} **Evergreen** \\ **Forest** \\ \end{tabular} & \
\begin{tabular}{c} **Bare Rock** \\ \end{tabular} &
\begin{tabular}{c} **Moors and** \\ **Healthland** \\ \end{tabular} \\ \hline (b1) Sill River & 853 km\({}^{2}\) & 25.1 m\({}^{3}\) s\({}^{-1}\) & 39\% & 20\% & 15\% & 31\% & 26\% & 23\% \\ (b2) Drava River & 669 km\({}^{2}\) & 13.7 m\({}^{3}\) s\({}^{-1}\) & 17\% & 31\% & 15\% & 46\% & 25\% & 17\% \\ (b3) Isel River & 1197 km\({}^{2}\) & 39.9 m\({}^{3}\) s\({}^{-1}\) & 40\% & 21\% & 21\% & 26\% & 40\% & 23\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dominant soil and land cover types in the pilot areas.  
Nachhaltigkeit und Tourismus--eHYD), see Table 4 for details. The time series of air temperature and other weather data (solar radiation, relative humidity, wind speed) were overtaken from the Climate Forecast System Reanalysis (CFSR) datasets [52], which are produced by combining gauge and satellite data and coupled with advanced atmospheric, oceanic and surface modelling components at a resolution of about 38 km [63]. The advantages of CFSR datasets for hydrological modelling include good applicability in remote areas with scarce gauging stations [52; 62; 63], good temporal coverage and no gaps in the data.  
The performance of the two GPPs was assessed and compared: Climate Hazards Group InfraRed Precipitation with Station (CHIRPS-V2.0) and the fifth generation of the global weather and climate reanalysis ERA5. The CHIRPS-V2.0 provides quasi-global (50S-50N) high-resolution precipitation grids. The product is based on CHPclim, which combines several data inputs: observed monthly means from the FAO (UN) Agromet Group, version 2 of the GHCN (Global Historical Climatology Network) [102] combined with five satellite products (microwave precipitation estimates from Tropical Rainfall Measuring Mission 2B31 [47], microwave plus infrared based precipitation estimates from CMORPH [45], monthly mean geostationary infrared brightness temperatures [103], and land surface temperature estimates [104]), all resampled to 0.05 degrees [38]. The CHIRPS-V2.0 dataset is available from 1981 to near real-time on the Climate Hazard Center server. The ERA5 dataset covers the period from 1979 to the near present and is produced by the Copernicus Climate Change Service (C3S) at ECMWF [50]. The ERA5 is generated by using Cycle 41r2 of the Integrated Forecast System (IFS) based on 4D-Var data assimilation in 2016 [105]. The ERA5 has a temporal resolution of 1 h and a spatial resolution about 31 km for atmospheric variables at 139 pressure levels in the vertical [106].