# Evaluating CHIRPS and ERA5 for Long-Term Runoff Modelling with SWAT in Alpine Headwaters
## 2 Materials and Methods
### Evaluation Using Hydrological Model  
In addition to the point-to-point analysis, the performance of the GPPs was evaluated on an area basis by comparing the runoff from the hydrological model for different precipitation inputs with the observed runoff. A physically based, semi-distributed model SWAT was selected, which has been successfully applied in catchments with snowmelt ([PERSON] et al. [115], [PERSON] et al. [116] and [PERSON] et al. [89]). Background data, including DEM (25 m grid resolution), soil grid (1000 m resolution) and land cover (100 m resolution), were available from the European land databases (Table 4). The analysis of land cover changes over four consecutive periods (1990-2000, 2000-2006, 2006-2012 and 2012-2018) did not reveal any significant changes, so land cover was considered unchanged in the model simulations. ArcSWAT (version 2012.10.24) was used to extract model parameter values from the background layers and databases. Components calculated in the model include interception, evapotranspiration, soil and snow evaporation, soil and root zone infiltration, surface runoff and groundwater flow. The calculation of the snow water equivalent (_SWE_) is influenced by the definition of the air temperatures of snowfall and snowmelt, and the calculation of the snowpack temperature (\(T_{snow}\)) is influenced by the depth, exposure and density of the snowpack [115]. The lapse rates of air temperature (\(dT/dZ\)) and precipitation (\(dP/dZ\)) were derived for each of the pilot catchments from the regression between elevation and annual means, as proposed by [PERSON] et al. [115]. Five classes of slope definitions and temperature bands were applied, as suggested in previous studies [92; 117]. The air temperature in an elevation band for each catchment was used to determine the precipitation type. To determine the amount of meltwater released, the melt coefficient (\(b_{mllt}\)) was used, which is defined as a  
Figure 3: RAPS results for the observed climate indices (\(TG\), \(P\), \(SD\)) at stations Sonnblick (SO), Züspütze (ZU), Hohenpeissenberg (HO) and Innoglstadt (IN), and runoff index (\(Q\)) at stations Puig (PUI), Innsbruck (INN), Rabland (RAB), Bruhl (BRU) and Lienz (LIE) in period 1951–2018. sine wave with a minimum of 0.20 cm/\({}^{\circ}\)C on 21 December and a maximum of 0.60 cm/\({}^{\circ}\)C on 21 June [89; 115; 118; 119]. To test the performance of the model, we used the SWAT Calibration Uncertainty Programs (SWAT-CUP) with the SUFI-2 algorithm, which has been shown to be appropriate in other similar studies [86; 88; 89; 120]. The aim of a successful calibration is to find the right balance between the two statistical factors (P-factor and R-factor). The performance of the model is considered adequate if the P-factor > 0.7 and the R-factor < 1.5. Nash-Sutcliffe efficiency (_NSE_) and the coefficient of determination (\(R^{2}\)), which are commonly used in snow-dominant catchments [89; 90; 116], were used to evaluate model skill. In each of the catchments, two hydrological stations are located in the upper and middle section, with the third at the outlet of the catchment (Figure 1). The model parameters were calibrated for the period 1996-2005, during which several wet and dry years were observed. The model was validated for the period 2009-2013. The snowmelt parameters (_SMTMP_ and _TIMP_) showed a significant influence on the model performance, as suggested by [PERSON] et al. [121]. The initial model parameter values were similar to those from the SWAT study in the region (about 100 km southwest of the pilot area) and from other studies in mountainous regions with snow accumulation/melt processes [86; 87; 89]. Table 8 shows final model parameters for weather, snow, surface runoff and groundwater parameters after the calibration process. The SWAT model was calibrated using observed precipitation at a monthly time step. Daily discharge observations from Austria's national database (eHYD) for nine hydrological stations were upscaled to mean monthly discharge time series.