# Evaluating CHIRPS and ERA5 for Long-Term Runoff Modelling with SWAT in Alpine Headwaters
###### Abstract  
Reliable gridded precipitation products (GPPs) are essential for effective hydrological simulations, particularly in mountainous regions with limited ground-based observations. This study evaluates the performance of two widely used GPPs, CHIRPS and ERA5, in estimating precipitation and supporting runoff generation using the Soil and Water Assessment Tool (SWAT) across three headwater catchments (Sill, Drava and Isel) in the Austrian Alps from 1991 to 2018. The region's complex topography and climatic variability present a rigorous test for GPP application. The evaluation methods combined point-to-point comparisons with gauge observations and assessments of generated runoff and runoff trends at annual, seasonal and monthly scales. CHIRPS showed a lower precipitation error (RMAE = 25%) and generated more consistent runoff results (RMAE = 12%), particularly in smaller catchments, whereas ERA5 showed higher spatial consistency but higher overall precipitation bias (RMAE = 37%). Although both datasets successfully reproduced the seasonal runoff regime, CHIRPS outperformed ERA5 in trend detection and monthly runoff estimates. Both GPPs systematically overestimate annual and seasonal precipitation amounts, especially at lower elevations and during the cold season. The results highlight the critical influence of GPP spatial resolution and its alignment with catchment morphology on model performance. While both products are viable alternatives to observed precipitation, CHIRPS is recommended for hydrological modelling in smaller, topographically complex alpine catchments due to its higher spatial resolution. Despite its higher precipitation bias, ERA5's superior correlation with observations suggests strong potential for improved model performance if bias correction techniques are applied. The findings emphasize the importance of selecting GPPs based on the scale and geomorphological and climatic conditions of the study area. g riderd precipitation products (GPPs); hydrological modelling; soil and water assessment tool; mountainous catchments; ERA5; CHIRPS; trend analysis 2025 17 2116 10.3390/w17142116 10.3390/w17142116 10.3390/w1714216 10. (\(P\)) is generally the most important for model performance [8], while air temperature (\(T\)) governs evapotranspiration and controls snow and ice accumulation and melt processes. A study of 156 Austrian catchments found that weak model performance was largely related to precipitation inhomogeneities and the omission of changes in vegetation dynamics [7]. In sparsely populated, mountainous areas, where in-situ weather gauges networks are typically sparse, gridded \(P\) and \(T\) products are especially valuable. They enable climate and hydrological studies in otherwise inaccessible or data-scar areas [9; 10; 11]. European Alpine catchments, like many regions worldwide, have experienced climate and land-use changes in recent decades, which have significantly altered the long-term runoff regime. Studies report a statistically significant rise in air temperature across the Alps [12; 13; 14], with a +2 \({}^{\circ}\)K increase in minimum daily air temperature (\(TN\)) in the 1901-1992 period, particularly at lower elevations [13], and more frequent days with \(TN\)\(>\) 0 \({}^{\circ}\)C in winter and spring [12]. Precipitation trends show a statistically significant increase in winter in Switzerland (1901-1990) [15] and for winter and spring during the 1931-2000 period [12], though the \(P\) trend was less pronounced than runoff increases in the same seasons. Snow depth trends in the Alps show fluctuations around the mean until 1980 but then a decline, especially at lower elevations [16; 17] due to rising \(TN\)[13]. Above 2000 m a.s.l., snow depths show no changes [16; 18]. Overall, 85% of the stations show decreasing trends (26% significant) and 15% increasing (less than 1% significant), indicating high spatial and inter-annual variability in snow depths across the region [16].