Daily information of snow cover area was retrieved from a digital camera (Campbell CC640 digital camera) with a resolution of 640 × 480 pixels that allows to cover around 90% of the catchment (Figure 1). Daily photos were orthorectified and binarized into snow cover maps (presence/absence of snow). Data affected by low clouds were not included in the analysis. The daily photos were used to create series summarizing the snow cover area that authors consider fully representative of the conditions over the entire catchment (Revuelto et al., 2020). In addition, periodic field surveys were performed to derive distributed information on snow depth. In 2017 and 2018, snow maps were made using a terrestrial laser scanner (TLS) at dates close to the maximum snow accumulation (Revuelto et al., 2014). Normally, snow depth maps of the catchment were made by merging point clouds from two different scan positions (Figure 1). However, due to the harsh meteorological conditions of the 2018 snow season, we only obtained information from one scan position for this year. Therefore, the 2018 snow depth map presents large areas affected by topographic shadows. In 2019 and 2020, snow depth maps were created by photogrammetry based on Structure from Motion (SfM) algorithms with photos retrieved from a fix wing unmanned aerial vehicle (Ebee+) following the methodology presented by Revuelto et al. (2021). Meteorological and snow information were used to characterize the meteorological conditions and the magnitude and persistence of snow in the catchment during spring over the 2017-2020 period.