High-resolution hydrometeorological and snow data for the Dischma catchment in Switzerland

Abstract

We present an hourly hydrometeorological and snow dataset with 100 m spatial resolution from the alpine Dischma watershed and its surroundings in eastern Switzerland, including station measurements of variables such as snow depth and catchment runoff. This dataset is particularly suited for different modelling experiments using distributed and process-based models, including physics-based snow and hydrological models. Additionally, the data are highly useful for testing various snow data assimilation schemes and for developing models representing snow–forest interactions. The dataset covers 7 water years from 1 October 2016 to 30 September 2023. The complete domain spans an area of 333 km2 with altitudes ranging from 1250 to 3228 m. The Dischma Basin, with its outlet at 1671 m elevation, occupies 42.9 km2. Included in the dataset are high-resolution (100 m) hourly meteorological data (air temperature, relative humidity, wind speed and direction, precipitation, and long- and shortwave radiation) from a numerical weather predication model and rain radar, land cover characteristics (primarily forest properties), and a digital elevation model. Notably, the dataset includes snow depth acquisitions obtained from airborne lidar and photogrammetry surveys, constituting the most extensive spatial snow depth dataset derived using such techniques in the European Alps. Along with these gridded datasets, we provide daily quality-controlled snow depth recordings from seven sites, biweekly snow water equivalent measurements from two locations, and hourly runoff and stream temperature observations for the Dischma watershed. The data compiled in this study will be useful to further develop our ability to forecast snow and hydrological conditions in high-alpine headwater catchments that are particularly sensitive to ongoing climate change. All data are available for download at https://doi.org/10.16904/envidat.568 (Magnusson et al., 2024).