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This dataset supports the study “Predicting Stream Water Temperature: A Data-Driven Approach Highlighting the Impact of Riparian Vegetation”. It combines in-situ measurements, meteorological variables, and spatially explicit upstream vegetation features derived from remote sensing to model river water temperature across multiple stations in the Canton of Vaud (Switzerland). The dataset is organized into three main components: Stations data (stations_data_public.parquet)A tabular dataset containing daily observations at monitoring stations, including: Meteorological variables (e.g., humidity, radiation, evaporation, wind) Spatial information (coordinates, altitude, river name) Temporal features (date, year, trigonometric encoding of day-of-year) Water and air temperature variables Upstream vegetation features (upstream_slavi_df.parquet)Spatially distributed vegetation indicators derived from SLAVI and VHM (see references), computed along upstream river networks.For each station and year, vegetation is summarized as mean pixel values within buffers of varying: Distances upstream (e.g., 20 m to 500 m) Buffer widths (e.g., 4 m, 8 m, 12 m, 16 m)These features capture the spatial structure of riparian vegetation influencing water temperature at monitoring stations. Slope data (slopes.json)A dictionary mapping each station to its local slope, used to characterize flow conditions and modulate upstream influence. Purpose and Usage This dataset is designed for: Modeling river water temperature using machine learning approaches Studying the influence of riparian vegetation on thermal regimes Exploring spatial interactions between upstream environments and local hydrology It supports reproducibility of the results presented in the associated article and can support further research on climate change impacts, river restoration, and data-driven environmental modeling. Notes Meteorological variables were spatially interpolated from nearby stations (from MeteoSwiss) and should be considered approximations of local conditions. For data sharing reasons, water temperature (`water_90_percentile`) values are set to zero in this initial public release. The original temperature data can nevertheless be obtained upon request: River temperature data are partially available from the General Directorate of the Environment of the Canton of Vaud (https://www.vhv-qualite.ch/) upon request Additional data are owned by La Maison de la Rivière (https://www.maisondelariviere.ch/) Code Code used for preprocessing and modeling is available at: https://github.com/SimWalther/ML4Water/ Funding This research was supported by the grant IA-RECHERCHE23-18 of the I&A Engineering and Architecture domain of the HES-SO University of Applied Sciences and Arts Western Switzerland.