Science Case Name Multi-Hazards in the Downstream Area of the Adige River Basin. Dataset Name/Title DBSCAN 3D Clusters of SPEI-90 Days Values – Italian NUTS3 (ITH10, 20, 31, 32, 33, 34, 35, 36, 37), 1981–2023 Dataset Description Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm output based on the daily Standardized Precipitation Evapotranspiration Index (SPEI) with a timescale of 90 days applying the threshold SPEI-90 days ≤ -1. Key Methodologies The DBSCAN algorithm included in the scikit-learn package in Python environment (https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html) was applied to detect spatio-temporal drought clusters, using the SPEI-90 days values as input. Three parameters guide the DBSCAN clustering procedure: the neighborhood parameter (ε), which defines the search radius around a point (a SPEI value); the spatio-temporal ratio (r), which controls the importance of spatial distance relative to temporal lag when computing the Euclidean distance between data points; the density threshold parameter (μ), representing the minimum number of neighbors required for a point to be considered as a core point (a point representing a suitable point to generate a new cluster). The selected parameter values are: neighborhood parameter (ε) = 20, spatio-temporal ratio (r) = 4 and density threshold (μ) = 20. These values were selected based on their physical significance and through the comparison with drought historical events retrieved from newspapers, official regional bulletins and technical reports. Temporal Domain 1981–2023 Spatial Domain The spatial domain of the dataset is represented by grid points within the Italian Provinces identified by the NUTS3 codes ITH10 (South-Tyrol), ITH20 (Trento), ITH31 (Verona), ITH32 (Vicenza), ITH33 (Belluno), ITH34 (Treviso), ITH35 (Venezia), ITH36 (Padova), ITH37 (Rovigo). Key Variables/Indicators Spatio-temporal clusters with SPEI-90 days ≤ -1 Data Format Comma Separated Values (CSV) Source Data SCIA dataset (the Italian National System for the collection, processing and dissemination of climate data, www.scia.isprambiente.it) Accessibility https://doi.org/10.5281/zenodo.15212462 Stakeholder Relevance Both the daily SPEI index and its use as an input to the DBSCAN algorithm for identifying spatio-temporal drought clusters represent a key step in detecting the spatial and temporal footprints of hazard events. The cluster identification enables a greater understanding of hazard dynamics, facilitates integration with other hazard footprints and fosters the use of Earth Observation (EO) data. This approach, based on observed meteorological data, provides a robust method for identifying hazard events, which can be further refined through the use of higher spatial resolution EO data capable of capturing finer spatial variations (e.g., drought induced changes in soil moisture or variations in land surface temperature in response to different land uses during extreme hot conditions). Limitations/Assumptions None. Additional Outputs/information The dataset access is currently restricted due to pending related publication. Contact Information Masina, Marinella (CMCC Foundation - Euro-Mediterranean Center on Climate Change; Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice) - Data manager Ferrario, Davide Mauro (CMCC Foundation - Euro-Mediterranean Center on Climate Change; Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice) - Data manager Maraschini, Margherita (CMCC Foundation - Euro-Mediterranean Center on Climate Change; Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice) - Data manager Furlanetto, Jacopo (CMCC Foundation - Euro-Mediterranean Center on Climate Change; Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice; National Biodiversity Future Center) - Data manager Torresan, Silvia (CMCC Foundation - Euro-Mediterranean Center on Climate Change; Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, National Biodiversity Future Center) - Data manager