DBSCAN 3D Clusters of SPEI-90 days Values – Italian NUTS3 (ITH31, 32, 34, 35, 36, 37), 1950–2023

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 (ITH31, 32, 34, 35, 36, 37), 1950–2023 Dataset Description Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm output based on the daily Standardized Precipitation Evapotranspiration Index (SPEI) with a 90-day timescale for elevation lower than 1500 m a.s.l. (https://doi.org/10.5281/zenodo.13778103) 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 used to detect the spatio-temporal clusters of droughts, taking the SPEI index as input. Three parameters guide the DBSCAN clustering procedure: the neighbourhood parameter (ε), which defines the search radius around a point (a SPEI index 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 neighbouring SPEI pixels required for a point to be considered as a core point (a point representing a suitable point to generate a new drought cluster). The selected parameter values are: neighbourhood parameter (ε) = 30, spatio-temporal ratio (r) = 3 and density threshold (μ) = 10. These parameters were selected based on their physical significane and then refined through the comparison with the drought historical events retrieved from newspapers, official technical reports and regional council resolutions. Temporal Domain 1950–2023 Spatial Domain The spatial domain of the dataset is represented by the Italian Provinces identified by the NUTS3 codes ITH31 (Verona), ITH32 (Vicenza), ITH34 (Treviso), ITH35 (Venezia), ITH36 (Padova), ITH37 (Rovigo) considering elevation lower than 1500 m a.s.l.. Key Variables/Indicators Spatio-temporal clusters of dry/drought events Data Format Comma Separated Values (CSV) Source Data E-OBS dataset (v29.0e), https://zenodo.org/records/13778103 Accessibility Zenodo, https://doi.org/10.5281/zenodo.13785996 Stakeholder Relevance Both the SPEI index and its adoption as an input in the DBSCAN algorithm used to identify drought spatio-temporal clusters represent a key step to identify the spatio-temporal footprints of hazard events. The identification of spatial patterns enables a greater understanding of hazard dynamics, it can be coupled with other hazard footprints (e.g., heatwaves) and fosters the use of EO data by providing a robust meteorological-based event identification to be then further refined by the use of higher spatial resolution EO data capable of capturing subtle spatial patterns (e.g., soil moisture as influenced by droughts, or land surface temperature as a response to different land uses during hot events). Limitations/Assumptions Excludes areas above 1500 m a.s.l. Additional Outputs/information Daily SPEI-90 Days Values - Elevation Below 1500 m a.s.l. (https://zenodo.org/records/13778103)The dataset access is currently restricted due to pending related publication. Contact Information Maraschini, Margherita (CMCC Foundation - Euro-Mediterranean Center on Climate Change) - Data managerMasina, Marinella (CMCC Foundation - Euro-Mediterranean Center on Climate Change) - Data curatorFurlanetto, Jacopo (CMCC Foundation - Euro-Mediterranean Center on Climate Change, National Biodiversity Future Center) - Data curatorFerrario, Davide Mauro (CMCC Foundation - Euro-Mediterranean Center on Climate Change) - Data curatorTorresan, Silvia (CMCC Foundation - Euro-Mediterranean Center on Climate Change, National Biodiversity Future Center) - Data manager