DBSCAN 3D Clusters of hot conditions – Italian NUTS3 (ITH10, 20, 31, 32, 33, 34, 35, 36, 37), 1981–2023

Science Case Name

Multi-Hazards in the Downstream Area of the Adige River Basin.

Dataset Name/Title

DBSCAN 3D Clusters of hot conditions – 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 maximum temperature (T_max) exceeding the calendar day 90^th percentile of the reference 1991-2020 long-term climatological distribution for at least three consecutive days. The 90^th percentile of T_max for each calendar day was calculated using a centered 15-day running window (i.e., 7 days before and after each calendar day).

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 clusters of hot weather conditions. Three parameters guide the DBSCAN clustering procedure: the neighborhood parameter (ε), which defines the search radius around a point; 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) = 10 and density threshold (μ) = 10. These values were selected based on their physical significance and through the comparison with heatwave 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, ITH20, ITH31, ITH32, ITH33, ITH34, ITH35, ITH36, ITH37.

Key Variables/Indicators

Spatio-temporal clusters of hot weather conditions, identified through the daily maximum temperature

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

NA

Stakeholder Relevance

The use of daily maximum temperature as an input to the DBSCAN algorithm for identifying spatio-temporal clusters of hot weather conditions 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