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Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
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DBSCAN 3D Clusters of SPEI-90 days – Linguere, Senegal, 1981-2023

Authors: Prikaziuk, Egor; University of Twente; Van Westen, Cees; Manzella, Irene;

DBSCAN 3D Clusters of SPEI-90 days – Linguere, Senegal, 1981-2023

Abstract

Science Case Name Multi-Hazards in Senegal. Dataset Name/Title DBSCAN 3D Clusters of SPEI-90 days – Linguere, Senegal, 1981-2023 Dataset Description The dataset contains gridded data on SPEI-90 days over Linguere area of Senegal. Key Methodologies Droughts were computed with SPEI-90, with daily precipitation from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) (1981-2023) and daily maximum, average and minimum air temperature from ERA5-Land. Potential evapotranspiration (PET) was computed with the Hargreaves equation from the SPEI R package. Water balance, the difference between precipitation and PET, was aggregated to 90-day rolling sums and Z-scores were computed from distributions of values from day of year (43 points). Days with Z-scores below or equal to –1 were marked as droughts. Spatio-temporal DBSCAN was conducted with Python packages st_dbscan https://github.com/eren-ck/st_dbscan (Cakmak et al., 2021). Spatial proximity (epsilon 1) was set to 0.5 (0.5 degree), temporal proximity (epsilon 2) was set to 1.5 (1.5 days); min number of samples was set to 30. The values in the NetCDF files represent cluster numbers (from 0 to 11), with values -1 (negative 1) representing outliers. A summary table in CSV represents rows for each cluster with start and end dates, average severity and intensity, and maximum number of affected cells. Temporal Domain 1981–2023, daily Spatial Domain Linguere, Senegal, West Africa Spatial resolution ca 0.1°x0.1° (EPGS:4326) Key Variables/Indicators Spatio-temporal clusters of dry/drought events Data Format netCDF CSV Source Data ERA5-Land daily min, max and average air temperature and CHIRPS daily precipitation Accessibility Zenodo, https://doi.org/10.5281/zenodo.15212446 Stakeholder Relevance Identifying and assessing past drought events for multi-hazard events monitoring, prediction and preparedness. Limitations/Assumptions The clustering was done over the specified region only. Each calendar year was clustered independetly. Additional Outputs/information The dataset access is currently restricted due to pending related publication. Contact Information Egor Prikaziuk (UT-ITC, Faculty of Geo-Information Science and Earth Observation, ITC, University of Twente, the Netherlands)

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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