DBSCAN 3D Clusters of SPEI-90 days – Linguere, Senegal, 1981-2023
Creators
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1.
University of Twente
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2.
University of Plymouth
Description
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Science Case Name |
Multi-Hazards in Senegal. |
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Dataset Name/Title |
DBSCAN 3D Clusters of SPEI-90 days – Linguere, Senegal, 1981-2023 |
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Dataset Description |
The dataset contains gridded data on SPEI-90 days over Linguere area of Senegal. |
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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. |
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Temporal Domain |
1981–2023, daily |
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Spatial Domain |
Linguere, Senegal, West Africa Spatial resolution ca 0.1°x0.1° (EPGS:4326) |
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Key Variables/Indicators |
Spatio-temporal clusters of dry/drought events |
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Data Format |
netCDF CSV |
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Source Data |
ERA5-Land daily min, max and average air temperature and CHIRPS daily precipitation |
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Accessibility |
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Stakeholder Relevance |
Identifying and assessing past drought events for multi-hazard events monitoring, prediction and preparedness. |
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Limitations/Assumptions |
The clustering was done over the specified region only. Each calendar year was clustered independetly. |
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Additional Outputs/information |
The dataset access is currently restricted due to pending related publication. |
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Contact Information |
Egor Prikaziuk (UT-ITC, Faculty of Geo-Information Science and Earth Observation, ITC, University of Twente, the Netherlands) |
Files
Restricted
The record is publicly accessible, but files are restricted to users with access.
Additional details
Funding
- European Space Research Institute
- EO4Multihazards (Earth Observation for High-Impact Multi-Hazards Science), funded by the European Space Agency and launched as part of the joint ESA-European Commission Earth System Science Initiative
References
- Cakmak, E., Plank, M., Calovi, D. S., Jordan, A., & Keim, D. (2021). Spatio-temporal clustering benchmark for collective animal behavior. Proceedings of the 1st ACM SIGSPATIAL International Workshop on Animal Movement Ecology and Human Mobility, 5–8. https://doi.org/10.1145/3486637.3489487