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| Glavni autor: | |
|---|---|
| Format: | Recurso digital |
| Jezik: | |
| Izdano: |
Zenodo
2025
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| Online pristup: | https://doi.org/10.5281/zenodo.17410178 |
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- <p>This dataset provides the Python implementation, GEE workflow, and trained Random Forest models used for agricultural and hydrological drought assessment in Ethiopia from 1982–2100. The work integrates ERA5-Land, FLDAS, CHIRPS, CHIRTS, and multi-model CMIP6 datasets (SSP245, SSP585) to analyze past and future drought dynamics.<br>The Random Forest model used for prediction of agricultural (SSMI/based) and hydrological (SRI-based) drought indices is openly available for reuse and adaptation.</p>