CDI_Dataset

This dataset provides a high-resolution (1 km) Composite Drought Index (CDI) for Tunisia covering the period 2000–2025, developed to support drought monitoring and forecasting in arid and semi-arid environments. The CDI was generated by integrating multi-source remote sensing and reanalysis products, including CHIRPS precipitation, ERA5-Land climate variables, and MODIS vegetation and temperature data. Five drought-related indicators were derived: the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Soil Moisture Anomaly (SMA), NDVI anomaly (NDVI-A), and Land Surface Temperature anomaly (LST-A). These indicators were combined through a logic-based cause–effect framework to capture drought interactions across meteorological, agricultural, and thermal conditions. The dataset reveals strong spatio-temporal drought variability and highlights major drought episodes such as 2002–2003, 2016–2018, and 2020–2024, particularly affecting central and southern Tunisia. Validation against in-situ rainfall observations shows strong agreement between station-based SPI and CHIRPS-derived estimates. Wavelet analysis indicates a significant relationship between CDI variability and the North Atlantic Oscillation (NAO), emphasizing the influence of large-scale atmospheric circulation on drought dynamics. In addition, the dataset was used to benchmark twelve deep learning forecasting models, where attention-based architectures such as TimeFormer achieved the best performance. This CDI dataset constitutes a valuable resource for operational drought early warning, climate resilience studies, and sustainable water resource management in Tunisia.