Abstract

Drought is becoming more frequent and severe in many regions, particularly in Mediterranean climates, where water demand is increased by warming and changes in precipitation regimes. A long-term assessment of meteorological drought at the Lisbon climatological station is provided in this study using the Standardized Precipitation Index (SPI) and the Reconnaissance Drought Index (RDI) over the period 1864–2021. Monthly precipitation and temperature data are used to compute SPI and RDI at 3-, 6-, and 12-month time scales, so that short-, mid-, and long-term droughts and their temporal evolution can be characterized. RDI is evaluated with three widely used empirical potential evapotranspiration (PET) formulations—Hargreaves, Thornthwaite, and Blaney–Criddle—in order to examine how PET estimations influence drought classification. Given the absence of a physically based reference PET—such as FAO-56 Penman–Monteith—for this station, the focus is on the internal consistency of the PET methods. Furthermore, the Hargreaves formulation is retained as a representative empirical PET for subsequent SPI–RDI comparison. The results show broadly consistent standardized RDI behavior across PET methods; it is indicated that drought conditions are captured more comprehensively by RDI than by SPI because both precipitation deficits and enhanced evaporative demand are included. At the Lisbon station, the estimated average return periods for short-, mid-, and long-term droughts are 3.79, 7.31, and 7.92 years according to RDI, compared with 3.86, 5.69 and 10.88 years from SPI. Several severe drought episodes are identified, including the years 1907, 1922–1923, 1944–1945, 1976, 1981, 1992–1993, 2005, and 2018. While no formal attribution analysis is performed, the drought characteristics are interpreted in the context of observed long-term warming and documented rainfall variability in Lisbon. The findings provide a single-station benchmark of historical drought behavior, by which local water-resources management can be supported and which can serve as a basis for future multi-station and climate-projection-based studies in Portugal.

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