Generalized drought index: A novel multi-scale daily approach for drought assessment

Abstract

Abstract. Drought is a complex climatic phenomenon characterized by water scarcity recognized as the most widespread and insidious natural hazard, posing significant challenges to ecosystems and human society. In this study, we propose a new daily based index for characterizing droughts, which involves standardizing precipitation and/or precipitation minus potential evapotranspiration data. The performance of this new index is assessed with data from the evaluation runs of the Coordinated Regional Climate Downscaling Experiment for the European domain and the observational data from the Iberian Gridded Dataset, covering the period from 1989 to 2009. Comparative assessments are conducted against the daily Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), and a simple Z-Score standardization of climatic variables. Seven different accumulation periods are considered (7, 15, 30, 90, 180, 360, and 720 days) with three drought levels: moderate, severe, and extreme. The evaluation focuses mainly on the direct comparison amongst indices, added value assessment using the Distribution Added Value and a simple bias difference for drought characteristics. Results reveal that not only does the new index allow the characterization of flash droughts, but also demonstrates added value when compared to SPI and SPEI, especially for longer accumulation periods. In comparison to the Z-Score, the new index shows slightly greater gains, particularly for extreme drought events at lower accumulation periods. Furthermore, an assessment of the spatial extent of drought for the 2004–2005 event is performed using the observational dataset. All three indices generally provide similar representations, except for the Z-Score, which exhibits limitations in capturing extreme drought events at lower accumulation periods. Overall, the findings suggest that the new index offers improved performance and adds value comparatively to similar indices with a daily time step.