Spatiotemporal Drought Assessment Based on Gridded Standardized Precipitation Index (SPI) in Vulnerable Agroecosystems

Drought is one of the most critical environmental hazards for the viability and productive development of crops, especially in a climate change environment. To this end, drought assessment is a process of paramount importance to make vulnerable agricultural regions more resilient. The primary aim of this paper is an integrated drought assessment through time and space in one of the most susceptible (in terms of water availability limitations) and agriculturally productive regions in Greece and the Mediterranean, namely, the Thessaly region. Supplementary objectives consist of the determination of the two most extreme years in terms of drought and wetness, so that we may reveal any potential climatological cycles/patterns from 1981 to 2020. Additionally, the methodology includes the annual and seasonal analysis using one of the most widely used drought indices, namely, the Standardized Precipitation Index (SPI), so that consistent measurements are available across a large study area, avoiding the possible scarcity/deficiency of data coming from a sparse land weather network. The innovative element of this paper is the integrated spatiotemporal drought assessment in multiple time scales through the estimation of the SPI making use of remotely sensed data, such as CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data). The outcomes highlight that the study area faced two severe years of drought in 1988 and 1989, which led to moderate and extreme drought conditions, respectively. In contrast, extremely wet conditions were observed in 2002–2003, whereas 2009–2010 experienced moderately wet conditions. The central and western part of the region tends to suffer the most in terms of drought severity, especially at the most extreme years. The validity of the results has been confirmed by the adoption of R 2 where the index is approaching 0.67 despite the large size of the pixels (5 × 5 km). In this context, the mapping of spatial and seasonal variability across the study area permits more targeted measures (e.g., precision farming) instead of horizontal policies.