Monitoring Spatiotemporal Drought Events by Moving Coincidence Index Approach

Drought is a complex and multi-dimensional natural hazard including hydro-climatic driven and socio-economic aspects. The impacts of drought are generally shaped by spatial variability, duration and its persistence as well. Therefore, monitoring and forecasting drought are challenging task in many folds: i) Traditional drought indices such as Standardized Precipitation Index (SPI) or its variant Standardized Precipitation-Evapotranspiration Index (SPEI) are highly accepted but such indices often focus on the deviations from normal conditions within a particular time scale, which limits their ability to capture comprehensive assessment of a given region. ii) These indices require a statistical distribution describing variable of climatic factors in concern, which is extremely difficult to obtain a unique distribution that may fit to basin characteristic entirely. iii) Those are not capable of assessing drought severity and persistence over a basin. To overcome these limitations, Successive Coincidence Deficit Index (SCDI) was previously introduced in order to establish drought severity, persistence, and spatial characteristics. This study offers a new variant of SCDI, referred to as Moving Coincidence Index (MCI) based on the idea that identifies drought events triggered by simultaneous occurrence of precipitation deficits and temperature anomalies, without relying on probability distribution fitting or data normalization. The proposed MCI was applied to the Upper Tigris River Catchment (UTRC), Türkiye, which is one of important trans-boundary catchments in the Middle East. Historical analyses were conducted using long-term gauge-based precipitation and temperature observations for the period 1972–2011. The propose methodology was extended to investigate future drought behavior by using bias-corrected CMIP6 climate projections under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. Drought characteristics were evaluated across multiple temporal windows (1-, 3-, 6-, and 12-month) to represent meteorological, agricultural, and hydrological drought processes. Results from the historical period indicate that MCI effectively captures prolonged and successive drought conditions and provides consistent spatial patterns when compared with commonly used drought indices. Shorter time scales reveal highly localized drought behavior, while longer accumulation periods highlight persistent and basin-wide drought structures. Future projections show a pronounced increase in drought persistence and spatial coherence, particularly under higher emission scenarios. The application of MCI for CMIP6 projections enables the identification of potential changes in the spatial distribution and seasonal characteristics of coincident hot–dry conditions across the basin. As a conclusion, the integration of MCI with CMIP6 projections provides a robust and flexible framework for assessing present and future drought dynamics. The findings suggest critical insights for climate adaptation strategies, reservoir operation, and sustainable water resource management in drought-prone and transboundary river basins.