Analysing drought-state transition dynamics across Great Britain using a multi-tier Markov framework

Drought is one of the most widespread hydroclimatic hazards, characterised by slow onset, long duration, and complex propagation. While Markov chains have recently gained attention for drought prediction, their potential to characterise changing drought-state dynamics has not yet been fully explored. This study proposes a multi-tier Markov chain (MC) framework to evaluate shifts in drought transition behaviour across 133 catchments in Great Britain under observed and future climate conditions.

Using SPI- and SSI-based drought classifications defined over seven discrete categories, 7×7 MC matrices were constructed for each catchment. The analysis employs the eFLaG dataset derived from the UKCP18 regional climate projections, combining simulations from 12 regional climate models and four hydrological models (G2G, GR6J, GR4J, PDM). Three time periods were assessed: the observed baseline (1989-2018), the near future (2020-2049), and the far future (2050-2079), yielding three MC transition matrices per catchment.

The first tier of the framework applies a non-parametric permutation test to determine whether differences between transition matrices across time periods represent statistically significant shifts rather than sampling variability. For catchments exhibiting significant changes, the second tier decomposes each matrix into interpretable components- such as persistence (matrix trace), upward and downward mobility, and direction-specific transitions (Wet to Wet, Dry to Dry, Wet to Dry, Dry to Wet). This approach identifies which transition pathways drive observed temporal changes and whether future climates are associated with increased persistence, greater drying tendencies, or altered recovery patterns.

The proposed multi-tier MC framework provides a systematic means to detect, localise, and interpret evolving drought-state dynamics, offering insights relevant for water-resource planning and climate-adaptation strategies. The results will contribute to an improved understanding of potential future changes in spatio-temporal drought behaviour across Great Britain and demonstrate the broader utility of Markov chains for drought-risk assessment beyond purely predictive applications.