A Novel Geospatial Simulation Framework for Projecting Climate Dynamics

This study presents a novel geospatial approach to model, predict, and analyze climate change patterns in Iran. The methodology began with calculating the UNEP aridity index using data from 34 stations for the 1967–2024 period. Subsequently, these data were used to generate interpolation maps via the Inverse Distance Weighting (IDW) method. The study area was then discretized into approximately 41,000 pixels, for which future climatic conditions (2025–2034) were predicted using the integrated Circular Automata-Markov Chain and Log-Normal Distribution (CAMLND) model. Validation using NSE, CCC, and R² indices confirmed the robust performance of both the IDW and CAMLND models. Projections for 2025–2034 indicate a significant expansion of hyper-arid (from 10% to 20%) and humid (from 3% to 12%) zones of Iran's total area. Conversely, arid regions are anticipated to shrink by 18% and semi-arid regions by 2%, while sub-humid regions are projected to expand by 1.5%. The trend assessment extending to 2034 projects a decline in the area exhibiting a significant decreasing trend (p < 0.01), from 65.14% to 57.73%. In contrast, the analysis forecasts increases in the proportion of pixels with non-significant decreasing trends (by 4%), non-significant increasing trends (by 3%), and slight increases for significant trends at the p < 0.05 level. Collectively, these findings point to a substantial and complex transformation of Iran's climatic landscape.