Climate‑risk hotspot mapping from rainfall extremes and land use dynamics

Understanding how climate extremes interact with land use and land cover (LULC) dynamics is essential for assessing climate-risk patterns in rapidly changing environments. This study develops an integrated geomatics-driven framework to identify climate-risk hotspots across Odisha, India, a region highly exposed to hydro-meteorological hazards. Using IMD gridded daily rainfall data (1960–2020), extreme precipitation indices were analyzed to characterize hydroclimatic changes. Results revealed a major rainfall regime shift around 1989, marking a transition toward a wetter climate characterized by shorter wet spells and more intense rainfall events, reflected in declining consecutive wet days and increasing extreme indices, particularly across coastal, northeastern, and southwestern Odisha. To evaluate the combined influence of climatic extremes and land-surface dynamics, precipitation indices were integrated with remotely sensed Normalized Difference Vegetation Index and Land Surface Temperature within a Self-Organizing Map (SOM) framework. The optimized SOM classified climate risk into low-, medium-, and high-risk categories for 2001–2010 and 2011–2020. High-risk grids increased by 17% during the latter decade, with hotspots concentrated in coastal Odisha. While nearly 70% of grids remained stable, fewer than 10% shifted to lower-risk categories.The proposed framework provides spatially explicit insights to support climate adaptation, risk-informed land management, and targeted mitigation strategies.