Spatial Twin Framework for Climate Risk Assessment Using Geospatial and Machine Learning Approaches

Traditional flood risk management approaches often rely on historical data, limiting their ability to account for the increasing severity and frequency of climate-induced hazards. This study presents the Spatial Twin framework, a data-driven methodology that integrates geospatial analysis and machine learning to assess climate risks dynamically. The framework enables decision-makers to identify vulnerabilities, quantify flood risk under evolving climate scenarios, and develop informed adaptation strategies. Using bias-corrected CMIP5 climate projections, the framework is demonstrated through a case study in Texas, where community flood risk prediction is done under multiple emission scenarios. Results indicate that under RCP 8.5, community vulnerability is projected to increase by 14%, leading to an estimated 28% rise in economic damages ($1.8B per decade by 2050) and heightened socio-economic disruptions, including displacement and infrastructure failures. By identifying the most influential climatological factors affecting community resilience, our approach underscores the urgent need for global intervention to mitigate extreme climate scenarios and demonstrates the scalability and adaptability of the Spatial Twin framework, underscoring its potential as a decision-support tool for climate risk assessment and adaptive planning.