Digital Twins for Climate Adaptation Science

This interdisciplinary review examines digital twins for climate adaptation science. The organizing question is how environmental models, sensors, infrastructure decisions, and community knowledge can form an updateable adaptation twin. Ten related scholarly sources are synthesized through a decision-centered framework spanning problem definition, mechanism, measurement, evaluation, implementation, and governance. The review does not invent experiments, pooled estimates, or unreported quantitative results. It instead evaluates the strength and transferability of the available evidence, with particular attention to presenting a visually detailed simulation as a validated forecast. The resulting framework links technical or empirical performance to explicit use conditions and identifies tests that should precede wider adoption in regional and urban climate adaptation.

1. Bianchini, S., Damioli, G., & Ghisetti, C. (2022). The environmental effects of the “twin” green and digital transition in European regions. Environmental and Resource Economics, 84(4), 877-918. https://doi.org/10.1007/s10640-022-00741-7 DOI
2. Dale, K. I., Pope, E. C. D., Hopkinson, A. R., McCaie, T., & Lowe, J. A. (2023). Environment-Aware Digital Twins: Incorporating Weather and Climate Information to Support Risk-Based Decision-Making. Artificial Intelligence for the Earth Systems, 2(4). https://doi.org/10.1175/aies-d-23-0023.1 DOI
3. Feroz, A. K., Zo, H., & Chiravuri, A. (2021). Digital Transformation and Environmental Sustainability: A Review and Research Agenda. Sustainability, 13(3), 1530. https://doi.org/10.3390/su13031530 DOI
4. Ghaith, M., Yosri, A., & El-Dakhakhni, W. (2022). Synchronization-Enhanced Deep Learning Early Flood Risk Predictions: The Core of Data-Driven City Digital Twins for Climate Resilience Planning. Water, 14(22), 3619. https://doi.org/10.3390/w14223619 DOI
5. Henriksen, H., Schneider, R., Koch, J., Ondracek, M., Troldborg, L., Seidenfaden, I., Kragh, S., Bøgh, E., & Stisen, S. (2022). A New Digital Twin for Climate Change Adaptation, Water Management, and Disaster Risk Reduction (HIP Digital Twin). Water, 15(1), 25. https://doi.org/10.3390/w15010025 DOI
6. Kaewunruen, S., AbdelHadi, M., Kongpuang, M., Pansuk, W., & Remennikov, A. M. (2022). Digital Twins for Managing Railway Bridge Maintenance, Resilience, and Climate Change Adaptation. Sensors, 23(1), 252. https://doi.org/10.3390/s23010252 DOI
7. Otgonbaatar, S., Nurmi, O., Johansson, M., Mäkelä, J., kocman, T., Gawron, P., Puchała, Z., Mielzcarek, J., Miroszewski, A., & Dumitru, C. O. (2023). Quantum Computing for Climate Change Detection, Climate Modeling, and Climate Digital Twin. Scholarly publication. https://doi.org/10.36227/techrxiv.24478663.v1 DOI
8. Pylianidis, C., Kallenberg, M. G. J., & Athanasiadis, I. N. (2024). Domain adaptation with transfer learning for pasture digital twins. Environmental Data Science, 3. https://doi.org/10.1017/eds.2024.6 DOI
9. Riaz, K., McAfee, M., & Gharbia, S. S. (2023). Management of Climate Resilience: Exploring the Potential of Digital Twin Technology, 3D City Modelling, and Early Warning Systems. Sensors, 23(5), 2659. https://doi.org/10.3390/s23052659 DOI
10. Yitmen, I., Almusaed, A., Hussein, M., & Almssad, A. (2025). AI-Driven Digital Twins for Enhancing Indoor Environmental Quality and Energy Efficiency in Smart Building Systems. Buildings, 15(7), 1030. https://doi.org/10.3390/buildings15071030 DOI