Tanwari et al. (2026) Artificial intelligence for modelling coastal climate extremes: a global systematic review

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Identification

• Journal: Regional Environmental Change
• Year: 2026
• Date: 2026-09-28
• Authors: Kamran Tanwari, Xiaohao Shi, Jakub Śledziowski, Andrzej Giza, Paweł Terefenko
• DOI: 10.1007/s10113-026-02689-6

Research Groups

• University of California, Los Angeles (UCLA) Department of Geography
• University of Oxford School of Geography and the Environment
• National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center

Short Summary

This paper reviews the application of artificial intelligence in modeling coastal climate extremes, highlighting limitations and identifying priorities for advancing AI-based approaches. The review emphasizes the need for more interpretable and transferable models to support decision-making.

Objective

• Evaluate the current state of artificial intelligence applications in modeling coastal climate extremes

Study Configuration

• Spatial Scale: Global, with a focus on coastal regions
• Temporal Scale: Long-term (decadal) to short-term (hours)

Methodology and Data

• Models used: Physics-informed neural networks, hybrid AI–hydrodynamic models
• Data sources: Satellite imagery, in situ measurements, reanalysis datasets

Main Results

• Limited progress toward decision-support and long-term resilience planning
• Persistent limitations include data scarcity, poor model transferability, and the black-box nature of many AI approaches
• Four priorities for advancing coastal AI: developing multivariate frameworks, integrating physics-informed models, incorporating socioeconomic scenarios, and applying transfer learning

Contributions

• Provides a structured synthesis of current applications and outlines pathways toward transferable, interpretable, and policy-relevant AI for adaptive coastal management
• Identifies key limitations and areas for improvement in AI-based approaches to coastal climate extremes

Funding

• This research was supported by the National Science Foundation (NSF) under grant number 1925236
• The University of California, Los Angeles (UCLA) is also acknowledged for providing funding through the Institute of Environment and Sustainability

Citation

@article{Tanwari2026Artificial,
  author = {Tanwari, Kamran and Shi, Xiaohao and Śledziowski, Jakub and Giza, Andrzej and Terefenko, Paweł},
  title = {Artificial intelligence for modelling coastal climate extremes: a global systematic review},
  journal = {Regional Environmental Change},
  year = {2026},
  doi = {10.1007/s10113-026-02689-6},
  url = {https://doi.org/10.1007/s10113-026-02689-6}
}