Gumbs et al. (2026) Anticipated Compound Flooding in Miami-Dade Under Extreme Hydrometeorological Events

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Hydrology
• Year: 2026
• Date: 2026-01-16
• Authors: Alan E. Gumbs, Alemayehu Dula Shanko, Abiodun Tosin-Orimolade, Assefa M. Melesse
• DOI: 10.3390/hydrology13010034

Research Groups

Not specified in the provided text.

Short Summary

This study applied HEC-RAS 2D and machine learning metamodels to assess Miami's vulnerability to extreme flood events, revealing that 35.4 km² of the city, particularly the inner bay coastline, is at risk, with 38% classified as medium to extreme risk.

Objective

• To understand and estimate Miami’s vulnerability to extreme flood events, such as 50- and 100-year return storms, focusing on its economic epicenter.

Study Configuration

• Spatial Scale: Miami, Florida, specifically its inner bay coastline and downtown area, covering an area of 35.4 km².
• Temporal Scale: Extreme flood events (50- and 100-year return periods); Hurricane Irma used for model validation and calibration.

Methodology and Data

• Models used: HEC-RAS 2D (one- and two-dimensional water flow models); Novel machine learning metamodels for robust sensitivity analysis of the hydrologic model.
• Data sources: Observational data (e.g., Hurricane Irma data for validation and calibration).

Main Results

• Miami’s inner bay coastline, particularly the downtown coastline, is severely impacted by extreme hydrometeorological events.
• Under extreme event circumstances, an area of 35.4 km² of Miami is at risk of flooding.
• 38% of the identified at-risk areas are classified by FEMA as having medium to extreme risk, indicating severe infrastructural and community vulnerability.

Contributions

• Provides in-depth research on flood vulnerability in Miami's economic epicenter, addressing a gap in existing literature.
• Offers insights into vulnerability thresholds to inform flood mitigation strategies for unprecedented and intensified weather events.
• Explores novel machine learning metamodels for robust sensitivity analysis in hydrologic modeling.

Funding

Not specified in the provided text.

Citation

@article{Gumbs2026Anticipated,
  author = {Gumbs, Alan E. and Shanko, Alemayehu Dula and Tosin-Orimolade, Abiodun and Melesse, Assefa M.},
  title = {Anticipated Compound Flooding in Miami-Dade Under Extreme Hydrometeorological Events},
  journal = {Hydrology},
  year = {2026},
  doi = {10.3390/hydrology13010034},
  url = {https://doi.org/10.3390/hydrology13010034}
}