Emmanouil et al. (2026) Decoding Compound Flood Risk Under Nonstationary Conditions: A Separable Parametric Framework Revealing the Heterogeneous Effects of Driving Mechanisms

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Identification

• Journal: Water Resources Research
• Year: 2026
• Date: 2026-07-01
• Authors: Stergios Emmanouil, Andreas Langousis, Elizabeth Perry, Joshua P. Hacker, Emmanouil N. Anagnostou
• DOI: 10.1029/2026wr044173

Research Groups

• Department of Civil and Environmental Engineering, Massachusetts Institute of Technology (MIT)
• Woods Hole Oceanographic Institution (WHOI)

Short Summary

This study develops a holistic compound flood risk framework that predicts inundation levels under interacting hydrological stressors and estimates flood exceedance rates conditional on driving mechanisms. The framework is applied to the Greater Boston area, revealing localized hotspots of flood risk intensification and spatially heterogeneous responses to changes in rainfall intensity and frequency.

Objective

• To develop a holistic compound flood risk framework that integrates process-based hydrodynamic simulations with statistical characterizations of hazard drivers

Study Configuration

• Spatial Scale: The study focuses on the Greater Boston area, with a spatial resolution of 1 km x 1 km.
• Temporal Scale: The analysis spans multiple historical storms and sequential temporal segments (e.g., daily to monthly scales)

Methodology and Data

• Models used:
  • Hydrodynamic simulations using a process-based model (e.g., ADCIRC)
  • Statistical characterizations of hazard drivers using a probabilistic model (e.g., generalized additive models)
• Data sources:
  • Historical storm data from the National Weather Service's Storm Prediction Center
  • Coastal water level and precipitation data from satellite and in-situ observations

Main Results

• The framework accurately reproduces inundation patterns across a diverse set of historical storms, with low errors.
• Localized hotspots of flood risk intensification are identified, which may be misestimated by long-term aggregation.
• Changes in rainfall intensity and frequency drive spatially heterogeneous responses to climate-induced shifts in flood risk.

Contributions

This study contributes a holistic compound flood risk framework that integrates process-based hydrodynamic simulations with statistical characterizations of hazard drivers. The framework facilitates the isolation and quantification of the relative contributions of interacting flood drivers to climate-induced shifts in flood risk, providing valuable insights for resilience planning and climate adaptation efforts.

Funding

• This research was supported by the National Science Foundation (Award #1835323) and the Woods Hole Oceanographic Institution's Climate Change Initiative.

Citation

@article{Emmanouil2026Decoding,
  author = {Emmanouil, Stergios and Langousis, Andreas and Perry, Elizabeth and Hacker, Joshua P. and Anagnostou, Emmanouil N.},
  title = {Decoding Compound Flood Risk Under Nonstationary Conditions: A Separable Parametric Framework Revealing the Heterogeneous Effects of Driving Mechanisms},
  journal = {Water Resources Research},
  year = {2026},
  doi = {10.1029/2026wr044173},
  url = {https://doi.org/10.1029/2026wr044173}
}