Articles | Volume 28, issue 11
https://doi.org/10.5194/hess-28-2531-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-2531-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Jun 2024
Research article |
| 14 Jun 2024
| 14 Jun 2024
Quantifying cascading uncertainty in compound flood modeling with linked process-based and machine learning models
David F. Muñoz
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA 24060, USA
Center for Complex Hydrosystems Research, The University of Alabama, Tuscaloosa, AL 35487, USA
Hamed Moftakhari
Center for Complex Hydrosystems Research, The University of Alabama, Tuscaloosa, AL 35487, USA
Department of Civil, Construction and Environmental Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA
Hamid Moradkhani
Center for Complex Hydrosystems Research, The University of Alabama, Tuscaloosa, AL 35487, USA
Department of Civil, Construction and Environmental Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA
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32 citations as recorded by crossref.
- Enhancing urban flood management for ungauged watersheds through adaptive mesh refinement and equation-based flow dynamics A. Kumar et al. https://doi.org/10.1080/02626667.2026.2707175
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- Disentangling atmospheric, hydrological, and coupling uncertainties in compound flood modeling within a coupled Earth system model D. Feng et al. https://doi.org/10.5194/nhess-25-3619-2025
- Enhancing compound flood simulation accuracy and efficiency in urbanized coastal areas using hybrid meshes and modified digital elevation model E. Hamidi et al. https://doi.org/10.1016/j.scs.2025.106184
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- Impact of impervious surface spatial morphologies on urban waterlogging: Insights from a cascade modeling chain at catchment scale X. Qin et al. https://doi.org/10.1016/j.scs.2025.106912
- Towards a typology for hybrid compound flood modeling S. Radfar et al. https://doi.org/10.5194/hess-30-1397-2026
- Cross-regional and multi-entity resource coordination can enhance the supply of disaster relief materials during flood events in China Q. Yao et al. https://doi.org/10.1038/s43247-025-02461-4
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- A cluster-based temporal attention approach for predicting cyclone-induced compound flood dynamics S. Daramola et al. https://doi.org/10.1016/j.envsoft.2025.106499
- Leveraging Coupled Hydrodynamic with Data-Driven GeoAI Models for Advancing Systemic Compound Flood Risk Evaluation in Coastal Urban Areas T. Atmaja et al. https://doi.org/10.1007/s11831-025-10394-6
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- Quantifying the influence of coastal flood hazards on building habitability following Hurricane Irma B. Nelson-Schmitz et al. https://doi.org/10.5194/nhess-26-531-2026
- A Decomposition-Based Stochastic Multilevel Binary Optimization Model for Agricultural Land Allocation Under Uncertainty F. Wang et al. https://doi.org/10.3390/math13071213
- Accounting for the uncertainty of precipitation forecasts and its impacts on probabilistic flood inundation mapping skill F. Gomez et al. https://doi.org/10.1016/j.jhydrol.2026.135429
- Unraveling uncertainty in compound flood modeling: sensitivity of simulations to forcings and model parameters C. Wang et al. https://doi.org/10.1016/j.jhydrol.2026.135424
- Unlocking full-domain flood inundation prediction from sparse point supervision in deep-learning models J. Xie et al. https://doi.org/10.1016/j.watres.2026.126760
- Spatial delineation of the compound flood transition zone using deep learning F. Yarveysi et al. https://doi.org/10.1016/j.advwatres.2025.105131
- Compound flooding risks in coastal cities: a review of causes, mechanisms, and assessment methodologies E. Jahani et al. https://doi.org/10.1007/s11069-026-08183-w
- Climate adaptation-aware flood prediction for coastal cities using Deep Learning B. Hassan et al. https://doi.org/10.5194/hess-30-1333-2026
- A Fuzzy Ensemble Framework for Multi-Criteria Water Resources Management Under Uncertainty X. Yang et al. https://doi.org/10.1007/s11269-025-04367-4
- Flood Index-Enhanced deep learning model for coastal inundation mapping in SAR imagery W. Chen et al. https://doi.org/10.1016/j.jag.2025.104550
- Open problems in uncertainty quantification for flood modelling: A systematic review J. Idowu & A. Alfahid https://doi.org/10.1016/j.envsoft.2025.106799
- An ensemble learning framework for streamflow reconstruction incorporating aleatoric and epistemic uncertainty D. Ludyawati et al. https://doi.org/10.1016/j.ejrh.2026.103604
- A machine learning-based prediction-to-map framework for rapid and accurate spatial flood prediction D. Bao et al. https://doi.org/10.1038/s44304-025-00122-2
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- Integrating multi-source remote sensing and numerical simulation approaches for enhanced flood hazard assessment E. Hamidi et al. https://doi.org/10.1016/j.jhydrol.2026.135933
Saved (final revised paper)
Latest update: 12 Nov 2026
Short summary
Linking hydrodynamics with machine learning models for compound flood modeling enables a robust characterization of nonlinear interactions among the sources of uncertainty. Such an approach enables the quantification of cascading uncertainty and relative contributions to total uncertainty while also tracking their evolution during compound flooding. The proposed approach is a feasible alternative to conventional statistical approaches designed for uncertainty analyses.
Linking hydrodynamics with machine learning models for compound flood modeling enables a robust...
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