Articles | Volume 28, issue 11
https://doi.org/10.5194/hess-28-2531-2024
https://doi.org/10.5194/hess-28-2531-2024
Research article
 | 
14 Jun 2024
Research article |  | 14 Jun 2024

Quantifying cascading uncertainty in compound flood modeling with linked process-based and machine learning models

David F. Muñoz, Hamed Moftakhari, and Hamid Moradkhani

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Cited articles

Abbaszadeh, P., Moradkhani, H., and Daescu, D. N.: The Quest for Model Uncertainty Quantification: A Hybrid Ensemble and Variational Data Assimilation Framework, Water Resour. Res., 55, 2407–2431, https://doi.org/10.1029/2018WR023629, 2019. 
Abbaszadeh, P., Muñoz, D. F., Moftakhari, H., Jafarzadegan, K., and Moradkhani, H.: Perspective on uncertainty quantification and reduction in compound flood modeling and forecasting, iScience, 25, 105201, https://doi.org/10.1016/j.isci.2022.105201, 2022. 
Ahrens, B.: Distance in spatial interpolation of daily rain gauge data, Hydrol. Earth Syst. Sci., 10, 197–208, https://doi.org/10.5194/hess-10-197-2006, 2006. 
Alipour, A., Ahmadalipour, A., Abbaszadeh, P., and Moradkhani, H.: Leveraging machine learning for predicting flash flood damage in the Southeast US, Environ. Res. Lett., 15, 024011, https://doi.org/10.1088/1748-9326/ab6edd, 2020. 
Alipour, A., Jafarzadegan, K., and Moradkhani, H.: Global sensitivity analysis in hydrodynamic modeling and flood inundation mapping, Environ. Model. Softw., 152, 105398, https://doi.org/10.1016/j.envsoft.2022.105398, 2022. 
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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.