Articles | Volume 24, issue 8
https://doi.org/10.5194/nhess-24-2647-2024
© Author(s) 2024. This work is distributed under the Creative Commons Attribution 4.0 License.
Research article
| 
02 Aug 2024
Research article |
| 02 Aug 2024
| 02 Aug 2024
Probabilistic flood inundation mapping through copula Bayesian multi-modeling of precipitation products
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Cited articles
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Abbaszadeh, P., Gavahi, K., Alipour, A., Deb, P., and Moradkhani, H.: Bayesian Multi-modeling of Deep Neural Nets for Probabilistic Crop Yield Prediction, Agr. Forest Meteorol., 314, 108773, https://doi.org/10.1016/j.agrformet.2021.108773, 2022a.
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, 2022b.
Alipour, A., Jafarzadegan, K., and Moradkhani, H.: Global sensitivity analysis in hydrodynamic modeling and flood inundation mapping, Environ. Modell. Softw., 152, 105398, https://doi.org/10.1016/j.envsoft.2022.105398, 2022.
Andreas, E. L., Mahrt, L., and Vickers, D.: A New Drag Relation for Aerodynamically Rough Flow over the Ocean, J. Atmos. Sci., 69, 2520–2537, https://doi.org/10.1175/JAS-D-11-0312.1, 2012.
