Articles | Volume 25, issue 9
Research article
16 Sep 2021
Research article |  | 16 Sep 2021

Sequential data assimilation for real-time probabilistic flood inundation mapping

Keighobad Jafarzadegan, Peyman Abbaszadeh, and Hamid Moradkhani

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Cited articles

Abbaszadeh, P., Moradkhani, H., and Yan, H.: Enhancing hydrologic data assimilation by evolutionary Particle Filter and Markov Chain Monte Carlo, Adv. Water Resour., 111, 192–204,, 2018. 
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,, 2019. 
Abbaszadeh, P., Gavahi, K., and Moradkhani, H.: Multivariate remotely sensed and in-situ data assimilation for enhancing community WRF-Hydro model forecasting, Adv. Water Resour., 145, 103721,, 2020. 
Ahmadisharaf, E., Kalyanapu, A. J., and Bates, P. D.: A probabilistic framework for floodplain mapping using hydrological modeling and unsteady hydraulic modeling, Hydrolog. Sci. J., 63, 1759–1775,, 2018. 
Alemohammad, S. H., McLaughlin, D. B., and Entekhabi, D.: Quantifying precipitation uncertainty for land data assimilation applications, Mon. Weather Rev., 143, 3276–3299,, 2015. 
Short summary
In this study, daily observations are assimilated into a hydrodynamic model to update the performance of modeling and improve the flood inundation mapping skill. Results demonstrate that integrating data assimilation with a hydrodynamic model improves the performance of flood simulation and provides more reliable inundation maps. A flowchart provides the overall steps for applying this framework in practice and forecasting probabilistic flood maps before the onset of upcoming floods.