Articles | Volume 25, issue 9
https://doi.org/10.5194/hess-25-4995-2021
https://doi.org/10.5194/hess-25-4995-2021
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

Related authors

Real-time coastal flood hazard assessment using DEM-based hydrogeomorphic classifiers
Keighobad Jafarzadegan, David F. Muñoz, Hamed Moftakhari, Joseph L. Gutenson, Gaurav Savant, and Hamid Moradkhani
Nat. Hazards Earth Syst. Sci., 22, 1419–1435, https://doi.org/10.5194/nhess-22-1419-2022,https://doi.org/10.5194/nhess-22-1419-2022, 2022
Short summary

Related subject area

Subject: Catchment hydrology | Techniques and Approaches: Uncertainty analysis
Use of expert elicitation to assign weights to climate and hydrological models in climate impact studies
Eva Sebok, Hans Jørgen Henriksen, Ernesto Pastén-Zapata, Peter Berg, Guillaume Thirel, Anthony Lemoine, Andrea Lira-Loarca, Christiana Photiadou, Rafael Pimentel, Paul Royer-Gaspard, Erik Kjellström, Jens Hesselbjerg Christensen, Jean Philippe Vidal, Philippe Lucas-Picher, Markus G. Donat, Giovanni Besio, María José Polo, Simon Stisen, Yvan Caballero, Ilias G. Pechlivanidis, Lars Troldborg, and Jens Christian Refsgaard
Hydrol. Earth Syst. Sci., 26, 5605–5625, https://doi.org/10.5194/hess-26-5605-2022,https://doi.org/10.5194/hess-26-5605-2022, 2022
Short summary
Pitfalls and a feasible solution for using KGE as an informal likelihood function in MCMC methods: DREAM(ZS) as an example
Yan Liu, Jaime Fernández-Ortega, Matías Mudarra, and Andreas Hartmann
Hydrol. Earth Syst. Sci., 26, 5341–5355, https://doi.org/10.5194/hess-26-5341-2022,https://doi.org/10.5194/hess-26-5341-2022, 2022
Short summary
Benchmarking global hydrological and land surface models against GRACE in a medium-sized tropical basin
Silvana Bolaños Chavarría, Micha Werner, Juan Fernando Salazar, and Teresita Betancur Vargas
Hydrol. Earth Syst. Sci., 26, 4323–4344, https://doi.org/10.5194/hess-26-4323-2022,https://doi.org/10.5194/hess-26-4323-2022, 2022
Short summary
Guidance on evaluating parametric model uncertainty at decision-relevant scales
Jared D. Smith, Laurence Lin, Julianne D. Quinn, and Lawrence E. Band
Hydrol. Earth Syst. Sci., 26, 2519–2539, https://doi.org/10.5194/hess-26-2519-2022,https://doi.org/10.5194/hess-26-2519-2022, 2022
Short summary
Quantifying input uncertainty in the calibration of water quality models: reordering errors via the secant method
Xia Wu, Lucy Marshall, and Ashish Sharma
Hydrol. Earth Syst. Sci., 26, 1203–1221, https://doi.org/10.5194/hess-26-1203-2022,https://doi.org/10.5194/hess-26-1203-2022, 2022
Short summary

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, https://doi.org/10.1016/j.advwatres.2017.11.011, 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, https://doi.org/10.1029/2018WR023629, 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, https://doi.org/10.1016/j.advwatres.2020.103721, 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, https://doi.org/10.1080/02626667.2018.1525615, 2018. 
Alemohammad, S. H., McLaughlin, D. B., and Entekhabi, D.: Quantifying precipitation uncertainty for land data assimilation applications, Mon. Weather Rev., 143, 3276–3299, https://doi.org/10.1175/MWR-D-14-00337.1, 2015. 
Download
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.