Preprints
https://doi.org/10.5194/hess-2021-181
https://doi.org/10.5194/hess-2021-181

  18 May 2021

18 May 2021

Review status: this preprint is currently under review for the journal HESS.

Sequential Data Assimilation for Real-Time Probabilistic Flood Inundation Mapping

Keighobad Jafarzadegan, Peyman Abbaszadeh, and Hamid Moradkhani Keighobad Jafarzadegan et al.
  • Center for Complex Hydrosystems Research, Department of Civil, Construction, and Environmental Engineering, University of Alabama, Tuscaloosa, AL

Abstract. Real-time probabilistic flood inundation mapping is crucial for flood risk warning and decision making during the emergency of an upcoming flood event. Considering high uncertainties involved in the modeling of a nonlinear and complex flood event, providing a deterministic flood inundation map can be erroneous and misleading for reliable and timely decision making. The conventional flood hazard maps provided for different return periods cannot also represent the actual dynamics of flooding rivers. Therefore, a real-time modeling framework that forecasts the inundation areas before the onset of an upcoming flood is of paramount importance. Sequential Data Assimilation (DA) techniques are well-known for real-time operation of physical models while accounting for existing uncertainties. In this study, we present a Data Assimilation (DA)-hydrodynamic modeling framework where multiple gauge observations are integrated into the LISFLOOD-FP model to improve its performance. This study utilizes the Ensemble Kalman Filter (EnKF) in a multivariate fashion for dual estimation of model state variables and parameters where the correlations among point source observations are taken into account. First, a synthetic experiment is designed to assess the performance of the proposed approach, then the method is used to simulate the Hurricane Harvey flood in 2017. Our results indicate that the multivariate assimilation of point-source observations into hydrodynamic models can improve the accuracy and reliability of probabilistic flood inundation mapping by 5–7% while it also provides the basis for sequential updating and real-time flood inundation mapping.

Keighobad Jafarzadegan et al.

Status: open (until 13 Jul 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-181', Anonymous Referee #1, 13 Jun 2021 reply
  • RC2: 'Comment on hess-2021-181', Anonymous Referee #2, 18 Jun 2021 reply

Keighobad Jafarzadegan et al.

Keighobad Jafarzadegan et al.

Viewed

Total article views: 320 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
248 66 6 320 2 0
  • HTML: 248
  • PDF: 66
  • XML: 6
  • Total: 320
  • BibTeX: 2
  • EndNote: 0
Views and downloads (calculated since 18 May 2021)
Cumulative views and downloads (calculated since 18 May 2021)

Viewed (geographical distribution)

Total article views: 304 (including HTML, PDF, and XML) Thereof 304 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 23 Jun 2021
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 to apply this framework in practice and forecast probabilistic flood maps before the onset of upcoming floods.