A probabilistic model for real-time flood warning based on deterministic flood inundation mapping

2011 ◽  
Vol 26 (7) ◽  
pp. 1079-1089 ◽  
Author(s):  
Jiun-Huei Jang ◽  
Pao-Shan Yu ◽  
Sen-Hai Yeh ◽  
Jin-Cheng Fu ◽  
Chen-Jia Huang
Keyword(s):  
Real Time ◽  
Probabilistic Model ◽  
Flood Inundation ◽  
Flood Warning ◽  
Inundation Mapping ◽  
Flood Inundation Mapping

scholarly journals Sequential Data Assimilation for Real-Time Probabilistic Flood Inundation Mapping

2021 ◽  
Author(s):  
Keighobad Jafarzadegan ◽  
Peyman Abbaszadeh ◽  
Hamid Moradkhani
Keyword(s):  
Decision Making ◽  
Data Assimilation ◽  
Point Source ◽  
Real Time ◽  
Sequential Data ◽  
Flood Inundation ◽  
Modeling Framework ◽  
Inundation Mapping ◽  
Flood Inundation Mapping ◽  
Sequential Data Assimilation

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.

scholarly journals Sequential data assimilation for real-time probabilistic flood inundation mapping

2021 ◽  
Vol 25 (9) ◽  
pp. 4995-5011
Author(s):  
Keighobad Jafarzadegan ◽  
Peyman Abbaszadeh ◽  
Hamid Moradkhani
Keyword(s):  
Decision Making ◽  
Data Assimilation ◽  
Point Source ◽  
Real Time ◽  
Sequential Data ◽  
Flood Inundation ◽  
Modeling Framework ◽  
Inundation Mapping ◽  
Flood Inundation Mapping ◽  
Sequential Data Assimilation

Abstract. Real-time probabilistic flood inundation mapping is crucial for flood risk warning and decision-making during the emergency period before an upcoming flood event. Considering the 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 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.

Near-real-time non-obstructed flood inundation mapping using synthetic aperture radar

2019 ◽  
Vol 221 ◽  
pp. 302-315 ◽  
Author(s):  
Xinyi Shen ◽  
Emmanouil N. Anagnostou ◽  
George H. Allen ◽  
G. Robert Brakenridge ◽  
Albert J. Kettner
Keyword(s):  
Synthetic Aperture Radar ◽  
Real Time ◽  
Synthetic Aperture ◽  
Flood Inundation ◽  
Inundation Mapping ◽  
Flood Inundation Mapping ◽  
Aperture Radar
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