Sequential data assimilation for real-time probabilistic flood inundation mapping

Abstract

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.

Perspective on uncertainty quantification and reduction in compound flood modeling and forecasting

“…This is mainly because of limited access to high spatiotemporal resolution remote sensing data needed for assimilation in HD modeling. As an alternative to remote sensing data, recent studies demonstrated that point-source observations can be assimilated and provide a robust characterization of uncertainty in HD models ( Annis et al., 2022 ; Jafarzadegan et al., 2021a ; Muñoz et al., 2022 ). Xu et al.…”

“…(2017a , 2017b) used a Particle Filter technique to assimilate several point-source WL observations into two-dimensional (2D) HD models. Jafarzadegan et al. (2021a) introduced a DA framework that assimilates both discharge and WL while considering correlations among point-source observations.…”

“…More recent studies have accounted for all sources of uncertainty in HD modeling using a pre-established EnKF method for model state-variable and parameter estimation ( Moradkhani et al., 2005 ). This method has been applied for the probabilistic flood inundation mapping ( Jafarzadegan et al., 2021a ) and further adapted to WL prediction and CF hazard assessment in near-real time ( Muñoz et al., 2022 ).…”

River network and hydro-geomorphological parameters at 1∕12° resolution for global hydrological and climate studies

“…Makungu and Hughes, 2021) or large-scale flood forecasting (e.g. GloFAS, Alfieri et al, 2013;Jafarzadegan et al, 2021).…”

River network and hydro-geomorphology parametrization for global river routing modelling at 1/12° resolution

“…By simulating the flow routing processes through river networks, they allow climate models to close the water budget at the global scale. Then, several applications have been developed based on RRMs, including studies on the impact of climate change on extreme flows (floods and droughts, see e.g., Hirabayashi et al (2013); Yamazaki et al (2018)), water resources monitoring (e.g., Makungu and Hughes , 2021) or large scale flood forecasting (e.g., GloFAS, Alfieri et al , 2013;Jafarzadegan et al , 2021).…”