Improvement of Flood Extent Representation With Remote Sensing Data and Data Assimilation

Flooding is one of the most devastating natural hazards to which our society worldwide must adapt, especially as its severity and occurrence tend to increase with climate changes. This research work focuses on the assimilation of two‐dimensional (2D) flood observations derived from remote‐sensing images acquired during overflowing events. To do so, the resulting binary wet/dry maps are expressed in terms of wet surface ratios (WSR) over a number of floodplain subdomains. This ratio is assimilated jointly with in‐situ water‐level gauge observations to improve the flow dynamics within the floodplain. An Ensemble Kalman Filter (EnKF) with a dual state‐parameter analysis approach is implemented on top of a TELEMAC‐2D hydrodynamic model. The EnKF control vector is composed of spatially‐distributed friction coefficients and a corrective parameter of the inflow discharge. It is extended with the hydraulic states within the floodplain subdomains. This data assimilation strategy was validated and evaluated over a reach of the Garonne river. The observation operator associated with the WSR observations, as well as the dual state‐parameter sequential correction, was first validated in the context of Observing System Simulation Experiments. It was then applied to two real flood events that occurred in 2019 and 2021. The merits of assimilating synthetic aperture radar‐derived WSR observations, in complement to the in‐situ water‐level observations, are shown in the parameter and observation spaces with assessment metrics computed over the entire flood events. It is also shown that the hydraulic state correction within the dual state‐parameter analysis approach significantly improves the flood dynamics, especially during the flood recess.

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“…The specifications of the flood extent mapping method applied to S1 images are detailed in Nguyen, Ricci, Fatras, et al. (2022).…”

Section: Methodsmentioning

confidence: 99%

Dual State‐Parameter Assimilation of SAR‐Derived Wet Surface Ratio for Improving Fluvial Flood Reanalysis

Nguyen

Ricci

Piacentini

et al. 2022

Water Resources Research

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“…The non-conservative form of SWEs is written in terms of water level ℎ [m] and velocity 𝒖 = (𝑢, 𝑣) (with horizontal components 𝑢 and 𝑣 [m.s -1 ]). Using similar mathematical notations to [4,7], the SWEs read:…”

Section: Shallow Water Equations (Swes) In T2dmentioning

confidence: 99%

“…The initial design of this model by EDF consisted in only three friction segments within the river bed. Based on our previous study [4,5], a finer zoning definition was favored, which led to the former second and third segments (covering, respectively, the middle and downstream parts of the river bed) were subdivided into two and three smaller segments, respectively 𝐾 𝑠 2,3 and 𝐾 𝑠 4,5,6 . Due to the absence of in-situ data for all of these zones, their a priori values are set based on the calibration process, yet they will allow for a fine adjustment in the DA algorithm.…”

Section: Study Area and Description Of The Uncertaintiesmentioning

confidence: 99%

“…In this work, the observation operator also includes a correction to account for a systematic model-observation bias, denoted by 𝐲 𝑏𝑖𝑎𝑠 . Such a bias was estimated based on the 24-hour quasi-stationary non-overflowing period of 2021-01-15, prior to the real flood event in January-February 2021 [4].…”

Section: Forecast Stepmentioning

confidence: 99%

“…Indeed, flood extent delineation is relatively straightforward on SAR images, with several exceptions, namely built environment and vegetation areas. In our previous works [4,5], flood extent maps were derived from Sentinel-1 (S1) SAR images by applying a Random Forest classifier. Being used as independent validation observations, they allowed to improve the DA results in both re-analysis and forecast modes.…”

Section: Introductionmentioning

confidence: 99%

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Assimilation of SAR-derived flood extent observations for improving fluvial flood forecast – A proof-of-concept

Nguyen

Ricci

Piacentini

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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As the severity and occurrence of flood events tend to intensify with climate change, the need for flood forecasting capability increases. In this regard, the Flood Detection, Alert and rapid Mapping (FloodDAM) project, funded by Space for Climate Observatory initiatives, was set out to develop pre-operational tools dedicated to enabling quick responses in flood-prone areas, and to improve the reactivity of decision support systems. This work focuses on the assimilation of 2D flood extent data (expressed in terms of wet surface ratios) and in-situ water level data to improve the representation of the flood plain dynamics with a Telemac-2D model and an Ensemble Kalman Filter (EnKF). The EnKF control vector was composed friction coefficients and corrective parameter to the input forcing. It is then augmented with the water level state averaged over several floodplain zones. This work was conducted in the context of Observing System Simulation Experiments (OSSE) based on a real flood event occurred in January-February 2021 on the Garonne Marmandaise catchment. This allows to validate the observation operator associated to the wet surface ratio observations as well as the dual state-parameter sequential correction implemented in this work. The merits of assimilating SAR-derived flood plain data complementary to in-situ water level observations are shown in the control parameter and observation spaces with 1D and 2D assessment metrics. It was also shown that the correction of the hydraulic state significantly improved the flood dynamics, especially during the recession. This proof-of-concept study paves the way towards near-real-time flood forecast, making the most of remote sensing-derived flood observations.

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Classification and detection of natural disasters using machine learning and deep learning techniques: A review

Abraham,

Abdelwahab,

Abo-Zahhad

2023

Earth Sci Inform

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Improvement of Flood Extent Representation With Remote Sensing Data and Data Assimilation

Cited by 15 publications

References 60 publications

Dual State‐Parameter Assimilation of SAR‐Derived Wet Surface Ratio for Improving Fluvial Flood Reanalysis

Dual State‐Parameter Assimilation of SAR‐Derived Wet Surface Ratio for Improving Fluvial Flood Reanalysis

Assimilation of SAR-derived flood extent observations for improving fluvial flood forecast – A proof-of-concept

Classification and detection of natural disasters using machine learning and deep learning techniques: A review

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