Earth Observation and Hydraulic Data Assimilation for Improved Flood Inundation Forecasting

Dasgupta, Antara; Hostache, Renaud; Ramsankaran, Raaj; Grimaldi, Stefania; Matgen, Patrick; Chini, Marco; Pauwels, Valentijn R. N.; Walker, Jeffrey P.

doi:10.1016/b978-0-12-819412-6.00012-2

Cited by 8 publications

(4 citation statements)

References 151 publications

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“…Therefore, Hostache et al (2018) proposed the assimilation of probabilistic flood maps from SAR images in their pioneering study whereby forecasts were updated through direct flood extent comparisons. More recently, Dasgupta et al (2021) proposed a novel model-data integration method, sensitive to slight variations in the flooded area, and verified its performance through synthetic experiments. At each time step shared information between the model predicted and the observed flooded area was quantified and used to combine their information content, obtaining persistent improvements in predictions of flood extent, depth, and velocity.…”

Section: Data Assimilationmentioning

confidence: 99%

“…In terms of algorithms, the Particle Filter, the Ensemble Kalman Filter and the 4D-Variational technique are the most widely used in hydrology (Dasgupta, et al 2021). In spite of its popularity in hydrological data assimilation literature, one of the key limitations of the EnKF is the assumption of Gaussianity for model and observation errors.…”

Section: Data Assimilationmentioning

confidence: 99%

See 1 more Smart Citation

Flood Modeling and Prediction Using Earth Observation Data

Schumann

Giustarini²,

Tarpanelli

et al. 2022

Surv Geophys

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The ability to map floods from satellites has been known for over 40 years. Early images of floods were rather difficult to obtain, and flood mapping from satellites was thus rather opportunistic and limited to only a few case studies. However, over the last decade, with a proliferation of open-access EO data, there has been much progress in the development of Earth Observation products and services tailored to various end-user needs, as well as its integration with flood modeling and prediction efforts. This article provides an overview of the use of satellite remote sensing of floods and outlines recent advances in its application for flood mapping, monitoring and its integration with flood models. Strengths and limitations are discussed throughput, and the article concludes by looking at new developments.

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Section: Data Assimilationmentioning

confidence: 99%

Section: Data Assimilationmentioning

confidence: 99%

Flood Modeling and Prediction Using Earth Observation Data

Schumann

Giustarini²,

Tarpanelli

et al. 2022

Surv Geophys

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“…Indeed, compared to in-situ data, RS data provide useful flood extent and flood edge information at a large coverage, usually covering the whole considered catchment, but they are much sparser in terms of frequency. Dasgupta et al (2021) provides an updated review on the assimilation of Earth Observation data with hydraulic models for the purpose of improved flood inundation forecasting. Over the last decades, the literature on DA associated with hydrodynamic models has mainly focused on the assimilation of WSE observations from in-situ stations or derived from RS data (Hostache et al, 2010), essentially because this is a state variable in any hydraulic model, thereby rendering the DA more straightforward.…”

Section: Assimilation Of Remote Sensing Flood-related Datamentioning

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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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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“…As stated by Thomas et al (2023), an adequate data validation would even strengthen satellite-based flood mapping that can drive flood index insurance applications. Further, the data can be used for data assimilation and validation of hydrological model outputs used in flood forecasting systems (Hostache et al, 2018;Dasgupta et al, 2021;.…”

Section: Introductionmentioning

confidence: 99%

Sentinel-1-based analysis of the severe flood over Pakistan 2022

Roth,

Bauer-Marschallinger,

Tupas

et al. 2023

Nat. Hazards Earth Syst. Sci.

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Abstract. In August and September 2022, Pakistan was hit by a severe flood, and millions of people were impacted. The Sentinel-1-based flood mapping algorithm developed by Technische Universität Wien (TU Wien) for the Copernicus Emergency Management Service (CEMS) global flood monitoring (GFM) component was used to document the propagation of the flood from 10 August to 23 September 2022. The results were evaluated using the flood maps from the CEMS rapid mapping component. Overall, the algorithm performs reasonably well with a critical success index of up to 80 %, while the detected differences can be primarily attributed to the time difference of the algorithm's results and the corresponding reference. Over the 6-week time span, an area of 30 492 km2 was observed to be flooded at least once, and the maximum extent was found to be present on 30 August. The study demonstrates the ability of the TU Wien flood mapping algorithm to fully automatically produce large-scale results and how key data of an event can be derived from these results.

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Earth Observation and Hydraulic Data Assimilation for Improved Flood Inundation Forecasting

Cited by 8 publications

References 151 publications

Flood Modeling and Prediction Using Earth Observation Data

Flood Modeling and Prediction Using Earth Observation Data

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

Sentinel-1-based analysis of the severe flood over Pakistan 2022

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