Gaussian Anamorphosis for Ensemble Kalman Filter Analysis of SAR-Derived Wet Surface Ratio Observations - Archive ouverte HAL Access content directly
Journal Articles IEEE Transactions on Geoscience and Remote Sensing Year : 2024

Gaussian Anamorphosis for Ensemble Kalman Filter Analysis of SAR-Derived Wet Surface Ratio Observations

Abstract

Flood simulation and forecast capability have been greatly improved thanks to advances in data assimilation (DA) strategies incorporating various types of observations; many are derived from Earth Observations from space. This paper focuses on the assimilation of 2D flood observations derived from Synthetic Aperture Radar (SAR) images acquired during a flood event with a dual state-parameter Ensemble Kalman Filter (EnKF). Resulting binary wet/dry maps are here expressed in terms of wet surface ratios (WSR) over a number of subdomains of the floodplain. This ratio is assimilated jointly with in-situ water-level observations to improve the flow dynamics within the floodplain. However, the non-Gaussianity of the observation errors associated with these SAR-derived measurements break a major hypothesis for the application of the EnKF, thus jeopardizing the optimality of the filter analysis. The novelty of this paper lies in the treatment of the non-Gaussianity of the SAR-derived WSR observations with a Gaussian anamorphosis (GA) process. This DA strategy was validated and applied over the Garonne Marmandaise catchment (South-west of France) represented with a TELEMAC-2D hydrodynamic model, first in a twin experiment and then for a major flood event that occurred in January-February 2021. It was shown that assimilating SAR-derived WSR observations, in complement to the in-situ water-level observations significantly improves the representation of the flood dynamics. The GA process brings further improvement to the DA analysis, while has also been demonstrated to be a non-essential element. This study heralds a reliable solution for flood forecasting over poorly gauged catchments thanks to available remote-sensing datasets.

Dates and versions

hal-04318160 , version 1 (01-12-2023)

Identifiers

Cite

Thanh Huy Nguyen, Sophie Ricci, Andrea Piacentini, Ehouarn Simon, Raquel Rodriguez-Suquet, et al.. Gaussian Anamorphosis for Ensemble Kalman Filter Analysis of SAR-Derived Wet Surface Ratio Observations. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62, pp.1-21. ⟨10.1109/TGRS.2023.3338296⟩. ⟨hal-04318160⟩
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