Assimilation of SWOT Altimetry and Sentinel-1 Flood Extent Observations for Flood Reanalysis -- A Proof-of-Concept

TL;DR

This paper demonstrates a proof-of-concept for integrating SWOT satellite altimetry, Sentinel-1 flood extent data, and in-situ water levels into a 2D hydrodynamic model using Ensemble Kalman Filter to improve flood reanalysis and prediction.

Contribution

It introduces a novel data assimilation approach combining SWOT, Sentinel-1, and in-situ data within a 2D hydrodynamic model for flood analysis.

Findings

Successful assimilation of multi-source remote sensing data improves flood state estimation.

The approach enhances flood reanalysis accuracy in hindcast mode.

The method paves the way for real-time flood nowcasting using heterogeneous satellite data.

Abstract

In spite of astonishing advances and developments in remote sensing technologies, meeting the spatio-temporal requirements for flood hydrodynamic modeling remains a great challenge for Earth Observation. The assimilation of multi-source remote sensing data in 2D hydrodynamic models participates to overcome such a challenge. The recently launched Surface Water and Ocean Topography (SWOT) wide-swath altimetry satellite provides a global coverage of water surface elevation at a high resolution. SWOT provides complementary observation to radar and optical images, increasing the opportunity to observe and monitor flood events. This research work focuses on the assimilation of 2D flood extent maps derived from Sentinel-1 C-SAR imagery data, and water surface elevation from SWOT as well as in-situ water level measurements. An Ensemble Kalman Filter (EnKF) with a joint state-parameter analysis is implemented on top of a 2D hydrodynamic TELEMAC-2D model to account for errors in roughness, input forcing and water depth in floodplain subdomains. The proposed strategy is carried out in an Observing System Simulation Experiment based on the 2021 flood event over the Garonne Marmandaise catchment. This work makes the most of the large volume of heterogeneous data from space for flood prediction in hindcast mode paves the way for nowcasting.