A Localized Particle Filtering Approach to Advance Flood Frequency Estimation at Large Scale Using Satellite Synthetic Aperture Radar Image Collection and Hydrodynamic Modelling

Autor: Marina Zingaro, Renaud Hostache, Marco Chini, Domenico Capolongo, Patrick Matgen
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Remote Sensing, Vol 16, Iss 12, p 2179 (2024)
Druh dokumentu: article
ISSN: 2072-4292
DOI: 10.3390/rs16122179
Popis: This study describes a method that combines synthetic aperture radar (SAR) data with shallow-water modeling to estimate flood hazards at a local level. The method uses particle filtering to integrate flood probability maps derived from SAR imagery with simulated flood maps for various flood return periods within specific river sub-catchments. We tested this method in a section of the Severn River basin in the UK. Our research involves 11 SAR flood observations from ENVISAT ASAR images, an ensemble of 15 particles representing various pre-computed flood scenarios, and 4 masks of spatial units corresponding to different river segmentations. Empirical results yield maps of maximum flood extent with associated return periods, reflecting the local characteristics of the river. The results are validated through a quantitative comparison approach, demonstrating that our method improves the accuracy of flood extent and scenario estimation. This provides spatially distributed return periods in sub-catchments, making flood hazard monitoring effective at a local scale.
Databáze: Directory of Open Access Journals
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