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Journal Article

Citation

Tegos A, Ziogas A, Bellos V, Tzimas A. Hydrology 2022; 9(5): e93.

Copyright

(Copyright © 2022, MDPI: Multidisciplinary Digital Publications Institute)

DOI

10.3390/hydrology9050093

PMID

unavailable

Abstract

On 18 September 2020, the Karditsa prefecture of Thessaly region (Greece) experienced a catastrophic flood as a consequence of the IANOS hurricane. This intense phenomenon was characterized by rainfall records ranging from 220 mm up to 530 mm, in a time interval of 15 h. Extended public infrastructure was damaged and thousands of houses and commercial properties were flooded, while four casualties were recorded. The aim of this study was to provide forensic research on a reconstruction of the flood event in the vicinity of Karditsa city. First, we performed a statistical analysis of the rainfall. Then, we used two numerical models and observed data, either captured by satellites or mined from social media, in order to simulate the event a posteriori. Specifically, a rainfall-runoff CN-unit hydrograph model was combined with a hydrodynamic model based on 2D-shallow water equations model, through the coupling of the hydrological software HEC-HMS with the hydrodynamic software HEC-RAS. Regarding the observed data, the limited available gauged records led us to use a wide spectrum of remote sensing datasets associated with rainfall, such as NASA GPM-IMREG, and numerous videos posted on social media, such as Facebook, in order to validate the extent of the flood. The overall assessment proved that the exceedance probability of the IANOS flooding event ranged from 1:400 years in the low-lying catchments, to 1:1000 years in the upstream mountainous catchments. Moreover, a good performance for the simulated flooding extent was achieved using the numerical models and by comparing their output with the remote sensing footage provided by SENTINEL satellites images, along with the georeferenced videos posted on social media.


Language: en

Keywords

HEC-HMS; HEC-RAS; IANOS; Karditsa; medicane; remote sensing; SENTINEL

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