Using Sentinel-1 Imagery to Assess Predictive Performance of a Hydraulic Model
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Using Sentinel-1 Imagery to Assess Predictive Performance of a Hydraulic Model Ioanna Zotou 1 & Vasilis Bellos 1 & Angeliki Gkouma 1 & Vassilia Karathanassi 2 & Vassilios A. Tsihrintzis 1 Received: 7 January 2020 / Accepted: 3 June 2020/ # Springer Nature B.V. 2020
Abstract
This study seeks to test the predictive performance of a hydraulic model using as reference the flood extent extracted through Sentinel-1 imagery. A precipitation event which took place between the 22nd and 28th of February 2018 in Pineios river basin, Central Greece, was analyzed. A threshold technique was performed to delineate the inundation extent from the satellite image, whereas both HEC-HMS and HEC-RAS software were coupled to simulate the examined storm event. To assess model response, the flooded area derived through the modeling approach was compared against that derived from the satellite image processing, using an area-based measure of fit. Furthermore, an uncertainty analysis on the parameters of the hydrologic model was elaborated to investigate their impact on the results of the hydraulic model. The sensitivity of the latter to the value of the roughness coefficient as well as to changes in the spatial resolution of the utilized topography was also examined. Considering as a perfect response of the model its complete coincidence with the satellite image product, it was found that the hydraulic model performance ranged between 61.04%-65.49%, depending on the selected upstream flow hydrograph, topography and roughness coefficient. The upstream flow conditions proved to play a more critical role, while roughness coefficient and topography were found to cause slighter changes in model response. Keywords Flood map . Sentinel-1 imagery . Remote sensing . HEC-HMS . HEC-RAS . Uncertainty
1 Introduction Floods are among the most catastrophic natural events with detrimental social and economic impacts. Therefore, being able to monitor and most importantly predict their magnitude, so as
* Ioanna Zotou [email protected]; [email protected] * Vassilios A. Tsihrintzis [email protected]; [email protected] Extended author information available on the last page of the article
Zotou I. et al.
to mitigate the subsequent damages, is very important. The implementation of a flood prediction system typically requires the use of a flood flow simulation model previously calibrated against in situ data, e.g., depth and velocity measurements, acquired during specific historical flood events that occurred in a given area. However, due to the extreme nature of the above phenomena, carrying out the required measurements during flood is often particularly difficult, placing severe limitations on calibration and validation processes. In this context, the contribution of Remote Sensing has proved to be of vital importance over the last decades (Twele et al. 2016). Specifically, the acquisition of satellite images capturing the extent of a specific flood over an area, not only provides immediate information on the significance of the ev
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