Volumetric Quantification of Flash Flood Using Microwave Data on a Watershed Scale in Arid Environments, Saudi Arabia

Abstract

Actual flood mapping and quantification in an area provide valuable information for the stakeholder to prevent future losses. This study presents the actual flash flood quantification in Al-Lith Watershed, Saudi Arabia. The study is divided into two steps: first is actual flood mapping using remote sensing data, and the second is the flood volume calculation. Two Sentinel-1 images are processed to map the actual flood, i.e., image from 25 May 2018 (dry condition), and 24 November 2018 (peak flood condition). SNAP software is used for the flood mapping step. During SNAP processing, selecting the backscatter data representing the actual flood in an arid region is challenging. The dB range value from 7.23–14.22 is believed to represent the flood. In GIS software, the flood map result is converted into polygon to define the flood boundary. The flood boundary that is overlaid with Digital Elevation Map (DEM) is filled with the same elevation value. The Focal Statistics neighborhood method with three iterations is used to generate the flood surface elevation inside the flood boundary. The raster contains depth information is derived by subtraction of the flood surface elevation with DEM. Several steps are carried out to minimize the overcalculation outside the flood boundary. The flood volume can be derived by the multiplication of flood depth points with each cell size area. The flash flood volume in Al-Lith Watershed on 24 November 2018 is 155,507,439 m3. Validity checks are performed by comparing it with other studies, and the result shows that the number is reliable.

Flood Extent and Volume Estimation Using Remote Sensing Data

“…Moreover, previous studies often leverage classification models based on threshold rules [26], which might lead to inaccuracy in predictions. In [27], Budiman et al defined the range for dB values that represent flood areas on Sentinel-1 data. The authors noted the challenging of such values estimation due to similar reflectance of other surfaces.…”

“…The authors suggest using floodwater extent maps derived from remote sensing classification. A similar approach is explored in [27], where Budiman et al used Sentinel-1 data and DEM to estimate flash flood volume by multiplying depth and each pixel size. However, these approaches do not involve usage of such advanced algorithms as convolutional neural networks.…”