Using multisource remote sensing data to predict the maximum flood extent in poorly gauged basins

Abstract

For small- or medium-sized river basins that suffer from floods, the lack of river gauging data is an important reason why flood warning at the appropriate time is difficult for local stakeholders. As a valuable dataset observed from spaceborne or airborne platforms, remote sensing images can capture slices of time-series information to cover the whole river basin. We consider the Nilwara Ganga tributaries in southern Sri Lanka for our research. The maximum flood extents and the corresponding elevations of flooded areas were determined in the post-flood Sentinel-1 images. Relying only on commonly available remote sensing datasets, the rainfall-triggered flood inundation models were proposed to simulate the maximum inundation extent of a potential flood. The results show that the models proposed here can be well applied in the determination of peak stage, especially for the non-tributary river reach (root mean square error = 0.59, R-square = 0.76). The average of Intersection over Union (IoU) accuracy of the maximum flood extent predictions was 0.75. Validations of two flood events had the IoU accuracy of 0.79 and 0.69, respectively. For gauging data-scarce areas, this approach has great potential to provide a different perspective for flood early warning and will benefit the subsequent risk assessment and disaster mitigation.