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Climate change intensifies the occurrence of severe flood events, increasing the demand for flood modeling studies. Hydrodynamic models can effectively represent flood events, but they are limited by the quality of available observations. Accurate topographic elevation is essential to replicate channel-floodplain interaction. Elevation is normally retrieved using satellite-based DEMs. However, freely available DEMs have a low spatial resolution, which is a limitation in identifying small-scale channel features in complex floodplain topography. In addition, these products can present issues such as vertical offset, random noise, or vegetation biases. These issues can lead to large errors when used in hydraulic modeling to simulate water levels and inundation extent. FABDEM is a 1 arcsec DEM, that removes forest and building artifacts from Copernicus DEM, but to map complex floodplain topography, finer resolution is needed. ICESat-2 mission offers a large spatial coverage with an along-track resolution down to 70 cm in the ATL03 product. This data product has shown great potential when mapping river topography and identifying small-scale channel features. ATL03 can be used as a control point dataset, to correct biases and refine DEMs To improve the accuracy of 2D hydraulic models, FABDEM was corrected on selected floodplain areas using supplementary data and machine learning methods. Artificial Neural Network (ANN) was used in the correction of FABDEM. This regression algorithm can predict differences between FABDEM floodplain elevation and ATL03 reference elevation, inputting data from Sentinel-2 and water occurrence maps produced from spectral and SAR imagery. The output floodplain elevation has a reduced vertical offset and a spatial resolution of 10 m, which can detect small-scale channel features. Flood inundation was simulated using the updated DEM. The high computational cost of 2D hydraulic models is a limitation when using discharge time series. To deal with computational cost, discharge classes were defined to represent different inundation scenarios that provide a good indicator for flood risk management, and steady-state inundation patterns were simulated for each discharge class. The method is demonstrated in a section of the Upstream Yellow River characterized by large floodplains with complex topography, and small-scale channels. Discharge observations from the Jimai in-situ station are used to define discharge classes. The discharge classes are defined by calculating the exceedance probability of a discharge value. The inundation scenarios are simulated for high flow discharge values for an exceedance probability of 25% (Q25) and 10% (Q10), and medium flow discharge values with 50% (Q50), and are compared with the corresponding water occurrence map produced from spectral and SAR imagery for the given discharge class. The critical success index (CSI) of the inundation map improves by about 5% using FABDEM corrected version for Q10 and Q25, and about 4% for Q50. In addition, we observe a consistent Bias reduction of about 20% for Q10 and Q25.
Frías et al. (Sat,) studied this question.