Azizian, Asghar; Brocca, Luca, 2020, Determining the best remotely sensed DEM for flood inundation mapping in data sparse regions, International journal of remote sensing (Print) (2020). doi_10.1080/01431161.2019.1677968,
URL: http://www.cnr.it/prodotto/i/410403

One of the most essential inputs in flood inundation mapping is the geometric description of the floodplains and river channel that often derives from the digital elevation models (DEMs). By increasing the satellite-based technologies during the past 30 years, several DEM sources ranging from fine-resolution and accurate, but costly, to low-cost and low-resolution have been developed. In most parts of the world, especially developing countries and data sparse regions, the coarse resolution DEMs is the only available data set for hydraulic modelling and flood inundation mapping. This research addressed the usefulness and efficiency of the recently released Advanced Land Observing Satellite (ALOS) DEM in flood inundation mapping using 1D Hydrologic Engineering Centre- River Analysis System (HEC-RAS) model. In addition, other DEM sources such as Shuttle Radar Topography Mission (SRTM-90 m), SRTM-30 m, and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER-30 m) are used to carry out a comprehensive evaluation of remotely sensed DEMs for flood inundation mapping. Findings indicate that using ALOS-30m for hydraulic simulation approximately leads to the similar results as well as ground-based DEM (GDEM). For example, the Mean Absolute Percentage Error (MAPE) in simulating mean Water Surface Elevation (WSE) and mean inundated extents based on this dataset, within the cross-sections, is lower than 8% and 13% for SojasRood river, respectively, while for Sarbaz river these values are 9% and 2%. Moreover, in both rivers, SRTM-30 m relative to ASTER-30 m and SRTM-90 m DEMs presents better results in deriving the geometric model and hydraulic simulation. Also, Hydraulic modelling based on ASTER-30 m, even relative to SRTM-90 m as a coarser resolution DEM, shows a significant discrepancy compared to GDEM. Moreover, in both rivers, the MAPE in predicting inundated extents, within the reaches, is higher than 38%.

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