Abstract
Global digital elevation model (DEM) datasets, such as the Shuttle Radar Topography Mission (SRTM), are widely used for flood inundation modeling in developing nations. However, errors in these datasets can significantly impact the accuracy of analyses and applications. Attempts to improve DEM accuracy have used supplementary data like flood observations, vegetation data, and drainage networks, but these approaches are limited in data-scarce regions where such information is unavailable. To address this issue, this study proposes bias correction of DEM elevation values using ground truth data from field measurements and outlines a systematic framework to improve flood inundation mapping. Various ground truth sampling strategies are tested, and the minimum number of points required is identified. The framework is applied to the Brazos River, Texas, USA, using the HEC-RAS 2D model for a major flood event. Results demonstrate significant improvements in DEM accuracy following bias correction. The mean DEM error was reduced from 4.5 m to 0.0 m, with a notable decrease in error variance. The RMSE decreased by 55–70% across sampling strategies. Performance indices for inundation extent, F-index and C-index, improved by 47% and 60%, respectively, while the maximum depth simulation showed a 350% enhancement in Nash-Sutcliffe Efficiency (NSE), increasing from 0.13 to 0.59 when the corrected DEM was used. The results demonstrate that the proposed farmwork aids in significantly improving the vertical accuracy of global DEMs, enabling more precise hydrological simulations and flood risk assessments.
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I.J. conceptualized the study, designed the methodology, performed software implementation, validation, formal analysis, investigation, resources acquisition, data curation, and prepared the original draft. S.M.B. and K.P.S. contributed to conceptualization, methodology design, validation, investigation, and resources. S.M.B. and I.J. performed manuscript review and editing, with S.M.B. supervising the work. All authors reviewed the manuscript.
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Appendix I
Appendix I
Map layers used for stratified random sampling
Landuse/landcover
The LULC map extracted from the National Land Cover Database (NLCD) product for the study area is shown in Fig. a. There were 32 classes in the original data layer. They have been reclassified into five major classes: agriculture, forest, developed area, open water/barren land and wetlands.
Landuse/landcover map of the study area
Elevation
The Shuttle Radar Topography Mission (SRTM) - global Digital Elevation Model (DEM) data derived from radar measurements, with a spatial resolution of 30 m (1 arc-second) for the study extent, is shown in Fig. b. The elevation values are divided into five classes with the aid of GIS tools using the Natural Jenks method, which groups elevation values into ranges that minimize variance within classes and maximize variance between classes.
Elevation map of the study area
Slope
The slope of the terrain is derived from the DEM using GIS tools by calculating the maximum rate of change between each cell and its neighbours. The output can be classified into percent slope classes, where slope values are expressed as percentages. Similar to the elevation classes, the slope values are divided into five classes, as shown in Fig. c.
Slope map of the study area
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Jesna, I., Bhallamudi, S.M. & Sudheer, K.P. Improving the usability of global SRTM DEM for reach-scale floodplain inundation mapping in data-scarce regions through bias correction. Earth Sci Inform 18, 294 (2025). https://doi.org/10.1007/s12145-025-01812-1
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DOI: https://doi.org/10.1007/s12145-025-01812-1