Articles | Volume 28, issue 6
https://doi.org/10.5194/hess-28-1287-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-1287-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Mar 2024
Research article |
| 22 Mar 2024
| 22 Mar 2024
Effects of high-quality elevation data and explanatory variables on the accuracy of flood inundation mapping via Height Above Nearest Drainage
Fernando Aristizabal
CORRESPONDING AUTHOR
Earth Resources Technology, 14401 Sweitzer Lane Suite 300, Laurel, MD 20707, USA
National Water Center, Office of Water Prediction, National Oceanic and Atmospheric Administration, 205 Hackberry Ln, Tuscaloosa, AL 35401, USA
Center for Remote Sensing, Agricultural and Biological Engineering, University of Florida, 1741 Museum Rd, Gainesville, FL 32603, USA
Taher Chegini
Civil and Environmental Engineering, University of Houston, 4226 Martin Luther King Boulevard, Houston, TX 77204, USA
Gregory Petrochenkov
National Water Center, Office of Water Prediction, National Oceanic and Atmospheric Administration, 205 Hackberry Ln, Tuscaloosa, AL 35401, USA
Lynker, 338 E Market St, Leesburg, VA 20176, USA
Fernando Salas
National Water Center, Office of Water Prediction, National Oceanic and Atmospheric Administration, 205 Hackberry Ln, Tuscaloosa, AL 35401, USA
Jasmeet Judge
Center for Remote Sensing, Agricultural and Biological Engineering, University of Florida, 1741 Museum Rd, Gainesville, FL 32603, USA
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Cited
25 citations as recorded by crossref.
- Evaluating the Impact of Digital Elevation Models on Urban Flood Modeling: A Comprehensive Analysis of Flood Inundation, Hazard Mapping, and Damage Estimation Z. Zandsalimi et al. https://doi.org/10.1007/s11269-024-03862-4
- Remote sensing-based 2D hydrodynamic modeling and assessment of the 2014 flood in the Jhelum river basin R. Mansha et al. https://doi.org/10.1007/s40808-026-02724-9
- Drone-mounted LiDAR Risk Mapping of Water Surges in Tropical Waterfall Environments: A Case Study of Recreational Area of Che Minah Sayang, Pahang M. Albarqouni et al. https://doi.org/10.1088/1755-1315/1605/1/012026
- Data-driven flood susceptibility assessment using hybrid machine learning and optimization techniques: case of the Sedrata Watershed, NE Algeria E. Mechentel et al. https://doi.org/10.1038/s41598-026-43262-9
- Evaluating High-Resolution LiDAR DEMs for Flood Hazard Analysis: A Comparison with 1:5000 Topographic Maps T. Kim et al. https://doi.org/10.3390/app16021029
- The HAND of flood mapping: multi-dimensional evaluation across data-rich and data-poor basins using existing maps, ground observations, and remote sensing data K. Patel et al. https://doi.org/10.1088/2515-7620/ae6236
- Impact of Elevation and Hydrography Data on Modeled Flood Map Accuracy Using ARC and Curve2Flood T. Miskin et al. https://doi.org/10.3390/hydrology12080202
- Evaluating the Vertical Accuracy of Global DEMs Using ICESat-2 and Its Cascading Impact on HAND-Based Flood Modeling in a Low-Gradient Coastal Plain Y. Sun et al. https://doi.org/10.3390/rs18101511
- Integrating Remote Sensing Indices and Ensemble Machine Learning Model with Independent HEC-RAS 2D Model for Enhanced Flood Prediction and Risk Assessment in the Ottawa River Watershed T. Oluwadare et al. https://doi.org/10.3390/app16010070
- Improving continental and global scale digital elevation models via estimation of a riverine topobathymetric surface J. Gutenson et al. https://doi.org/10.1016/j.envsoft.2025.106487
- Deep learning-based downscaling of global digital elevation models for enhanced urban flood modeling Z. Zandsalimi et al. https://doi.org/10.1016/j.jhydrol.2025.132687
- Community-oriented data integration and communication framework for streamflow forecast models and flood inundation map products K. Sugiyama et al. https://doi.org/10.1016/j.envsoft.2026.107036
- Urban drainage efficiency evaluation and flood simulation using integrated SWMM and terrain structural analysis X. Zhang et al. https://doi.org/10.1016/j.scitotenv.2024.177442
- Applying Machine Learning Algorithms for Spatial Modeling of Flood Susceptibility Prediction over São Paulo Sub-Region T. Oluwadare et al. https://doi.org/10.3390/land14050985
- Hydrological-hydrodynamic modeling of climate-induced urban flooding of design storms using HydroPol2D: a case study in São Carlos, Brazil M. Sousa et al. https://doi.org/10.1590/2318-0331.312620250090
- Intercomparison of flood inundation models across land use types and hydrological flood stages P. Nikrou et al. https://doi.org/10.1016/j.jhydrol.2026.135410
- Influence of DEM Spatial Resolution on the Accuracy and Computational Efficiency of HEC-RAS 1D and 2D Flood Inundation Modelling: A Case Study of the Cimanceuri Basin, Indonesia R. Fikri et al. https://doi.org/10.3390/w18101203
- FlDepth: A New Method for Estimating Fluvial and Pluvial Flood Depths from Near Real-Time Satellite-Derived Inundation Map and Topography A. Akkimi et al. https://doi.org/10.1007/s11269-025-04405-1
- Decadal SAR Evidence of Re-Encroachment into Hazardous Floodplains Following the 2020 Relocation Policy in Beledweyne, Somalia I. Heo et al. https://doi.org/10.3390/su18147060
- PyFlood: Rapid high-resolution coastal flood mapping with digital elevation model, land cover and water level data A. Santos Cruz et al. https://doi.org/10.1016/j.envsoft.2026.107010
- Merging Remote Sensing Derived River Slope Datasets with High-Resolution Hydrofabrics for the United States Y. Chen et al. https://doi.org/10.1038/s41597-025-05941-6
- Predicting synthetic rating curve adjustment factors with explainable machine learning for enhancing the United States operational flood inundation mapping framework A. Baruah et al. https://doi.org/10.1016/j.jhydrol.2025.134086
- Evaluating the effect of digital elevation model resolution in lahar hazard simulations: insights from the 1877 Cotopaxi scenario, Ecuador F. Vasconez et al. https://doi.org/10.3389/feart.2025.1611579
- Evaluating terrain-based HAND-SRC flood mapping model in low-relief rural plains using high resolution topography and crowdsourced data H. Sabeh et al. https://doi.org/10.1016/j.jhydrol.2024.132649
- Balancing Flood Hazard and Livelihood: A GIS–AHP–WLC Framework with Non-Monotonic River Scoring for Resilient Resettlement in Beledweyne, Somalia I. Heo et al. https://doi.org/10.3390/land15071275
25 citations as recorded by crossref.
- Evaluating the Impact of Digital Elevation Models on Urban Flood Modeling: A Comprehensive Analysis of Flood Inundation, Hazard Mapping, and Damage Estimation Z. Zandsalimi et al. https://doi.org/10.1007/s11269-024-03862-4
- Remote sensing-based 2D hydrodynamic modeling and assessment of the 2014 flood in the Jhelum river basin R. Mansha et al. https://doi.org/10.1007/s40808-026-02724-9
- Drone-mounted LiDAR Risk Mapping of Water Surges in Tropical Waterfall Environments: A Case Study of Recreational Area of Che Minah Sayang, Pahang M. Albarqouni et al. https://doi.org/10.1088/1755-1315/1605/1/012026
- Data-driven flood susceptibility assessment using hybrid machine learning and optimization techniques: case of the Sedrata Watershed, NE Algeria E. Mechentel et al. https://doi.org/10.1038/s41598-026-43262-9
- Evaluating High-Resolution LiDAR DEMs for Flood Hazard Analysis: A Comparison with 1:5000 Topographic Maps T. Kim et al. https://doi.org/10.3390/app16021029
- The HAND of flood mapping: multi-dimensional evaluation across data-rich and data-poor basins using existing maps, ground observations, and remote sensing data K. Patel et al. https://doi.org/10.1088/2515-7620/ae6236
- Impact of Elevation and Hydrography Data on Modeled Flood Map Accuracy Using ARC and Curve2Flood T. Miskin et al. https://doi.org/10.3390/hydrology12080202
- Evaluating the Vertical Accuracy of Global DEMs Using ICESat-2 and Its Cascading Impact on HAND-Based Flood Modeling in a Low-Gradient Coastal Plain Y. Sun et al. https://doi.org/10.3390/rs18101511
- Integrating Remote Sensing Indices and Ensemble Machine Learning Model with Independent HEC-RAS 2D Model for Enhanced Flood Prediction and Risk Assessment in the Ottawa River Watershed T. Oluwadare et al. https://doi.org/10.3390/app16010070
- Improving continental and global scale digital elevation models via estimation of a riverine topobathymetric surface J. Gutenson et al. https://doi.org/10.1016/j.envsoft.2025.106487
- Deep learning-based downscaling of global digital elevation models for enhanced urban flood modeling Z. Zandsalimi et al. https://doi.org/10.1016/j.jhydrol.2025.132687
- Community-oriented data integration and communication framework for streamflow forecast models and flood inundation map products K. Sugiyama et al. https://doi.org/10.1016/j.envsoft.2026.107036
- Urban drainage efficiency evaluation and flood simulation using integrated SWMM and terrain structural analysis X. Zhang et al. https://doi.org/10.1016/j.scitotenv.2024.177442
- Applying Machine Learning Algorithms for Spatial Modeling of Flood Susceptibility Prediction over São Paulo Sub-Region T. Oluwadare et al. https://doi.org/10.3390/land14050985
- Hydrological-hydrodynamic modeling of climate-induced urban flooding of design storms using HydroPol2D: a case study in São Carlos, Brazil M. Sousa et al. https://doi.org/10.1590/2318-0331.312620250090
- Intercomparison of flood inundation models across land use types and hydrological flood stages P. Nikrou et al. https://doi.org/10.1016/j.jhydrol.2026.135410
- Influence of DEM Spatial Resolution on the Accuracy and Computational Efficiency of HEC-RAS 1D and 2D Flood Inundation Modelling: A Case Study of the Cimanceuri Basin, Indonesia R. Fikri et al. https://doi.org/10.3390/w18101203
- FlDepth: A New Method for Estimating Fluvial and Pluvial Flood Depths from Near Real-Time Satellite-Derived Inundation Map and Topography A. Akkimi et al. https://doi.org/10.1007/s11269-025-04405-1
- Decadal SAR Evidence of Re-Encroachment into Hazardous Floodplains Following the 2020 Relocation Policy in Beledweyne, Somalia I. Heo et al. https://doi.org/10.3390/su18147060
- PyFlood: Rapid high-resolution coastal flood mapping with digital elevation model, land cover and water level data A. Santos Cruz et al. https://doi.org/10.1016/j.envsoft.2026.107010
- Merging Remote Sensing Derived River Slope Datasets with High-Resolution Hydrofabrics for the United States Y. Chen et al. https://doi.org/10.1038/s41597-025-05941-6
- Predicting synthetic rating curve adjustment factors with explainable machine learning for enhancing the United States operational flood inundation mapping framework A. Baruah et al. https://doi.org/10.1016/j.jhydrol.2025.134086
- Evaluating the effect of digital elevation model resolution in lahar hazard simulations: insights from the 1877 Cotopaxi scenario, Ecuador F. Vasconez et al. https://doi.org/10.3389/feart.2025.1611579
- Evaluating terrain-based HAND-SRC flood mapping model in low-relief rural plains using high resolution topography and crowdsourced data H. Sabeh et al. https://doi.org/10.1016/j.jhydrol.2024.132649
- Balancing Flood Hazard and Livelihood: A GIS–AHP–WLC Framework with Non-Monotonic River Scoring for Resilient Resettlement in Beledweyne, Somalia I. Heo et al. https://doi.org/10.3390/land15071275
Saved (final revised paper)
Latest update: 16 Aug 2026
Short summary
Floods are significant natural disasters that affect people and property. This study uses a simplified terrain index and the latest lidar-derived digital elevation maps (DEMs) to investigate flood inundation extent quality. We examined inundation quality influenced by different spatial resolutions and other variables. Results showed that lidar DEMs enhance inundation quality, but their resolution is less impactful in our context. Further studies on reservoirs and urban flooding are recommended.
Floods are significant natural disasters that affect people and property. This study uses a...
