93 citations as recorded by crossref.

1. Flood Susceptibility Assessment through Statistical Models and HEC-RAS Analysis for Sustainable Management in Essaouira Province, Morocco A. Khouz et al. https://doi.org/10.3390/geosciences13120382
2. Inundation mapping using hydraulic modeling with high-resolution remote sensed data: a case study in the Acre River Basin, Brazil L. Antunes da Silva et al. https://doi.org/10.1007/s40808-024-01972-x
3. An Operational Method for Flood Directive Implementation in Ungauged Urban Areas G. Papaioannou et al. https://doi.org/10.3390/hydrology5020024
4. 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
5. Optimizing Flood Susceptibility Detection Using Ensemble Learning Methods Z. Dai et al. https://doi.org/10.1007/s11269-025-04288-2
6. Integrating rainfall return periods in MCDA-based flood risk mapping: a fuzzy-AHP case study in an ungauged watershed M. Syarifuddin et al. https://doi.org/10.1007/s00477-026-03211-4
7. Effect of the spatial resolution of digital terrain data obtained by drone on urban fluvial flood modeling of mountainous regions X. Zhou et al. https://doi.org/10.5194/hess-29-1963-2025
8. Understanding the effects of digital elevation model resolution and building treatment for urban flood modelling W. Jiang et al. https://doi.org/10.1016/j.ejrh.2022.101122
9. Assessment of vertical accuracy of open source 30m resolution space-borne digital elevation models R. Talchabhadel et al. https://doi.org/10.1080/19475705.2021.1910575
10. AUTOMATIC EXTRACTION OF RIVER CHANNEL LONGITUDINAL AND CROSS-SECTIONAL DATA FROM DIGITAL ELEVATION MODEL S. SEGI https://doi.org/10.2208/jscejj.25-00026
11. MSFlood-Net: A physically informed deep learning model integrating multi-source data for flood inundation mapping L. Zhou et al. https://doi.org/10.1016/j.envsoft.2025.106779
12. Qualitative simulation of bathymetric changes due to reservoir sedimentation: A Japanese case study A. Bilal et al. https://doi.org/10.1371/journal.pone.0174931
13. Benchmarking the Indian National CartoDEM against SRTM for 1D hydraulic modelling A. Kumar et al. https://doi.org/10.1080/15715124.2019.1606816
14. FLOOD INUNDATION ANALYSIS IN LOW-LYING RIVER BASIN USING DIFFERENT DIGITTAL ELEVATION MODEL K. KOURA et al. https://doi.org/10.2208/jscejj.23-16183
15. Evaluation of the sensitivity of hydraulic model parameters, boundary conditions and digital elevation models on ice-jam flood delineation A. Das & K. Lindenschmidt https://doi.org/10.1016/j.coldregions.2020.103218
16. 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
17. Effects of digital elevation model data source on HSPF-based watershed-scale flow and water quality simulations M. Roostaee & Z. Deng https://doi.org/10.1007/s11356-022-24449-9
18. Determining the best remotely sensed DEM for flood inundation mapping in data sparse regions A. Azizian & L. Brocca https://doi.org/10.1080/01431161.2019.1677968
19. Comparison of two modelling strategies for 2D large-scale flood simulations S. Dazzi et al. https://doi.org/10.1016/j.envsoft.2021.105225
20. The influence of climate model uncertainty on fluvial flood hazard estimation L. Beevers et al. https://doi.org/10.1007/s11069-020-04282-4
21. HydroPredicT_Extreme: A probabilistic method for the prediction of extremal high-flow hydrological events J. Molina et al. https://doi.org/10.1016/j.jhydrol.2022.127929
22. Towards a continental-scale riverine bathymetry dataset using readily-available data and simple hydraulic models M. Follum et al. https://doi.org/10.1016/j.jhydrol.2023.129769
23. Global Digital Elevation Model Comparison Criteria: An Evident Need to Consider Their Application C. López-Vázquez & F. Ariza-López https://doi.org/10.3390/ijgi12080337
24. Using 1D HEC-RAS Modeling and LiDAR Data to Improve Flood Hazard Maps Accuracy: A Case Study from Jijia Floodplain (NE Romania) E. Huţanu et al. https://doi.org/10.3390/w12061624
25. How much spatial resolution do we need to model a local flood event? Benchmark testing based on UAV data from Biga River (Turkey) M. Utlu & H. Özdemir https://doi.org/10.1007/s12517-020-06318-2
26. Enhancing the resilience to flooding induced by levee breaches in lowland areas: a methodology based on numerical modelling A. Ferrari et al. https://doi.org/10.5194/nhess-20-59-2020
27. Evaluation of Digital Elevation Models (DEM) Generated from the InSAR Technique in a Sector of the Central Andes of Chile, Using Sentinel 1 and TerraSar-X Images F. Flores et al. https://doi.org/10.3390/app16010392
28. WebFRIS: An efficient web-based decision support tool to disseminate end-to-end risk information for flood management M. Mohanty & S. Karmakar https://doi.org/10.1016/j.jenvman.2021.112456
29. Probabilistic flood inundation mapping at ungauged streams due to roughness coefficient uncertainty in hydraulic modelling G. Papaioannou et al. https://doi.org/10.5194/adgeo-44-23-2017
30. High-resolution floodplain topography mapping from ICESat-2 and Sentinel-2 using slope-corrected inundation frequency J. Ma et al. https://doi.org/10.1016/j.jhydrol.2026.136052
31. Flood Risk Assessment Based on Hydrodynamic Model—A Case of the China–Pakistan Economic Corridor X. Sun et al. https://doi.org/10.3390/w15244295
32. The Effects of Topographic Map Scale and Costs of Land Surveying on Geometric Model and Flood Inundation Mapping A. Azizian https://doi.org/10.1007/s11269-019-2202-y
33. Evaluation of 1D hydraulic models for the simulation of mountain fluvial floods: a case study of the Santa Bárbara River in Ecuador J. Pinos et al. https://doi.org/10.2166/wpt.2019.018
34. Kotrupi landslide deformation study in non-urban area using DInSAR and MTInSAR techniques on Sentinel-1 SAR data K. Niraj et al. https://doi.org/10.1016/j.asr.2021.11.042
35. An interpretable and scalable model for rapid flood extent forecasting using satellite imagery and machine learning with rotated EOF analysis H. Wan et al. https://doi.org/10.1016/j.envsoft.2025.106562
36. A consensus-based framework for urban flood risk assessment under DEM uncertainty F. Ma et al. https://doi.org/10.1016/j.envsoft.2026.107175
37. River Bathymetry Model Based on Floodplain Topography L. Bures et al. https://doi.org/10.3390/w11061287
38. Comparison of bathymetric data sources used in hydraulic modelling of floods L. Bures et al. https://doi.org/10.1111/jfr3.12495
39. Tsunami Induced by the Strike‐Slip Fault of the 2018 Palu Earthquake (Mw = 7.5), Sulawesi Island, Indonesia T. Ho et al. https://doi.org/10.1029/2020EA001400
40. Modelling the future climate impacts on hydraulic infrastructure development in tropical (peri-)urban region: Case of Kigali, Rwanda P. Iradukunda et al. https://doi.org/10.1016/j.heliyon.2024.e27126
41. 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
42. Effects of digital elevation model resolution on rain-on-grid simulations: a case study in a Slovenian watershed M. Alexopoulos et al. https://doi.org/10.1080/02626667.2024.2378487
43. Asymmetric Interaction Between Terrain and Rainfall Data Resolution in Flood Inundation Mapping of Large Basins P. He et al. https://doi.org/10.1007/s13753-026-00785-z
44. UAV based comprehensive modelling approach for flood hazard assessment and mitigation planning K. Darji et al. https://doi.org/10.1016/j.pce.2024.103609
45. A review of integrated multicriteria decision support analysis in the climate resilient infrastructure development P. Iradukunda et al. https://doi.org/10.1016/j.indic.2023.100312
46. Monitoring and simulation of Chennai floods through a high-resolution topographic datasets A. .K et al. https://doi.org/10.1007/s44327-026-00238-8
47. The Future Impacts of ESL Events in Euro-Mediterranean Coastal Cities: The Coast-RiskBySea Model to Assess the Potential Economic Damages in Naples, Marseille and Barcelona M. Clemente https://doi.org/10.3390/su141610096
48. Survey on the resolution and accuracy of input data validity for SWAT-based hydrological models N. Rasheed et al. https://doi.org/10.1016/j.heliyon.2024.e38348
49. Sensitivity of Flood Hazard and Damage to Modelling Approaches C. Lyddon et al. https://doi.org/10.3390/jmse8090724
50. Accuracy Comparison of 30m Resolution SRTM and ASTER Derived Digital Elevation Models over AL-khamisah Region Using RTK-DGPS Data H. Hamid https://doi.org/10.1088/1742-6596/1804/1/012084
51. Improved accuracy and efficiency of flood inundation mapping of low-, medium-, and high-flow events using the AutoRoute model M. Follum et al. https://doi.org/10.5194/nhess-20-625-2020
52. Evaluation of topographic data sources for 2D flood modelling: case study of Kelani basin, Sri Lanka A. Suja & R. Rajapakse https://doi.org/10.1088/1755-1315/612/1/012043
53. UAV-borne, LiDAR-based elevation modelling: a method for improving local-scale urban flood risk assessment K. Trepekli et al. https://doi.org/10.1007/s11069-022-05308-9
54. Using volunteered geographic information data for flood mapping – Wadi Deffa El Bayadh Algeria A. Otmani et al. https://doi.org/10.1080/23249676.2022.2155716
55. Effect of DEM resolution in flood modeling: a case study of Gorganrood River, Northeastern Iran S. Khojeh et al. https://doi.org/10.1007/s11069-022-05283-1
56. Probabilistic hydro-geomorphological hazard assessment based on UAV-derived high-resolution topographic data: The case of Basento river (Southern Italy) M. La Salandra et al. https://doi.org/10.1016/j.scitotenv.2022.156736
57. Testing UAV-derived topography for hydraulic modelling in a tropical environment M. Mazzoleni et al. https://doi.org/10.1007/s11069-020-03963-4
58. Flood inundation mapping sensitivity to riverine spatial resolution and modelling approach G. Papaioannou et al. https://doi.org/10.1007/s11069-016-2382-1
59. Flood-Prone Area Assessment Using GIS-Based Multi-Criteria Analysis: A Case Study in Davao Oriental, Philippines J. Cabrera & H. Lee https://doi.org/10.3390/w11112203
60. A Novel Method for the Automatic Extraction of Quality Non-Planar River Cross-Sections from Digital Elevation Models I. Petikas et al. https://doi.org/10.3390/w12123553
61. Damage and protection cost curves for coastal floods within the 600 largest European cities B. Prahl et al. https://doi.org/10.1038/sdata.2018.34
62. Impact of identical digital elevation model resolution and sources on morphometric parameters of Tena watershed, Ethiopia T. Tesema https://doi.org/10.1016/j.heliyon.2021.e08345
63. Estimating Flood-Affected Houses as an SDG Indicator to Enhance the Flood Resilience of Sahel Communities Using Geospatial Data M. Belenguer-Plomer et al. https://doi.org/10.3390/rs17122087
64. Catchment-Scale Flood Modelling in Data-Sparse Regions Using Open-Access Geospatial Technology I. Ekeu-wei & G. Blackburn https://doi.org/10.3390/ijgi9090512
65. An Overview of Flood Risk Analysis Methods D. Diaconu et al. https://doi.org/10.3390/w13040474
66. Sponge City Planning and Information System Development Based on Geographic Information Fuzzy Processing M. Gan et al. https://doi.org/10.1155/2022/9464785
67. The Role of DEM Resolution and Evapotranspiration Assessment in Modeling Groundwater Resources Estimation: A Case Study in Sicily I. Borzì et al. https://doi.org/10.3390/w12112980
68. Quantifying the inundation impacts of earthquake-induced surface elevation change by hydrological and hydraulic modeling M. Ouyang et al. https://doi.org/10.1038/s41598-021-83309-7
69. Flood-Induced Geomorphic Change of Floodplain Extent and Depth: A Case Study of Hurricane Maria in Puerto Rico Y. Li et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0002199
70. Flood Hazard Assessment Supported by Reduced Cost Aerial Precision Photogrammetry S. Zazo et al. https://doi.org/10.3390/rs10101566
71. Evaluation of riverbed representation methods for one‐dimensional flood hydraulics model A. Reil et al. https://doi.org/10.1111/jfr3.12304
72. Real-Time Urban Flood Forecasting Systems for Southeast Asia—A Review of Present Modelling and Its Future Prospects D. Chitwatkulsiri & H. Miyamoto https://doi.org/10.3390/w15010178
73. Application of CryoSat-2 altimetry data for river analysis and modelling R. Schneider et al. https://doi.org/10.5194/hess-21-751-2017
74. Evaluation of Copernicus DEM and Comparison to the DEM Used for Landsat Collection-2 Processing S. Franks & R. Rengarajan https://doi.org/10.3390/rs15102509
75. A hybrid machine learning-based multi-DEM ensemble model of river cross-section extraction: Implications on streamflow routing S. Biswal et al. https://doi.org/10.1016/j.jhydrol.2023.129951
76. Comparative Analysis of Global DEMs for Dam-Break Flood Modeling and Inundation Mapping S. Toledo Salgado et al. https://doi.org/10.1007/s11269-025-04427-9
77. A copula model of extracting DEM-based cross-sections for estimating ecological flow regimes in data-limited deltaic-branched river systems S. Biswal et al. https://doi.org/10.1016/j.jenvman.2023.118095
78. Sensitivity of various topographic data in flood management: Implications on inundation mapping over large data-scarce regions M. Prakash Mohanty et al. https://doi.org/10.1016/j.jhydrol.2020.125523
79. Seasonality of Roughness - the Indicator of Annual River Flow Resistance Condition in a Lowland Catchment S. Song et al. https://doi.org/10.1007/s11269-017-1656-z
80. Sensitivity of habitat hydraulic model outputs to DTM and computational mesh resolution G. Papaioannou et al. https://doi.org/10.1002/eco.2182
81. Hydrodynamic Modeling for Flood Hazard Assessment in a Data Scarce Region: a Case Study of Bharathapuzha River Basin X. Jacob et al. https://doi.org/10.1007/s10666-019-09664-y
82. Procedure of Numerical Modelling and Estimation of Sieve Curve Changes as a Tool to Define Riverbed’s Erodibility J. Florek & M. Wyrębek https://doi.org/10.3390/su15021468
83. Spatial network autoregressive propagation (SNAP): An innovative GIS model for probabilistic road network failure in urban areas S. Mansour https://doi.org/10.1016/j.urbmob.2026.100274
84. Multi‐Level Monte Carlo Models for Flood Inundation Uncertainty Quantification G. Aitken et al. https://doi.org/10.1029/2022WR032599
85. Optimization of Manning’s roughness coefficient using 1-dimensional hydrodynamic modelling in the perennial river system: A case of lower Narmada Basin, India A. Bhargav et al. https://doi.org/10.1007/s10661-024-12883-w
86. National-Scale Built-Environment Exposure to 100-Year Extreme Sea Levels and Sea-Level Rise R. Paulik et al. https://doi.org/10.3390/su12041513
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88. Uncertainty estimation in hydrodynamic modeling using Bayesian techniques V. Pinheiro et al. https://doi.org/10.1590/2318-0331.241920180110
89. Does the topographic data source truly influence the routing modelling of debris flows in a torrent catchment? M. Boreggio et al. https://doi.org/10.1002/esp.5366
90. Influence of DEM resolution on modelling hydrological connectivity in a complex agricultural catchment with woody crops M. López‐Vicente & S. Álvarez https://doi.org/10.1002/esp.4321
91. A river channel terrain reconstruction method for flood simulations based on coarse DEMs J. Hou et al. https://doi.org/10.1016/j.envsoft.2021.105035
92. On the Simulation of Floods in a Narrow Bending Valley: The Malpasset Dam Break Case Study C. Biscarini et al. https://doi.org/10.3390/w8110545
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