One Dimensional Hydrodynamic Modeling of River Flow Using DEM Extracted River Cross-sections

River cross-sections are the prime input to any river hydraulic model for simulation of water level and discharge. Field measurements of river cross-sections are labour intensive and expensive activities. Availability of measured river cross-sections is scanty in most of the developing countries, thereby making it difficult to simulate the water level and discharge using hydraulic models. A methodology for extracting river cross-sections from Shuttle Radar Topographic Mission digital elevation model (SRTM DEM) of 3-arc second has been proposed in the reported study. The extracted river cross-sections were used to simulate the magnitude of flood in the deltaic reaches of Brahmani river basin located in the eastern India. Forty cross-sections along the reaches of the rivers were extracted from the DEM and were used in the MIKE 11 hydrodynamic (MIKE 11HD) model. Prior to using the DEM-extracted river cross-sections in the model, the cross-sections were modified based on the results of the DEM error analysis. Four available measured river cross-sections were compared with the DEM-extracted modified cross-sections to examine their geometric and hydraulic similarity. By changing Manning’s roughness coefficient (n), same stage-discharge relationship could be obtained in both types of cross-sections. Subsequently, the DEM-extracted cross-sections were used in the MIKE 11HD model for the simulation of discharge and water levels at various sections of the rivers. The model was calibrated for the period of June 15–October 31 of the year 1999 and validated for the year 2003. The model validation results showed a close agreement between the simulated and observed stage hydrographs. The calibrated values of Manning’s n were found to vary within the range of 0.02 to 0.033. The study revealed that freely available SRTM DEM-extracted river cross-sections could be used in hydraulic models to simulate stage and discharge hydrographs with considerable accuracy under the scarcity of measured cross-section data.