4.6 Article

Flood inundation mapping and hazard assessment of Baitarani River basin using hydrologic and hydraulic model

Journal

NATURAL HAZARDS
Volume 109, Issue 1, Pages 389-403

Publisher

SPRINGER
DOI: 10.1007/s11069-021-04841-3

Keywords

Hydrologic model; Hydraulic model; Flood hazard map; Land use; cover; FLO-2D; HEC-HMS

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This study utilized a continuous hydrologic-hydraulic model to map flood caused by the Baitarani River in Odisha, India, and relied on the HEC-HMS and FLO-2D models to assess the extent of flooding.
Frequent flood is a concern for most of the coastal regions of India. The importance of flood maps in governing strategies for flood risk management is of prime importance. Flood inundation maps are considered dependable output generated from simulation results from hydraulic models in evaluating flood risks. In the present work, a continuous hydrologic-hydraulic model has been implemented for mapping the flood, caused by the Baitarani River of Odisha, India. A rainfall time-series data were fed into the hydrologic model and the runoff generated from the model was given as an input into the hydraulic model. The study was performed using the HEC-HMS model and the FLO-2D model to map the extent of flooding in the area. Shuttle Radar Topographic Mission (SRTM) 90 m Digital Elevation Model (DEM) data, Land use/Land cover map (LULC), soil texture data of the basin area were used to compute the topographic and hydraulic parameters. Flood inundation was simulated using the FLO-2D model and based on the flow depth, hazard zones were specified using the MAPPER tool of the hydraulic model. Bhadrak District was found to be the most hazard-prone district affected by the flood of the Baitarani River. The result of the study exhibited the hydraulic model as a utile tool for generating inundation maps. An approach for assessing the risk of flooding and proper management could help in mitigating the flood. The automated procedure for mapping and the details of the study can be used for planning flood disaster preparedness in the worst affected area.

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