Hydraulic Modelling of Flood Assessment in Chenab Basin Pakistan

Abstract

Flooding represents one of the most recurrent and economically damaging natural hazards in Pakistan, particularly within the Indus River system, where extensive floodplains and dense human settlement exacerbate vulnerability. This study presents an integrated flood-assessment framework combining optical remote sensing, geographic information systems (GIS), and physically based hydraulic modelling to delineate and quantify flood inundation in the Chenab Basin, Punjab. Multi-temporal Landsat 8 OLI imagery acquired during pre-, peak-, and post-flood stages was processed to derive the Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), and Water Ratio Index (WRI) for flood detection. Comparative accuracy assessment using field observations and high-resolution reference imagery demonstrated that MNDWI outperformed other indices, achieving an overall accuracy of 91% compared to 83% for NDWI and 85% for WRI. Supervised maximum-likelihood land-use/land-cover (LULC) classification yielded an overall accuracy of 91.6% with a Kappa coefficient of 0.89, confirming strong agreement between classified outputs and ground reference data. A 30 m SRTM-derived Digital Terrain Model was employed to develop a one-dimensional hydraulic model in HEC-RAS, simulating flood scenarios for return periods ranging from 2.5 to 100 years (455,000–1,665,000 cusecs) along the Chenab River reach between Head Trimmu and Head Panjnad. Modelled water-surface elevations showed close correspondence with GPS-recorded flood marks, with positional deviations below 50 m and sensitivity analysis indicating a maximum ±0.15 m variation in water level for ±0.01 changes in Manning’s roughness coefficient. Results indicate that approximately 68% of the study area was inundated during the 2010 flood, with cropland accounting for nearly 61% of the affected area and settlements for 18%. The integration of satellite-derived water indices with hydraulic simulation proved effective for accurate flood delineation and hazard zoning, providing a robust and operationally scalable framework for flood-risk assessment and spatial planning in data-scarce river basins of Pakistan.