Characterization of the 2014 Indus River Flood Using Hydraulic Simulations and Satellite Images

• Published in: Remote sensing (Basel, Switzerland) Vol. 13; no. 11; p. 2053
• Main Authors: Tariq, Aqil, Shu, Hong, Kuriqi, Alban, Siddiqui, Saima, Gagnon, Alexandre S., Lu, Linlin, Linh, Nguyen Thi Thuy, Pham, Quoc Bao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2021
• Subjects: Agricultural land; Crop damage; Damage assessment; flood characterization; Flood damage; Flood frequency; Flood mapping; Floods; Frequency analysis; Geographic information systems; HEC-RAS; hydraulic simulation; Hydrologic models; Hydrology; Image classification; Indus River; Land cover; Land use; Landsat; Landsat satellites; Metadata; Model accuracy; MODIS; Remote sensing; Risk reduction; River basins; Rivers; Satellite imagery; Sensors; Software; Spatial analysis; Spectroradiometers; United States > US; Indus River; Pakistan
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13112053

Rivers play an essential role to humans and ecosystems, but they also burst their banks during floods, often causing extensive damage to crop, property, and loss of lives. This paper characterizes the 2014 flood of the Indus River in Pakistan using the US Army Corps of Engineers Hydrologic Engineering Centre River Analysis System (HEC-RAS) model, integrated into a geographic information system (GIS) and satellite images from Landsat-8. The model is used to estimate the spatial extent of the flood and assess the damage that it caused by examining changes to the different land-use/land-cover (LULC) types of the river basin. Extreme flows for different return periods were estimated using a flood frequency analysis using a log-Pearson III distribution, which the Kolmogorov–Smirnov (KS) test identified as the best distribution to characterize the flow regime of the Indus River at Taunsa Barrage. The output of the flood frequency analysis was then incorporated into the HEC-RAS model to determine the spatial extent of the 2014 flood, with the accuracy of this modelling approach assessed using images from the Moderate Resolution Imaging Spectroradiometer (MODIS). The results show that a supervised classification of the Landsat images was able to identify the LULC types of the study region with a high degree of accuracy, and that the most affected LULC was crop/agricultural land, of which 50% was affected by the 2014 flood. Finally, the hydraulic simulation of extent of the 2014 flood was found to visually compare very well with the MODIS image, and the surface area of floods of different return periods was calculated. This paper provides further evidence of the benefit of using a hydrological model and satellite images for flood mapping and for flood damage assessment to inform the development of risk mitigation strategies.