Since the dawn of human civilization, rivers have profoundly shaped both landscape evolution and population dynamics. Rivers have historically attracted human settlements due to their accessibility for navigation, trade, and resources. However, with urbanization and population growth, particularly near rivers, flood hazards have intensified, leading to significant human and economic losses. This thesis focuses on flood risks in the Indus-Ganga Basin, the world’s most populous river basin, which frequently experiences severe floods. Traditional flood risk assessments often use outdated datasets that may not capture the dynamic nature of human presence and infrastructure. To address these limitations, this study incorporates novel data from night-time lights (NTL), a proxy for human activity and settlement, in flood exposure and risk assessments.

The thesis applies a remote sensing and GIS-based approach to integrate NTL data, analysing spatial patterns of human activity and their interactions with landscape factors and flood hazards. Three key components are explored: landscape drivers shaping human presence, the socio-economic impacts of floods, and the development of a comprehensive flood risk assessment framework.

I first investigate NTL data to examine the relationship between river geomorphology and human settlements in the Indus Basin. Results highlight how settlement patterns near rivers are shaped by geomorphological factors, influencing flood vulnerability. In my second project, the impact of the 2022 Indus floods is analysed through NTL, revealing significant declines in radiance, indicating disruptions to human activity. This novel approach captures flood impacts beyond the inundation zone, emphasising the compound effects on both urban and rural areas. Lastly, I introduce a flood risk assessment model for the Ganga Basin, combining flood hazard, exposure, and vulnerability indices. The study showcases the value of NTL data in assessing flood exposure and vulnerability, offering an innovative, dynamic, and cost-effective method to enhance flood risk understanding in vulnerable regions.