Flood risk assessment in the Ganga Basin, India: A multi-criteria geospatial analysis with NASA’s Black Marble Nighttime light Data

Changing climate, intense rainfall, and geomorphological conditions within the Indo-Gangetic Basin (IGB) have led to recurring flooding within the area in the recent past. The devastating August 2022 floods in Pakistan affected 33 million people causing severe loss of life and property. The occurrence of such flooding events has increased the need to understand the complexities of the interplay between flood hazards, exposure, vulnerability, and risk. This study delves into flood risk within India's Ganga Basin, examining the flood-inducing factors, vulnerability, and exposure through an innovative approach using NASA's Black Marble Nighttime Lights Product Suite (VNP46).  The product suite, available globally on daily, monthly, and annual composite scales, corrects extraneous sources of noise in nighttime light (NTL) radiance signals and has proven effective in disaster monitoring, risk assessment and reduction, humanitarian response, preparedness, resilience, and sustainable development.

Our work to date has successfully utilized these NTL data to quantify flood exposure and the impact of flooding in both urban and rural areas by analyzing changes in radiance across time and space. However, to improve our understanding of human response to floods, we now focus on a more intricate analysis: incorporating geomorphological and socio-hydrological factors into a risk assessment approach.

Our study evaluates flood hazard, exposure, and vulnerability as three separate entities and combines them using a multi-criterion decision tool to assess flood risk within the basin. Flood hazards are studied as a relationship between geomorphological and hydrological parameters, whereas flood vulnerability is studied using land use and land cover data. The novelty of this research is using NASA’s Black Marble nightlights as a proxy to study flood exposure. We argue that the NTL data can more effectively capture the human presence and economic activities compared to some conventional parameters for flood exposure such as population count, household density, and literacy amongst others. By integrating these diverse data layers using the robust Analytical Hierarchical Process (AHP), we generate comprehensive flood risk maps across the Ganga Basin spanning a decade. The accuracy of these maps is validated against historical flood event data from the EM-DAT database. Ultimately, our research culminates in a spatially explicit and data-driven approach to flood risk assessment, which can empower targeted mitigation strategies and proactive planning within the basin.