Remote sensing for sustainable river management: Evaluating watershed vulnerability for Ganga, the world’s most densely populated river basin

Anthony Acciavatti , Sarthak Arora , Michael Warner , Ariel Chamberlain , James C. Smoot, Nikhil Raj Deep , Claire Gorman Hanly

When standing water mixes with wastewater, it can create serious public health and environmental concerns. This scenario is particularly dangerous in densely populated urban areas with inadequate infrastructure. Such contamination threatens to cause major public health crises in the Ganga River basin where monsoonal flooding, which is exacerbated by climate change, converges with 6 billion liters of untreated sewage that is discharged daily into the river by 650 million people. To prioritize areas of the watershed for actions ranging from conservation to intervention, it is vital to perform vulnerability assessments. While the Analytic Hierarchy Process (AHP) is widely regarded as the standard in decision making methodologies, uncertainties arise from its dependence on expert judgments, especially when applied to remote sensing data, where expert knowledge might not fully capture spatial and spectral complexities inherent in such data. To constrain model uncertainties, AHP alongside a suite of alternative existing and novel variants of AHP-based decision analysis was applied on remote sensing data to assess the vulnerability of the river Ganga to pollution. Model outputs were compared to identify areas where variants may provide additional insights over AHP, and a composite variable of these results was utilized to robustly define the vulnerability of the river Ganga to waterway pollution. Together, these analyses located areas of extreme vulnerability at the nexus of river Ganga and urban landscapes as well as regions of low vulnerability potentially suitable for conservation efforts or sustainable development practices to prevent their degradation. This approach contributes to a more comprehensive understanding of remote sensing data applications in environmental assessment, and these decision-making variants can also have broader applications in other areas of environmental management and sustainability, facilitating more precise and adaptable decision support frameworks in densely populated watersheds.