Gandhinagar: For the last few years, India is facing the issue of severe infrastructural damage, casualties, and social-economic disruptions caused by highway flooding, landslides, and associated debris flows that follow incessant monsoon rainfall in different parts of the country. Recently, Shri Nitin Gadkari, Minister for Road Transport and Highways, had directed key officials to solve highway-flooding issues that arise as a consequence of extreme rainfall events, which in turn cripple the country’s transportation systems every year.
A team of researchers led by Professor Udit Bhatia, including Raviraj Dave and Srikrishnan Siva Subramanian, from the Indian Institute of Technology Gandhinagar (IITGN) has been working on this crucial issue and has developed an integrated predictive model to accurately understand and assess the real magnitude of heavy rainfall-induced damages in road transport networks. This hazard model can help administrators identify the ‘hotspots’ which should be reinforced and protected to solve highway-flooding issues and minimise the socio-economic disruptions.
The research team uses satellite imageries, ingeniously developed landslide and debris flow models, and state-of-the-art flood predictions models to predict the occurrence time and geographical locations that have a high susceptibility to the simultaneous occurrence of flooding, landslides and debris flow events after heavy rainfall episodes. It also predicts the magnitude of infrastructure disruptions that may happen due to landslides, debris flows, and floods in those regions.
How the model works:
The framework requires a high-resolution Digital Elevation Model (DEM) overlaid by the road network of the terrain and daily precipitation data to analyse the shallow landslides, debris flows, and a gauged reservoir discharge data for flood inundation. The occurrence timing (temporal), locations (spatial, hotspot pixels), and magnitude of infrastructure disruptions provided by this model are predicted using iterative numerical calculations based on equations of force, moment equilibrium and fundamental conservation laws (mass and momentum balance).
The results identify the least to most weak links (hotspot pixels) within a road network and helps in deciding the locations of road segments that need strengthening, which in turn, can minimise the societal and economic disruptions during heavy rainfall emergencies. The landslide models developed by the team also take into account the existing vegetation cover, land use characteristics, water flow characteristics, and binding properties of the soil. All these parameters are fed into their mathematical model to calculate the slope stability at each pixel. On the other hand, this model can also estimate the effect each stabilisation effort (e.g. changes in vegetation, reinforcements at each location) would have on the slope stability.
As a part of their study, the researchers reconstructed the sequence of events that unfolded in the Periyar river basin in Kerala in 2018 and quantified the exact magnitude of connectivity losses as a consequence of events triggered by once in 100 years rainfall.
Explaining the significance of this model, Prof Udit Bhatia, Assistant Professor of Civil Engineering at IITGN said, “The first step in the direction of enhancing resilience is to understand the risk well for pre-disaster preparedness and post-disaster recovery. The results of our modelling framework show that we could underestimate the functionality losses by 70% if we do not take into account the concurrence of extreme events, which in turn, can undermine our disaster preparedness.”
Adding further, Prof Bhatia said, “When we look at the road transportation systems within the region, we end up with thousands of intersections and hundreds of thousands of road segments. Hence, it is virtually impossible for any stakeholder to give the same priority to each infrastructure element in systems with such a high complexity. Our patented complex network framework in combination with our recently developed hazard model used in this study helps us identify the ‘hotspots’ which should be reinforced and protected to minimise the societal and economic disruptions.”
