IIT Guwahati Creates Technique to Track Glacier Hazards in Eastern Himalayas

In a major scientific breakthrough, researchers from Indian Institute of Technology Guwahati have developed a new method to predict glacial hazards in the fragile Eastern Himalayan region. The study identifies hundreds of locations where glacial lakes may form in the future, raising concerns about floods, infrastructure safety, and climate resilience in high-mountain areas.

Why in News?

IIT Guwahati researchers have developed a predictive framework that has identified 492 potential glacial lake formation sites in the Eastern Himalayas. The findings were recently published in Scientific Reports, highlighting new risks of Glacial Lake Outburst Floods (GLOFs).

Understanding Glacial Hazards and GLOFs

• Glacial hazards mainly arise due to the formation and sudden bursting of glacial lakes.
• These events, known as Glacial Lake Outburst Floods (GLOFs), release massive volumes of water, ice, and debris downstream within a short time.
• Such floods can destroy villages, roads, hydropower projects, and agricultural land. With glaciers retreating rapidly due to climate change, new lakes are forming at unprecedented rates, especially in the Himalayas.
• Predicting where these lakes might appear is crucial for disaster preparedness, long-term water management, and safeguarding mountain communities.

What Did IIT Guwahati’s Study Do Differently?

• The research team used high-resolution Google Earth satellite images along with digital elevation models (DEMs) to study terrain features in detail.
• Unlike earlier studies that focused mainly on glacier size or temperature trends, this framework closely examined landscape structure.
• By analysing slope, surface shape, cirques, and neighbouring lakes, the researchers captured complex terrain behaviour.
• Importantly, the model also estimated uncertainty levels, making predictions more realistic.
• This approach significantly improves reliability and helps authorities focus on zones that need urgent monitoring and preventive action.

Advanced Predictive Models Used in the Study

• To ensure accuracy, the researchers tested three predictive techniques.
• These included Logistic Regression (LR), Artificial Neural Networks (ANN), and Bayesian Neural Networks (BNN).
• Among these, BNN emerged as the most accurate model. Its strength lies in handling uncertainty, which is common in high-altitude terrain data.
• The BNN model identified key predictors such as retreating glaciers, gentle slopes, cirques, and nearby lakes.
• This confirms that landform characteristics, often ignored earlier, play a decisive role in glacial lake formation.

Key Findings: 492 High-Risk Locations Identified

• Using the developed framework, the team identified 492 locations in the Eastern Himalayas where new glacial lakes are likely to form.
• These areas are potential future hazard zones. According to Prof. Ajay Dashora, Assistant Professor at IIT Guwahati, the framework can guide early-warning systems for GLOFs, support safer planning of roads and hydropower projects, and help decide suitable settlement locations.
• This makes the research directly relevant for disaster management authorities, planners, and policymakers working in Himalayan states.

Global Relevance and Future Scope

• Beyond India, the framework developed by IIT Guwahati is adaptable to other glaciated mountain regions worldwide.
• From the Andes to the Alps, glacial hazards are increasing due to global warming.
• The research team plans to further strengthen the model by integrating moraine development histories, automating data preparation, and adding field-based validation.
• These upgrades will improve accuracy and enable large-scale monitoring.
• This positions India as a contributor to global climate science and resilience planning.

Key Summary at a Glance

Aspect	Details
Why in News?	IIT Guwahati identified 492 potential glacial lake sites
Region	Eastern Himalayas
Main Risk	Glacial Lake Outburst Floods (GLOFs)
Technology Used	Satellite images, DEMs, AI-based models
Best Model	Bayesian Neural Network (BNN)
Applications	Early warnings, infrastructure planning
Global Use	Adaptable to other mountain regions

Question

Q. Which institute developed a predictive framework to identify glacial hazard zones in the Eastern Himalayas?

A. IIT Delhi
B. IIT Bombay
C. IIT Guwahati
D. IISc Bengaluru