Western Ghats Landslides Highlight the Need for a Robust Landslide Early Warning System in India

Why in News?

• Recent landslides in the Western Ghats, including the one affecting the under-construction twin tunnel project in Wayanad, Kerala, have once again highlighted the urgent need for a Landslide Early Warning System (LEWS) in India.
• Experts believe that scientific forecasting, real-time monitoring, and timely evacuation can significantly reduce casualties and damage in highly vulnerable regions such as the Himalayas and the Western Ghats.
  
  

What is a Landslide Early Warning System (LEWS) ?

• A Landslide Early Warning System (LEWS) is a scientific mechanism that monitors landslide-prone areas, predicts the likelihood of slope failure, and issues timely alerts to authorities and communities, enabling evacuation before a disaster occurs.

Why Does India Need a Landslide Early Warning System ?

• Landslides are largely predictable in identified high-risk zones.
• Early warnings help save lives through timely evacuation.
• They reduce damage to infrastructure, transportation networks, and public property.
• They enable disaster management agencies to shift from reactive relief to proactive risk reduction.
• With climate change increasing extreme rainfall events, the frequency and intensity of landslides are expected to rise.

Successful Examples

• Switzerland has effectively minimized casualties through advanced landslide forecasting and evacuation systems.
• In Munnar, Kerala (2024), scientific warnings enabled authorities to evacuate residents before landslides occurred, preventing fatalities.

India's Landslide Vulnerability

• According to the National Disaster Management Authority (NDMA):
• Around 13% of India's geographical area (about 0.42 million sq. km) is vulnerable to landslides.
• The Himalayan region and the Western Ghats are the country's most landslide-prone zones.

Highly Vulnerable Regions

• Tehri Garhwal and Uttarkashi (Uttarakhand)
• Mandi and Shimla (Himachal Pradesh)
• Aizawl region (Mizoram)
• Several parts of Manipur

Comparatively Less Vulnerable

• Although Sikkim frequently experiences landslides, its overall vulnerability is relatively lower due to:
• Limited road expansion
• Reduced mountain cutting
• Better slope stability

Major Approaches to Landslide Forecasting

1. Sensor-Based Monitoring System

• Developed by institutions such as Amrita University, this approach involves installing scientific instruments on highly vulnerable slopes.

Key Instruments

• Tilt meters
• Pressure gauges
• Accelerometers
• Ground movement sensors
• Vibration sensors

How It Works

• The sensors continuously monitor changes in slope stability. If any parameter crosses a predefined safety threshold, an automatic warning is sent to local authorities, allowing evacuation before slope failure occurs.

Advantages

• Highly accurate and scientifically reliable
• Provides sufficient lead time for evacuation
• Successfully tested in Kerala

Limitations

• Monitors only the instrumented slope
• Cannot predict failures on nearby unmonitored slopes
• Installation and maintenance are expensive

2. Probabilistic Forecasting Model

• Developed by IIT Mandi, this model estimates landslide probability over large geographical regions.

Methodology 

• The model integrates :
  • Historical landslide inventory from satellite data
  • Local rainfall forecasts
  • Soil characteristics
  • Rock stability
  • Slope gradient
  • Population density
  • Seven to ten rainfall-related parameters for every location

Validation

• The model has been successfully validated against nearly 80 actual landslides in the Himalayan region.

Advantages

• Covers large geographical areas
• Identifies multiple vulnerable locations simultaneously
• Suitable for remote and inaccessible regions

Limitations

• Depends heavily on high-resolution rainfall forecasts
• Current rainfall predictions are generally available only one day in advance
• Improved forecasting by the India Meteorological Department (IMD) would significantly enhance its performance

Towards a National Landslide Early Warning System

• Experts believe that India can establish a comprehensive nationwide Landslide Early Warning System (LEWS) within the next two years with adequate institutional support and investment.

Key Priorities

• Identify high-frequency landslide hotspots.
• Prepare detailed hazard zonation and risk maps.
• Install sensor networks in the most vulnerable locations.
• Integrate satellite monitoring with ground-based observations.
• Use GIS, Remote Sensing, and Artificial Intelligence (AI).
• Improve high-resolution weather forecasting.
• Strengthen coordination among NDMA, IMD, Geological Survey of India (GSI), State Disaster Management Authorities (SDMAs), and local administrations.

Challenges

• Lack of comprehensive mapping of landslide-prone hotspots.
• Limited deployment of sensor networks.
• Dependence on short-term rainfall forecasts.
• High installation and maintenance costs.
• Weak inter-agency coordination.
• Limited community awareness and preparedness in vulnerable regions.

Way Forward

• Develop an integrated National Landslide Early Warning System combining sensor-based monitoring and probabilistic forecasting.
• Expand landslide susceptibility mapping using GIS, Remote Sensing, satellite imagery, and AI.
• Strengthen IMD's high-resolution rainfall forecasting capability.
• Prioritize monitoring of critical infrastructure such as highways, tunnels, railways, dams, and densely populated hill settlements.
• Improve coordination among NDMA, IMD, GSI, SDMAs, and local governments.
• Conduct regular community awareness programmes, mock drills, and evacuation exercises.

Institutions Involved