Why in News?

• Recent landslides in the Western Ghats (that struck the under-construction twin tunnel project in Wayanad, Kerala) and other parts of India have renewed focus on the urgent need for effective landslide early warning systems (LEWS).
• Experts argue that scientific forecasting, coupled with timely evacuation, can significantly reduce casualties in highly vulnerable regions such as the Western Ghats and the Himalayas.

Why in News?

• Why Early Warning Systems Matter?
• Major Approaches to Landslide Forecasting
• Towards a Comprehensive National Landslide Warning System
• Challenges and Way Forward
• Conclusion

Why Early Warning Systems Matter?

• Landslides are predictable to a considerable extent, particularly in identified high-risk zones.
• Early warning systems enable timely evacuation, reducing loss of life and property.
• Countries such as Switzerland have successfully prevented casualties through advance warnings and planned evacuations.
• In India, the 2024 Munnar landslides (Kerala) demonstrated the effectiveness of an early warning system, where evacuations based on scientific advice prevented fatalities.
• India's landslide vulnerability:
  • According to the National Disaster Management Authority (NDMA), around 13% of India's landmass (0.42 million sq km) is prone to landslides.
  • The Himalayan region and the Western Ghats are the most vulnerable zones.
  • Highly vulnerable regions: Tehri Garhwal and Uttarkashi (Uttarakhand), Mandi and Shimla (Himachal Pradesh), Aizawl region (Mizoram), and parts of Manipur.
  • Relatively less vulnerable:
    • Sikkim, despite frequent attention, has comparatively lower vulnerability because road networks are less extensive.
    • Reduced mountain cutting and slope disturbance improve geological stability.

Major Approaches to Landslide Forecasting:

• Sensor-based monitoring system:
  • Developed by research groups such as Amrita University, this method involves installing sensors at high-risk slopes.
  • Key instruments: Tilt meters, pressure gauges, accelerometers, ground movement and vibration sensors.
  • Working mechanism:
    • Sensors continuously monitor slope stability.
    • When readings exceed predefined safety thresholds, warnings are issued to local authorities for evacuation.
  • Advantages: Scientifically robust and highly accurate. Provides sufficient lead time for evacuation. Successfully tested in Kerala.
  • Limitations: Monitors only the instrumented slope. Cannot predict landslides on nearby, unmonitored slopes. Installation and maintenance involve significant costs.
• Probabilistic forecasting model:
  • Developed by IIT Mandi, this approach predicts landslide probability across large regions.
  • Methodology:
    • Uses satellite-based mapping of historical landslides.
    • Integrates localised rainfall forecasts, soil characteristics, rock stability, slope gradient, and population density.
    • Employs 7–10 rainfall-derived parameters for each location.
  • Validation: Successfully validated against around 80 actual landslides in the Himalayan region.
  • Advantages: Covers extensive geographical areas, including remote locations. Identifies multiple vulnerable sites simultaneously.
  • Limitations:
    • Dependent on high-resolution rainfall forecasts.
    • Current rainfall predictions are available only one day in advance, limiting lead time.
    • Improved forecasts from the India Meteorological Department (IMD) could significantly enhance predictive capability.

Towards a Comprehensive National Landslide Warning System:

• Experts believe India can develop an effective nationwide LEWS within two years, provided adequate resources and institutional support are available.
• Priority actions: Identify high-frequency, high-impact landslide zones. Prepare detailed hazard zonation and risk maps. Install sensor networks at the most vulnerable locations.
• Integrate: Satellite monitoring, sensor-based observations, high-resolution weather forecasting, and GIS and remote sensing technologies.
• Strengthen coordination: Among IMD, NDMA, Geological Survey of India (GSI), State Disaster Management Authorities (SDMAs), and local administrations.

Challenges and Way Forward:

• Absence: Of comprehensive mapping of high-risk landslide hotspots. Develop an integrated National Landslide Early Warning System combining sensor-based monitoring with probabilistic forecasting models.
• Limited deployment of sensor networks: Expand landslide susceptibility mapping using remote sensing, GIS and AI-based analytics.
• Dependence on short-term rainfall forecasts: Accelerate development of high-resolution rainfall forecasting by IMD.
• High costs of monitoring infrastructure: Prioritise vulnerable infrastructure, transport corridors and densely populated hill settlements.
• Need: For greater inter-agency coordination and sustained investment. Promote community awareness, evacuation drills and local disaster preparedness.

Conclusion:

• With climate change increasing the frequency of extreme rainfall events, landslides are becoming a growing disaster risk in India.
• A combination of above suggestions can transform landslide management from reactive relief to proactive risk reduction, saving lives and protecting critical infrastructure.