Context
Recent landslides in the Western Ghats, including those affecting the under-construction twin tunnel project in Wayanad (Kerala), have reinforced the need for a robust Landslide Early Warning System (LEWS). Scientific forecasting and timely evacuation can substantially reduce the loss of life and damage to infrastructure in landslide-prone regions.
Significance of a Landslide Early Warning System
- Facilitates timely evacuation, minimising loss of life and property.
- Promotes a shift from disaster response to disaster risk reduction.
- Provides authorities with sufficient lead time for preparedness, evacuation, and emergency response.
- In India, the 2024 Munnar landslides demonstrated the effectiveness of early warning systems, where timely evacuation prevented fatalities.
- International experience, particularly from Switzerland, shows that scientific forecasting and advance warnings can significantly reduce disaster impacts.
Landslide Vulnerability in India
- According to the National Disaster Management Authority (NDMA), nearly 13% of India’s geographical area (about 0.42 million sq. km) is susceptible to landslides.
- The Himalayan region and the Western Ghats are the most vulnerable zones.
- Major landslide-prone regions include:
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- Tehri Garhwal and Uttarkashi (Uttarakhand)
- Mandi and Shimla (Himachal Pradesh)
- Aizawl region (Mizoram)
- Parts of Manipur
- Despite frequent landslides, Sikkim has comparatively lower overall vulnerability due to limited road construction and reduced slope disturbance.
Major Approaches to Landslide Forecasting
- Sensor-Based Monitoring
Developed by institutions such as Amrita University, this approach continuously monitors slope stability using specialised sensors.
Key Instruments
- Tilt meters
- Pressure gauges
- Accelerometers
- Ground movement and vibration sensors
Mechanism
- Sensors continuously monitor changes in slope conditions.
- Warnings are generated when monitored parameters exceed predefined safety thresholds.
Advantages
- Provides highly accurate, real-time assessment of slope stability.
- Offers sufficient lead time for evacuation.
- Successfully demonstrated in parts of Kerala.
Limitations
- Covers only instrumented slopes.
- Cannot detect failures on nearby unmonitored slopes.
- Involves high installation and maintenance costs.
- Probabilistic Forecasting Model
Developed by IIT Mandi, this model estimates landslide probability over large geographical areas.
Methodology: Integrates satellite-derived landslide inventories with rainfall forecasts, terrain characteristics, geological conditions, soil properties, and slope gradients.
Advantages
- Covers extensive and remote regions.
- Identifies multiple vulnerable locations simultaneously.
- Successfully validated against historical landslide events in the Himalayan region.
Limitations
- Depends on high-resolution rainfall forecasts.
- Limited forecast lead time reduces predictive effectiveness.
- Improved rainfall forecasting by the India Meteorological Department (IMD) can significantly enhance forecasting accuracy.
Towards a National Landslide Early Warning System
A comprehensive National Landslide Early Warning System should:
- Identify high-risk zones through detailed hazard zonation and risk mapping.
- Deploy sensor networks in high-risk locations.
- Integrate satellite observations, sensor networks, GIS, remote sensing, and high-resolution weather forecasting into a unified decision-support platform.
- Strengthen coordination among the India Meteorological Department (IMD), National Disaster Management Authority (NDMA), Geological Survey of India (GSI), State Disaster Management Authorities (SDMAs), and local administrations.
Challenges and Way Forward
| Challenges | Way Forward |
| Inadequate mapping of landslide-prone areas limits accurate risk assessment. | Develop an integrated National Landslide Early Warning System combining sensor-based monitoring with probabilistic forecasting models. |
| Limited coverage of monitoring infrastructure restricts effective real-time surveillance. | Expand sensor networks and strengthen landslide susceptibility mapping using GIS, remote sensing, and AI-based analytics. |
| Dependence on short-range rainfall forecasts reduces warning lead time. | Enhance high-resolution rainfall forecasting through advanced meteorological models and forecasting systems. |
| High deployment and maintenance costs hinder large-scale implementation. | Prioritise critical infrastructure, transport corridors, and densely populated hill settlements for phased deployment. |
| Weak institutional coordination delays dissemination of warnings and emergency response. | Strengthen coordination through integrated disaster management protocols involving central, state, and local agencies. |
| Limited community preparedness increases disaster vulnerability. | Strengthen community awareness, conduct regular evacuation drills, and promote local disaster preparedness. |
Conclusion
With climate change increasing the frequency and intensity of extreme rainfall events, landslide risks are expected to rise across India. An integrated National Landslide Early Warning System, supported by scientific forecasting, advanced technologies, and coordinated institutional action, can significantly strengthen disaster resilience and enable a shift from reactive disaster response to proactive disaster risk reduction, thereby safeguarding lives, livelihoods, and critical infrastructure.
