India Moves Towards Comprehensive Landslide Early Warning System After Increasing Disaster Risks

Recent Developments:

• Recent landslides in the Western Ghats, including the incident affecting the under-construction twin tunnel project in Wayanad, Kerala, have highlighted the urgent need for effective Landslide Early Warning Systems (LEWS) in vulnerable regions.
• Experts emphasise that scientific forecasting combined with timely evacuation can significantly reduce casualties in landslide-prone areas such as the Western Ghats and Himalayan region.
• The increasing frequency of extreme rainfall events due to climate change has strengthened the need to shift from reactive disaster response towards proactive risk reduction strategies.

Importance of Landslide Early Warning Systems:

Role in Disaster Risk Reduction:

• Landslides are predictable to a significant extent, especially in identified high-risk zones, when geological, climatic and environmental factors are continuously monitored.
• Effective early warning systems provide sufficient time for evacuation, thereby reducing loss of human lives, property damage and infrastructure disruption.
• Countries such as Switzerland have successfully reduced landslide-related casualties through advance warnings, hazard monitoring and planned evacuation systems.

Indian Experience with Early Warning Systems:

• The 2024 Munnar landslides in Kerala demonstrated the effectiveness of scientific-based warnings, where timely evacuation based on expert advice prevented fatalities.
• Such examples highlight the importance of integrating scientific technology with local administration and community participation.

Landslide Vulnerability in India:

Extent of Landslide-Prone Areas:

• According to the National Disaster Management Authority (NDMA), approximately 13% of India's geographical area, covering around 0.42 million sq km, is vulnerable to landslides.
• The major vulnerable regions include:
• Himalayan region, Western Ghats, North-Eastern hill states and unstable mountainous zones.

Highly Vulnerable Regions:

• Major landslide-prone areas include:
• Tehri Garhwal and Uttarkashi in Uttarakhand,
• Mandi and Shimla in Himachal Pradesh,
• Aizawl region in Mizoram,
• Parts of Manipur.

Relatively Less Vulnerable Regions:

• Despite frequent landslide events, Sikkim has comparatively lower vulnerability in certain areas due to:
• Less extensive road construction,
• Reduced mountain cutting,
• Lower slope disturbance, which improves geological stability.

Major Approaches for Landslide Forecasting:

Sensor-Based Monitoring System:

Concept and Development:

• Sensor-based monitoring systems have been developed by research institutions such as Amrita University for continuous monitoring of unstable slopes.
• These systems involve installing advanced sensors at identified high-risk locations.

Key Instruments Used:

• Important monitoring devices include:
• Tilt meters, pressure gauges, accelerometers, ground movement sensors and vibration sensors.

Working Mechanism:

• Sensors continuously measure changes in:
• Slope movement, ground pressure, vibration levels and stability conditions.
• When recorded values cross predefined safety limits, warnings are issued to local authorities for timely evacuation.

Advantages:

• The system is:
• Scientifically reliable, highly accurate and capable of providing sufficient evacuation time.
• Such systems have been successfully tested in vulnerable areas of Kerala.

Limitations:

• The system can monitor only those slopes where instruments are installed.
• It cannot predict landslides occurring on nearby unmonitored slopes.
• Installation, maintenance and expansion of sensor networks require significant financial resources.

Probabilistic Forecasting Model:

Concept and Development:

• The Indian Institute of Technology Mandi has developed a probabilistic model to estimate landslide probability over large geographical areas.

Methodology:

• The model combines:
• Satellite-based historical landslide mapping, rainfall forecasts, soil characteristics, rock stability, slope gradient and population density.
• It uses approximately 7–10 rainfall-related parameters for analysing landslide probability at specific locations.

Validation:

• The model has been validated against approximately 80 actual landslide events in the Himalayan region.

Advantages:

• The model can cover:
• Large geographical regions, including remote areas where physical monitoring is difficult.
• It can identify multiple vulnerable locations simultaneously.

Limitations:

• The accuracy of the model depends heavily on high-resolution rainfall forecasts.
• Current rainfall predictions are generally available only around 1 day in advance, which limits evacuation time.
• Improved forecasting capability of the India Meteorological Department (IMD) can significantly enhance landslide prediction accuracy.

Towards a National Landslide Early Warning System:

Need for Integrated Approach:

• Experts suggest that India can develop an effective nationwide Landslide Early Warning System within approximately 2 years with adequate resources, technology and institutional cooperation.
• A comprehensive system should combine:
• Satellite monitoring, sensor-based observations, high-resolution weather forecasting, Geographical Information System (GIS) and remote sensing technologies.

Priority Actions Required:

• India should focus on:
• Identification of high-frequency and high-impact landslide zones,
• Preparation of detailed hazard zonation maps and risk assessment maps,
• Installation of sensor networks in extremely vulnerable locations.

Institutional Coordination:

• Effective implementation requires coordination among:
• India Meteorological Department (IMD),
• National Disaster Management Authority (NDMA),
• Geological Survey of India (GSI),
• State Disaster Management Authorities (SDMAs),

Local administration and communities.

Challenges and Way Forward:

Major Challenges:

Lack of Comprehensive Mapping:

• India lacks complete mapping of all high-risk landslide hotspots, which affects targeted monitoring and preventive planning.
• A national system combining sensor-based monitoring and probabilistic forecasting models is required.

Limited Sensor Network Coverage:

• Existing sensor deployment is limited and needs expansion through:
• Remote sensing, GIS-based analysis and artificial intelligence-based prediction tools.

Dependence on Short-Term Rainfall Forecasts:

• Current rainfall prediction limitations reduce the available warning period.
• Development of high-resolution rainfall forecasting by the India Meteorological Department is essential.

High Infrastructure Costs:

• Monitoring infrastructure requires significant investment.
• Priority should be given to:

Critical infrastructure, transport corridors and densely populated hill settlements.

Way Forward Measures:

• India should promote:
• Greater inter-agency coordination,
• Long-term investment in monitoring infrastructure,
• Community awareness programmes,
• Evacuation drills and local disaster preparedness.
• Disaster management should shift from post-disaster relief towards anticipatory action and risk reduction.

Conclusion:

• Climate change-induced extreme rainfall events are increasing the frequency and intensity of landslides in India.
• A combination of scientific forecasting, advanced monitoring systems, institutional coordination and community preparedness can transform landslide management from reactive response to proactive disaster risk reduction.
• A comprehensive national Landslide Early Warning System can protect human lives, infrastructure and sustainable development in vulnerable regions.

Value Addition for UPSC:

Important Facts:

• Disaster Type: Geological Disaster
• Major Vulnerable Regions: Himalayas, Western Ghats, North-Eastern Hills
• Landslide-Prone Area in India: Around 13% of geographical area
• Key Institution: National Disaster Management Authority
• Scientific Agencies: Geological Survey of India, India Meteorological Department
• Key Technologies: Remote Sensing, Geographic Information System, Artificial Intelligence, Sensor Networks