Revisiting Early Warning Systems Amid Western Ghats Landslides

Source: The post “Revisiting Early Warning Systems Amid Western Ghats Landslides” has been created based on “Revisiting Early Warning Systems Amid Western Ghats Landslides” published in “Indian Express” on 11th July 2026.

UPSC Syllabus: GS  3 – Disaster Management

Context: Landslides are one of the most frequent natural disasters in India’s mountainous regions, particularly in the Western Ghats and the Himalayas. Around 13% of India’s land area (0.42 million sq km) is prone to landslides, according to the National Disaster Management Authority (NDMA). The recent landslides in Wayanad and other parts of the Western Ghats have highlighted the urgent need for robust Early Warning Systems (EWS) to minimise loss of life and property.

Need for Landslide Early Warning Systems

1. Early Warning Systems can predict landslides in highly vulnerable locations and provide sufficient time for evacuation.
2. Timely evacuation can significantly reduce casualties, as demonstrated by successful experiences in Switzerland (2023 and 2025).
3. The evacuation before the 2024 Munnar landslides in Kerala prevented loss of life because warnings were issued by researchers from Amrita University.
4. Early warning systems strengthen disaster preparedness and improve the response capacity of district administrations.

Existing Initiatives in India

1. Sensor-based Early Warning System (Amrita University Model)

1. Researchers have installed instruments such as tilt meters, pressure gauges and accelerometers at vulnerable slopes.
2. These sensors continuously monitor ground movement, vibrations and slope stability.
3. A warning is issued when sensor readings cross predefined safety thresholds.
4. This method is scientifically robust and provides sufficient lead time for evacuation.

2. Probabilistic Forecasting Model (IIT Mandi Model)

1. The IIT Mandi team has developed a satellite-based landslide forecasting model.
2. The model identifies vulnerable locations using a database of previous landslides.
3. It combines highly localised rainfall forecasts with soil conditions, rock stability, slope characteristics and population density.
4. Around 7–10 rainfall-derived parameters are analysed for every location.
5. The model has been validated against nearly 80 actual landslides in the Himalayan region.

Challenges

1. Sensor-based systems provide information only for the specific slope where instruments are installed and cannot monitor neighbouring slopes.
2. Landslides are highly localised events, making prediction difficult across large regions.
3. High-resolution local rainfall forecasts are currently available only on the same day or one day in advance, limiting the warning period.
4. India has not yet comprehensively identified all high-risk landslide-prone zones for targeted deployment of sensors.
5. Resource constraints and inadequate institutional coordination slow down large-scale implementation.

Way Forward

1. India should comprehensively identify high-risk landslide-prone areas where frequent landslides threaten lives and property.
2. Sensor networks should be installed at the most vulnerable locations across the Western Ghats and the Himalayan region.
3. The India Meteorological Department should strengthen high-resolution local rainfall forecasting to provide longer lead times.
4. Satellite-based forecasting should be integrated with ground-based sensor networks for more accurate predictions.
5. Strong coordination among research institutions, IMD, NDMA, State Disaster Management Authorities and district administrations should be ensured.
6. Adequate financial resources and sustained policy support should be provided to establish a nationwide landslide Early Warning System within the next few years.

Conclusion: Landslides cannot be completely prevented, but their devastating impacts can be significantly reduced through scientific prediction, timely warnings and efficient evacuation. A combination of sensor-based monitoring, satellite-based probabilistic forecasting and improved weather prediction can build a reliable and comprehensive landslide Early Warning System, making India’s disaster management framework more resilient.

Source: Indian Express

Question: Landslides in the Western Ghats and Himalayan region highlight the urgent need for effective Early Warning Systems (EWS). Discuss the significance, existing initiatives, challenges, and the way forward for landslide early warning systems in India.