Subject: Landslides

Why in the News?

Recent landslides across the Western Ghats and other parts of India have revived the debate on installing early warning systems (EWS) for landslides. The renewed discussion exposes a gap between what landslide-prediction technology has already proven capable of and the absence of any single, scaled system deploying it nationally.

Why has landslide prediction returned to the policy conversation, and does the science actually work?

1. Trigger: Recent landslides in the Western Ghats and other parts of India reignited discussion on installing EWS for such events.
2. Proven feasibility: Landslides can be predicted in high-risk zones. The 2024 Wayanad landslide killed more than 300 people, illustrating the human cost when prediction is absent.
3. Working precedent: Two weeks before the Wayanad disaster, landslides in Munnar caused no fatalities. The Idukki district administration evacuated residents on the advice of an Amrita University research team, led by Maneesha Vinodini Ramesh, that was testing an EWS.
4. Global validation: EWS already operates effectively in multiple countries, establishing that the underlying approach is proven rather than experimental.

What are the two competing methodologies India is currently developing for landslide early warning?

1. Amrita University approach: Deploys a network of on-site sensors, tilt meters, pressure gauges, accelerometers, at high-risk slopes to measure vibration and ground movement.
2. Threshold-based alerts: When sensor readings cross well-defined thresholds, an automated warning is issued, allowing the administration to act.
3. IIT Mandi approach: Professor Dericks Praise Shukla’s team uses probabilistic forecasting instead of physical sensors, currently being validated against ongoing landslide events in the Himalayan region.
4. Satellite-based mapping: The IIT Mandi team has mapped vulnerable spots across the Himalayan region using a satellite-based database of past landslide events.
5. Multi-factor modelling: The probabilistic model factors in localised rainfall forecasts along with soil conditions, rock stability, extent of slope, and population density.

Why does neither current methodology, on its own, deliver a complete early warning solution?

1. Sensor method’s blind spot: Amrita’s sensor network reports data only for the specific slope where instruments are installed. Neighbouring slopes remain unmonitored, even though landslides are highly localised events.
2. Rainfall model’s lead-time constraint: Shukla’s probabilistic model depends on rainfall forecasts, but highly localised forecasts are currently available only for the day of the event or one day earlier, giving very little lead time.
3. Trade-off exposed: The sensor method provides adequate lead time but incomplete geographic coverage. The probabilistic method provides wider coverage but insufficient lead time.
4. Scale limitation: Both methods remain validated only at pilot or regional scale. Neither is currently integrated into a single nationwide operational system.

What must change before India moves from pilot-scale projects to a comprehensive national system?

1. Precondition 1: high-risk zone identification: A comprehensive system first requires identifying high-risk areas where landslides are frequent, before sensors or models can be meaningfully deployed at scale.
2. Risk zones already flagged: Shukla identifies the north-western Himalayan region and parts of Manipur and Mizoram as highly vulnerable. Sikkim is relatively less vulnerable due to a less dense road network, which implies greater slope stability.
3. Precondition 2: higher-resolution rainfall forecasting: The probabilistic method’s lead-time limitation can only be resolved once the India Meteorological Department develops higher-resolution rainfall forecasts, which is currently in progress.
4. Timeline and resourcing: A comprehensive and effective landslide EWS can be built in about two years if resources and effort are properly dedicated to it, according to Shukla.
5. Sequencing: The stated roadmap identifies high-risk zones nationally first, and installs sensors at selected sites only afterward, mapping precedes instrumentation, not the reverse.

Conclusion

Landslide early warning technology is scientifically proven and has already prevented casualties in India, as seen in Munnar in 2024. No standardised national system exists, however; current efforts are split between a sensor-based method and a rainfall-probability-based method, each constrained by a different limitation, localised coverage in one case, short lead time in the other. Scaling to a comprehensive national system depends on two preconditions currently absent: systematic identification of high-risk zones across India, and higher-resolution rainfall forecasting infrastructure from the India Meteorological Department. Until both are in place, early warning capability will remain confined to isolated pilot projects rather than a nationwide shield.

PYQ Relevance

[UPSC 2021] Describe the various causes and the effects of landslides. Mention the important components of the National Landslide Risk Management Strategy.

Linkage: The PYQ examines India’s institutional approach to landslide risk reduction through the National Landslide Risk Management Strategy (NLRMS) and disaster preparedness. The article directly complements this PYQ by highlighting early warning systems, sensor networks, vulnerability mapping, localized rainfall forecasting, and timely evacuation, all of which are core components of proactive landslide risk management envisaged under the NLRMS.