Establishing an Early Warning System (EWS) for the Himalayas

The Himalayas face rising climate disasters like floods and glacial bursts, highlighting the urgent need for robust, AI-driven early warning systems.

Strengthening Climate Resilience: Establishing an Early Warning System (EWS) for the Himalayas

Syllabus: Climate & Geography (UPSC GS III)
Source: The Hindu

Context

The Himalayas are facing an alarming rise in climate-induced disasters — including floods, landslides, and glacial lake outbursts (GLOFs). Scientists are calling for a robust Early Warning System (EWS) across this fragile region to minimise loss of life and property.

Rising Climate Risks in the Himalayas

Between 1900–2022, India recorded 687 disasters, of which 240 occurred in the Himalayan belt (DTE, 2024).
The number of events rose sharply — only 5 before 1962, but 68 between 2013–2022, accounting for 44% of India’s disasters.
NASA data (2007–2017) recorded 1,121 landslides, reflecting growing instability.
The region is warming 0.15°C–0.60°C per decade, faster than the global average, increasing snowmelt, flash floods, and GLOFs.
Cloudbursts, avalanches, and erratic rainfall are becoming more frequent and severe.

Why an Early Warning System is Crucial

Life-saving Mechanism: Timely alerts enable evacuation and disaster response in vulnerable valleys.
Disaster Preparedness: Enables real-time monitoring and forecasting of hazards like GLOFs and cloudbursts.
Scientific Backbone: Generates continuous, data-based risk models for safer infrastructure and planning.
Community Resilience: Involving local residents ensures awareness, accountability, and faster ground response.
Global Precedent: Countries such as Switzerland and China have shown how EWS can prevent glacier-related catastrophes.

Technological and Global Examples

Switzerland: Community-based alerts prevented glacier-collapse disasters.
China (Cirenmaco Lake): Satellite-fed glacial lake monitoring using unmanned boats.
India: The Environment Ministry is funding AI-based hailstorm EWS for apple farmers in Himalayan states.

Role of Artificial Intelligence and Modern Technology

AI-driven models can process live data to generate predictive alerts with high accuracy.
Satellites and unmanned boats monitor lake levels and glacier movements in real time.
Drone mapping helps in local hazard assessment though limited by terrain and cost.
AI-integrated EWS prototypes are being tested for hailstorms and cloudbursts in Uttarakhand and Himachal Pradesh.

Challenges in Implementing EWS

Rugged Terrain: Difficult installation and maintenance of sensors.
Poor Connectivity: Lack of telecom and internet in remote valleys restricts data transfer.
High Costs and Technology Gaps: Limited access to affordable, weather-resistant indigenous systems.
Fragmented Governance: Overlapping institutional roles delay coordination.
Low Community Involvement: Absence of local ownership leads to poor utilisation of alerts.

Way Forward

Develop Indigenous Systems: Build solar-powered, AI-enabled, low-cost EWS tailored to Himalayan conditions.
Expand Valley-Level Coverage: Set up networks across major valleys and transboundary watersheds.
Leverage AI and Satellites: Integrate advanced forecasting and imaging for real-time hazard mapping.
Empower Local Communities: Train local task forces for independent management of EWS alerts.
Institutional Reform: Establish a National Himalayan Early Warning Mission under NDMA for unified action.

Conclusion

The Himalayas—often called the “third pole”—are on the frontline of climate change but remain poorly equipped for disaster alerts.
A community-based, technology-driven Early Warning System is critical for protecting lives, livelihoods, and fragile ecosystems.
Ensuring the resilience of the Himalayas must now be treated as a national climate-security priority.