On August 26, a massive glacier and rock collapse struck near the town of Nuwakot in Nepal, resulting in the deaths or disappearance of more than 7,000 people. The event, one of the largest recorded collapses in the Himalayas, occurred when an estimated seven billion cubic feet of ice and rock detached from Langtang Lirung, a peak close to the Nepal-China border, and surged down the valley.

At approximately 9:10 a.m., a school staff member warned students at a local school of the approaching flood. Principal Rajendra Dawadi quickly organized the evacuation of around 900 students to higher ground, preventing any fatalities among them. However, much of the valley floor, including the school, was destroyed by 9:23 a.m. Most residents in the area were not so fortunate.

The disaster highlights the growing instability of Himalayan glaciers, driven by climate change. Rising temperatures have accelerated glacier retreat and thawing of permafrost, destabilizing mountain slopes and increasing the risk of such catastrophic collapses. These environmental changes place communities in the region at heightened risk, complicating efforts to safely develop infrastructure.

Nepal relies heavily on its river corridors for hydroelectric power generation, which has become a critical component of the country’s economic growth and energy supply. At least a dozen hydropower projects along the affected rivers were damaged or destroyed in this collapse. With current installed capacity at about 4,300 megawatts, Nepal aims to increase hydropower output to 30,000 megawatts by 2035. Yet, the recent disaster underscores the precarious nature of building dams, tunnels, and facilities beneath unstable glaciers and mountain slopes.

Satellite radar data collected months before the collapse showed signs of gradual slope movement at Langtang Lirung, but the warnings went unnoticed until after the event. Experts suggest that artificial intelligence (AI) could play a vital role in mitigating future disasters by continuously analyzing vast quantities of satellite imagery to detect early signs of glacier instability. Machine learning could identify accelerating slopes among thousands across the Himalayas, providing crucial advance warnings.

Implementation of such early-warning systems would require significant collaboration between international glaciologists, geomorphologists, and policymakers. With effective monitoring, communities might receive hours or days of notice to evacuate, rather than only minutes. This approach could benefit neighboring Himalayan countries facing similar challenges, including China, India, Pakistan, and Bhutan.

Nepal’s vulnerability underscores broader issues of climate justice. The country’s contribution to global greenhouse gas emissions is minimal, yet it bears disproportionate consequences from warming-induced hazards. While international climate finance mechanisms, such as the United Nations Fund for Responding to Loss and Damage established in 2022, aim to support countries like Nepal, funding remains inadequate, and processes are slow.

As monsoon rains and glacier retreat continue, Nepal must enhance its disaster preparedness through improved monitoring, infrastructure development, and evacuation routes. Wealthier nations most responsible for climate change are urged to provide financial assistance to build resilience in vulnerable regions. The urgency remains: how many more disasters Nepal will endure before sufficient support arrives remains uncertain.