𝐀𝐈 𝐓𝐨𝐨𝐥𝐬 𝐒𝐭𝐫𝐞𝐧𝐠𝐭𝐡𝐞𝐧 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐎𝐟 𝐆𝐥𝐚𝐜𝐢𝐞𝐫𝐬 𝐀𝐧𝐝 𝐂𝐥𝐢𝐦𝐚𝐭𝐞 𝐑𝐢𝐬𝐤𝐬 𝐈𝐧 𝐇𝐢𝐦𝐚𝐥𝐚𝐲𝐚𝐧 𝐑𝐞𝐠𝐢𝐨𝐧

The National Centre for Hydrology and Meteorology (NCHM) is hosting the first Regional AI Training on Cryosphere Research, a five-day programme bringing together participants from across the Hindu Kush Himalaya (HKH) region. Conducted in collaboration with the International Centre for Integrated Mountain Development (ICIMOD), the training forms part of ICIMOD’s regional capacity-building initiative. Researchers and technical professionals from five ICIMOD member countries, including Bhutan, are attending the workshop. The programme aims to strengthen understanding of machine learning and deep learning applications in cryosphere studies, an area increasingly important for climate monitoring in the HKH region.

The Director General of NCHM graced the opening session and highlighted the value of advanced AI tools in analysing data and tracking changes in the cryosphere. He said that reliable, science-based information is crucial for informed planning, improved decision-making, and climate adaptation in Bhutan and neighbouring mountain countries. He added that the regional training would play an important role in supporting these efforts. The programme will include hands-on exercises and practical applications of AI technologies to enhance regional capacity in studying glaciers, snow cover, and related environmental changes.

An official from NCHM explained that hosting the first Regional AI Training on Cryosphere Research was driven by a combination of national vulnerability, regional responsibility, and strategic scientific partnerships. Firstly, Bhutan is located in the HKH, one of the most climate-sensitive regions in the world. The country’s glaciers, snow cover, and permafrost systems are critical for sustaining water security and regulating climate-induced disaster risks, particularly floods and landslides. Changes in the cryosphere directly affect river flows, agriculture, hydropower generation, and the safety of downstream communities.

Secondly, Bhutan is home to 567 glacial lakes, of which 17 are classified as potentially dangerous. Several of these lakes, particularly within the Lunana glacial complex, are considered critically hazardous due to rapid expansion of lake areas, unstable surrounding landforms, and the presence of settlements downstream. These factors significantly increase the risk of glacial lake outburst floods, making continuous and accurate monitoring a national priority.

Thirdly, NCHM maintains a strong institutional partnership with ICIMOD. When ICIMOD proposed conducting the regional AI training in Bhutan, NCHM recognised the urgency and strategic importance of the initiative for both Bhutan and the wider HKH region. Hosting the training reflects Bhutan’s commitment to strengthening scientific capacity and advancing regional collaboration in climate and cryosphere research.

The initiative is also driven by the rapidly expanding availability of Earth observation data and the growing need to apply advanced analytical tools, including artificial intelligence, to interpret this data effectively. Cryosphere research in high-altitude and data-scarce environments requires sophisticated methods to detect subtle yet critical changes over time. NCHM already possesses a substantial repository of climate and hydrological data, complemented by freely available global Earth observation datasets. However, the sheer volume and complexity of these datasets demand the use of advanced AI-based techniques for meaningful analysis.

The training is expected to significantly enhance Bhutan’s technical capacity to integrate and analyse large datasets and generate robust scientific evidence. In practical terms, the skills and knowledge gained will strengthen glacier and glacial lake monitoring, enable early detection of hazardous changes, and improve the assessment of climate impacts in remote and inaccessible high-altitude areas.

From a policy perspective, AI-driven insights will support evidence-based decision-making in climate adaptation, disaster risk reduction, water resource management, and infrastructure planning. The outcomes of the training align directly with national priorities, particularly in managing glacial lake outburst flood risks and advancing long-term climate resilience planning.

Currently, NCHM is already applying AI tools to automatically map glaciers and glacial lakes using satellite imagery and to detect changes in glacier extent and lake surface area. Looking ahead, the centre plans to expand the use of AI for glacier mass balance estimation, identification of potentially dangerous glacial lakes, snow cover mapping, and permafrost monitoring. NCHM is also exploring the integration of satellite data and climate models with ground-based observations, such as those from automatic weather stations, to improve climate monitoring and forecasting across the country.

These AI-enabled tools allow for faster, more accurate, and cost-effective monitoring, particularly in rugged and inaccessible terrain where field measurements are limited. The HKH region presents additional complexity due to the influence of two major climate systems. The eastern HKH, including Bhutan, is primarily influenced by the Indian monsoon, while the western HKH is dominated by westerly weather systems. Understanding the combined impact of these systems on the cryosphere requires extensive data from both space-based and ground-based observations.

Satellite data quality is often compromised by persistent cloud cover and complex terrain, while ground-based data remain sparse due to accessibility challenges and extreme environmental conditions, including severe cold. As a result, significant data gaps persist. While AI techniques are powerful, they require large, high-quality datasets, specialised technical expertise, substantial computing resources, and reliable data infrastructure, all of which are still evolving in many countries across the region.

This regional training directly addresses these challenges by building technical expertise, encouraging data sharing, and strengthening collaboration among scientists working in similar mountain environments. In the long term, it will help establish a regional network of skilled cryosphere scientists capable of applying AI tools to monitor climate change impacts more effectively. For Bhutan, the training will strengthen national capacity in cryosphere research, enhance disaster preparedness, and support climate-resilient development.

At the regional level, it will promote harmonised methodologies, shared datasets, and collaborative research across HKH countries. Such a collective approach is essential for addressing transboundary cryosphere risks and ensuring sustainable water and climate security for the millions of people dependent on Himalayan river systems.

Karma Toeb, a researcher, said that integrating artificial intelligence into climate and cryosphere research remains challenging, largely because AI is still relatively new within the field. As a result, there is a limited pool of expertise in applying these advanced techniques to cryospheric studies, particularly in developing mountain regions. Another major constraint is the lack of high-performance computing infrastructure.

He explained, “Working with climate and cryosphere data requires powerful computing systems capable of processing and analysing large and complex datasets derived from satellite observations, climate models, and ground-based measurements. At present, access to such computing facilities remains limited, posing a significant barrier to the effective use of AI in research.”

He emphasised that cryosphere components such as glaciers and snow are critical to water resources in every country, especially in the HKH region. The region contains numerous transboundary river systems that support millions of people downstream. He said, “Given this interconnectedness, sharing data and scientific information across borders is essential for developing a more accurate understanding of both the current condition and future behaviour of these river systems under a changing climate. In this context, regional collaboration is not optional but fundamental.

Collaborative platforms enable researchers to access high-quality datasets, exchange knowledge, and develop harmonised scientific approaches.”

According to him, regional cooperation provides the foundation for credible climate and cryosphere research in the HKH, where no single country can comprehensively monitor the entire system in isolation. Reflecting on the training, Karma Toeb noted that several AI techniques introduced during the programme show strong potential for advancing cryosphere research.

He added, “Among these, gradient-boosting machine learning models and convolutional neural networks were particularly promising. Gradient-boosting models, such as XGBoost, are highly effective for reconstructing and downscaling meteorological variables, including temperature, at glacier sites where direct observations are often sparse or unavailable. These models can capture complex, non-linear relationships in climate data, making them especially useful for improving local-scale climate information in high-altitude environments.”
He continued, “Convolutional neural networks, on the other hand, are well suited for analysing satellite imagery and have proven effective in mapping glaciers, snow cover, and glacial lakes. By learning from vast volumes of satellite data, CNNs can detect spatial patterns and subtle changes that are difficult to identify using traditional methods.

Importantly, these AI techniques can be designed to respect physical constraints and quantify uncertainty, ensuring that the outputs remain scientifically credible and useful for decision-making.”
He concluded, “Overall, the training has strengthened both technical understanding and confidence in applying AI tools to cryosphere research. With continued investment in computing infrastructure, skills development, and regional collaboration, AI has the potential to significantly improve monitoring of cryospheric change, enhance water resource assessments, and support more effective climate adaptation and disaster risk management across the HKH region.”