Mountain snow and meltwater from glaciers in the Himalayas determine how much water is available for drinking, farming and hydropower for millions of people in India, Pakistan, Nepal, China and Afghanistan.

This is especially important during the spring and early summer crop-growing season when meltwater helps sustain the flow of rivers and provides the region with a vital buffer against the worst impacts of a dry season or drought. Because many of these rivers cross national borders, changes in water supply can also heighten regional tensions when resources are scarce.

Meltwater from snow is one of the pillars of survival – but it’s proved difficult in the past to get accurate measurements across this challenging terrain.

So measuring where and how much snow falls is essential. My team’s new research gives improved estimates of snowfall across the west-central Himalayas. Results show that in just one winter, the previous best available snowfall analyses underestimated total seasonal snowfall by 37% over Lake Hampta area in Himachal Pradesh. By including new snowfall observations, we reduced that error dramatically.

Why snowfall predictions have been wrong

Because most instruments were originally designed to measure rain over very small areas, rather than snowfall spread across large areas, most calculations of snowfall levels have historically been inaccurate. A rain gauge around the size of a dinner plate was used in the past – too small for the purpose and therefore missing large amounts of snow. These small devices were particularly bad at catching snow during strong winds. As a result, mountain water resources are often misunderstood and have been underestimated by 50–100% over the years.

To change this, we developed a new way to measure snowfall using commercially available water-pressure sensors deployed on the lakebeds. The team installed these instruments at three lakes – Ghepan and Hampta in the western Himalayas, and Mugu in Nepal. Unlike conventional instruments, these sense the whole lake surface – an area of thousands to billions of square metres – to measure the timing and intensity of snowfall. These use water pressure in lakes to directly measure the mass (water content) of accumulating snow, providing an accurate and unbiased estimate of snowfall. This is based on Archimedes principle of displacement: as snow accumulates on a frozen lake, it changes the water pressure beneath.

Snowfall from these devices was analysed and used to refine physical processes in a weather model. This is the same model which provides weather predictions over the UK but is customised for mountainous regions. The results show the model can generally reproduce both when the snow falls and how much accumulates, and is particularly good at representing extreme snowfall events. We now have new insights into where and when snow falls across the region at much finer detail than previous estimates, making mountain water resources more predictable. In contrast, a snowfall map based on previous analysis shows a much more uniform distribution and considerably less snowfall.

Planning for water shortages

This information could help regional communities adapt and improve water planning, help manage reservoirs and protect vulnerable infrastructure from snow-related hazards. It could also support engineering interventions such as snow harvesting in areas that receive abundant snowfall, for example, by artificially storing meltwater in community-managed reservoirs and into ice stupas (an artificial glacier) designed to gradually release water during the growing season.

An understanding of when snow falls will be crucial for predicting when it will melt and how much water will flow into rivers and help communities and policymakers better prepare for future water shortages.

The Himalayan ice fields are important because they are the largest permanent ice-cover outside of the polar regions and contain some of the largest reservoirs of freshwater on earth.

With this region starting to see more water shortages it’s time to start reassessing reliance on mountain water supplies. But how much water the mountains provide, and how it will change, remain remarkably uncertain.

Good measurements of snowfall are now more important than ever for predicting the future of water resources, which until now have been lacking. For example, a single long-running active weather station above 4,000 metres has provided one of the few continuous snowfall records across the vast 566,000 square kilometres of Himalayan headwaters that feed the Brahmaputra, Indus and Ganges river basins.

This work is also deeply personal for me. I grew up in a city just 50 kilometres from the outer Himalayas. On family holidays, I was captivated by the thriving beauty of pines and junipers against the backdrop of the Himalayas, while learning that mountains and rivers are more than just landscapes. Those experiences fostered a lasting connection to the region, and I continue to return to the mountains whenever I can.

Water is a precious commodity upon which every society depends, and shortages can cause untold misery.

With better snowfall estimates, we are already beginning to understand how to make the future of the mountain eco-system more predictable, and help people cope.

Siddharth Gumber receives funding from the National Environmental Research Council (NERC), The Big Thaw project (NE/X005267/1).

Young people in low- and middle-income countries appear generally more optimistic about how AI can enhance their work prospects and social lives than their western peers, according to our new survey of people in ten countries in Africa and South Asia.

While debates in higher-income countries often focus on job losses, misinformation and mental health risks, many of the 1,864 young adults we surveyed (all aged 18-35) placed more emphasis on the opportunities artificial intelligence tools present for them. In particular, nearly 80% of our participants expect AI to deliver improved education and learning for them.

Until now, there has been little evidence of how young people in middle- and low-income countries use AI, and how they feel about this technology – for good and bad. The 2026 youth AI survey – run by global charity Restless Development and academics from the universities of Makerere in Uganda and Cambridge in the UK – seeks to correct this imbalance.

It comes immediately after the UN Independent International Scientific Panel released its preliminary report on AI. Commenting on the need for more science about AI’s use around the world, the UN’s secretary general, António Guterres, admitted: “The world cannot govern what it cannot understand.”

Our responses were collected in Angola, Kenya, India, Nepal, Nigeria, Sierra Leone, Tanzania, Uganda, Zambia and Zimbabwe – with the UK included for comparison. Two-thirds of respondents said they use AI every day, with Meta AI the most common tool, followed by Claude.

The extent of AI use is striking given the obstacles many young people encounter. In every African country surveyed except Nigeria, more than half said high data costs limit their access. Weak internet connections and a lack of digital skills were also raised, with many relying on basic-feature phones. By contrast, UK participants said ethical concerns played a stronger role in limiting their AI usage.

AI work opportunities

While few respondents (14%) said they earn income directly from AI tools yet, half expected AI to improve their employment and economic opportunities within the next five years.

Currently, AI’s main work-related uses among our respondents are learning new skills and job searches. In almost every lower-income country, young people held broadly positive attitudes about AI’s future role in their working lives – although many (32%-50% per country) expressed concerned about its impact on their future income.

How will your income be affected by AI?

Among those from lower-income countries, more than seven in ten thought AI would bring them better education and learning opportunities in the near future. A 28-year-old self-employed man in Nepal commented: “AI has the potential to empower young people in developing countries like Nepal by improving access to knowledge, digital skills, and economic opportunities.”

Our sentiment analysis found respondents to be most positive in Tanzania and Zambia, with young women there even more upbeat than young men. In the UK, young men diverged from their international peers in feeling more negative towards AI and work.

Biggest AI concerns

Some respondents described AI tools as “Americanised”, built around western norms and unable to grasp local nuance. They worried this could lead to their languages being further marginalised.

“These AI tools are fed major languages that are spoken worldwide, unlike Shona,” said a 25-year-old student in urban Zimbabwe. Shona is spoken by around 14 million people mainly in southern Africa, but is severely underserved by AI and other technology tools.

A Ugandan respondent was similarly critical about provision for her native language, Luganda – the country’s most widely spoken Indigenous language. “AI barely knows the spellings, grammar and how to interpret sentences. It’s not even familiar with our culture at all, which increases mistakes when we get to things concerning our own world.”

What most concerns you about AI?

Despite the widespread belief that AI would improve education and training, some held strong reservations. A 21-year-old female student in rural Uganda said: “Slowly by slowly, AI is making us dumb. We can no longer think for ourselves.”

The more often these young people use AI, the more worried they appear to be about it. The biggest concerns highlighted by daily users were around who owns their data and the power of AI companies.

Concern about AI’s environmental impact and the welfare of AI workers followed a similar pattern of higher usage co-existing with higher concern. Mental health is the only domain where non-users worry slightly more than daily users.

Gender differences

Significantly more young men (38%) than women (23%) reported having “a lot” of AI knowledge. This probably reflects a combination of diverging gender self-confidence, which tends to emerge from school age, and different levels of AI use. “AI is scary but helpful too,” said one female homemaker in urban Nepal.

Almost twice as many men than women use AI to search for work (33% vs. 17%). This difference is probably rooted in gendered access to data and technology, as well as differing social roles – such as the tendency for women to do more care work.

What have you used AI for in the past month?

In most of the ten lower-income countries surveyed, young women expressed greater concern about AI than men – with their biggest concern being over who owns their data. In the UK, 68% of young women were strongly concerned about data ownership, compared with 29% of men.

What young people want from AI

Many of our participants stressed the need for cheaper data and internet connections that work well. But they also want AI tools that fit their lives and culture – and speak their language. Until then, most recognise that talk of AI “leapfrogging” traditional stages of economic and social development is just that – talk.

Some efforts to make AI more inclusive and relevant for lower-income countries are underway. These include initiatives for “frugal AI”, as well as regional collaborations such as Masakhane for African languages and QuechuaBase in Peru.

How can AI be made safer?

Most of our participants framed AI safety as much more of a governance problem than a technical one. Many countries are establishing new AI policies, shaped in part by global forums such as the UN-led AI for Good summits. Overwhelmingly, our respondents agreed that governments, not tech companies, should be setting the rules when it comes to AI regulation and privacy protection.

Anna Barford receives funding from UKRI. She has previously been funded by the British Academy, Leverhulme Trust, ESRC, the Asian Development Bank, the Mastercard Foundation, and a philanthropic gift to the University from Unilever. She is a co-director of the Business Fights Poverty Institute and does consultancy work for the International Labour Organization.

Anthony Mugeere works as a Research Fellow for the Advocates Coalition for Development, Uganda's leading thinktank specialising in advocacy and analysis on climate change. He has received funding from the British Academy and the Mastercard Foundation to conduct research on climate change and young people.

Giulia Occhini does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.