The Hindu Editorial Summary

Editorial Topic : Hindu Kush Himalaya Snow Update (ICIMOD, 2024)

 GS-3 Mains Exam : Environment Conversation 

Revision Notes

 

Question : Analyze the role of snowmelt from the Hindu Kush Himalaya (HKH) mountains in supporting water resources for major Asian river systems. How does the variability in snow persistence affect the water supply for millions of people?

Context

According to the Hindu Kush Himalaya snow update by the International Centre for Integrated Mountain Development (ICIMOD), the Ganga river basin, which is the largest in India, experienced its lowest recorded snow persistence in 2024.

More information

• Snow Persistence Defined: It refers to the duration snow remains on the ground. This snowmelt is a crucial source of water for people and ecosystems.
• Importance of HKH Snowmelt: Snowmelt from the Hindu Kush Himalaya (HKH) mountains is the primary source of water in the region’s rivers, contributing 23% of annual runoff to 12 major basins.
• The Water Towers of Asia: Spanning eight countries (Afghanistan, Bangladesh, Bhutan, China, India, Myanmar, Nepal, and Pakistan), the HKH mountains are the source of 10 major Asian river systems: Amu Darya, Indus, Ganga, Brahmaputra, Irrawaddy, Salween, Mekong, Yangtze, Yellow, and Tarim.
• Water Source for Millions: These basins provide water to nearly a quarter of the world’s population and serve as a significant freshwater source for 240 million people living in the HKH region.
• Fluctuations in Snow Persistence: The 2024 HKH snow update analyzed data (2003-2024) revealing significant variations in snow persistence between November and April (snow accumulation period).
• 2024 Snow Persistence in India: Compared to normal levels, significant drops were observed in all three major Indian basins:
  • Ganga – 17% below average
  • Brahmaputra – 14.6% below average (worse than 2021’s 15.5% below average)
  • Indus – 23.3% below average (partially offset by higher persistence in lower regions)
• 2024 Snow Persistence Outside India: Record lows were recorded in basins of:
  • Amu Darya River (Central Asia) – 28.2% below average
  • Helmand River (Iran, Afghanistan) – 31.9% below average (worse than 2018 record)
• Mekong River: Snow persistence in the Himalayan source region of the Mekong River (Vietnam’s “rice bowl”) was only slightly below normal in 2024.

Reasons for Lower Snow Persistence in Hindu Kush Himalaya

• Main Culprit: Weaker Western Disturbances – These low-pressure systems originating in the Mediterranean, Caspian, and Black Seas bring winter rain and snow to the HKH region.
• Climate Change Impact: A changing climate and global warming are making weather patterns like western disturbances less stable.
• La Niña-El Niño Link: Global warming is suspected to be intensifying La Niña and El Niño events, which significantly influence global weather patterns including western disturbances.
• Disrupted Western Disturbances:
  • Persistently high sea-surface temperatures in the region where these storms originate weakened and delayed their arrival.
  • This resulted in less winter precipitation and snowfall in the HKH region (explains record low snow persistence in 2024).

Higher Snow Persistence (Yellow River Basin)

• East Asian Winter Monsoon: This cold, dry air mass from Siberia and Mongolia brings snowfall to the higher altitudes of the upper Yellow River basin when it interacts with moist air from the Pacific Ocean.

Impact on India

• Snowmelt’s Importance: For the Ganga river basin, snowmelt contributes a significant 10.3% of its water compared to just 3.1% from glaciers.
  • Similar trends are seen in Brahmaputra (13.2% vs 1.8%) and Indus (40% vs 5%) basins.
• Water Availability Concerns: Lower snow persistence in 2024, especially in the Indus basin, could lead to water scarcity if there’s less early-season rainfall.

Long-Term Solutions

• Reforestation: Planting native trees can help the ground retain more snow.
• Improved Weather Forecasting: Better forecasts and early warnings can help communities prepare for water stress.
• Water Infrastructure: Upgrading water infrastructure is crucial.
• Policy and Regional Cooperation: Protection policies for snowfall areas and regional cooperation are vital for sustainable snow management.
• Emission Reduction: Reducing emissions is key to mitigating rising sea-surface and ground temperatures, which are detrimental to snow persistence.
  • G20 countries, major emitters (81% of global emissions), need to take the lead in cutting fossil fuel dependence.

 

 

 

The Hindu Editorial Summary

Editorial Topic : How AI is Revolutionizing Protein Structure Prediction

 GS-3 Mains Exam : Science and Tech.

Revision Notes

 

 

Question : Critically Analysis the limitations of AlphaFold 3 in terms of predicting small molecule-protein interactions and the potential for generating incorrect structures. How do these limitations impact its application in research?

Proteins and their importance

• Proteins are life’s building blocks, regulating nearly every biological function.
• Each protein folds from a linear chain of amino acids into a unique 3D structure.

Protein Folding Problem

• Understanding how proteins fold is crucial for comprehending cellular and organism function.
• Predicting protein structures has been a challenge due to the complex folding process.

AI to the Rescue

• Google DeepMind’s AlphaFold emerged in 2020, using AI to predict protein structures from amino acid sequences.
• AlphaFold 2 (2021) significantly improved accuracy.
• AlphaFold 3 (May 2024) represents a breakthrough:
  • Predicts protein-protein interactions.
  • Predicts structures of DNA, RNA, and other molecules.
  • Predicts interactions between various biomolecules.
  • More user-friendly for non-machine learning experts.

AlphaFold 3’s Advantages

• Unprecedented accuracy in protein structure prediction.
• Broader capability – predicts structures beyond proteins.
• Increased usability for researchers.

How AlphaFold 3 Works

• Trained on existing protein data, including structures from the protein data bank.
• Employs a diffusion model (like image-generating software):
  • Learns from protein structures by adding noise and then removing it.
  • Enables handling larger datasets.

Limitations

• High accuracy for protein-protein interactions, but less reliable for small molecule-protein interactions.
• Potential for generating plausible but incorrect structures due to vast vocabulary of small molecules.
• Restricted access to the full code limits customization for specific research.

Future Directions

• AlphaFold 3 holds immense potential for drug discovery by facilitating the identification of protein-binding drug candidates.
• Open-source alternatives to AlphaFold 3 are being developed to address access limitations.