Question

डिस्क्राइब द थ्री बैलेंस ऑफ हिमालया​

Answer 1

Answer:

Explanation:

Introduction

The Himalaya-Karakoram (HK), covering a glacierized area of ~41,000 km2 is one of the largest mountain ranges on Earth1. HK is surrounded by densely populated countries of south Asia and more than 800 million people depend strongly on water originating in its river systems (Indus, Ganges and Brahmaputra) for drinking, irrigation, hydropower, and industrial purposes2,3. Given the intrinsic variability of regional precipitation fed by Western Disturbances (WDs) and Indian Summer Monsoon (ISM), HK glaciers buffer a significant fraction of the population against droughts3. Climate change is threatening the future of glaciers in the HK4, putting this dynamic water reserve under severe stress and causing serious hydrological changes3,5,6. This is likely to expose a significant fraction of regional population to serious climate impacts3,7,8. Understanding the details of the climatic forcing that is driving the glacier changes over the HK, therefore becomes an imperative for future adaptation and mitigation strategies.

The glaciers over Karakoram are mostly fed by snowfall occurring during winter months (November to April) derived from WDs while glaciers in the Himalaya receive snowfall from both WDs and ISM9. Region-wide remote-sensing glacier mass balance (MB) estimates have revealed that HK glaciers have mostly been experiencing a mass loss in recent decades10,11. Available field data from a handful of Himalayan glaciers also confirm this fact12,13. However, the pattern of the mass loss is quite heterogeneous; glaciers in the Himalaya are losing mass at rates that vary locally11,12 while Karakoram glaciers have been in a near mass-balanced state over the past few decades10,14,15. The anomalous behavior, initially termed as “Karakoram Anomaly”16,17, is now found to be centered in Kun Lun Shan10. The heterogeneous mass balance behavior over HK points to local idiosyncrasies of net climate forcing and glacier response that await a clear explanation.

Due to the harsh field conditions, only a few studies have been attempted to understand the meteorological forcing of glacier MB in the HK using in-situ data18,19. Relevant meteorological field-data from the glacierized high mountains of the region are quite limited20,21. A few model studies have investigated the regional glacier MB patterns utilizing various interpolated/reanalyzed meteorological data products21,22,23,24. A recent study25 elucidated the importance of local MB sensitivity to temperature in determining glacier response. Another study using station data over the Karakoram and Tibetan Plateau suggested that cooling summertime surface air temperatures induce less glacier melting over the Karakoram. This cooling is further linked with the “Karakoram Vortex” under climate warming scenarios26,27. The vortex modulates the air temperature over the western Tibetan Plateau mainly through adiabatic sinking/rising processes28. However, the role of other meteorological parameters in driving the observed spatiotemporal pattern of glacier mass loss in the HK remains largely unexplored. A clear answer to the questions about drivers of HK glaciers in a warming world is needed to guide future developments related to measurement and modelling strategies to advance process and predictive understanding, and to deliver reliable projections for this dynamic storage of water.

Glacier MB variability is a net result of climatic forcing, which consists of surface MB and a relatively slow response of ice flow29. While the long-term MB trends reflect the combined effects of these, the short-term interannual fluctuations of MB are mostly due to the meteorological forcing. Therefore, the correlations between annual time series of glacier MB and the meteorological variables encode the sensitivity of the former to changes in the latter. In fact, the interannual variability of glacier MB is rather large relative to the recent long-term net MB especially in the Himalaya12. It is also expected that some of the drivers themselves vary coherently due to mutual feedbacks.