The Big Thaw: gauging the past, present and future of our mountain water resources

大解冻：衡量山区水资源的过去、现在和未来

• 批准号: NE/X005194/1
• 负责人: Richard Essery
• 金额: $ 47.08万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FX005194%2F1
• 关键词: Big; Thaw; gauging; past; present

The world's mountains store and release frozen water when it is most valuable, as summer meltwater in the growing season. This service is an extraordinary generator of wealth and well-being, sustaining a sixth of the global population and a quarter of global GDP, but is highly vulnerable to climate change. Over the next 30 years, the Alps, Western North America, Himalayas and Andes will lose 10-40% of their snow, hundreds of cubic kilometres of summer water supply, and by end of century, mountain glaciers will lose 20-60% of their ice.To map our mountain water resources and predict their future, we must rely on models of snowfall, seasonal snowpacks, glacier gains and losses, and river runoff. The skill of these models is, however, fundamentally limited by the quality and availability of observations needed to test and develop them, and the mountain cryosphere is so large, varied and inhospitable that we lack many of these key observations. In most mountain ranges, snowfall is underestimated by 50-100%, and weather records are too short to have captured a history of their climate extremes. The thickness of only 6 of 41,000 glaciers has been surveyed in the Himalayan headwaters of the Brahmaputra, Indus and Ganges basins, so the lifespan of a water resource used by 800 million people remains unpredictable.This project aims to fill four of the key observation gaps: 1) snowfall, 2) glacier thickness, 3) runoff, and 4) weather extremes, by taking a targeted approach to provide not blanket coverage of the mountain cryosphere but carefully-selected datasets designed to test and improve model skill. Importantly, through the calibration and refinement of relevant model processes at these target sites we can eliminate gross biases and reduce uncertainties in model outputs that can then apply not just locally but across all model scales, in the past, present and future.We will make new snowfall observations with a pioneering method that, for the first time, makes unbiased measurements over areas thousands to billions of times larger than rain gauges, and use these to test and improve snowfall models that are run worldwide. To capture and understand the extremes of mountain precipitation, we will extend the decades-long instrumental record back by centuries to millennia by identifying the signals of wet and dry years preserved in high, undisturbed Himalayan-lake sediments that we will core and analyse at very high resolution. In parallel, we will use a recently acquired and uniquely extensive glacier survey from Nepal to improve glacier-thickness models on the mountain-range scale. We will use our new snowfall maps and projections to drive detailed models of snowpack and glacier evolution over the 21st century for two targeted catchments in the Alps and Himalayas. We will apply our models to our glacier thickness maps to determine how long these glaciers will survive under a changing climate, how much meltwater will flow into their catchments and how this will change. We will test the performance of our models against cutting-edge new flux and hydrochemistry observations of the contribution of different water sources to downstream river flow. Finally, we will determine which climate factors affect the frequency and severity of extreme wet and dry years for the two catchments, and how these events are likely to change through the 21st century.Together, our targeted, data-driven modelling advances will demonstrably improve our ability to quantify how much seasonal snow accumulates in the mountain cryosphere and predict how it will change in the future, what the timescales and potential trajectories for change are for glacier-ice resources, how frequently dry and wet years occur, what climate factors cause this, and how these extremes will change. By making the mountain cryosphere more predictable, we will support societies in managing change in this critical but vulnerable water resource.

在生长季节，夏季融水是世界山脉的商店并释放冷冻水。这项服务是财富和福祉的非凡生成者，维持了全球人口的六分之三和全球GDP的四分之一，但极易受到气候变化的影响。 Over the next 30 years, the Alps, Western North America, Himalayas and Andes will lose 10-40% of their snow, hundreds of cubic kilometres of summer water supply, and by end of century, mountain glaciers will lose 20-60% of their ice.To map our mountain water resources and predict their future, we must rely on models of snowfall, seasonal snowpacks, glacier gains and losses, and river runoff.但是，这些模型的技能从根本上受到测试和开发所需的观察值的质量和可用性的限制，而山冰层是如此之大，多样化和荒凉，以至于我们缺乏许多关键观察结果。在大多数山区范围内，降雪被低估了50-100％，天气记录太短，无法捕捉到其极端气候的历史。在布拉马普特拉（Brahmaputra），印度河和恒河盆地的喜马拉雅河水中，仅在41,000个冰川中只有6个冰川中的6个厚度，因此8亿人使用的水资源的寿命仍然不可预测。该项目的目的仍然是可预测的。此项目的目的是填补四个关键的观察缺口：1）降雪量，2）距离造成距离的距离，以及偏僻的距离，以及4）偏僻的距离，以及4）偏僻的距离，4）越过距离，4）山冰圈的覆盖范围，但精心挑选的数据集旨在测试和提高模型技能。重要的是，通过对这些目标站点上相关模型过程的校准和改进，我们可以消除严重的偏见并减少模型输出中的不确定性，然后在过去，现在和未来中不仅可以在本地而是在所有模型量表中应用，在过去，现在和未来中，我们将通过降雪方法进行新的降雪方法，以使他们的降雨范围更大，而不是降雨，这使得越来越多的降雨范围更大，而不是次数，而不是次数，而不是次数，而不是次数的数千次，并且数千次的时间和数量的数千次降低了数千，并且在全球范围内运行的降雪模型。为了捕捉和理解山水降水的极端，我们将通过确定保存在高，不受干扰的喜马拉雅湖沉积物中的潮湿和干燥年份的信号，将长达数十年的工具记录延长了几个世纪，我们将以高分辨率进行分析并分析，并以非常高的分辨率进行分析。同时，我们将使用来自尼泊尔的最近获得且独特的冰川调查来改善山地范围的冰川厚度模型。我们将使用新的降雪地图和预测来推动21世纪的详细模型和冰川进化的详细模型，用于阿尔卑斯山和喜马拉雅山的两个有针对性的集水区。我们将把模型应用于冰川厚度图，以确定这些冰川在不断变化的气候下将存活多长时间，融化了多少流水以及如何变化。我们将测试模型的性能，以针对不同水源对下游河流的贡献的最新新通量和水化学观察结果。最后，我们将确定这两个流域的极端潮湿和干燥年的频率和严重性以及这些事件在21世纪可能会发生变化的频率和严重性。发生，什么气候因素导致了这一点，以及这些极端将如何改变。通过使山冰圈更可预测，我们将支持社会管理这种关键但脆弱的水资源的变化。