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/X004635/1
• 负责人: David Hannah
• 金额: $ 31.87万
• 依托单位: University of Birmingham
• 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%2FX004635%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.

世界上的山脉在最有价值的时候储存和释放冰水，就像生长季节的夏季融水一样。这项服务是财富和福祉的非凡创造者，维持着全球六分之一的人口和全球四分之一的国内生产总值，但极易受到气候变化的影响。未来30年，阿尔卑斯山、北美西部、喜马拉雅山和安第斯山脉将失去10-40%的积雪和数百立方公里的夏季供水，到本世纪末，山地冰川将失去20-60%的冰。为了绘制山区水资源图并预测其未来，我们必须依靠降雪、季节性积雪、冰川增减和河流径流的模型。然而，这些模型的技能从根本上受到测试和开发它们所需的观测的质量和可用性的限制，而山区冰冻圈是如此之大、多样且不适宜居住，以至于我们缺乏许多这些关键观测。在大多数山脉，降雪量被低估了 50-100%，而且天气记录太短，无法记录极端气候的历史。在雅鲁藏布江、印度河和恒河盆地的喜马拉雅源头，仅对 41,000 条冰川中的 6 条进行了厚度测量，因此，8 亿人使用的水资源的寿命仍然无法预测。该项目旨在通过采取有针对性的方法来填补四个关键观测空白：1) 降雪、2) 冰川厚度、3) 径流和 4) 极端天气。 覆盖山区冰冻圈，但精心挑选的数据集旨在测试和提高模型技能。重要的是，通过在这些目标地点校准和完善相关模型过程，我们可以消除总体偏差并减少模型输出的不确定性，这些输出不仅可以应用于本地，而且可以应用于过去、现在和未来的所有模型尺度。我们将采用开创性的方法进行新的降雪观测，该方法首次对比雨量计大数千至数十亿倍的区域进行无偏差测量，并使用这些数据来测试和改进 全球范围内运行的降雪模型。为了捕捉和了解山区降水的极端情况，我们将通过识别保存在高处、未受干扰的喜马拉雅湖沉积物中的潮湿和干燥年份的信号，将长达数十年的仪器记录追溯到几个世纪到几千年前，我们将以非常高分辨率对这些沉积物进行取芯和分析。与此同时，我们将利用最近从尼泊尔获得的独特的广泛冰川调查来改进山脉规模的冰川厚度模型。我们将使用新的降雪地图和预测来驱动 21 世纪阿尔卑斯山和喜马拉雅山两个目标流域的积雪和冰川演化的详细模型。我们将把我们的模型应用到冰川厚度图上，以确定这些冰川在气候变化下能存活多久、有多少融水将流入其集水区以及这将如何变化。我们将根据不同水源对下游河流贡献的尖端新通量和水化学观测来测试我们模型的性能。最后，我们将确定哪些气候因素影响两个流域极端潮湿和干旱年份的频率和严重程度，以及这些事件在21世纪可能如何变化。总之，我们有针对性的、数据驱动的建模进展将明显提高我们量化山区冰冻圈季节性积雪量并预测其未来如何变化的能力，冰川冰资源的时间尺度和潜在变化轨迹，干旱和潮湿年份的频率 会发生什么气候因素导致这种情况以及这些极端情况将如何变化。通过使山区冰冻圈更加可预测，我们将支持社会管理这一重要但脆弱的水资源的变化。