Cluster of excellence in water resources and climate modeling in alpine and polar environments

高山和极地环境水资源和气候建模卓越集群

• 批准号: RGPIN-2019-04858
• 负责人: Radic, Valentina
• 金额: $ 2.62万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=751309
• 关键词: Cluster; excellence; water; resources; climate

The retreat of mountain glaciers worldwide is a key indicator of modern climate change, with mass loss from glaciers contributing to sea level rise and to river flows during dry and warm parts of the year. In western Canada, maintenance of low flows by glacier runoff is essential for hydro-power generation, agricultural irrigation and water supply for major cities and small towns. The coming decades will see rapid and unprecedented changes to glaciers worldwide. However, current projections are unable to resolve with certainty how individual river basins will be impacted by the loss of glacier ice in their headwaters.  The long-term objective of my research program is to use state-of-the-art field, modelling and data analysis methods to narrow the uncertainties in projections of glacial contributions to streamflow and sea level rise. My short-term objectives, each envisioned as a PhD project, will address the following key uncertainties: A) Uncertainty in future climate scenarios, given by Global Climate Models (GCMs), which drive the glacier mass changes. We will evaluate GCMs with historical climate observations and use the best performing GCMs for the projections.  B) Uncertainty in simple downscaling methods used to convert coarsely-resolved climate fields from GCMs to finely-resolved ones that drive melt processes at the scale of individual glaciers. We will develop advanced downscaling methods that incorporate regional climate modelling and machine learning methods.  C) Uncertainty in empirical melt models which cannot be adequately calibrated in the absence of observations. We will develop a physics-based model that calculates melt from the difference between energy input into the glacier (e.g. solar radiation) and energy output (e.g. thermal radiation). We will evaluate model performance against novel field observations of energy and mass fluxes at glaciers in western Canada.  D) Uncertainty in the treatment of glacier dynamics. We will incorporate a high-complexity model of glacier geometry evolution in our model of glacier mass balance.  E) Uncertainty in glacier mass loss on regional scale which cannot be adequately quantified in the absence of a historical record of mass changes at this scale. We will develop novel data analysis methods to use available long-term streamflow measurements as a proxy of glacier mass changes.  By resolving these uncertainties, we will provide projections of glacier contribution to streamflow at the level of detail necessary to inform local policy makers and user communities. The outcomes will define how climate change will tangibly express itself for Canadians and most of the world. Funded personnel will contribute to the cutting-edge research on risk assessment of freshwater resources under climate change, and will receive high-level training in geosciences, computing and data analysis preparing them for a broad range of opportunities in academia, government or private industry.

世界各地山地冰川的消退是现代气候变化的一个关键指标，冰川的大量流失导致海平面上升，并在一年中的干旱和温暖地区造成河流流动。在加拿大西部，通过冰川径流维持低流量对于水力发电、农业灌溉和大城市和小城镇的供水至关重要。未来几十年，全球冰川将发生前所未有的快速变化。然而，目前的预测无法确定个别河流流域将如何受到源头冰川融化的影响。我的研究计划的长期目标是使用最先进的现场、建模和数据分析方法来缩小冰川对溪流和海平面上升的影响预测中的不确定性。我的短期目标，每个都被设想为一个博士项目，将解决以下关键的不确定性：a)全球气候模型(GCM)给出的未来气候情景的不确定性，它推动了冰川质量的变化。我们将使用历史气候观测对GCM进行评估，并使用表现最好的GCM进行预测。(B)用于将粗略解析的气候场从GCM转换为精细解析的气候场的简单降尺度方法中的不确定性，这些方法推动单个冰川规模的融化过程。我们将开发先进的缩减尺度方法，将区域气候建模和机器学习方法结合在一起。(C)经验熔融模型中的不确定性，在没有观测的情况下无法充分校准。我们将开发一个基于物理的模型，根据输入冰川的能量(例如太阳辐射)和输出的能量(例如热辐射)之间的差值计算融化。我们将根据加拿大西部冰川能量和质量通量的新的现场观测来评估模型的性能。(D)冰川动力学处理的不确定性。我们将在我们的冰川质量平衡模型中纳入冰川几何演化的高复杂性模型。(E)区域尺度上冰川质量损失的不确定性，在没有这种规模质量变化的历史记录的情况下，这种不确定性无法充分量化。我们将开发新的数据分析方法，使用现有的长期径流测量作为冰川质量变化的替代。通过解决这些不确定性，我们将以必要的详细程度提供冰川对径流的贡献预测，以告知当地政策制定者和用户社区。这些结果将定义气候变化将如何切实地为加拿大人和世界大部分地区带来影响。获得资助的人员将为气候变化下淡水资源风险评估的前沿研究做出贡献，并将接受地球科学、计算和数据分析方面的高水平培训，为他们在学术界、政府或私营行业的广泛机会做好准备。